Related references
Note: Only part of the references are listed.Solving the electronic Schrodinger equation for multiple nuclear geometries with weight-sharing deep neural networks
Michael Scherbela et al.
NATURE COMPUTATIONAL SCIENCE (2022)
Assessing conformer energies using electronic structure and machine learning methods
Dakota Folmsbee et al.
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2021)
A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
Tsz Wai Ko et al.
NATURE COMMUNICATIONS (2021)
Machine Learning Force Fields
Oliver T. Unke et al.
CHEMICAL REVIEWS (2021)
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Wojciech Samek et al.
PROCEEDINGS OF THE IEEE (2021)
QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
Johannes Hoja et al.
SCIENTIFIC DATA (2021)
Unsupervised Learning Methods for Molecular Simulation Data
Aldo Glielmo et al.
CHEMICAL REVIEWS (2021)
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
John A. Keith et al.
CHEMICAL REVIEWS (2021)
Teaching a neural network to attach and detach electrons from molecules
Roman Zubatyuk et al.
NATURE COMMUNICATIONS (2021)
Machine Learning for Molecular Simulation
Frank Noé et al.
Annual Review of Physical Chemistry (2020)
FCHL revisited: Faster and more accurate quantum machine learning
Anders S. Christensen et al.
JOURNAL OF CHEMICAL PHYSICS (2020)
Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations
Xiaowei Xie et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2020)
Exploring chemical compound space with quantum-based machine learning
O. Anatole von Lilienfeld et al.
NATURE REVIEWS CHEMISTRY (2020)
Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
Huziel E. Sauceda et al.
JOURNAL OF CHEMICAL PHYSICS (2020)
OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features
Zhuoran Qiao et al.
JOURNAL OF CHEMICAL PHYSICS (2020)
Deep-neural-network solution of the electronic Schrodinger equation
Jan Hermann et al.
NATURE CHEMISTRY (2020)
A general-purpose machine-learning force field for bulk and nanostructured phosphorus
Volker L. Deringer et al.
NATURE COMMUNICATIONS (2020)
Ab initio solution of the many-electron Schrodinger equation with deep neural networks
David Pfau et al.
PHYSICAL REVIEW RESEARCH (2020)
High-dimensional potential energy surfaces for molecular simulations: from empiricism to machine learning
Oliver T. Unke et al.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2020)
sGDML: Constructing accurate and data efficient molecular force fields using machine learning
Stefan Chmiela et al.
COMPUTER PHYSICS COMMUNICATIONS (2019)
GFN2-xTB-An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions
Christoph Bannwarth et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)
Unmasking Clever Hans predictors and assessing what machines really learn
Sebastian Lapuschkin et al.
NATURE COMMUNICATIONS (2019)
A generally applicable atomic-charge dependent London dispersion correction
Eike Caldeweyher et al.
JOURNAL OF CHEMICAL PHYSICS (2019)
PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges
Oliver T. Unke et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)
Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network
Roman Zubatyuk et al.
SCIENCE ADVANCES (2019)
Incorporating long-range physics in atomic-scale machine learning
Andrea Grisafi et al.
JOURNAL OF CHEMICAL PHYSICS (2019)
Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
K. T. Schuett et al.
NATURE COMMUNICATIONS (2019)
Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
Tristan Bereau et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Hierarchical modeling of molecular energies using a deep neural network
Nicholas Lubbers et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
SchNet - A deep learning architecture for molecules and materials
K. T. Schuett et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information
Oliver T. Unke et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
wACSF-Weighted atom-centered symmetry functions as descriptors in machine learning potentials
M. Gastegger et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Charge Separation in Donor-C60 Complexes with Real-Time Green Functions: The Importance of Nonlocal Correlations
Emil Vinas Bostrom et al.
NANO LETTERS (2018)
Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Stefan Elfwing et al.
NEURAL NETWORKS (2018)
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
Linfeng Zhang et al.
PHYSICAL REVIEW LETTERS (2018)
The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
Kun Yao et al.
CHEMICAL SCIENCE (2018)
Toolkit for the Construction of Reproducing Kernel-Based Representations of Data: Application to Multidimensional Potential Energy Surfaces
Oliver T. Unke et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2017)
Interpolation of intermolecular potentials using Gaussian processes
Elena Uteva et al.
JOURNAL OF CHEMICAL PHYSICS (2017)
Machine learning molecular dynamics for the simulation of infrared spectra
Michael Gastegger et al.
CHEMICAL SCIENCE (2017)
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
J. S. Smith et al.
CHEMICAL SCIENCE (2017)
Quantum-chemical insights from deep tensor neural networks
Kristof T. Schuett et al.
NATURE COMMUNICATIONS (2017)
Machine learning unifies the modeling of materials and molecules
Albert P. Bartok et al.
SCIENCE ADVANCES (2017)
Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela et al.
SCIENCE ADVANCES (2017)
First-Principles Models for van der Waals Interactions in Molecules and Materials: Concepts, Theory, and Applications
Jan Hermann et al.
CHEMICAL REVIEWS (2017)
Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
A. P. Thompson et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2015)
Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network
S. Alireza Ghasemi et al.
PHYSICAL REVIEW B (2015)
A charge optimized many-body (COMB) potential for titanium and titania
Yu-Ting Cheng et al.
JOURNAL OF PHYSICS-CONDENSED MATTER (2014)
Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
Katja Hansen et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)
A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections
Tobias Morawietz et al.
JOURNAL OF PHYSICAL CHEMISTRY A (2013)
On representing chemical environments
Albert P. Bartok et al.
PHYSICAL REVIEW B (2013)
A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges
Tobias Morawietz et al.
JOURNAL OF CHEMICAL PHYSICS (2012)
An Extended Charge Equilibration Method
Christopher E. Wilmer et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2012)
Resolution-of-identity approach to Hartree-Fock, hybrid density functionals, RPA, MP2 and GW with numeric atom-centered orbital basis functions
Xinguo Ren et al.
NEW JOURNAL OF PHYSICS (2012)
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Matthias Rupp et al.
PHYSICAL REVIEW LETTERS (2012)
Accurate and Efficient Method for Many-Body van der Waals Interactions
Alexandre Tkatchenko et al.
PHYSICAL REVIEW LETTERS (2012)
Atom-centered symmetry functions for constructing high-dimensional neural network potentials
Joerg Behler
JOURNAL OF CHEMICAL PHYSICS (2011)
High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
Nongnuch Artrith et al.
PHYSICAL REVIEW B (2011)
Nonlocal Activation of a Bistable Atom through a Surface State Charge-Transfer Process on Si(100)-(2 x 1):H
A. Bellec et al.
PHYSICAL REVIEW LETTERS (2010)
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Albert P. Bartok et al.
PHYSICAL REVIEW LETTERS (2010)
Ab initio molecular simulations with numeric atom-centered orbitals
Volker Blum et al.
COMPUTER PHYSICS COMMUNICATIONS (2009)
970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13
Lorenz C. Blum et al.
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2009)
Generalized neural-network representation of high-dimensional potential-energy surfaces
Joerg Behler et al.
PHYSICAL REVIEW LETTERS (2007)
Resonant inelastic x-ray scattering at the oxygen K resonance of NiO:: Nonlocal charge transfer and double-singlet excitations -: art. no. 067402
LC Duda et al.
PHYSICAL REVIEW LETTERS (2006)
Ab initio quantum chemistry:: Methodology and applications
RA Friesner
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2005)
Long-range charge-transfer excited states in time-dependent density functional theory require non-local exchange
A Dreuw et al.
JOURNAL OF CHEMICAL PHYSICS (2003)
Molecular dynamics simulations of biomolecules
M Karplus et al.
NATURE STRUCTURAL BIOLOGY (2002)
Molecular dynamics simulations of biological reactions
A Warshel
ACCOUNTS OF CHEMICAL RESEARCH (2002)
An introduction to kernel-based learning algorithms
KR Müller et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS (2001)
Share Paper
