Self-Consistency Training for Density-Functional-Theory Hamiltonian
Prediction

Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction

14 March 2024

Siyuan Liu

Papers citing "Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction"

6 / 6 papers shown

Title
TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials Philipp Thölke Gianni De Fabritiis AI4CE 61 193 0 05 Feb 2022
Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties Jigyasa Nigam M. J. Willatt Michele Ceriotti 24 57 0 24 Sep 2021
The Open Catalyst 2020 (OC20) Dataset and Community Challenges L. Chanussot Abhishek Das Siddharth Goyal Thibaut Lavril Muhammed Shuaibi ... Brandon M. Wood Junwoong Yoon Devi Parikh C. L. Zitnick Zachary W. Ulissi 239 513 0 20 Oct 2020
DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory Yixiao Chen Linfeng Zhang Han Wang E. Weinan 35 72 0 01 Aug 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 302 42,038 0 03 Dec 2019
Finding Density Functionals with Machine Learning John C. Snyder M. Rupp K. Hansen K. Müller K. Burke 94 476 0 22 Dec 2011