Digital Discovery, 2023, 2,1404-1413
DOI: 10.1039/D3DD00114H, Paper
DOI: 10.1039/D3DD00114H, Paper
Open Access
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.Weiyi Gong, Tao Sun, Hexin Bai, Shah Tanvir ur Rahman Chowdhury, Peng Chu, Anoj Aryal, Jie Yu, Haibin Ling, John P. Perdew, Qimin Yan
We demonstrate that contrastive representation learning is a computationally efficient and flexible method to incorporate physical constraints, especially those defined by equalities, in machine-learning-based density functional design.
The content of this RSS Feed (c) The Royal Society of Chemistry
We demonstrate that contrastive representation learning is a computationally efficient and flexible method to incorporate physical constraints, especially those defined by equalities, in machine-learning-based density functional design.
The content of this RSS Feed (c) The Royal Society of Chemistry