Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning

31 May 2022

Papers citing "Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning"

21 / 21 papers shown

Title
Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space Lixue Cheng Jiace Sun Thomas F. Miller 24 13 0 21 Apr 2022
Molecular Energy Learning Using Alternative Blackbox Matrix-Matrix Multiplication Algorithm for Exact Gaussian Process Jiace Sun Lixue Cheng Thomas F. Miller 46 3 0 20 Sep 2021
Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry Zhuoran Qiao Anders S. Christensen Matthew Welborn F. Manby Anima Anandkumar Thomas F. Miller 93 74 0 31 May 2021
Spherical Message Passing for 3D Graph Networks Yi Liu Limei Wang Meng Liu Xuan Zhang Bora Oztekin Shuiwang Ji GNN 26 199 0 09 Feb 2021
Equivariant message passing for the prediction of tensorial properties and molecular spectra Kristof T. Schütt Oliver T. Unke M. Gastegger 52 522 0 05 Feb 2021
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules Johannes Klicpera Shankari Giri Johannes T. Margraf Stephan Günnemann 48 319 0 28 Nov 2020
Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces Zhuoran Qiao Feizhi Ding Matthew Welborn P. J. Bygrave Daniel G. A. Smith Anima Anandkumar F. Manby Thomas F. Miller 34 7 0 05 Nov 2020
OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features Zhuoran Qiao Matthew Welborn Anima Anandkumar F. Manby Thomas F. Miller AI4CE 41 215 0 15 Jul 2020
Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles M. Veit D. Wilkins Yang Yang R. DiStasio Michele Ceriotti 22 90 0 27 Mar 2020
Deep neural network solution of the electronic Schrödinger equation J. Hermann Zeno Schätzle Frank Noé 189 450 0 16 Sep 2019
Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning Lixue Cheng Nikola B. Kovachki Matthew Welborn Thomas F. Miller 30 44 0 04 Sep 2019
Generative Models for Automatic Chemical Design Daniel Schwalbe-Koda Rafael Gómez-Bombarelli MedIm AI4CE 45 81 0 02 Jul 2019
Deep learning for molecular design - a review of the state of the art Daniel C. Elton Zois Boukouvalas M. Fuge Peter W. Chung AI4CE 3DV 50 328 0 11 Mar 2019
A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules Lixue Cheng Matthew Welborn Anders S. Christensen Thomas F. Miller 44 93 0 10 Jan 2019
Optimization of Molecules via Deep Reinforcement Learning Zhenpeng Zhou S. Kearnes Li Li R. Zare Patrick F. Riley AI4CE 62 537 0 19 Oct 2018
Deep Reinforcement Learning for De-Novo Drug Design Mariya Popova Olexandr Isayev Alexander Tropsha 52 1,017 0 29 Nov 2017
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions Kristof T. Schütt Pieter-Jan Kindermans Huziel Enoc Sauceda Felix Stefan Chmiela A. Tkatchenko K. Müller 103 1,063 0 26 Jun 2017
MoleculeNet: A Benchmark for Molecular Machine Learning Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes C. Geniesse Aneesh S. Pappu K. Leswing Vijay S. Pande OOD 286 1,808 0 02 Mar 2017
By-passing the Kohn-Sham equations with machine learning Felix Brockherde Leslie Vogt Li Li M. Tuckerman K. Burke K. Müller AI4CE 26 605 0 09 Sep 2016
Molecular Graph Convolutions: Moving Beyond Fingerprints S. Kearnes Kevin McCloskey Marc Berndl Vijay S. Pande Patrick F. Riley GNN 78 1,443 0 02 Mar 2016
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning M. Rupp A. Tkatchenko K. Müller O. A. von Lilienfeld AI4CE 78 1,581 0 12 Sep 2011