Digital Discovery Blog

We are delighted to announce the recipients of the Outstanding Early Career Research Award 2025 from Digital Discovery: a team comprising Martin Fitzner, Adrian Šošić, Alexander V. Hopp, Marcel Müller, Rim Rihana, Karin Hrovatin, Fabian Liebig, Mathias Winkel, Wolfgang Halter and Jan Gerit Brandenburg.

Their award-winning paper, “BayBE: a Bayesian Back End for experimental planning in the low-to-no-data regime“, describes an open-source software framework that empowers researchers to use Bayesian optimisation techniques. These seek optima, such as the best set of reaction conditions, through a minimum number of experiments.

In their own words:

“The Bayesian Back End (BayBE) was born at Merck KGaA from the merger of two independent Bayesian optimization projects. Anticipating a rapid increase in use cases benefiting from this technology, we unified these efforts to build an easy-to-use toolkit and scalable foundation. In doing so, BayBE became a blueprint for professional code development, also proving the value of open-source software in a traditionally guarded industrial setting.

“Today, its footprint has expanded significantly, utilized by various companies across the chemical and pharmaceutical sectors, as well as by universities for research and teaching. Back at Merck KGaA, the internal BayBE ecosystem continues to rapidly evolve through comprehensive APIs, MCPs, and graphical user interfaces, alongside efforts to drive fully autonomous robotic lab equipment. To push these capabilities even further, our ongoing research focuses on enabling robust transfer learning, multi-fidelity optimization, hybrid search spaces, and adapting Bayesian optimization for sequence-based domains.”

Dr Fitzner shared these remarks on the award, on behalf of the team:

“As industrial scientists, open-sourcing code and publishing methods aren’t always the default, but with BayBE, we really wanted to prioritize open collaboration. We are so grateful to Digital Discovery for this award and for recognizing that effort. The research around Bayesian optimization and the BayBE ecosystem is ongoing, in particular around areas such as transfer learning, hybrid spaces and sequence-based domains. Whether you have feature requests or developed a new algorithm that you might want to bring to a wider audience by integrating it into our framework, we encourage BO enthusiasts around the globe to get in touch via GitHub issues / discussions / email.”

We are proud to celebrate this outstanding contribution to the field and look forward to what this team uncovers next!

Read the winners’ paper here.

About the team:

Martin Fitzner

Martin holds a PhD in Computational Physics from University College London (UCL). As Principal Data Scientist for Digital Chemistry he aims to unite the fields of cheminformatics, machine learning, computational chemistry, experimental planning and professional software development. Martin is one of the founders and main developers of BayBE and product owner for various related technologies at Merck KGaA.

Adrian Šošić

Adrian holds a PhD in Electrical Engineering from TU Darmstadt, with a research focus on probabilistic modeling, Bayesian methods, and machine learning. Driven by a deep passion for mathematics and software engineering, he channels these interests into his work as a creator and core developer of BayBE. As a Lead Data Scientist at Merck KGaA, his goal is to establish Bayesian optimization as an industry standard — bridging the gap between cutting-edge probabilistic methods and real-world experimental workflows.

Alexander Hopp

Alex joined Merck directly after earning a PhD in Mathematics in 2020. As a Senior AI Research Scientist, he works at the forefront of cutting-edge technologies and leverages them for Merck KGaA. He is one of the founders and members of the BayBE Core Team, and contributes as a leading developer, maintainer, and product owner of the full BayBE ecosystem. In addition to BayBE, he participates in other interdisciplinary projects and supports them with his programming expertise.

Marcel Müller

Marcel holds a PhD in Theoretical Chemistry from the University of Bonn, where he developed efficient quantum-mechanical methods for molecular screening. After working with Merck KGaA on digital chemistry and Bayesian optimization workflows based on BayBE, he joined Alán Aspuru-Guzik at the University of Toronto, where he develops agentic AI systems for chemistry, early-stage drug discovery, and autonomous optimization.

Rim Rihana

Rim holds a bachelor’s degree in Computer Science from Darmstadt University of Applied Sciences, earned through a dual-study program with Merck KGaA. She contributed to BayBE during a practical phase of her bachelor’s studies, where she helped develop an early-stopping criterion for BayBE based on model internal data and created a function to visualize PI iterations in a 3D plot. Rim is currently a Data Culture Manager responsible for community and enablement around myGPT Suite, Merck’s internal conversational AI, fostering user empowerment and responsible usage.

Karin Hrovatin

Karin is an applied machine learning researcher at Merck KGaA. She focuses on understanding how machine learning models interact with real-world data in order to advance commercially relevant use-cases. She worked in different biological domains, such as protein design and target identification with single-cell data, where she developed approaches for integration of different data sources and efficient protein candidate design with small training datasets. Besides, she is interested in entrepreneurial activities.

Fabian Liebig

Fabian holds a bachelor’s degree in computer science from Darmstadt University of Applied Sciences, earned through a dual-study program with Merck KGaA. He first became involved with BayBE during the practical phase of his bachelor’s studies, where he developed a benchmarking package for BayBE, integrated it into the cloud-based evaluation pipeline, and contributed to the underlying cloud infrastructure. Fabian is currently pursuing a dual master’s degree while continuing his work with Merck KGaA. His focus is on benchmarking, evaluation, and software implementation within the BayBE ecosystem.

Mathias Winkel

Being trained as a physicist, Mathias is inherently curious, which has driven him across numerous scientific fields—primarily in digital domains: large-scale numeric simulations of laser-plasma interactions, parallel-in-time methods, finite-element modeling of cardiac activity, commercial software development for particle simulations, brain-inspired AI research, modular automation for laboratories and production, and more. Today, Mathias leads the AI & Quantum Lab at Merck KGaA, Darmstadt, Germany. The team identifies and transfers disruptive technologies into the company to globally accelerate scientific discovery and production processes. One of these technologies is BayBE, in which Mathias serves as user voice and advisor.

Wolfgang Halter

Wolfgang earned his Dr.-Ing. in Control Theory and Synthetic Biology at the University of Stuttgart, studying how feedback loops govern biological systems. At Merck KGaA, he heads a global team of 40 in AI, Data Science & Bioinformatics, building AI solutions across the full Life Science business. He contributes to BayBE in an advisory and strategic capacity, helping steer the project where it matters most.

Jan Gerit Brandenburg

Gerit holds a PhD in Theoretical Chemistry from the University of Bonn and a master’s degree in Physics from Heidelberg University. He is Senior Director for AI & Automation in Drug Discovery at Merck KGaA, Darmstadt, Germany, where he builds AI-driven and automated discovery platforms connecting machine learning, computational chemistry, and lab automation. Previously, he led Digital Chemistry and helped scale computational and AI capabilities across the company. Gerit has authored more than 60 peer-reviewed publications and brings deep scientific expertise to the interface of computation, AI, and experimentation. He led the BayBE paper as a strong example of cutting-edge technology development with substantial relevance and traction in industry.

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We invite you to read our new Digital Discovery Emerging Investigators Collection 2025.

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

Read the collection

Digital Discovery is committed to supporting and recognizing the excellent work of early career researchers. We are thus proud to present our first annual Emerging Investigators collection. The collection showcases research carried out by internationally recognised, up-and-coming scientists in the early stage of their independent careers across the Digital Discovery community who are making outstanding contributions to their respective fields.

A selection of the articles has been provided below. Be sure to visit the collection the read the rest!

All articles in Digital Discovery are open access and free to read.

If you would like to nominate a colleague or yourself as an Emerging Investigator for our next collection, please contact us for further details by reply to this email.

Profile

Contributors to the Digital Discovery Emerging Investigators collection 2025

Digital Discovery, 2026, 5, DOI: 10.1039/D6DD90017H

Papers

A self-driving fluidic lab for data-driven synthesis of lead-free perovskite nanocrystals

Sina Sadeghi, Karl Mattsson, Joshua Glasheen, Victoria Lee, Christine Stark, Pragyan Jha, Nikolai Mukhin, Junbin Li   Arup Ghorai, Negin Orouji, Christopher H. J. Moran, Alireza Velayati, Jeffrey A. Bennett, Richard B. Canty, Kristofer G. Reyes and Milad Abolhasani

Digital Discovery, 2025, 4, 1722-1733, DOI: 10.1039/D5DD00062A

Programmable aerosol chemistry coupled to chemical imaging establishes a new arena for automated chemical synthesis and discovery

Jakub D. Wosik, Chaoyi Zhu, Zehua Li and S. Hessam M. Mehr

Digital Discovery, 2025, 4, 2423-2430, DOI: 10.1039/D5DD00100E

Going beyond SMILES enumeration for data augmentation in generative drug discovery

Helena Brinkmann, Antoine Argante, Hugo ter Steege and Francesca Grisoni

Digital Discovery, 2025, 4, 2752-2764, DOI: 10.1039/D5DD00028A

Harnessing surrogate models for data-efficient predictive chemistry: descriptors vs. learned hidden representations

Guanming Chen and Thijs Stuyver

Digital Discovery, 2025, 4, 3227-3237, DOI: 10.1039/D5DD00256G

Active learning meets metadynamics: automated workflow for reactive machine learning interatomic potentials

Valdas Vitartas, Hanwen Zhang, Veronika Juraskova, Tristan Johnston-Wood and Fernanda Duarte

Digital Discovery, 2026, 5, 108-122, DOI: 10.1039/D5DD00261C

One step retrosynthesis of drugs from commercially available chemical building blocks and conceivable coupling reactions

Babak Mahjour, Felix Katzenburg, Emil Lammi and Tim Cernak

Digital Discovery, 2026, 5, 153-160, DOI: 10.1039/D5DD00310E

FiberForge: enabling high-throughput simulations of the mechanical properties of helical fibrils

Kieran Nehil-Puleo and Zhongyue John Yang

Digital Discovery, 2026, 5, 919-930, DOI: 10.1039/D5DD00307E

shnitsel-tools: a toolkit for the full lifecycle of surface hopping trajectory data

Kevin Höllring, Theodor E. Röhrkasten and Carolin Müller

Digital Discovery, 2026, 5, DOI: 10.1039/D5DD00299K

Physics-informed machine learning for predicting temperature-dependent chemical properties

Mahyar Rajabi-Kochi, Hanie Rezaei, Sartaaj Takrim Khan Bhanu Mamillapalli, Maryam Ebrahimiazar, Haoming Ye, Rose Moosavian, Mohammad Zargartalebi, David Sinton and Seyed Mohamad Moosavi

Digital Discovery, 2026, 5, DOI: 10.1039/D5DD00489F

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

Sincerely,

Digital Discovery Editorial Office

Royal Society of Chemistry

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.

Follow us on LinkedIn and Bluesky for new articles and the latest news from Digital Discovery and related journals at the Royal Society of Chemistry.

We are pleased to welcome Professor Jun Jiang to the Advisory Board of Digital Discovery

Professor Jiang is a Professor in the School of Chemistry and Materials Science at the University of Science and Technology of China (USTC) in Hefei, China. His research lies at the intersection of intelligent chemistry, data-driven scientific discovery, and automated robotic platforms for precision science.

By integrating artificial intelligence, robotics, and high-throughput experimentation, Professor Jiang has made significant contributions to advancing materials and chemical research. He is the developer of the “Robotic AI-Chemist”, a system that autonomously designs, executes, and iterates experiments in China. The latest iteration, Robotic AI-Chemist 2.0, leverages cloud-connected infrastructure and large-language-model-driven agents to support scalable, reproducible, and collaborative scientific exploration.

On joining the Advisory Board, Professor Jiang said:

“Being part of Digital Discovery aligns with my vision of accelerating science through openness and innovation. I look forward to supporting the journal’s mission to disseminate transformative research that bridges computation, experimentation, and real-world impact.”

We are delighted to welcome Professor Jiang and look forward to his insights and contributions in supporting the mission and future direction of Digital Discovery.

As we enter the last quarter of 2025, we are excited to share our latest updates from Digital Discovery with the community.

Get future updates directly to your inbox with our email alerts. Sign up here.

Latest News

Digital Discovery has 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 have 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. Our congratulations to the winners! Find out more about the winners and their work in our blog post.

Research Spotlight

We were delighted to feature a manuscript by Omar M. Yaghi et al. on the journal’s cover on the same day that Professor Yaghi received a Nobel Prize for his work on metal-organic frameworks. Read our October front cover article here: “Comparison of LLMs in extracting synthesis conditions and generating Q&A datasets for metal–organic frameworks”.

Data-driven accelerated science depends on well-organised, accessible datasets. In “A FAIR research data infrastructure for high-throughput digital chemistry”, Cousty et al. discuss the research data infrastructure of SwissCat+, which transforms experimental metadata in to RDF graphs using an ontology-driven semantic model.

We’re pleased to share the first article from our collaboration with the AI in Drug Discovery workshop at ICANN 2025: “MARCUS: molecular annotation and recognition for curating unravelled structures”.  This web-based tool from Steinbeck et al. provides a human-in-the-loop approach to extracting and curating data from the natural product literature. Access MARCUS at https://marcus.decimer.ai/.

Digital Discovery in the Community

We were proud to offer our support to a number of events and awards in the busy 2025 conference season. Our congratulations to all of the winners!

We were pleased to again sponsor a prize for the best talk at the Machine Learning and AI in Bio(Chemical) Engineering Conference, won by Calvin Yu (University of Bristol, UK). Our colleagues at Molecular Systems Design & Engineering also sponsored the award for best poster, won by ZhengJie Liew (University of Cambridge, UK).

Our partnership with the Accelerate Conference continues, with prizes for best posters to Florian Boser (Universität Münster, Germany), Xu Chen (University of Toronto, Canada) and Joy-Lynn Kobti (University of Windsor, Canada). We look forward to inviting all of the presenters at the meeting to contribute to a themed collection in the journal in the near future.

Finally, our new Deputy Editor Alexander Whiteside presented the People’s Prize sponsored by Digital Discovery at the 8th Artificial Intelligence in Chemistry Symposium, and was pleased to meet the winner, Sara Tanovic (University of Oxford, UK).

Editorial Board

The journal held its annual Editorial Board meeting online in October, shaping its strategy for 2026 and discussing our successes and challenges throughout the preceding year. We were pleased to welcome three new Associate Editors to the Board:

Prof. Milad Abolhasani of North Carolina State University, United States provides his expertise on self-driving labs. Prof. Abolhasani is the ALCOA Professor, a University Faculty Scholar, and the Director of the Graduate Program in the Department of Chemical and Biomolecular Engineering at North Carolina State University. He also serves as the Director of Accelerated Technologies within NC State’s Integrative Sciences Initiative.

Prof. Xin Hong of Zhejiang University, China contributes knowledge on the intersection of synthetic chemistry and data science. Prof. Hong is a tenured Associate Professor in the Department of Chemistry at Zhejiang University, and research focuses on understanding reaction mechanisms and structure–performance relationships in molecular synthesis, with a particular interest in integrating mechanistic insight with data-driven modelling.

Dr Melodie Christiansen from Merck & Co., Inc., United States, offers insights bridging chemistry, automation, and data science. Prof. Christiansen serves as Director of Data-Rich Experimentation in Process Research & Development Enabling Technologies at Merck & Co., Inc., leading the development and deployment of AI-driven automation technologies that accelerate pharmaceutical process research.

We also bid farewell to Editorial Board member Dr Linda Hung of the Toyota Research Institute, United States, whose term as an Associate Editor ends this year. 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.

Collections

In addition to our upcoming themed collection with the Accelerate Conference 2025, 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 and Technology. We look forward to working with the participants in due course. Follow the journal on social media or watch out for our next newsletter for information on our planned call for papers on Large Language Models, and the publication of our collection on Quantum Computing.

Upcoming Events

Executive Editor Anna Rulka will be representing Digital Discovery at this year’s Pacifichem symposium in Honolulu, Hawaii, on 15-20 December 2025. This year’s meeting features sessions organised by our Editor-in-Chief Alán Aspuru-Guzik and Associate Editor Yousung Jung. We look forward to meeting you there!

Stay Connected

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We are thrilled to announce the winners of the Outstanding Early Career Researcher Award 20245: Daniel Probst and Jan Weinreich, recognized for their innovative paper published in Digital Discovery, “Learning on compressed molecular representations“

In their work, Daniel and Jan present MolZip, a simple yet powerful method that leverages string compression to predict molecular properties. By compressing concatenated SMILES and protein sequences, MolZip competes impressively with more complex graph-based methods outperforming 9 out of 10 state-of-the-art graph neural networks in predicting protein-ligand binding affinities.

Their approach is a compelling reminder that elegant, data-efficient solutions can still hold their ground even in the era of sophisticated machine learning models.

About the winners:
Daniel Probst received his PhD in Chemistry and Molecular Sciences from the University of Bern. After research roles at IBM and EPFL, he is now a tenure-track assistant professor at Wageningen University, focusing on sustainable machine learning applications in biology and chemistry.

Jan Weinreich specializes in machine learning for chemistry and materials science. He develops scalable algorithms for molecular property prediction and is co-founder of Chembricks AI, a company building AI platforms for materials discovery.

“Winning this prize is really cool and was a great surprise, especially for an article in one of my favourite journals. Of course, it is, as it is always the case, the contributions of scientific collectives rather than single groups or individuals that make a paper happen.”

– Daniel Probst

“We are truly honoured to receive the Outstanding Early Career Researcher Award from Digital Discovery. This recognition means a great deal to us. We hope that our work on compressed molecular representations will be of some use to the community to build AI models in chemistry that do not require huge computational resources but give quite competitive accuracy. Even with powerful GPUs at hand – sometimes being able to quickly build ML models can be helpful to “debug” data pipelines and test if allocating more attention/compute to a more “sophisticated” AI architecture”

– Jan Weinreich

🔗 Read the winner’s paper here!