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