Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D4DD90009J, Editorial Open Access   This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.Jason E. Hein, Joshua SchrierIn this editorial we set expectations and requirements for submissions describing discovery-enabling hardware.To cite this article before page numbers are
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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
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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
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