Park, Hyun https://orcid.org/0000-0001-5550-5610
Zhu, Ruijie https://orcid.org/0000-0001-9316-7245
Huerta, E A https://orcid.org/0000-0002-9682-3604
Chaudhuri, Santanu https://orcid.org/0000-0002-4328-2947
Tajkhorshid, Emad https://orcid.org/0000-0001-8434-1010
Cooper, Donny https://orcid.org/0000-0002-2432-972X
Funding for this research was provided by:
National Science Foundation (OAC 2005572)
Department of Energy, Office of Science, Advanced Scientific Computing Research (DE-AC02-06CH11357)
Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science (DE-AC02-06CH11357)
Article Title: End-to-end AI framework for interpretable prediction of molecular and crystal properties
Journal Title: Machine Learning: Science and Technology
Article Type: paper
Copyright Information: © 2023 The Author(s). Published by IOP Publishing Ltd
Publication dates
Date Received: 2022-12-21
Date Accepted: 2023-05-10
Online publication date: 2023-06-29