RSC - Digital Discovery latest articles

A reproducible Python workflow for absorber–light-source spectral matching: overlap-calculator

Digital Discovery, 2026, Accepted Manuscript
DOI: 10.1039/D6DD00247A, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Pinar Seyitdanlioglu
A reproducible Python workflow for absorber-light-source spectral matching: overlap-calculator Pinar Seyitdanlioglu* aThe spectral compatibility between an organic absorber and the illumination source is an important but often underquantified descriptor in...
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Protein language visualizer: a repository for homology exploration with language model embeddings

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00472A, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Javier Espinoza-Herrera, María F. Manríquez-García, Sofía Medina-Bermejo, Ailyn López-Jasso, Juan P. Ruiz-Alcocer, Adriana Siordia, Sarah M. Veskimägi, Nate Roethler, Adrian Jinich
The PLVis repository turns protein language model embeddings into maps of proteomic relationships, enabling accessible comparative and functional proteomic analysis.
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Good enough is better: feasibility vs. Pareto-optimality in alloy design

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00517E, Paper

Open Access

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Cayden Maguire, Christofer Hardcastle, Trevor Hastings, Raymundo Arróyave, Brent Vela
In this work, we treat alloy design as a probabilistic constraint satisfaction problem and demonstrate the advantages of this framework over traditional optimization-based approaches.
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Automatic generation of input files with optimised k-point meshes for Quantum ESPRESSO self-consistent field single-point total energy calculations

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00565E, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Elena Patyukova, Junwen Yin, Susmita Basak, Samuel Pinilla, Alin M. Elena, Gilberto Teobaldi
Performing density functional theory (DFT) calculations requires a careful choice of computational parameters to ensure convergence and obtain meaningful results.
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Back to the future of lead optimization: benchmarking compound prioritization strategies

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00387C, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Pablo Mas, Bruno Filoche-Rommé, Marc Bianciotto, Rodolphe Vuilleumier
We introduce a framework based on the Design–Make–Test–Analyze (DMTA) paradigm for simulating the outcome of prioritization strategies during lead optimization and assessing their performance in exploring and exploiting the chemical space.
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Developing a machine-learning interatomic potential for non-covalent interactions in proteins

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D6DD00056H, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Lejia Zeng, Xintong Zhang, Yuchan Pei, Lifeng Zhao, Lan Hua, Jincai Yang, Niu Huang
A PAirwise Non-covalent Interaction Potential (PANIP) with accuracy comparable to the ωB97X-D3BJ/def2-TZVPP level for non-covalent interactions in proteins.
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Journal research data policies in materials science

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D6DD00111D, Perspective

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Lukas Hörmann, Hemanadhan Myneni, Rwayda Kh. S. Al-Hamd, Katarina Batalović, Silvia Bonfanti, Federico Grasselli, Saulius Gražulis, Bahattin Koç, Konstantinos Konstantinou, Ivor Lončarić, Nataliya Lopanitsyna, José Manuel Oliveira, Paolo Pegolo, Patrícia Ramos, Kevin Rossi, Sebastian P. Schwaminger, Edith Simmen, Milica Todorović, Markus Stricker, Jonathan Schmidt
Open and reproducible research in materials science relies on the availability of data, code, and established metadata standards.
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Accelerating ligand discovery by combining Bayesian optimization with MMGBSA-based binding affinity calculations

Digital Discovery, 2026, Accepted Manuscript
DOI: 10.1039/D5DD00522A, Paper

Open Access

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Lucas Andersen, Max Rausch-Dupont, Alejandro Martínez León, Andrea Volkamer, Jochen Hub, Dietrich Klakow
Predicting protein–ligand binding affinity with high accuracy is critical in structure-based drug discovery. While docking methods offer computational efficiency, they often lack the precision required for reliable affinity ranking. In...
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Towards 'on-demand' van der Waals epitaxy with adaptive ensemble sampling atomistic workflows

Digital Discovery, 2026, Accepted Manuscript
DOI: 10.1039/D6DD00049E, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

S Bagchi, Ankita Biswas, Prasanna Venkataraman Balachandran, Ayana Ghosh, Panchapakesan Ganesh
Traditional approaches to achieve targeted epitaxial growth involves exploring a vast parameter space of thermodynamical and kinetic drivers (e.g., temperature, pressure, chemical potential etc). This tedious and time-consuming approach becomes...
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Learning Rates: Predicting Rate Coefficients for Hydrogen Abstraction Reactions

Digital Discovery, 2026, Accepted Manuscript
DOI: 10.1039/D6DD00113K, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Calvin Pieters, Alon Grinberg Dana
Accelerating the discovery of complex chemical systems, from sustainable aviation fuels to atmospheric models, requires the rapid determination of thousands of elementary rate coefficients, a task fundamentally bottlenecked by traditional,...
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A modular approach to studying polymer processing using a self-driving lab

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D6DD00146G, Paper

Open Access

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Adedire D. Adesiji, Dylan J. Balter, Zhaoji Yang, Kelsey L. Snapp, Joseph M. Palomba, Keith A. Brown
We report an autonomous experimentation system designed to study processing-dependent properties of polymers in a modular fashion. We test this by spray coating polymer films and discovering conditions under which they are highly iridescent.
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A property–agnostic framework for scalable molecular inverse design via quantum annealing

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D6DD00012F, Paper

Open Access

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Yuki Deguchi, Masato Taki
Technologies for designing molecules with desired properties have the potential to drive innovation across a wide range of fields.
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Text-to-flowsheet: an LLM-assisted pipeline for expert-level digitization and automated simulation of chemical processes

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D6DD00060F, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Jan-Frederic Laub, Luca Bosetti, André Bardow
Using a unique dataset of expert-drawn flowsheets, we develop and validate an LLM-assisted pipeline that digitizes chemical processes from natural language descriptions and automates their simulation.
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Leveraging active site information for deep learning prediction of enzyme–substrate Michaelis constants

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D6DD00094K, Paper

Open Access

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Daniil Lepikhov, Laura Sandner, Ariane Nunes-Alves
The Michaelis constant (K_M) is a key parameter in enzymology. Active site for K_M (AS4Km) is a deep learning model that leverages explicit active site information to predict K_M values for enzyme–substrate complexes.
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A soft sensor based on pH for real-time monitoring of mRNA medicine production

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00417A, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Mahdi Ahmed, Shady Hamed, Ricardo Cardoso, Charley Kenyon, Manoj Pohare, Mabrouka Maamra, Mark Dickman, Joan Cordiner, Zoltán Kis
A simple pH signal unlocks real-time, extensive monitoring of mRNA production via mechanistic and semi-empirical models. This aids continuous quantification of RNA yield, NTP depletion and ∼40 IVT components for monitoring and advanced control.
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Scientists might be reaffirming the relevance of human oversight as AI lands in labs

Digital Discovery, 2026, Accepted Manuscript
DOI: 10.1039/D6DD00030D, Opinion

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Renan Leonel, Li Du, Gil Eyal
Artificial Intelligence (AI) is prompting scientists to reflect on the shifting role of human judgment, interpretation, and oversight in experimental practice. As AI increasingly assumes critical roles in scientific discovery,...
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Bayesian active learning to accelerate high throughput phase diagram exploration

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00459D, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Mingzhou Fan, Yucheng Wang, Guillermo Vazquez, Ruida Zhou, Ibrahim Karaman, Raymundo Arróyave, Xiaoning Qian
BALPI accelerates phase diagram discovery by adaptively sampling thermodynamic space with quantified uncertainty, efficiently identifying sparse and disconnected phase stability regions.
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Achieving a scalable machine learning workflow for crystal structure discovery with experimental validation

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D6DD00132G, Review Article

Open Access

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Danila Shiryaev, Emil I. Jaffal, Sangjoon Lee, Balaranjan Selvaratnam, Anton O. Oliynyk
Interpretable and explainable machine learning models extract physical knowledge to enable the workflow to translate predictions into laboratory experimental discoveries.
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GEOMIND: A hybrid generative artificial intelligence for geopolymer design and optimization

Digital Discovery, 2026, Accepted Manuscript
DOI: 10.1039/D5DD00383K, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Sebastien Rousseau, Assil Bouzid, Sylvie Rossignol, Ameni Gharzouni
Geopolymers are an emerging class of eco-friendly materials with a wide range of applications. Nevertheless, achieving compounds for a specific application requires extensive experimental efforts on finding the accurate formulation...
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Hybrid quantum algorithm for simulating real-time thermal correlation functions

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00381D, Paper

Open Access

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Elliot C. Eklund, Nandini Ananth
We introduce a hybrid Path Integral Monte Carlo (hPIMC) algorithm to calculate quantum, real-time, thermal correlation functions for condensed phase systems.
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Taming T-REX: a canonical language for geometry-aware generative design of transition-metal complexes

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D6DD00129G, Paper

Open Access

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Ilia Kevlishvili, Devmin Dorabawila
T-REX strings enable coordination isomer discovery, novel structure generation and robust ML representation.
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A graph-based approach to selection of feasible compositions for compositionally graded alloy design

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D6DD00006A, Paper

Open Access

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Mikayla Obrist, James Hanagan, Marshall Allen, Bernard Gaskey, Richard Malak, Raymundo Arróyave
Graph-based subgraph analysis to identify connected regions of feasible alloy compositions, enabling selection of continuous paths that balance manufacturability, phase stability, and performance for compositionally graded alloy design.
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A critical examination of active learning workflows in materials science

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D6DD00081A, Perspective

Open Access

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Akhil S. Nair, Lucas Foppa
Data-centric materials science requires carefully designed active learning workflows, but existing practices remain limited by weak design frameworks and generic implementations.
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Gradient-enhanced neural networks for model parameter estimation applied to flow chemistry automated platforms

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00526D, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Francisco Bolaños-García, Jean-Marc Commenge, Laurent Falk
The acceleration of chemical process development through flow chemistry depends on obtaining reliable kinetic models.
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Spectroscopy-assisted Bayesian optimization for efficient refolding of inclusion body proteins

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D6DD00035E, Paper

Open Access

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Florian Gisperg, Robert Klausser, Matthias Kierein, Eva Prada Brichtova, Mohamed Elshazly, Julian Kopp, Oliver Spadiut
Combining Bayesian optimization with intrinsic tryptophan fluorescence spectroscopy enables efficient inclusion body refolding process development with decreased experimental effort.
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optimade-maker: automated generation of interoperable materials APIs from static datasets

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D6DD00125D, Paper

Open Access

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Kristjan Eimre, Matthew L. Evans, Bud Macaulay, Xing Wang, Jusong Yu, Nicola Marzari, Gian-Marco Rignanese, Giovanni Pizzi
optimade-maker converts atomistic structure datasets into OPTIMADE APIs, enabling interoperable access and analysis within the OPTIMADE ecosystem.
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Novelty-aware evolutionary Bayesian optimisation for multi-objective discovery science

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D6DD00134C, Paper

Open Access

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Maytham Aqeeli, Thatchathon Leelawat, David Shorthouse
We combine evolutionary algorithms with Bayesian optimisation, and introduce a novelty-aware selection strategy to efficiently explore complex experimental design spaces.
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Lightweight privacy-preserving human activity recognition from CSI data using a CNN-temporal attention network

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D6DD00077K, Paper

Open Access

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Khondakar Ashik Shahriar, Maruf Ahmed, Hafiz Imtiaz
We propose an end-to-end privacy-preserving CSI-based HAR framework integrating a CNN with temporal attention, which outperforms existing studies on multiple benchmark datasets with distance, height and environmental variations.
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A user-tunable machine learning framework for step-wise synthesis planning

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00562K, Paper

Open Access

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Shivesh Prakash, Nandan Patel, Hans-Arno Jacobsen, Viki Kumar Prasad
A mordern Hopfield network-based retrosynthetic tool for computer-aided synthesis planning that associates target molecules with reaction templates and uses tunable scoring of cost, temperature, and solvent toxicity to prioritize synthetic routes.
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Rapid prediction of single-site adsorbate probability distributions in metal–organic frameworks using graph neural networks

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D6DD00027D, Paper

Open Access

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Jake Burner, Olivier Marchand, Rosa Cicciarella, Marco Gibaldi, Tom K. Woo
Adsorbate probability distributions (APDs) of MOFs can be rapidly generated by machine learning, bypassing expensive atomistic simulations. Adsorption binding sites can be reliably extracted from the APDs.
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RobInHood: a robotic chemist in a fume hood

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D6DD00007J, Paper

Open Access

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Louis Longley, Francisco Munguia-Galeano, Yushu Han, Rob Clowes, Sriram Vijayakrishnan, Adam Edwards, Gabriella Pizzuto, Hatem Fakhruldeen, Andrew Cooper
Robot-In-a-Fume-Hood (RobInHood), a robotic arm in a fume hood capable of liquid/solid dispensing, capping, heating, filtration and visual analysis. The platform performs synthesis of a cage molecule, a phthalimide, and a dye-based porosity screen.
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ConforFormer: representation for molecules through understanding of conformers

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D6DD00096G, Paper

Open Access

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Mas Pieter Klein, Irina Rudenko, Evgeny A. Pidko, Ivan Bushmarinov
ConforFormer learns to align conformers while separating isomers, producing compact 3D molecular embeddings transferable across property prediction and similarity search tasks.
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AI-driven natural product-based antiviral drug development: a technical overview

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00504C, Review Article

Open Access

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Junxi Song, Kunhuan Yang, Yingcai Xiong, Keyu Tao, Liangyu Cai, Peng Cao, Jianjian Ji
This review highlights AI-driven innovations in natural product-based antiviral drug development, covering resource mining, target ID, screening, and preclinical/clinical applications, with opportunities and limitations.
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How digital is chemical research? insights from the second NFDI4Chem community survey on research data and FAIR workflows

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00584A, Paper

Open Access

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Jochen Ortmeyer, Vitali Sidorin, Daniela Adele Hausen, Ann-Christin Andres, John Jolliffe, Theo Bender, Giacomo Lanza, Steffen Neumann, Oliver Koepler, Nicole Jung, Christoph Steinbeck, Johannes Liermann, Sonja Herres-Pawlis
Chemical research data may be handled analogously or digitally in its life cycle, posing digitalisation challenges. The 2nd NFDI4Chem survey (>800 voices) gives updates on the community perspective which is evaluated in comparison to earlier surveys.
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PRISM: protocol refinement through intelligent simulation modeling

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D6DD00004E, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Brian Hsu, Priyanka V. Setty, Rory M. Butler, Ryan Lewis, Casey Stone, Rebecca Weinberg, Thomas Brettin, Rick Stevens, Ian Foster, Arvind Ramanathan
Digital-twin simulation catches physical errors in AI-generated lab protocols that model self-critique cannot detect. End-to-end autonomous execution produces results comparable to manual runs.
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Vision-guided adaptive scooping for powder weighing in autonomous chemistry laboratories

Digital Discovery, 2026, 5,2120-2127
DOI: 10.1039/D5DD00569H, Paper

Open Access

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Nikola Radulov, Thomas Little, Andrew I. Cooper, Gabriella Pizzuto
The integration of vision-guided perception into the powder scooping stage allowed our system to autonomously correct acquisition errors, enabling robust weighing of heterogeneous materials for solid-state materials chemistry workflows.
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Contributors to the Digital Discovery Emerging Investigators collection 2025

Digital Discovery, 2026, 5,1963-1967
DOI: 10.1039/D6DD90017H, Profile

Open Access

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This article profiles the early career researchers whose work features in the Digital Discovery Emerging Investigators collection 2025.
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First annual Digital Discovery Emerging Investigators collection

Digital Discovery, 2026, 5,1962-1962
DOI: 10.1039/D6DD90016J, Editorial

Open Access

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This collection showcases research carried out by internationally recognised, up-and-coming scientists in the early stage of their independent careers who are making outstanding contributions to their respective fields.
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Structured domain knowledge enables trustworthy materials science question-answering with large language models

Digital Discovery, 2026, 5,2243-2253
DOI: 10.1039/D6DD00028B, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Daegun Lee, Jiwoo Choi, Gyeong Hoon Yi, Seok Su Sohn, Byungju Lee, Donghun Kim
The domain-aligned question-answering system based on structured database construction and RAG pipeline.
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Discovery of hydrogen storage molecules using large language models and machine learning

Digital Discovery, 2026, 5,2089-2102
DOI: 10.1039/D6DD00102E, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Hassan Harb, Magali S. Ferrandon, Timothy A. Goetjen, Seryeong Lee, Omar K. Farha, Massimiliano Delferro, Rajeev Surendran Assary
Accelerating the discovery of new molecules with targeted properties is a central challenge in molecular design.
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Benchmarking physics-inspired machine learning models for transition metal complexes with diverse charge and spin states

Digital Discovery, 2026, 5,2103-2119
DOI: 10.1039/D5DD00571J, Paper

Open Access

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Yuri Cho, Ksenia R. Briling, Yannick Calvino Alonso, Ruben Laplaza, Clemence Corminboeuf
We benchmark two classes of physics-inspired machine learning models for predicting quantum-chemical properties of transition metal complexes using three complementary datasets.
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CReM-dock: de novo design of synthetically feasible structures guided by molecular docking

Digital Discovery, 2026, 5,2271-2291
DOI: 10.1039/D6DD00131A, Paper

Open Access

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Guzel Minibaeva, Haolin Du, Finlay Clark, Julien Michel, Pavel Polishchuk
The de novo generation of chemical compounds represents a compelling strategy for the exploration of a significantly broader chemical space compared to traditional virtual screening methods.
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Autonomous sampling and SHAP interpretation of deposition-rates in bipolar HiPIMS

Digital Discovery, 2026, 5,2221-2231
DOI: 10.1039/D6DD00063K, Paper

Open Access

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Alexander Wieczorek, Nathan Rodkey, Jan Sommerhäuser, Jason Hattrick-Simpers, Sebastian Siol
Autonomous loops and SHAP analysis allow for comprehensive, statistical analysis of >3000 different bipolar HiPIMS processes.
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Python-controlled, solvent-resistant fraction collector for automated flow synthesis

Digital Discovery, 2026, 5,2067-2073
DOI: 10.1039/D5DD00554J, Paper

Open Access

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Hongchen Wang, Owen A. Meville, Harrison A. Mills, Monique Ngan, Jay R. Werber, Nipun Kumar Gupta
We present a low-cost, solvent-resistant, open-source fraction collector that delivers real-time, volume-based sampling for automated flow chemistry.
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Uncertainty-aware active learning reveals reliability limits in lead-free halide perovskite screening

Digital Discovery, 2026, 5,2342-2352
DOI: 10.1039/D6DD00153J, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Xiyao Yu
Uncertainty-aware active learning maps reliability limits in lead-free halide perovskite screening, guiding high-fidelity calculations.
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Accelerating discovery across scientific disciplines through reproducible workflows with AiiDAlab

Digital Discovery, 2026, 5,2310-2324
DOI: 10.1039/D5DD00567A, Paper

Open Access

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Aliaksandr V. Yakutovich, Daniel Hollas, Edan Bainglass, Jusong Yu, Corsin Battaglia, Miki Bonacci, Lucas Fernandez Vilanova, Stephan Henne, Anders Kaestner, Michel Kenzelmann, Graham Kimbell, Jakob Lass, Fabio Lopes, Daniel G. Mazzone, Andres Ortega-Guerrero, Xing Wang, Nicola Marzari, Carlo A. Pignedoli, Giovanni Pizzi
AiiDAlab provides a Jupyter-based environment for reproducible computational workflows powered by AiiDA provenance tracking, integrating ELNs and experimental facilities to enable data-driven scientific discovery across a broad range of disciplines.
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Stoichiometrically-informed symbolic regression for extracting chemical reaction mechanisms from data

Digital Discovery, 2026, 5,2325-2341
DOI: 10.1039/D5DD00470E, Paper

Open Access

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Manuel Palma Banos, Joel D. Kress, Rigoberto Hernandez, Galen T. Craven
A data-driven computational method is introduced to extract chemical reaction mechanisms from time series chemical concentration data.
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WeChemSynOntology: semantic modeling of wet chemical syntheses in a self-driving lab for nano- and advanced materials

Digital Discovery, 2026, 5,2074-2088
DOI: 10.1039/D6DD00058D, Paper

Open Access

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Markus Schilling, Harald Bresch, Bernd Bayerlein, Bastian Ruehle
The WCSO formally describes wet chemical syntheses, enabling precise, machine-readable representations that improve reproducibility, sharing, querying, and autonomous workflow execution across self-driving labs and materials acceleration platforms.
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Masgent: an AI-assisted materials simulation agent

Digital Discovery, 2026, 5,2151-2171
DOI: 10.1039/D6DD00043F, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Guangchen Liu, Songge Yang, Yu Zhong
Masgent is an AI-driven materials simulation agent that translates user intent into automated workflows. Integrating DFT, MLPs, and ML with a feedback loop, it streamlines simulations and accelerates data-driven materials discovery.
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Data-driven exploration of AB2X4 (X = O, S, Se, Te) spinel chemical space

Digital Discovery, 2026, 5,2128-2136
DOI: 10.1039/D6DD00064A, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Panyalak Detrattanawichai, Zhenzhu Li, Hyunsoo Park, Kinga O. Mastej, Aron Walsh
The crystal structure of the mineral spinel is adopted by hundreds of known materials. We use machine learning to explore how many spinels remain to be discovered.
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Enhancing predictive modeling with molecular fingerprint fusion strategies

Digital Discovery, 2026, 5,2232-2242
DOI: 10.1039/D5DD00302D, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Viktoriia Turkina, Melanie R. W. Messih, Etienne Kant, Jelle T. Gringhuis, Annemieke Petrignani, Garry Corthals, Jake W. O'Brien, Saer Samanipour
QSAR performance depends on molecular representation; mid-level fusion of non-hashed fingerprints may improve accuracy across diverse tasks.
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Chemist Eye: a visual language model-powered system for safety monitoring and robot decision-making in self-driving laboratories

Digital Discovery, 2026, 5,2209-2220
DOI: 10.1039/D6DD00062B, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Francisco Munguia-Galeano, Zhengxue Zhou, Satheeshkumar Veeramani, Hatem Fakhruldeen, Louis Longley, Rob Clowes, Andrew I. Cooper
Safety risks in self-driving laboratories (SDLs) are amplified by robotics and automation, motivating the development of real-time AI-driven monitoring and hazard-response systems.
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A deep learning approach to searching property spaces of materials

Digital Discovery, 2026, 5,2172-2183
DOI: 10.1039/D5DD00422E, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Robert J. Appleton, Brian C. Barnes, Steven F. Son, Alejandro Strachan
Large scale discovery of melt-castable molecular materials using deep learning property prediction and genetic algorithms.
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Generalization of long-range machine learning potentials in complex chemical spaces

Digital Discovery, 2026, 5,2195-2208
DOI: 10.1039/D5DD00570A, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Michał Sanocki, Julija Zavadlav
Long-range interactions are key to making machine learning interatomic potentials accurate and transferable across chemical space.
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Large language models in materials science and the need for open-source approaches

Digital Discovery, 2026, 5,1981-1990
DOI: 10.1039/D5DD00499C, Review Article

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Fengxu Yang, Weitong Chen, Jack D. Evans
This work explores the use of large language models across data mining, chemical understanding, predictive insight, and autonomous experimental orchestration.
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MolRes-DTA: a molecular-multiview fusion and residue-aware model for drug-target affinity prediction

Digital Discovery, 2026, 5,2184-2194
DOI: 10.1039/D5DD00365B, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Hongli Hou, Qi Wei, Dian Huang, Minglu Zhao, Hongliang Duan, Shengzhong Feng
MolRes-DTA fuses drug and protein features for affinity prediction.
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AiiDA-TrainsPot: towards automated training of neural-network interatomic potentials

Digital Discovery, 2026, 5,2292-2309
DOI: 10.1039/D6DD00005C, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Davide Bidoggia, Nataliia Manko, Maria Peressi, Antimo Marrazzo
AiiDA-TrainsPot is an automated, modular active-learning workflow that efficiently trains neural-network interatomic potentials with minimal human supervision.
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FlowMol3: flow matching for 3D de novo small-molecule generation

Digital Discovery, 2026, 5,2052-2066
DOI: 10.1039/D5DD00363F, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Ian Dunn, David R. Koes
A generative model capable of sampling realistic molecules with desired properties could accelerate chemical discovery across a wide range of applications.
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Distilling and exploiting quantitative insights from large language models for enhanced Bayesian optimization of chemical reactions

Digital Discovery, 2026, 5,2042-2051
DOI: 10.1039/D6DD00052E, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Roshan A. Patel, Mingxuan Li, Chin-Fei Chang, Louis De Lescure, Paul Chauvin, Alan Cherney, Saeed Moayedpour, Sven Jager, Yasser Jangjou
Preference learning on LLM-completed surveys is used to distill their chemical insights into a utility function. The utility function is used to focus Bayesian optimization efforts, leading to significantly enhanced chemical reaction optimization.
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RAISE: a self-driving laboratory for interfacial property formulation discovery

Digital Discovery, 2026, 5,2254-2270
DOI: 10.1039/D5DD00531K, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Mohammad Nazeri, Sheldon Mei, Jeffrey Watchorn, Alex Zhang, Erin Ng, Tao Wen, Abhijoy Mandal, Kevin Golovin, Alán Aspuru-Guzik, Frank Gu
RAISE is the first autonomous closed-loop system linking formulations to surface wettability to discover formulations from user-defined goals.
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shnitsel-tools: a toolkit for the full lifecycle of surface hopping trajectory data

Digital Discovery, 2026, 5,2016-2027
DOI: 10.1039/D5DD00299K, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Kevin Höllring, Theodor E. Röhrkasten, Carolin Müller
uses a hierarchical, metadata-rich data model to enable consistent analysis of surface hopping simulations across molecules, methods, and simulation settings.
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A user's guide to your first self-driving liquid handling lab

Digital Discovery, 2026, 5,2028-2041
DOI: 10.1039/D5DD00525F, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Apostolos P. Maroulis, Dylan M. Waynor, Quinn M. Gallagher, Roshan A. Patel, Matthew Tamasi, D. Christopher Radford, Michael A. Webb, Adam J. Gormley
To contribute towards the democratization of self-driving laboratories, we created a foundation-level guide focusing on Bayesian optimization driven experimentation and low-cost open-source automated liquid handlers.
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Deep generative inverse design of biofunctional polymer coatings using conditional GANs

Digital Discovery, 2026, 5,2137-2150
DOI: 10.1039/D5DD00332F, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Wafa Benaatou, Mudasir Ahmad Wani, Kashish Ara Shakil
A conditional generative adversarial network (cGAN) enables the inverse design of biofunctional polymer coatings with tunable compositions and predictive biological performance.
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Self-driving laboratories in Korea: a new era of autonomous discovery

Digital Discovery, 2026, 5,1968-1980
DOI: 10.1039/D6DD00024J, Perspective

Open Access

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Jiho Hwang, Seongmin Kim, Sooyoun Lim, Juhwan Kim, Seungwoo Lee, Seonghyeon Min, Jisoo Song, Jeongwook Lim, Seonghun Hong, Jin-Ha Hwang, Youn-Suk Choi, Dong-Hwa Seo, Sang Soo Han, KangGeon Kim, Su-Hyun Yoo, Jungho Shin, Jang Wook Choi, Jaewook Nam, Jungwon Park, Jaeyune Ryu, Yousung Jung
This perspective highlights the current status and future prospects of self-driving laboratories in Korea. It illustrates how AI-robotics integration accelerates material discovery through closed-loop automation and cross-disciplinary collaboration.
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OPENXRD: a comprehensive benchmark framework for LLM/MLLM XRD question answering

Digital Discovery, 2026, 5,1991-2015
DOI: 10.1039/D5DD00519A, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Ali Vosoughi, Ayoub Shahnazari, Yufeng Xi, Zeliang Zhang, Griffin Hess, Chenliang Xu, Niaz Abdolrahim
We introduce OPENXRD, a comprehensive benchmarking framework for evaluating large language models (LLMs) and multimodal LLMs (MLLMs) in crystallography question answering.
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