no code implementations • 15 Nov 2021 • A. Gilad Kusne, Austin McDannald, Brian DeCost, Corey Oses, Cormac Toher, Stefano Curtarolo, Apurva Mehta, Ichiro Takeuchi
Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades.
no code implementations • 11 Jun 2020 • A. Gilad Kusne, Heshan Yu, Changming Wu, Huairuo Zhang, Jason Hattrick-Simpers, Brian DeCost, Suchismita Sarker, Corey Oses, Cormac Toher, Stefano Curtarolo, Albert V. Davydov, Ritesh Agarwal, Leonid A. Bendersky, Mo Li, Apurva Mehta, Ichiro Takeuchi
Active learning - the field of machine learning (ML) dedicated to optimal experiment design, has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics [1].
1 code implementation • 13 Mar 2018 • Corey Oses, Cormac Toher, Stefano Curtarolo
The expansion of programmatically-accessible materials data has cultivated opportunities for data-driven approaches.
Materials Science