Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D3DD00073G, Paper Open Access   This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.James L. McDonagh, Stamatia Zavitsanou, Alexander Harrison, Dimitry Zubarev, Theordore van Kessel, Benjamin H. Wunsch, Flaviu CipciganA chemical space analysis of carbon capture amines
Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D3DD00214D, Paper Open Access   This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.Shuyi Jia, Akaash R. Parthasarathy, Rui Feng, Guojing Cong, Chao Zhang, Victor FungGeneral pre-training strategy of graph neural networks for materials science.To cite this
Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D3DD00216K, Paper Open Access   This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.Gang Seob Jung, Jong Youl Choi, Sangkeun Matthew LeeDeveloping an automated active learning framework for Neural Network Potentials, focusing on accurately simulating bond-breaking in
Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D4DD00018H, Paper Open Access   This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.Robin Ruff, Patrick Reiser, Jan Stühmer, Pascal FriederichGraph neural networks (GNNs) have been applied to a large variety of applications in materials science and
Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D4DD00020J, Communication Open Access   This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.Flaviu Cipcigan, Jonathan Booth, Rodrigo Neumann Barros Ferreira, Carine Ribeiro dos Santos, Mathias SteinerGFlowNets discover reticular materials with simulated CO2 working capacity outperforming all
Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D3DD00200D, Paper Open Access   This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.Amer Marwan El-Samman, Incé Amina Husain, Mai Huynh, Stefano De Castro, Brooke Morton, Stijn De BaerdemackerThe embedding vectors from a Graph Neural Network trained
Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D3DD00252G, Paper Open Access   This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.Paula Torren-Peraire, Alan Kai Hassen, Samuel Genheden, Jonas Verhoeven, Djork-Arné Clevert, Mike Preuss, Igor V. TetkoSynthesis planning relies on retrosynthesis models, yet this relationship
Researchers have developed a framework that uses machine learning to accelerate the search for new proton-conducting materials, that could potentially improve the efficiency of hydrogen fuel cells.
Antineutrinos generated in nuclear fission can be measured to remotely monitor the operation of nuclear reactors and verify that they are not being used to produce nuclear weapons, report scientists. Thanks to a newly developed method, it is now possible to estimate a reactor's operation