RSC - Digital Discovery latest articleshttp://pubs.rsc.org/en/Journals/Journal/DDRSC - Digital Discovery latest articlesCopyright (c) The Royal Society of ChemistryFri, 29 Mar 2024 12:03:47 ZRSC - Digital Discovery latest articleshttp://pubs.rsc.org/content/NewImages/rsc_publishing_logo.gifRSC - Digital Discovery latest articleshttp://pubs.rsc.org/en/Journals/Journal/DDhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00165Bhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00165BExtended Similarity Methods for Efficient Data Mining in Imaging Mass Spectrometry<div><i><b>Digital Discovery</b></i>, 2024, Accepted Manuscript<br/><b>DOI</b>: 10.1039/D3DD00165B, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png' alt='Creative Commons Licence' border='none'/></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Nicholas R. Ellin, Yingchan Guo, Ramon Alain Miranda-Quintana, Boone M Prentice<br/>Imaging mass spectrometry is a label-free imaging modality that allows for the spatial mapping of many compounds directly in tissues. In an imaging mass spectrometry experiment, a raster of the...<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-03-27T00:00:00ZNicholas R. EllinYingchan GuoRamon Alain Miranda-QuintanaBoone M Prenticehttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00001Chttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00001CMLstructureMining: A machine learning tool for structure identification from X-ray pair distribution functions<div><i><b>Digital Discovery</b></i>, 2024, Accepted Manuscript<br/><b>DOI</b>: 10.1039/D4DD00001C, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png' alt='Creative Commons Licence' border='none'/></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Emil Kjær, Andy Sode Anker, Andrea Kirsch, Joakim Lajer, Olivia Aalling-Frederiksen, Simon J. L. Billinge, Kirsten Marie Ørnsbjerg Jensen<br/>Synchrotron X-ray techniques are essential for studies of the intrinsic relationship between synthesis, structure, and properties of materials. Modern synchrotrons can produce up to 1 petabyte of data per day....<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-03-27T00:00:00ZEmil KjærAndy Sode AnkerAndrea KirschJoakim LajerOlivia Aalling-FrederiksenSimon J. L. BillingeKirsten Marie Ørnsbjerg Jensenhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00212Hhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00212HThe Automated Discovery of Kinetic Rate Models – Methodological Frameworks<div><i><b>Digital Discovery</b></i>, 2024, Accepted Manuscript<br/><b>DOI</b>: 10.1039/D3DD00212H, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png' alt='Creative Commons Licence' border='none'/></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Miguel Ángel de Carvalho Servia, Ilya Orson Sandoval, King Kuok Mimi Hii, Klaus Hellgardt, Dongda Zhang, Antonio Del Rio Chanona<br/>The industrialization of catalytic processes requires reliable kinetic models for their design, optimization and control. Mechanistic models require significant domain knowledge, while data-driven and hybrid models lack interpretability. Automated knowledge...<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-03-27T00:00:00ZMiguel Ángel de Carvalho ServiaIlya Orson SandovalKing Kuok Mimi HiiKlaus HellgardtDongda ZhangAntonio Del Rio Chanonahttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00257Hhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00257HAutonomous millimeter scale high throughput battery research system<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00257H" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, Advance Article<br/><b>DOI</b>: 10.1039/D3DD00257H, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Fuzhan Rahmanian, Stefan Fuchs, Bojing Zhang, Maximilian Fichtner, Helge Sören Stein<br/>The high-throughput Auto-MISCHBARES platform streamlines reliable autonomous experimentation across laboratory devices through scheduling, quality control, live feedback, and real-time data management, including measurement, validation and analysis.<br/>To cite this article before page numbers are assigned, use the DOI form of citation above.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-03-21T00:00:00ZFuzhan RahmanianStefan FuchsBojing ZhangMaximilian FichtnerHelge Sören Steinhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00238Ahttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00238AChebifier: Automating semantic classification in ChEBI with AI to accelerate data-driven discovery<div><i><b>Digital Discovery</b></i>, 2024, Accepted Manuscript<br/><b>DOI</b>: 10.1039/D3DD00238A, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Martin Glauer, Fabian Neuhaus, Simon Flügel, Marie Wosny, Till Mossakowski, Adel Memariani, Johannes Schwerdt, Janna Hastings<br/>Connecting chemical structural representations with meaningful categories and semantic annotations representing existing knowledge enables data-driven digital discovery from chemistry data, since these semantic annotations may be used to determine patterns...<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-03-26T00:00:00ZMartin GlauerFabian NeuhausSimon FlügelMarie WosnyTill MossakowskiAdel MemarianiJohannes SchwerdtJanna Hastingshttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00021Hhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00021HMessage Passing Neural Network for Predicting Dipole Moment Dependent Core Electron Excitation Spectra<div><i><b>Digital Discovery</b></i>, 2024, Accepted Manuscript<br/><b>DOI</b>: 10.1039/D4DD00021H, Communication</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Kiyou Shibata, Teruyasu Mizoguchi<br/>Absorption near-edge structures in core electron excitation spectra reflect the anisotropy of orbitals in the final transition state and can be utilized for analyzing the local atomic environment, including its...<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-03-26T00:00:00ZKiyou ShibataTeruyasu Mizoguchihttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00019Fhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00019FGotta be SAFE: a new framework for molecular design<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D4DD00019F" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, Advance Article<br/><b>DOI</b>: 10.1039/D4DD00019F, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png' alt='Creative Commons Licence' border='none'/></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Emmanuel Noutahi, Cristian Gabellini, Michael Craig, Jonathan S. C. Lim, Prudencio Tossou<br/>SAFE is a novel SMILES-compatible, fragment-based molecular line notation that streamlines molecule generation tasks. Unlike existing line notations, it enforces a sequential depiction of molecular substructures, thus simplifying molecule design.<br/>To cite this article before page numbers are assigned, use the DOI form of citation above.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-03-21T00:00:00ZEmmanuel NoutahiCristian GabelliniMichael CraigJonathan S. C. LimPrudencio Tossouhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00217Ahttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00217APredicting small molecules solubility on endpoint devices using deep ensemble neural networks<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00217A" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, Advance Article<br/><b>DOI</b>: 10.1039/D3DD00217A, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Mayk Caldas Ramos, Andrew D. White<br/>We propose a new way of deploying deep learning models to improve reproducibility and usability, making predictions with uncertainty.<br/>To cite this article before page numbers are assigned, use the DOI form of citation above.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-03-13T00:00:00ZMayk Caldas RamosAndrew D. Whitehttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00024Bhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00024BLearning conditional policies for crystal design using offline reinforcement learning<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D4DD00024B" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, Advance Article<br/><b>DOI</b>: 10.1039/D4DD00024B, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Prashant Govindarajan, Santiago Miret, Jarrid Rector-Brooks, Mariano Phielipp, Janarthanan Rajendran, Sarath Chandar<br/>Conservative Q-learning for band-gap conditioned crystal design with DFT evaluations – the model is trained on trajectories constructed from crystals in the Materials Project. Results indicate promising performance for lower band gap targets.<br/>To cite this article before page numbers are assigned, use the DOI form of citation above.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-02-29T00:00:00ZPrashant GovindarajanSantiago MiretJarrid Rector-BrooksMariano PhielippJanarthanan RajendranSarath Chandarhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00010Bhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00010BInverse design of metal–organic frameworks for direct air capture of CO2 via deep reinforcement learning<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D4DD00010B" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, Advance Article<br/><b>DOI</b>: 10.1039/D4DD00010B, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Hyunsoo Park, Sauradeep Majumdar, Xiaoqi Zhang, Jihan Kim, Berend Smit<br/>A reinforcement learning framework enables the design and discovery of novel metal–organic frameworks (MOFs) for direct air capture of CO<small><sub>2</sub></small> (DAC) in terms of CO<small><sub>2</sub></small> heat of adsorption and CO<small><sub>2</sub></small>/H<small><sub>2</sub></small>O selectivity.<br/>To cite this article before page numbers are assigned, use the DOI form of citation above.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-03-12T00:00:00ZHyunsoo ParkSauradeep MajumdarXiaoqi ZhangJihan KimBerend Smithttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00027Ghttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00027GEGraFFBench: evaluation of equivariant graph neural network force fields for atomistic simulations<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D4DD00027G" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, Advance Article<br/><b>DOI</b>: 10.1039/D4DD00027G, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Vaibhav Bihani, Sajid Mannan, Utkarsh Pratiush, Tao Du, Zhimin Chen, Santiago Miret, Matthieu Micoulaut, Morten M. Smedskjaer, Sayan Ranu, N. M. Anoop Krishnan<br/>EGraFFBench: a framework for evaluating equivariant graph neural network force fields on dynamic atomistic simulations.<br/>To cite this article before page numbers are assigned, use the DOI form of citation above.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-03-04T00:00:00ZVaibhav BihaniSajid MannanUtkarsh PratiushTao DuZhimin ChenSantiago MiretMatthieu MicoulautMorten M. SmedskjaerSayan RanuN. M. Anoop Krishnanhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00205Ehttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00205EFSL-CP: a benchmark for small molecule activity few-shot prediction using cell microscopy images<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00205E" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, Advance Article<br/><b>DOI</b>: 10.1039/D3DD00205E, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Son V. Ha, Lucas Leuschner, Paul Czodrowski<br/>A benchmark of different methods for few-shot prediction of small molecule activity using cell painting data.<br/>To cite this article before page numbers are assigned, use the DOI form of citation above.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-02-26T00:00:00ZSon V. HaLucas LeuschnerPaul Czodrowskihttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00183Khttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00183KChemGymRL: A customizable interactive framework for reinforcement learning for digital chemistry<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00183K" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, Advance Article<br/><b>DOI</b>: 10.1039/D3DD00183K, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Chris Beeler, Sriram Ganapathi Subramanian, Kyle Sprague, Mark Baula, Nouha Chatti, Amanuel Dawit, Xinkai Li, Nicholas Paquin, Mitchell Shahen, Zihan Yang, Colin Bellinger, Mark Crowley, Isaac Tamblyn<br/>Demonstration of a new open source Python library for simulating chemistry experiments as a gymnasium-API, reinforcement learning environment. Allowing learning policies for material design tasks or pipelines using a modular, extendable design.<br/>To cite this article before page numbers are assigned, use the DOI form of citation above.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-02-20T00:00:00ZChris BeelerSriram Ganapathi SubramanianKyle SpragueMark BaulaNouha ChattiAmanuel DawitXinkai LiNicholas PaquinMitchell ShahenZihan YangColin BellingerMark CrowleyIsaac Tamblynhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00194Fhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00194FSPOTLIGHT: structure-based prediction and optimization tool for ligand generation on hard-to-drug targets – combining deep reinforcement learning with physics-based de novo drug design<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00194F" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, Advance Article<br/><b>DOI</b>: 10.1039/D3DD00194F, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png' alt='Creative Commons Licence' border='none'/></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Venkata Sai Sreyas Adury, Arnab Mukherjee<br/>SPOTLIGHT: a method capable of designing a diverse set of novel drug molecules through a combination of rule-based learning and reinforcement learning.<br/>To cite this article before page numbers are assigned, use the DOI form of citation above.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-03-08T00:00:00ZVenkata Sai Sreyas AduryArnab Mukherjeehttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00225Jhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00225JMulti-task scattering-model classification and parameter regression of nanostructures from small-angle scattering data<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00225J" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, Advance Article<br/><b>DOI</b>: 10.1039/D3DD00225J, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png' alt='Creative Commons Licence' border='none'/></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Batuhan Yildirim, James Doutch, Jacqueline M. Cole<br/>Machine learning (ML) can be employed at the data-analysis stage of small-angle scattering (SAS) experiments.<br/>To cite this article before page numbers are assigned, use the DOI form of citation above.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-03-12T00:00:00ZBatuhan YildirimJames DoutchJacqueline M. Colehttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00008Khttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00008KDeepSPInN – deep reinforcement learning for molecular structure prediction from infrared and 13C NMR spectra<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D4DD00008K" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, Advance Article<br/><b>DOI</b>: 10.1039/D4DD00008K, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png' alt='Creative Commons Licence' border='none'/></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Sriram Devata, Bhuvanesh Sridharan, Sarvesh Mehta, Yashaswi Pathak, Siddhartha Laghuvarapu, Girish Varma, U. Deva Priyakumar<br/>DeepSPInI is a deep reinforcement learning method that predicts the molecular structure when given infrared and <small><sup>13</sup></small>C nuclear magnetic resonance spectra with an accuracy of 91.5%.<br/>To cite this article before page numbers are assigned, use the DOI form of citation above.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-03-07T00:00:00ZSriram DevataBhuvanesh SridharanSarvesh MehtaYashaswi PathakSiddhartha LaghuvarapuGirish VarmaU. Deva Priyakumarhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00032Chttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00032CReconstructing Materials Tetrahedron: Challenges in Materials Information Extraction<div><i><b>Digital Discovery</b></i>, 2024, Accepted Manuscript<br/><b>DOI</b>: 10.1039/D4DD00032C, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Kausik Hira, Mohd Zaki, Dhruvil Sheth, Mausam Mausam, N. M. Anoop Krishnan<br/>Discovery of new materials has a documented history of propelling human progress for centuries and more. The behavior of a material is a function of its composition, structure, and properties,...<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-03-18T00:00:00ZKausik HiraMohd ZakiDhruvil ShethMausam MausamN. M. Anoop Krishnanhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00040Dhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00040DThe future of self-driving laboratories: From Human in the Loop Interactive AI to Gamification<div><i><b>Digital Discovery</b></i>, 2024, Accepted Manuscript<br/><b>DOI</b>: 10.1039/D4DD00040D, Perspective</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Holland Hysmith, Elham Foadian, Shakti P. Padhy, Sergei V. Kalinin, Rob G. Moore, Olga Ovchinnikova, Mahshid Ahmadi<br/>Recent developments in artificial intelligence (AI) and machine learning (ML), implemented through self-driving laboratories (SDLs), are rapidly creating unprecedented opportunities for the accelerated discovery and optimization of materials. This paper...<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-03-14T00:00:00ZHolland HysmithElham FoadianShakti P. PadhySergei V. KalininRob G. MooreOlga OvchinnikovaMahshid Ahmadihttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD90005Ghttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD90005GCorrection: Understanding the patterns that neural networks learn from chemical spectra<div><i><b>Digital Discovery</b></i>, 2024, <b>3</b>,610-610<br/><b>DOI</b>: 10.1039/D4DD90005G, Correction</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Laura Hannemose Rieger, Max Wilson, Tejs Vegge, Eibar Flores<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-03-06T00:00:00ZLaura Hannemose RiegerMax WilsonTejs VeggeEibar Floreshttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD90009Jhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD90009JGuidelines for hardware-focused articles<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D4DD90009J" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, <b>3</b>,447-448<br/><b>DOI</b>: 10.1039/D4DD90009J, Editorial</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Jason E. Hein, Joshua Schrier<br/>In this editorial we set expectations and requirements for submissions describing discovery-enabling hardware.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-02-29T00:00:00ZJason E. HeinJoshua Schrierhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00243Hhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00243Hhttps://2DMat.ChemDX.org: Experimental data platform for 2D materials from synthesis to physical properties<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00243H" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, <b>3</b>,573-585<br/><b>DOI</b>: 10.1039/D3DD00243H, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Jin-Hoon Yang, Habin Kang, Hyuk Jin Kim, Taeho Kim, Heonsu Ahn, Tae Gyu Rhee, Yeong Gwang Khim, Byoung Ki Choi, Moon-Ho Jo, Hyunju Chang, Jonghwan Kim, Young Jun Chang, Yea-Lee Lee<br/>https://2DMat.ChemDX.org is a comprehensive data platform tailored for 2D materials research, emphasizing the handling and analysis of experimental data through specialized data management, visualization, and machine learning tools.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-02-27T00:00:00ZJin-Hoon YangHabin KangHyuk Jin KimTaeho KimHeonsu AhnTae Gyu RheeYeong Gwang KhimByoung Ki ChoiMoon-Ho JoHyunju ChangJonghwan KimYoung Jun ChangYea-Lee Leehttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00214Dhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00214DDerivative-based pre-training of graph neural networks for materials property predictions<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00214D" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, <b>3</b>,586-593<br/><b>DOI</b>: 10.1039/D3DD00214D, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png' alt='Creative Commons Licence' border='none'/></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Shuyi Jia, Akaash R. Parthasarathy, Rui Feng, Guojing Cong, Chao Zhang, Victor Fung<br/>General pre-training strategy of graph neural networks for materials science.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-02-21T00:00:00ZShuyi JiaAkaash R. ParthasarathyRui FengGuojing CongChao ZhangVictor Funghttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00073Ghttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00073GChemical space analysis and property prediction for carbon capture solvent molecules<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00073G" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, <b>3</b>,528-543<br/><b>DOI</b>: 10.1039/D3DD00073G, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>James L. McDonagh, Stamatia Zavitsanou, Alexander Harrison, Dimitry Zubarev, Theordore van Kessel, Benjamin H. Wunsch, Flaviu Cipcigan<br/>A chemical space analysis of carbon capture amines and a computational screening framework for carbon capture solvents.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-02-21T00:00:00ZJames L. McDonaghStamatia ZavitsanouAlexander HarrisonDimitry ZubarevTheordore van KesselBenjamin H. WunschFlaviu Cipciganhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00216Khttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00216KActive learning of neural network potentials for rare events<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00216K" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, <b>3</b>,514-527<br/><b>DOI</b>: 10.1039/D3DD00216K, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png' alt='Creative Commons Licence' border='none'/></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Gang Seob Jung, Jong Youl Choi, Sangkeun Matthew Lee<br/>Developing an automated active learning framework for Neural Network Potentials, focusing on accurately simulating bond-breaking in hexane chains through steered molecular dynamics sampling and assessing model transferability.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-02-20T00:00:00ZGang Seob JungJong Youl ChoiSangkeun Matthew Leehttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00018Hhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00018HConnectivity optimized nested line graph networks for crystal structures<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D4DD00018H" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, <b>3</b>,594-601<br/><b>DOI</b>: 10.1039/D4DD00018H, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Robin Ruff, Patrick Reiser, Jan Stühmer, Pascal Friederich<br/>Graph neural networks (GNNs) have been applied to a large variety of applications in materials science and chemistry. We report a nested line-graph neural network achieving state-of-the-art performance in multiple benchmarks.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-02-20T00:00:00ZRobin RuffPatrick ReiserJan StühmerPascal Friederichhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00020Jhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D4DD00020JDiscovery of novel reticular materials for carbon dioxide capture using GFlowNets<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D4DD00020J" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, <b>3</b>,449-455<br/><b>DOI</b>: 10.1039/D4DD00020J, Communication</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Flaviu Cipcigan, Jonathan Booth, Rodrigo Neumann Barros Ferreira, Carine Ribeiro dos Santos, Mathias Steiner<br/>GFlowNets discover reticular materials with simulated CO<small><sub>2</sub></small> working capacity outperforming all materials in CoRE2019.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-02-16T00:00:00ZFlaviu CipciganJonathan BoothRodrigo Neumann Barros FerreiraCarine Ribeiro dos SantosMathias Steinerhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00200Dhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00200DGlobal geometry of chemical graph neural network representations in terms of chemical moieties<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00200D" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, <b>3</b>,544-557<br/><b>DOI</b>: 10.1039/D3DD00200D, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png' alt='Creative Commons Licence' border='none'/></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Amer Marwan El-Samman, Incé Amina Husain, Mai Huynh, Stefano De Castro, Brooke Morton, Stijn De Baerdemacker<br/>The embedding vectors from a Graph Neural Network trained on quantum chemical data allow for a global geometric space with a Euclidean distance metric. Moieties that are close in chemical sense, are also close in Euclidean sense.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-02-15T00:00:00ZAmer Marwan El-SammanIncé Amina HusainMai HuynhStefano De CastroBrooke MortonStijn De Baerdemackerhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00252Ghttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00252GModels Matter: the impact of single-step retrosynthesis on synthesis planning<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00252G" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, <b>3</b>,558-572<br/><b>DOI</b>: 10.1039/D3DD00252G, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Paula Torren-Peraire, Alan Kai Hassen, Samuel Genheden, Jonas Verhoeven, Djork-Arné Clevert, Mike Preuss, Igor V. Tetko<br/>Synthesis planning relies on retrosynthesis models, yet this relationship is under-analyzed. We investigate the effect of contemporary single-step models trained on public and proprietary reaction data to analyze the synthesis routes produced.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-02-14T00:00:00ZPaula Torren-PeraireAlan Kai HassenSamuel GenhedenJonas VerhoevenDjork-Arné ClevertMike PreussIgor V. Tetkohttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00126Ahttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00126APhysics-informed models of domain wall dynamics as a route for autonomous domain wall design via reinforcement learning<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00126A" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, <b>3</b>,456-466<br/><b>DOI</b>: 10.1039/D3DD00126A, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png' alt='Creative Commons Licence' border='none'/></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Benjamin R. Smith, Bharat Pant, Yongtao Liu, Yu-Chen Liu, Jan-Chi Yang, Stephen Jesse, Anahita Khojandi, Sergei V. Kalinin, Ye Cao, Rama K. Vasudevan<br/>Prompted by limited available data, we explore data-aggregation strategies for material datasets, aiming to boost machine learning performance. Our findings suggest that intuitive aggregation schemes are ineffective in enhancing predictive accuracy.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-02-07T00:00:00ZBenjamin R. SmithBharat PantYongtao LiuYu-Chen LiuJan-Chi YangStephen JesseAnahita KhojandiSergei V. KalininYe CaoRama K. Vasudevanhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00239Jhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00239JImage and data mining in reticular chemistry powered by GPT-4V<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00239J" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, <b>3</b>,491-501<br/><b>DOI</b>: 10.1039/D3DD00239J, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png' alt='Creative Commons Licence' border='none'/></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Zhiling Zheng, Zhiguo He, Omar Khattab, Nakul Rampal, Matei A. Zaharia, Christian Borgs, Jennifer T. Chayes, Omar M. Yaghi<br/>The integration of artificial intelligence into scientific research opens new avenues with the advent of GPT-4V, a large language model equipped with vision capabilities.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-02-02T00:00:00ZZhiling ZhengZhiguo HeOmar KhattabNakul RampalMatei A. ZahariaChristian BorgsJennifer T. ChayesOmar M. Yaghihttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00219Ehttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00219ERetro-BLEU: quantifying chemical plausibility of retrosynthesis routes through reaction template sequence analysis<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00219E" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, <b>3</b>,482-490<br/><b>DOI</b>: 10.1039/D3DD00219E, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png' alt='Creative Commons Licence' border='none'/></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Junren Li, Lei Fang, Jian-Guang Lou<br/>Retro-BLEU is a statistical metric to evaluate the plausibility of model-generated retrosynthesis routes based on reaction template sequences analysis.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-02-02T00:00:00ZJunren LiLei FangJian-Guang Louhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00179Bhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00179BA human-in-the-loop approach for visual clustering of overlapping materials science data<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00179B" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, <b>3</b>,502-513<br/><b>DOI</b>: 10.1039/D3DD00179B, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png' alt='Creative Commons Licence' border='none'/></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Satyanarayana Bonakala, Michael Aupetit, Halima Bensmail, Fedwa El-Mellouhi<br/>Our divide and conquer approach to enable the visual split or merge decision for each pair of Gaussian pairs.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-02-02T00:00:00ZSatyanarayana BonakalaMichael AupetitHalima BensmailFedwa El-Mellouhihttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00254Chttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00254CInfrared spectra prediction using attention-based graph neural networks<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00254C" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, <b>3</b>,602-609<br/><b>DOI</b>: 10.1039/D3DD00254C, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png' alt='Creative Commons Licence' border='none'/></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Naseem Saquer, Razib Iqbal, Joshua D. Ellis, Keiichi Yoshimatsu<br/>In this work, we present attention-based graph neural networks to predict infrared (IR) spectra from chemical structures.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-01-18T00:00:00ZNaseem SaquerRazib IqbalJoshua D. EllisKeiichi Yoshimatsuhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00227Fhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00227FPareto optimization to accelerate multi-objective virtual screening<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00227F" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, <b>3</b>,467-481<br/><b>DOI</b>: 10.1039/D3DD00227F, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png' alt='Creative Commons Licence' border='none'/></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by-nc/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Jenna C. Fromer, David E. Graff, Connor W. Coley<br/>Pareto optimization is suited to multi-objective problems when the relative importance of objectives is not known a priori. We report an open source tool to accelerate docking-based virtual screening with strong empirical performance.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-01-15T00:00:00ZJenna C. FromerDavid E. GraffConnor W. Coleyhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00175Jhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00175JBenchmarking machine-readable vectors of chemical reactions on computed activation barriers<div><i><b>Digital Discovery</b></i>, 2024, Accepted Manuscript<br/><b>DOI</b>: 10.1039/D3DD00175J, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Puck van Gerwen, Ksenia R. Briling, Yannick Calvino Alonso, Malte Franke, Clemence Corminboeuf<br/>In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches...<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-03-07T00:00:00ZPuck van GerwenKsenia R. BrilingYannick Calvino AlonsoMalte FrankeClemence Corminboeufhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00228Dhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00228DComparing software tools for optical chemical structure recognition<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00228D" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, Advance Article<br/><b>DOI</b>: 10.1039/D3DD00228D, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Aleksei Krasnov, Shadrack J. Barnabas, Timo Boehme, Stephen K. Boyer, Lutz Weber<br/>The extraction of chemical information from images, also known as Optical Chemical Structure Recognition (OCSR) has recently gained new attention.<br/>To cite this article before page numbers are assigned, use the DOI form of citation above.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-03-07T00:00:00ZAleksei KrasnovShadrack J. BarnabasTimo BoehmeStephen K. BoyerLutz Weberhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00232Bhttp://pubs.rsc.org/en/Content/ArticleLanding/2024/DD/D3DD00232BAccelerated screening of carbon dioxide capture by liquid sorbents<div><p><img align="center" src="/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D3DD00232B" /></p></div><div><i><b>Digital Discovery</b></i>, 2024, Advance Article<br/><b>DOI</b>: 10.1039/D3DD00232B, Paper</div><div><img alt='Open Access' src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png' /> Open Access</div><div><a rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window'> <img src='http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png' alt='Creative Commons Licence' border='none' /></a>&nbsp This article is licensed under a <a text-decoration=none rel='license' href='http://creativecommons.org/licenses/by/3.0/' target='_blank' title='This link will open in a new browser window' >Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Ryan J. R. Jones, Yungchieh Lai, Kevin Kan, Dan Guevarra, Joel A. Haber, Natalia M. Ramirez, Alessandra Zito, Clarabella Li, Jenny Y. Yang, Aaron M. Appel, John M. Gregoire<br/>The sustainability potential of carbon capture, concentration, and utilization technologies motivates accelerated discovery of carbon dioxide sorbents, for which we present a high throughput screening instrument.<br/>To cite this article before page numbers are assigned, use the DOI form of citation above.<br/>The content of this RSS Feed (c) The Royal Society of Chemistry</div>2024-02-28T00:00:00ZRyan J. R. JonesYungchieh LaiKevin KanDan GuevarraJoel A. HaberNatalia M. RamirezAlessandra ZitoClarabella LiJenny Y. YangAaron M. AppelJohn M. Gregoire