Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

12 September 2011

Papers citing "Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning"

6 / 6 papers shown

Title
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials Matthias Holzenkamp Dongyu Lyu Ulrich Kleinekathöfer Peter Zaspel 49 0 0 10 Jan 2025
Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies Vivin Vinod Peter Zaspel 62 0 0 15 Oct 2024
Multi-Type Point Cloud Autoencoder: A Complete Equivariant Embedding for Molecule Conformation and Pose Michael Kilgour Mark Tuckerman Jutta Rogal 50 0 0 22 May 2024
A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead Kamila Zaman Alberto Marchisio Muhammad Abdullah Hanif Mohamed Bennai 53 26 0 16 Oct 2023
Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles M. Veit D. Wilkins Yang Yang R. DiStasio Michele Ceriotti 28 90 0 27 Mar 2020
Deep Learning for Computational Chemistry Garrett B. Goh Nathan Oken Hodas Abhinav Vishnu AI4CE 45 674 0 17 Jan 2017