标题
Fast Near Ab Initio Potential Energy Surfaces Using Machine Learning
作者
关键词
-
出版物
JOURNAL OF PHYSICAL CHEMISTRY A
Volume 126, Issue 25, Pages 4013-4024
出版商
American Chemical Society (ACS)
发表日期
2022-06-18
DOI
10.1021/acs.jpca.2c02243
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Diffusion Monte Carlo approaches for studying nuclear quantum effects in fluxional molecules
- (2022) Ryan J. DiRisio et al. Wiley Interdisciplinary Reviews-Computational Molecular Science
- Δ-machine learning for potential energy surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) level of theory
- (2021) Apurba Nandi et al. JOURNAL OF CHEMICAL PHYSICS
- Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states
- (2021) Tamara Husch et al. JOURNAL OF CHEMICAL PHYSICS
- Analytical gradients for molecular-orbital-based machine learning
- (2021) Sebastian J. R. Lee et al. JOURNAL OF CHEMICAL PHYSICS
- GPU-Accelerated Neural Network Potential Energy Surfaces for Diffusion Monte Carlo
- (2021) Ryan J. DiRisio et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Using Diffusion Monte Carlo Wave Functions to Analyze the Vibrational Spectra of H7O3+ and H9O4+
- (2021) Ryan J. DiRisio et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
- (2021) Cheol Woo Park et al. npj Computational Materials
- FCHL revisited: Faster and more accurate quantum machine learning
- (2020) Anders S. Christensen et al. JOURNAL OF CHEMICAL PHYSICS
- Machine learning accurate exchange and correlation functionals of the electronic density
- (2020) Sebastian Dick et al. Nature Communications
- OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features
- (2020) Zhuoran Qiao et al. JOURNAL OF CHEMICAL PHYSICS
- Deep-neural-network solution of the electronic Schrödinger equation
- (2020) Jan Hermann et al. Nature Chemistry
- A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules
- (2019) Lixue Cheng et al. JOURNAL OF CHEMICAL PHYSICS
- PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges
- (2019) Oliver T. Unke et al. Journal of Chemical Theory and Computation
- Statistical Analysis of the Effect of Deuteration on Quantum Delocalization in CH5+
- (2019) Meredith E. Fore et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Machine learning for potential energy surfaces: An extensive database and assessment of methods
- (2019) Gunnar Schmitz et al. JOURNAL OF CHEMICAL PHYSICS
- An Efficient Approach for Studies of Water Clusters Using Diffusion Monte Carlo
- (2019) Victor G. M. Lee et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Regression Clustering for Improved Accuracy and Training Costs with Molecular-Orbital-Based Machine Learning
- (2019) Lixue Cheng et al. Journal of Chemical Theory and Computation
- Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
- (2018) Thuong T. Nguyen et al. JOURNAL OF CHEMICAL PHYSICS
- Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis
- (2018) Matthew Welborn et al. Journal of Chemical Theory and Computation
- Combining active-space coupled-cluster approaches with moment energy corrections via the CC(P;Q) methodology: connected quadruple excitations
- (2017) Nicholas P. Bauman et al. MOLECULAR PHYSICS
- ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
- (2017) J. S. Smith et al. Chemical Science
- Assessment of the accuracy of coupled cluster perturbation theory for open-shell systems. I. Triples expansions
- (2016) Janus J. Eriksen et al. JOURNAL OF CHEMICAL PHYSICS
- Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
- (2016) Alexander V. Shapeev MULTISCALE MODELING & SIMULATION
- Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
- (2015) Raghunathan Ramakrishnan et al. Journal of Chemical Theory and Computation
- Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials
- (2015) Suresh Kondati Natarajan et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Permutation invariant polynomial neural network approach to fitting potential energy surfaces
- (2013) Bin Jiang et al. JOURNAL OF CHEMICAL PHYSICS
- Development of a “First Principles” Water Potential with Flexible Monomers: Dimer Potential Energy Surface, VRT Spectrum, and Second Virial Coefficient
- (2013) Volodymyr Babin et al. Journal of Chemical Theory and Computation
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- (2012) Matthias Rupp et al. PHYSICAL REVIEW LETTERS
- Rovibrational spectra of ammonia. I. Unprecedented accuracy of a potential energy surface used with nonadiabatic corrections
- (2011) Xinchuan Huang et al. JOURNAL OF CHEMICAL PHYSICS
- Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
- (2010) Albert P. Bartók et al. PHYSICAL REVIEW LETTERS
- Infrared spectroscopy of protonated ethylene: The nature of proton binding in the non-classical structure
- (2009) Allen M. Ricks et al. CHEMICAL PHYSICS LETTERS
- An adaptive density-guided approach for the generation of potential energy surfaces of polyatomic molecules
- (2009) Manuel Sparta et al. THEORETICAL CHEMISTRY ACCOUNTS
- IR Spectrum of the Ethyl Cation: Evidence for the Nonclassical Structure
- (2007) Horia-Sorin Andrei et al. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
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