Probabilistic prediction of material stability: integrating convex hulls into active learning-Reference-Cited by-同舟云学术

Probabilistic prediction of material stability: integrating convex hulls into active learning

Published:2024 Issue: Volume: Page:
ISSN:2051-6347
Container-title:Materials Horizons
language:en
Short-container-title:Mater. Horiz.

Author:

Novick Andrew¹^ORCID,Cai Diana²^ORCID,Nguyen Quan³,Garnett Roman³,Adams Ryan⁴,Toberer Eric¹^ORCID

Affiliation:

1. Department of Physics, Colorado School of Mines, Golden, Colorado, USA

2. Center for Computational Mathematics, Flatiron Institute Address, New York, New York, USA

3. Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, Missouri, USA

4. Department of Computer Science, Princeton University, New Jersey, USA

Abstract

Stability prediction is accelerated by treating the convex hull as a probabilistic object, allowing for an efficient active learning process that minimizes the number of thermodynamic calculations necessary to define the convex hull.

Funder

Office of Advanced Cyberinfrastructure

Division of Information and Intelligent Systems

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/MH/D4MH00432A

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