标题
Deep-neural-network solution of the electronic Schrödinger equation
作者
关键词
-
出版物
Nature Chemistry
Volume 12, Issue 10, Pages 891-897
出版商
Springer Science and Business Media LLC
发表日期
2020-09-24
DOI
10.1038/s41557-020-0544-y
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Fermionic neural-network states for ab-initio electronic structure
- (2020) Kenny Choo et al. Nature Communications
- 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
- Backflow Transformations via Neural Networks for Quantum Many-Body Wave Functions
- (2019) Di Luo et al. PHYSICAL REVIEW LETTERS
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
- (2019) K. T. Schütt et al. Nature Communications
- Solving many-electron Schrödinger equation using deep neural networks
- (2019) Jiequn Han et al. JOURNAL OF COMPUTATIONAL PHYSICS
- SchNet – A deep learning architecture for molecules and materials
- (2018) K. T. Schütt et al. JOURNAL OF CHEMICAL PHYSICS
- Alchemical and structural distribution based representation for universal quantum machine learning
- (2018) Felix A. Faber et al. JOURNAL OF CHEMICAL PHYSICS
- Method to Solve Quantum Few-Body Problems with Artificial Neural Networks
- (2018) Hiroki Saito JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN
- Nonlinear Network Description for Many-Body Quantum Systems in Continuous Space
- (2018) Michele Ruggeri et al. PHYSICAL REVIEW LETTERS
- Fast and accurate quantum Monte Carlo for molecular crystals
- (2018) Andrea Zen et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis
- (2018) Matthew Welborn et al. Journal of Chemical Theory and Computation
- Transferable Machine-Learning Model of the Electron Density
- (2018) Andrea Grisafi et al. ACS Central Science
- H4: A model system for assessing the performance of diffusion Monte Carlo calculations using a single Slater determinant trial function
- (2017) Kevin Gasperich et al. JOURNAL OF CHEMICAL PHYSICS
- Solving the quantum many-body problem with artificial neural networks
- (2017) Giuseppe Carleo et al. SCIENCE
- ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
- (2017) J. S. Smith et al. Chemical Science
- Machine learning unifies the modeling of materials and molecules
- (2017) Albert P. Bartók et al. Science Advances
- Machine learning of accurate energy-conserving molecular force fields
- (2017) Stefan Chmiela et al. Science Advances
- Hard Numbers for Large Molecules: Toward Exact Energetics for Supramolecular Systems
- (2014) Alberto Ambrosetti et al. Journal of Physical Chemistry Letters
- Multideterminant Wave Functions in Quantum Monte Carlo
- (2012) Miguel A. Morales et al. Journal of Chemical Theory and Computation
- Optimizing large parameter sets in variational quantum Monte Carlo
- (2012) Eric Neuscamman et al. PHYSICAL REVIEW B
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- (2012) Matthias Rupp et al. PHYSICAL REVIEW LETTERS
- Quantum Monte Carlo and Related Approaches
- (2011) Brian M. Austin et al. CHEMICAL REVIEWS
- Multireference Nature of Chemistry: The Coupled-Cluster View
- (2011) Dmitry I. Lyakh et al. CHEMICAL REVIEWS
- Quantum Monte Carlo study of the first-row atoms and ions
- (2011) P. Seth et al. JOURNAL OF CHEMICAL PHYSICS
- Stochastic Coupled Cluster Theory
- (2010) Alex J. W. Thom PHYSICAL REVIEW LETTERS
- Fermion Monte Carlo without fixed nodes: A game of life, death, and annihilation in Slater determinant space
- (2009) George H. Booth et al. JOURNAL OF CHEMICAL PHYSICS
- Continuum variational and diffusion quantum Monte Carlo calculations
- (2009) R J Needs et al. JOURNAL OF PHYSICS-CONDENSED MATTER
- Full optimization of Jastrow–Slater wave functions with application to the first-row atoms and homonuclear diatomic molecules
- (2008) Julien Toulouse et al. JOURNAL OF CHEMICAL PHYSICS
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started