Digital Discovery Blog

We’re pleased to announce that a new themed collection from Digital Discovery has now been published online.

Read the collection

 

This new themed collection represents a collaboration between the editors of Digital Discovery and the Acceleration Consortium, organisers of the Accelerate Conference. The goal of the conference was to explore the power of self-driving labs (SDLs), which combine AI, automation, and advanced computing to accelerate materials and molecular discovery.

This themed collection, Guest Edited by Prof. Janine George (Federal Institute for Materials Research and Testing (BAM) and Friedrich Schiller University Jena, Germany), Prof. Claudiane Ouellet-Plamondon (École de Technologie Supérieure, Canada) and Prof. Kristofer Reyes (University at Buffalo, United States), features contributions that cover various aspects of this process, whether specifically presented at the conference or not.

The papers span innovations in algorithms, decision-making, and integrated self-driving laboratories—from efficient experimental design and probabilistic programming to orchestration frameworks coordinating sensing, actuation, and learning. Collectively, they illustrate new principles for accelerating and scaling discovery.

The articles from this collection have been provided below. All articles in Digital Discovery are open access and free to read.

A new collection to feature contributors to Accelerate Conference 2025 is currently underway, and we look forward to sharing further information in the near future.

 

Editorial

Introduction to the “Accelerate Conference 2023–2024” themed collection

Janine George, Claudiane Ouellet-Plamondon and Kristofer Reyes

Digital Discovery, 2026, 5, DOI: 10.1039/D5DD90057C

 

Opinion

Autonomous laboratories for accelerated materials discovery: a community survey and practical insights

Linda Hung, Joyce A. Yager, Danielle Monteverde, Dave Baiocchi, Ha-Kyung Kwon, Shijing Sun and Santosh Suram

Digital Discovery, 2024, 3, 1273-1279, DOI: 10.1039/D4DD00059E

 

Review

Democratizing self-driving labs: advances in low-cost 3D printing for laboratory automationSayan Doloi, Maloy Das, Yujia Li, Zen Han Cho, Xingchi Xiao, John V. Hanna, Matthew Osvaldoa and Leonard Ng Wei Tat

Digital Discovery, 2025, 4, 1685-1721, DOI: 10.1039/D4DD00411F

 

Tutorial Review

Review of low-cost self-driving laboratories in chemistry and materials science: the “frugal twin” concept

Stanley Lo, Sterling G. Baird, Joshua Schrier, Ben Blaiszik, Nessa Carson, Ian Foster, Andrés Aguilar-Granda, Sergei V. Kalinin, Benji Maruyama, Maria Politi, Helen Tran, Taylor D. Sparks and Alán Aspuru-Guzik

Digital Discovery, 2024, 3, 842-868, DOI: 10.1039/D3DD00223C

 

Communication

Stability and transferability of machine learning force fields for molecular dynamics applications

Salatan Duangdangchote, Dwight S. Seferos and Oleksandr Voznyy

Digital Discovery, 2024, 3, 2177-2182, DOI: 10.1039/D4DD00140K

 

Papers

Autonomous organic synthesis for redox flow batteries via flexible batch Bayesian optimization

Clara Tamura, Heather Job, Henry Chang, Wei Wang, Yangang Liang and  Shijing Sun

Digital Discovery, 2025, 4, 2737-2751, DOI: 10.1039/D5DD00017C

Advancing vanadium redox flow battery analysis: a deep learning approach for high-throughput 3D visualization and bubble quantification

André Colliard-Granero, Kangjun Duan, Roswitha Zeis, Michael H. Eikerling, Kourosh Malek and Mohammad J. Eslamibidgoli

Digital Discovery, 2025, 4, 2724-2736, DOI: 10.1039/D5DD000158G

twa: The World Avatar Python package for dynamic knowledge graphs and its application in reticular chemistry

Jiaru Bai, Simon D. Rihm, Aleksandar Kondinski, Fabio Saluz, Xinhong Deng, George Brownbridge, Sebastian Mosbach, Jethro Akroyd and Markus Kraft

Digital Discovery, 2025, 4, 123-2135, DOI: 10.1039/ D5DD00069F

BayBE: a Bayesian Back End for experimental planning in the low-to-no-data regime

Martin Fitzner, Adrian Šošić, Alexander V. Hopp, Marcel Müller, Rim Rihana, Karin Hrovatin, Fabian Liebig, Mathias Winkel, Wolfgang Halter and Jan Gerit Brandenburg

Digital Discovery, 2025, 4, 1991-2000, DOI: 10.1039/D5DD00050E

Atomate2: modular workflows for materials science

Alex M. Ganose, Hrushikesh Sahasrabuddhe, Mark Asta, Kevin Beck, Tathagata Biswas, Alexander Bonkowski, Joana Bustamante, Xin Chen, Yuan Chiang, Daryl C. Chrzan, Jacob Clary, Orion A. Cohen, Christina Ertural, Max C. Gallant, Janine George, Sophie Gerits, Rhys E. A. Goodall, Rishabh D. Guha, Geoffroy Hautier, Matthew Horton, T. J. Inizan, Aaron D. Kaplan, Ryan S. Kingsbury, Matthew C. Kuner, Bryant Li, Xavier Linn, Matthew J. McDermott, Rohith Srinivaas Mohanakrishnan, Aakash N. Naik, Jeffrey B. Neaton, Shehan M. Parmar, Kristin A. Persson, Guido Petretto, Thomas A. R. Purcell, Francesco Ricci, Benjamin Rich, Janosh Riebesell, Gian-Marco Rignanese, Andrew S. Rosen, Matthias Scheffler, Jonathan Schmidt Jimmy-Xuan Shen, Andrei Sobolev, Ravishankar Sundararaman, Cooper Tezak, Victor Trinquet, Joel B. Varley, Derek Vigil-Fowler, Duo Wang, David Waroquiers, Mingjian Wen, Han Yang, Hui Zheng, Jiongzhi Zheng, Zhuoying Zhu and Anubhav Jain

Digital Discovery, 2025, 4, 1944-1973, DOI: 10.1039/D5DD00019J

Predefined attention-focused mechanism using center-environment features: a machine learning study of alloying effects on the stability of Nb₅Si₃ alloys

Yuchao Tang, Bin Xiao, Shuizhou Chen, Quan Qian and Yi Liu

Digital Discovery, 2025, 4, 1870-1883, DOI: 10.1039/D5DD00079C

SynCoTrain: a dual classifier PU-learning framework for synthesizability prediction

Sasan Amariamir, Janine George and Philipp Benner

Digital Discovery, 2025, 4, 2737-2751, DOI: 10.1039/D4DD00394B

Large language models for knowledge graph extraction from tables in materials science

Max Dreger, Kourosh Malek and Michael Eikerling

Digital Discovery, 2025, 4, 1221-1231, DOI: 10.1039/D4DD00362D

ADEL: an automated drop-cast electrode setup for high-throughput screening of battery materials

Maha Ismail, Maria Angeles Cabañero, Joseba Orive, Lakshmipriya Musuvadhi Babulal, Javier Garcia, Maria C. Morant-Miñana, Jean-Luc Dauvergne, Francisco Bonilla, Iciar Monterrubio, Javier Carrasco Amaia Saracibarb and  Marine Reynaud

Digital Discovery, 2025, 4, 943-953, DOI: 10.1039/D4DD00381K

Archerfish: a retrofitted 3D printer for high-throughput combinatorial experimentation via continuous printing

Alexander E. Siemenn, Basita Das, Eunice Aissi, Fang Sheng, Lleyton Elliott, Blake Hudspeth, Marilyn Meyers, James Serdy and Tonio Buonassisi

Digital Discovery, 2025, 4, 896-909, DOI: 10.1039/D4DD00249K

Opentrons for automated and high-throughput viscometry

Beatrice W. Soh, Aniket Chitre, Shu Zheng Tan, Yuhan Wang, Yinqi Yi, Wendy Soh, Kedar Hippalgaonkar and D. Ian Wilson

Digital Discovery, 2025, 4, 711-722, DOI: 10.1039/D4DD00368C

Preferential Bayesian optimization improves the efficiency of printing objects with subjective qualities

James R. Deneault, Woojae Kim, Jiseob Kim, Yuzhe Gu, Jorge Chang, Benji Maruyama, Jay I. Myung and Mark A. Pitt

Digital Discovery, 2025, 4, 723-737, DOI: 10.1039/D4DD00320A

Multi-objective Bayesian optimization: a case study in material extrusion

Jay I. Myung, James R. Deneault, Jorge Chang, Inhan Kang, Benji Maruyama and Mark A. Pitt

Digital Discovery, 2025, 4, 464-476, DOI: 10.1039/D4DD00281D

A materials discovery framework based on conditional generative models applied to the design of polymer electrolytes

Arash Khajeh, Xiangyun Lei, Weike Ye, Zhenze Yang, Linda Hung, Daniel Schweigert and  Ha-Kyung Kwon

Digital Discovery, 2025, 4, 11-20, DOI: 10.1039/D4DD00293H

Data efficiency of classification strategies for chemical and materials design

Quinn M. Gallagher and  Michael A. Webb

Digital Discovery, 2025, 4, 135-148, DOI: 10.1039/D4DD00298A

Agent-based learning of materials datasets from the scientific literature

Mehrad Ansari and Seyed Mohamad Moosavi

Digital Discovery, 2025, 4, 2607-2617, DOI: 10.1039/D4DD00252K

Combining Hammett σ constants for Δ-machine learning and catalyst discovery

Diana Rakotonirina, Marco Bragato, Stefan Heinen and O. Anatole von Lilienfeld

Digital Discovery, 2025, 4, 2487-2496, DOI: 10.1039/D4DD00228H

Leveraging GPT-4 to transform chemistry from paper to practice

Wenyu Zhang, Mason A. Guy, Jerrica Yang, Lucy Hao, Junliang Liu, Joel M. Hawkins, Jason Mustakis, Sebastien Monfette and Jason E. Hein

Digital Discovery, 2024, 3, 2367-2376, DOI: 10.1039/D4DD00248B

Pellet dispensomixer and pellet distributor: open hardware for nanocomposite space exploration via automated material compounding

Miguel Hernández-del-Valle, Jorge Ilarraza-Zuazo, Enrique Dios-Lázaro, Javier Rubio, Joris Audoux and Maciej Haranczyk

Digital Discovery, 2024, 3, 2032-2041, DOI: 10.1039/D4DD00198B

 

We hope you enjoy this new themed collection from Digital Discovery.

Welcome to the first Digital Discovery newsletter of 2026! We’re pleased to share a recap of the most important developments in 2025, and highlight some upcoming events the journal is supporting. We wish all of our readers, authors, reviewers and editors a successful new year in 2026. Get future updates directly to your inbox with our email alerts. Sign up here.

Latest News

We’re delighted to welcome Dr Indra Priyadarsini to the Editorial Board of the journal as an Associate Editor. Dr Priyadarsini is a Research Scientist at IBM Research – Tokyo, where she focuses on developing models and algorithms for foundation models in materials discovery. She earned her B.E. in Electronics and Communication Engineering from PES Institute of Technology, Bangalore, India, in 2016, followed by an M.E. and Ph.D. from Shizuoka University, Japan, in 2019 and 2022, respectively. Her doctoral research centered on optimization algorithms and deep learning. She is currently actively involved in advanced research related to AI for Math and Science, multimodal foundation models, and their application to accelerated scientific discovery. She has contributed to open-source frameworks in AI for science and has been recognized with the IPSJ Industrial Achievement Award (2024) for her impactful work in AI-based materials research.

We are very excited to announce that the 2026 #RSCPoster Conference will be taking place for 24 hours starting 3rd March 2026, 12:00 UTC.

The #RSCPoster conference is an annual event that has become a staple on many scientific community calendars. Held entirely online on LinkedIn over 24 hours, the unique format removes the environmental and financial costs of attending a traditional conference, and helps scientific researchers share their work and network across disciplines, wherever they are in the world. Find out more and join in: https://rsc.li/poster

2025 in Review

Digital Discovery introduced a new article type, Commit, for incremental improvements to articles previously published in the journal. This could include improved hardware designs, new features in software, or expanded datasets. Find out more in our Editorial at DOI: 10.1039/D4DD90053G, read the first Commit in DOI: 10.1039/D5DD00089K, and contact the Editorial Office at digitaldiscovery-rsc@rsc.org with your questions or comments.

Jan Weinreich and Daniel Probst received the Digital Discovery Outstanding Early Career Researcher Award 2025 for their paper “Learning on compressed molecular representations”. Their work uses string compression to predict molecular properties, with performance competitive to state-of-the-art graph neural networks. Find out more about the winners and their work in our blog post.

We were pleased to welcome three new Associate Editors to the journal’s Editorial Board, Prof. Milad Abolhasani of North Carolina State University, United States; Prof. Xin Hong of Zhejiang University, China; and Dr Melodie Christiansen from Merck & Co., Inc., United States. We also bid farewell to Editorial Board member Dr Linda Hung of the Toyota Research Institute, United States. Her insights and support of the journal from the outset were essential to its present success, and we wish her well in her future endeavours.

Our editors were pleased to meet the community at events such as AI4AM, Pacifichem, and WATOC. We were pleased to support the Accelerate Conference, and invite the participants to contribute to a themed collection of articles in the journal. We look forward to sharing news on the launch of a further themed collection on large language models, and the line-up of accepted papers from our collection on quantum computing, in the coming months.

Upcoming events

Digital Discovery Associate Editor Prof. Joshua Schrier will be attending Chemical Compound Space Conference 2026 and presenting awards from Digital Discovery and PCCP for the best posters.

Digital Discovery will feature contributions from the second international symposium on High-Throughput Catalysis Design, in a joint collection with Reaction Chemistry & Engineering and Catalysis Science & Technology. We look forward to working with the participants in due course.

Follow our channels below to keep up to date on the events we’re supporting in 2026.

Submit your work to Digital Discovery

Find out more about Digital Discovery on our webpage, where you can also find our author guidelines. Digital Discovery has received a 2024 Impact Factor of 5.6, has an article acceptance rate of 67%, and provides a first decision on articles sent to peer review in an average of 45 days.

Publishing open access with RSC journals unlocks the full potential of your research – bringing increased visibility, wider readership and higher citation potential to your work. As a not-for-profit organisation serving the chemical sciences community, we ensure that our article processing charge (APC) remains the most competitive of major publishers. More details can be found here and the APC for Digital Discovery is £2200. You can also use our journal finder tool to check if your institution currently has an agreement with the RSC that may entitle you to a discount of the APC.

Stay Connected

Postdoc or early career researcher? Interested in building your peer review experience and helping improve open data at Digital Discovery? Consider becoming a data reviewer. Find out more on our blog post.

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