Digital Discovery Blog

Digital Discovery is delighted to welcome papers for its latest themed collection on Quantum Computing in Chemistry, Material Science and Biotechnology themed collection of Digital Discovery, led by Dr Matthias Degroote (Boehringer Ingelheim Quantum Lab), Prof. Joonho Lee (Harvard University) and Dr Pauline Ollitrault (QC Ware Corp.). If you do not directly work in this field, please do feel free to forward this email to any of your colleagues that might be interested in contributing to this themed collection.

Contributions are welcome in both theory for and applications of quantum computers in chemistry, material science and biotechnology. We would especially like to encourage manuscripts that expand the current area of applicability of quantum computers and introduce innovative ways to discover, characterize and produce new molecules. We will consider near-term and fault-tolerant algorithms as well as improvements over current algorithms and entirely new workflows.

We encourage submissions on topics including, but by no means limited to:

• Synergies between classical and quantum computers that leverage the strengths of both.
• Use of machine learning and data to bring down the cost of quantum computation.
• Tailored algorithms for specific subsets of chemical systems or types of interaction.
• Prediction of chemical properties with data that can efficiently be extracted from a quantum computer.

The deadline for submissions 11 August 2025.

If you would like to contribute to this collection, please let us know by email at digitaldiscovery-rsc@rsc.org, and we will set up a submission link for you to contribute your article.

Promotion of the collection is scheduled for promotion in late 2025, with articles published online as soon as they’re accepted. Authors are welcome to submit original research in the form of a Communication or Full Paper. Authors who would like to contribute a Review article should contact the Editorial office with their proposal. The Editorial Office reserves the right to check suitability of submissions for both the journal and the scope of the collection, and inclusion of accepted articles in the final themed collection is not guaranteed.

You can find out more detailed information about our journal scope and our valued editorial board members on our website. If you have any questions about the journal or the collection, please contact us at the above address.

We are pleased to announce the appointment of six new members to the Digital Discovery advisory board!

We are pleased to announce the expansion of our Advisory Board to include researchers with extensive industry experience. This strategic addition brings valuable perspectives and expertise from the forefront of industry innovations, ensuring that our work remains at the cutting edge of both academic and practical applications.

Meet the New Advisory Board Members:

• Dr Jehad Abed (FAIR, Meta): Researcher focused on autonomous laboratories, clean energy materials, and the application of machine learning to solve energy challenges.
• Dr Matteo Aldeghi (Bayer Research and Innovation Center): Director of Machine Learning Research, focusing on the application of digital tools to pharmaceutical R&D.
• Dr Jan Gerit Brandenburg (Merck KGaA Darmstadt): Director for Digital Chemistry, specializing in AI-driven research and computational simulations for molecular and materials science.
• Dr Kenichi Shimmei (Sekisui Chemical): Head of Informatics Technology Center, integrating Materials Informatics (MI) into R&D processes and leading laboratory automation initiatives.
• Dr Seiji Takeda (IBM Research): Research Scientist and manager, developing AI frameworks and fostering collaborations across academia and industry.

By incorporating industry professionals into our Advisory Board, we aim to strengthen the collaboration between academia and industry, and enhance the relevance of the research published in Digital Discovery.

Join Us in LinkedIn to welcome our new experts in the field to the team!

Digital Discovery is an international gold open-access journal. Join a community committed to impactful research and collaboration. Submit your article now Sign up now to get updates on all articles as they are published on Twitter, LinkedIn, and in our e-alerts.

With the increasing application of large language models (LLMs) in automation and data analysis, Digital Discovery is looking for experts in LLMs to act as peer reviewers. If you would like to take part, please follow the instructions below. Reviewers who have registered their interest will be entered into a prize draw to win an exclusive Digital Discovery mug in March of 2025!

If you have authored or reviewed for us previously, you can log in to your account at https://mc.manuscriptcentral.com/dd and update the “Research Interests” section of your profile to mention “LLMs”, and/or “large language models”. If you don’t currently have an account you can sign up at https://rsc.li/become-a-reviewer, and then complete your Research Interests once the process is complete.

If LLMs are not one of your areas of expertise, but you would be interested in reviewing other papers for Digital Discovery, please let us know, and update your research interests and keywords as mentioned above. We are also interested in recruiting reviewers to assess authors’ datasets and codes – please see this link for more information.

If you have a colleague who is an expert in LLMs, or who would be interested in reviewing for Digital Discovery in general, please feel free to pass this information to them!

We’re excited to announce that Prof Arnaud Demortière, an expert in energy materials and advanced microscopy techniques, is joining our Advisory Board for Digital Discovery. His background and  research will be a great addition to our board.

Prof Demortière has had a distinguished career, with a PhD from the Sorbonne University in Paris and five years of postdoctoral research at Argonne National Laboratory in Chicago. Since 2015, he has been conducting research at the CNRS in France, focusing on understanding the dynamics of Li-ion battery materials. His work primarily involves multi-scale and multimodal techniques, including in situ/operando methods like TEM (transmission electron microscopy) and X-ray techniques.

In addition to his research, Prof Demortière has been leading efforts to incorporate machine learning and deep learning into analysing experimental data, especially in image processing and computer vision. He also co-founded the startup PreDeeption, which focuses on predicting battery life, and he was awarded the CNRS Innovation RISE prize for this achievement.

Join Us in LinkedIn to welcome Prof Arnaud Demortière to the team!

Digital Discovery is an international gold open-access journal. Sign up now to get updates on all articles as they are published on Twitter, LinkedIn, and in our e-alerts.

We are thrilled to announce that Dr Matthias Degroote, a leading expert in quantum chemistry and quantum computing, has joined the editorial board of Digital Discovery. Dr Degroote’s experience and research in the application of quantum computers in drug design will bring great insights and much needed expertise to our journal.

Dr Matthias Degroote is currently investigating the application of quantum computers in drug design at Boehringer Ingelheim. His expertise spans both classical and quantum computing approaches to the quantum many-body problem.

Matthias earned his PhD in physics from Ghent University, where his research focused on Green’s functions. His academic journey continued with postdoctoral research in method development at Ghent University and Rice University. Since 2018, his research has concentrated on the field of quantum computing. He has held prestigious postdoctoral positions at both Harvard University and the University of Toronto.

Dr Degroote’s work at the intersection of quantum computing and drug design is pioneering. By leveraging quantum computers, he aims to revolutionize the drug design process, making it more efficient and effective. His unique approach and innovative research are expected to bring fresh perspectives to Digital Discovery, enhancing the quality and scope of our publications.

We invite the scientific community to join us in welcoming Dr Matthias Degroote to the editorial board of Digital Discovery. His expertise will significantly contribute to our mission of publishing cutting-edge research in digital and computational sciences.

We look forward to your contributions in quantum computing and beyond and to the exciting developments that lie ahead with Dr Degroote on board!

Digital Discovery is an international gold open-access journal. Sign up now to get updates on all articles as they are published on Twitter, LinkedIn, and in our e-alerts.

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

Read the collection

The AI for Accelerated Materials Design (AI4Mat) workshop at NeurIPS 2023 featured many of the ongoing major research themes in materials design, synthesis, and characterization by bringing together an international interdisciplinary community of researchers and enthusiasts. The AI4Mat 2023 organizing committee and the editors of Digital Discovery have curated a selection of research papers drawn from some of the most exciting and high-quality paper submissions from the workshop. We are pleased to share these papers, and a perspective on the workshop as a whole, in this themed collection.

You can find the line-up of the collection below. All articles in Digital Discovery are open access and free to read.

Editorial

Perspective on AI for Accelerated Materials Design at the AI4Mat-2023 Workshop at NeurIPS 2023
Santiago Miret, N. M. Anoop Krishnan, Benjamin Sanchez-Lengeling, Marta Skreta, Vineeth Venugopal and Jennifer N. Wei
Digital Discovery, 2024, 3, DOI: 10.1039/D4DD90010C

Communications

Discovery of novel reticular materials for carbon dioxide capture using GFlowNets
Flaviu Cipcigan, Jonathan Booth, Rodrigo Neumann Barros Ferreira, Carine Ribeiro dos Santos and Mathias Steiner
Digital Discovery, 2024, 3, 449–455, DOI: 10.1039/D4DD00020J

A message passing neural network for predicting dipole moment dependent core electron excitation spectra
Kiyou Shibata and Teruyasu Mizoguchi
Digital Discovery, 2024, 3, 649–653, DOI: 10.1039/D4DD00021H

Papers

Connectivity optimized nested line graph networks for crystal structures
Robin Ruff, Patrick Reiser, Jan Stühmer and Pascal Friederich
Digital Discovery, 2024, 3, 594–601, DOI: 10.1039/D4DD00018H

Learning conditional policies for crystal design using offline reinforcement learning
Prashant Govindarajan, Santiago Miret, Jarrid Rector-Brooks, Mariano Phielipp, Janarthanan Rajendran and Sarath Chandar
Digital Discovery, 2024, 3, 769–785, DOI: 10.1039/D4DD00024B

EGraFFBench: evaluation of equivariant graph neural network force fields for atomistic simulations
Vaibhav Bihani, Sajid Mannan, Utkarsh Pratiush, Tao Du, Zhimin Chen, Santiago Miret, Matthieu Micoulaut, Morten M. Smedskjaer, Sayan Ranu and N. M. Anoop Krishnan
Digital Discovery, 2024, 3, 759–768, DOI: 10.1039/D4DD00027G

Gotta be SAFE: a new framework for molecular design
Emmanuel Noutahi, Cristian Gabellini, Michael Craig, Jonathan S. C. Lim and Prudencio Tossou
Digital Discovery, 2024, 3, 796–704, DOI: 10.1039/D4DD00019F

Reconstructing the materials tetrahedron: challenges in materials information extraction
Kausik Hira, Mohd Zaki, Dhruvil Sheth, Mausam and N. M. Anoop Krishnan
Digital Discovery, 2024, 3, 1021–1037, DOI: 10.1039/D4DD00032C

Towards equilibrium molecular conformation generation with GFlowNets
Alexandra Volokhova, Michał Koziarski, Alex Hernández-García, Cheng-Hao Liu, Santiago Miret, Pablo Lemos, Luca Thiede, Zichao Yan, Alán Aspuru-Guzik and Yoshua Bengio
Digital Discovery, 2024, 3, 1038–1047, DOI: 10.1039/D4DD00023D

CoDBench: a critical evaluation of data-driven models for continuous dynamical systems
Priyanshu Burark, Karn Tiwari, Meer Mehran Rashid, Prathosh A. P. and N. M. Anoop Krishnan
Digital Discovery, 2024, 3, DOI: 10.1039/D4DD00028E

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

We are thrilled to announce our launch of the prestigious Outstanding Early Career Research Award, aimed at recognising and celebrating outstanding contributions to Digital Discovery. This initiative seeks to honour the dedication, innovation, and impactful research  of promising early career researchers.

Andrew White and Glen Hocky are two such remarkable individuals. Their paper, Assessment of chemistry knowledge in large language models that generate code, has not only shown how powerful language models can be in scientific research but has also opened doors for major advancements in chemistry and computational sciences.

In August 2021, OpenAI released the Codex model, a variant of GPT-3 specifically tailored for code generation. This development sparked the curiosity of White, Hocky, and their team, leading them to explore the model’s capabilities in the domain of chemistry. Their investigations revealed that Codex possessed an aptitude for generating code snippets relevant to chemical tasks, signalling a promising opportunity for computational chemistry.

Publishing their findings in Digital Discovery, the researchers unveiled their groundbreaking discoveries for the future landscape of computational chemistry. Their study not only highlighted Codex’s proficiency in handling diverse chemistry-related queries but also emphasized the significance of crafting effective prompts to optimize model performance.

Moreover, the team devised a sophisticated software tool, NLCC, enabling querying of language model APIs and code execution—evidence of their innovative outlook and commitment to advancing scientific inquiry. Although the advent of ChatGPT in November 2022 altered the scientific landscape by introducing a conversational interface, the enduring impact of their contributions remains indisputable.

Meet the winners:

Andrew White is currently a Founder and Head of Science at FutureHouse and an Associate Professor of Chemical Engineering at the University of Rochester. Glen Hocky is an Assistant Professor in the Department of Chemistry and Simons Centre for Computational Physical Chemistry at New York University. They overlapped as postdocs at the University of Chicago and have been collaborating since.

Authors Heta Gandhi, Sam Cox, Geemi Wellawatte, and Ziyue Yang were graduate students at the University of Rochester. Mehrad Ansari was a PhD student at the University of Rochester. Subarna Sasmal, Kangxin Liu, Yuvraj Singh, and William Peña Ccoa were graduate students at NYU at the time of this work.

In the light of receiving this award, the winners commented: “We are honoured to receive this recognition for our work. We have been very gratified to see that our study has resonated with other researchers at a time when the scientific community has rapidly taken up LLMs as tools in their own research. Our work led directly to Prof White’s founding of FutureHouse which is at the forefront of leveraging language models to advance scientific discovery, and we are excited to participate in driving this field forward!”

Join us in celebrating the winners on LinkedIn!

We’re pleased to share this new infographic on research from Haranczyk et al.

Read the article here:

Robotically automated 3D printing and testing of thermoplastic material specimens

In the fast-paced world of scientific research, staying abreast of the latest breakthroughs and innovations is crucial. The RSC, with journals like Digital Discovery, play a vital role in disseminating cutting-edge knowledge, and one of the ways we do so is by recognizing and celebrating outstanding contributions from the global research community. We are thrilled to unveil the winners of the Digital Discovery Outstanding Paper 2022 Award.

The selection process is rigorous, with the Editorial Board carefully evaluating each paper’s scientific merit and its potential to shape future research.

Without further ado, we are proud to introduce the winners of the Digital Discovery Outstanding Paper 2022 Award:

 Automating mix design for 3D concrete printing using optimization methods

Vasileios Sergis and Claudiane M. Ouellet-Plamondon

Digital Discovery, 2022, 1, 645-657

DOI 10.1039/D2DD00040G

Winning this prestigious award is a remarkable achievement for Vasileios Sergis and Claudiane M. Ouellet-Plamondon, the authors of the winning paper. Their work on automating mix design for 3D concrete printing using optimization methods has not only garnered recognition but has also demonstrated its potential to advance 3D concrete printing technology.

In response to the news, Vasileios Sergis expressed his enthusiasm, stating, “Winning the Outstanding Paper Award 2022 is a moment of great importance, honour, and a deeply meaningful achievement for me.” He added, “This award serves as a strong motivator for continuous exploration and commitment to excellence in my academic journey. I am genuinely thankful for this award.”

Claudiane Ouellet-Plamondon echoed the excitement, emphasizing the timeliness of their research, stating, “The timing is great as we want to solve more challenges with machine learning (ML).” She highlighted the potential of ML in designing binder and concrete with a lower CO2 impact, emphasizing its importance in mitigating climate change.

Meet the Authors

• Vasileios Sergis, PhD: A mechanical engineer specializing in automation, additive manufacturing, and artificial intelligence. He earned his bachelor’s degree in “Production Engineering and Management” from the Technical University of Crete, followed by a master’s degree in “Automation Systems” from the Technical University of Athens, where he delved into control system design, mechatronics, and robotics. His academic journey continued with a PhD in engineering at École de Technologie Supérieure – Université du Québec in Montreal, Canada, focusing on automating the development process of mortar mixtures and the quality monitoring of the layer deposition in 3D concrete printing technology by integrating statistics, artificial intelligence, optimization, and computer vision techniques.
• Claudiane Ouellet-Plamondon is a full professor in the Department of Construction Engineering at the École de technologie supérieure (ÉTS) in Montreal, Canada. She holds the Canada Research Chair in Sustainable Multifunctional Materials in the perspective of the ecological transition and the circular economy. She studied a bachelor of engineering at Dalhousie University, a master’s degree in biological sciences at the University of Montreal, a PhD in geoenvironmental engineering from the University of Cambridge. She was a postdoctoral fellow at ETH Zurich. Her research is on functional materials, robotic 3D printing of mortars, bio-based materials, earth construction, valorisation of industrial by-products in cement, concrete and other value-added materials, materials in a circular economy perspective, as well as the sustainability of buildings and cities. She firmly believes that modelling and artificial intelligence have become indispensable tools for designing advanced materials and understanding their behaviour.

For those eager to delve deeper into the award-winning research, Vasileios Sergis and Claudiane M. Ouellet-Plamondon will be presenting their findings in a webinar series scheduled for October. The exact date is yet to be defined, so be sure to follow @digital_rsc on Twitter for updates and details on how to join these insightful sessions.

The Outstanding Paper 2022 Award recognizes and celebrates excellence in digital science. We extend our heartfelt congratulations to Vasileios Sergis and Claudiane M. Ouellet-Plamondon for their outstanding achievement, and we look forward to the insights they will share in their upcoming webinar series. Stay tuned for more exciting discoveries in the world of digital science!

Digital Discovery is an international gold open-access journal. All article processing charges until mid-2024. Sign up now to get updates on all articles as they are published on Twitter and in our e-alerts.

Welcome to Digital Discovery!

We are delighted to welcome Professor Cesar de la Fuente-Nunez from the University of Pennsylvania, USA, as a new Associate Editor for Digital Discovery.

Cesar de la Fuente is a Presidential Assistant Professor at the University of Pennsylvania, where he leads the Machine Biology Group whose goal is to combine the power of machines and biology to help prevent, detect, and treat infectious diseases. Specifically, he pioneered the development of the first antibiotic designed by a computer with efficacy in animals, designed algorithms for antibiotic discovery, reprogrammed venoms into antimicrobials, created novel resistance-proof antimicrobial materials, and invented rapid low-cost diagnostics for COVID-19 and other infections.

De la Fuente is an NIH MIRA investigator and has received recognition and research funding from numerous other groups. Prof. de la Fuente has received over 50 awards. He was recognized by MIT Technology Review as one of the world’s top innovators for “digitizing evolution to make better antibiotics”. He was selected as the inaugural recipient of the Langer Prize, an ACS Kavli Emerging Leader in Chemistry, and received the AIChE’s 35 Under 35 Award and the ACS Infectious Diseases Young Investigator Award.

In 2021, he received the Thermo Fisher Award, and the EMBS Academic Early Career Achievement Award “For the pioneering development of novel antibiotics designed using principles from computation, engineering, and biology.” Most recently, Prof. de la Fuente was awarded the prestigious Princess of Girona Prize for Scientific Research, the ASM Award for Early Career Applied and Biotechnological Research and has been named a Highly Cited Researcher by Clarivate several times.

Professor de la Fuente has given over 200 invited lectures and his scientific discoveries have yielded over 110 publications, including papers in Nature Biomedical Engineering, Nature Communications, PNAS, ACS Nano, Cell, Nature Chemical Biology, Advanced Materials, and multiple patents.

Read some of Cesar’s recent papers below.

Deep generative models for peptide design

Fangping Wan, Daphne Kontogiorgos-Heintz and Cesar de la Fuente-Nunez

Digital Discovery, 2022, 1, 195-208

AI and drug discovery

Morgan Craig, Juan Caicedo, Payel Das, James Collins, Francesca Grisoni, Cesar de la Fuente-Núñez, Yu-Shan Lin, Jian Tang

Cell Reports Physical Science, 2022, 3, 101142

Submit your work to Cesar here.

Please join us in welcoming Professor Cesar de la Fuente to Digital Discovery!