Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery

6 May 2022

Papers citing "Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery"

7 / 7 papers shown

Title
Neural Polarization: Toward Electron Density for Molecules by Extending Equivariant Networks Bumju Kwak Jeonghee Jo 53 0 0 01 Jun 2024
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation Yan Xiang Yunhao Tang Zheng Gong Hongyi Liu Liang Wu Guang Lin Huai Sun AI4CE 20 0 0 01 Sep 2022
Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression Lixue Cheng Jiace Sun J. E. Deustua Vignesh C. Bhethanabotla Thomas F. Miller 11 6 0 17 Jul 2022
Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands Chenru Duan A. Ladera Julian C.-L. Liu Michael G. Taylor I. Ariyarathna Heather J. Kulik 13 10 0 05 May 2022
Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis Chenru Duan Aditya Nandy Husain Adamji Yuriy Román‐Leshkov Heather J. Kulik 14 6 0 02 Mar 2022
Distributed Representations of Atoms and Materials for Machine Learning Luis M. Antunes R. Grau‐Crespo K. Butler AI4CE 8 26 0 30 Jul 2021
Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles Chenru Duan Shuxin Chen Michael G. Taylor F. Liu Heather J. Kulik AI4CE 14 18 0 24 Jun 2021