Dr Matthias Degroote Joins the Editorial Board of Digital Discovery!

 

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 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 and to the exciting developments that lie ahead with Dr Degroote on board!

Digital Discovery is an international gold open-access journal.

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New themed collection with the NeurIPS AI4Mat 2023 workshop

The AI for Materials Design logo

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.

Meet the winners of the Digital Discovery Outstanding Early Career Researcher Award 2023

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!

Research infographic – Robotically automated 3D printing and testing of thermoplastic material specimens

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

An infographic summarising the linked article.

Read the article here:

Robotically automated 3D printing and testing of thermoplastic material specimens

“Formalizing chemical physics using the Lean theorem prover” featured on Breaking Math

Josephson et al.‘s paper “Formalizing chemical physics using the Lean theorem prover” is featured on a new episode of the Breaking Math podcast! Find it at the links below or in your favourite podcatcher.

Apple Podcasts

Spotify podcasts

Read the open access article here.

Research infographic – Digitisation of a modular plug and play 3D printed continuous flow system for chemical synthesis

Our new infographic highlights work from Hilton et al. on a 3D-printed, modular system for classical and photochemical synthesis:

An infographic summarising the linked article.

Read their paper below to find out more:

Digitisation of a modular plug and play 3D printed continuous flow system for chemical synthesis

Mireia Benito Montaner, Matthew R. Penny and Stephen T. Hilton, Digital Discovery, 2023, 2, 1797–1805

Research infographic – Evaluating the roughness of structure–property relationships using pretrained molecular representations

Work by Coley et al. features in the next Digital Discovery infographic, which introduces  a reformulation of the roughness index (ROGI) to help understand the roughtness of QSPR surfaces created by new models.

An infographic summarising the linked article.

Get the whole story in their article, available open access:

Evaluating the roughness of structure–property relationships using pretrained molecular representations

David E. Graff, Edward O. Pyzer-Knapp, Kirk E. Jordan, Eugene I. Shakhnovich and Connor W. Coley, Digital Discovery, 2023, 2, 1452–1460

Research infographic – Driving school for self-driving labs

Our latest research infographic shares Snapp and Brown’s heuristic framework for defining the operaiton of self-driving labs.

An infographic summarising the linked article

Find out more in their open access article here:

Driving school for self-driving labs

Kelsey L. Snapp and Keith A. Brown, Digital Discovery, 2023, 2, 1620–1629, DOI: 10.1039/D3DD00150D

Research infographic – Feature selection in molecular graph neural networks based on quantum chemical approaches

Discover new research on feature selection for molecular systems in this new infographic:

An infographic summarising the linked article

Read the open access full article at the link below:

Feature selection in molecular graph neural networks based on quantum chemical approaches

Daisuke Yokogawa and Kayo Suda, Digital Discovery, 2023, 2, 1089–1097, DOI: 10.1039/D3DD00010A

Research Infographic – Model-based evaluation and data requirements for parallel kinetic experimentation and data-driven reaction identification and optimization

Our newest infographic presents research from Jiscoot, Uslamin, and Pidko, developing an algorithm for constructing and evaluating kinetic models which has a a grounding in physical effects.

An infographic summarising the linked research article

Read their full article, open access, at the link below:

Model-based evaluation and data requirements for parallel kinetic experimentation and data-driven reaction identification and optimization

Nathan Jiscoot, Evgeny A. Uslamin, and Evgeny A. Pidko, Digital Discovery, 2023, 2, 994–1005, DOI: 10.1039/D3DD00016H