Modeling Electronic Response Properties with an Explicit-Electron Machine Learning Potential
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Modeling Electronic Response Properties with an Explicit-Electron Machine Learning Potential
Authors
Keywords
-
Journal
Journal of Chemical Theory and Computation
Volume -, Issue -, Pages -
Publisher
American Chemical Society (ACS)
Online
2022-02-17
DOI
10.1021/acs.jctc.1c00978
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- (2021) Tsz Wai Ko et al. Nature Communications
- Machine Learning Force Fields
- (2021) Oliver T. Unke et al. CHEMICAL REVIEWS
- Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules
- (2021) Valentin Vassilev-Galindo et al. JOURNAL OF CHEMICAL PHYSICS
- Four Generations of High-Dimensional Neural Network Potentials
- (2021) Jörg Behler CHEMICAL REVIEWS
- Ab Initio Machine Learning in Chemical Compound Space
- (2021) Bing Huang et al. CHEMICAL REVIEWS
- Machine learning potentials for extended systems: a perspective
- (2021) Jörg Behler et al. EUROPEAN PHYSICAL JOURNAL B
- Teaching a neural network to attach and detach electrons from molecules
- (2021) Roman Zubatyuk et al. Nature Communications
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
- (2021) Oliver T. Unke et al. Nature Communications
- FCHL revisited: Faster and more accurate quantum machine learning
- (2020) Anders S. Christensen et al. JOURNAL OF CHEMICAL PHYSICS
- SciPy 1.0: fundamental algorithms for scientific computing in Python
- (2020) Pauli Virtanen et al. NATURE METHODS
- The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules
- (2020) Justin S. Smith et al. Scientific Data
- Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles
- (2020) Max Veit et al. JOURNAL OF CHEMICAL PHYSICS
- Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations
- (2020) Xiaowei Xie et al. Journal of Chemical Theory and Computation
- Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials
- (2020) V. Zaverkin et al. Journal of Chemical Theory and Computation
- A Reactive Force Field with Coarse-Grained Electrons for Liquid Water
- (2020) Itai Leven et al. Journal of Physical Chemistry Letters
- Electron-Passing Neural Networks for Atomic Charge Prediction in Systems with Arbitrary Molecular Charge
- (2020) Derek P. Metcalf et al. Journal of Chemical Information and Modeling
- Organic piezoelectric materials: milestones and potential
- (2019) Sarah Guerin et al. NPG Asia Materials
- Accurate molecular polarizabilities with coupled cluster theory and machine learning
- (2019) David M. Wilkins et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Operators in quantum machine learning: Response properties in chemical space
- (2019) Anders S. Christensen et al. JOURNAL OF CHEMICAL PHYSICS
- Polarizable Force Fields for Biomolecular Simulations: Recent Advances and Applications
- (2019) Zhifeng Jing et al. Annual Review of Biophysics
- 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
- Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network
- (2019) Roman Zubatyuk et al. Science Advances
- C-GeM: Coarse-Grained Electron Model for Predicting the Electrostatic Potential in Molecules
- (2019) Itai Leven et al. Journal of Physical Chemistry Letters
- Wannier90 as a community code: new features and applications
- (2019) Giovanni Pizzi et al. JOURNAL OF PHYSICS-CONDENSED MATTER
- Hierarchical modeling of molecular energies using a deep neural network
- (2018) Nicholas Lubbers et al. JOURNAL OF CHEMICAL PHYSICS
- SchNet – A deep learning architecture for molecules and materials
- (2018) K. T. Schütt et al. JOURNAL OF CHEMICAL PHYSICS
- Towards exact molecular dynamics simulations with machine-learned force fields
- (2018) Stefan Chmiela et al. Nature Communications
- Review of force fields and intermolecular potentials used in atomistic computational materials research
- (2018) Judith A. Harrison et al. Applied Physics Reviews
- SchNetPack: A Deep Learning Toolbox For Atomistic Systems
- (2018) K. T. Schütt et al. Journal of Chemical Theory and Computation
- Calculation of the Infrared Intensity of Crystalline Systems. A Comparison of Three Strategies Based on Berry Phase, Wannier Function, and Coupled-Perturbed Kohn–Sham Methods
- (2018) R. Dovesi et al. Journal of Physical Chemistry C
- Psi4 1.1: An Open-Source Electronic Structure Program Emphasizing Automation, Advanced Libraries, and Interoperability
- (2017) Robert M. Parrish et al. Journal of Chemical Theory and Computation
- Advanced capabilities for materials modelling with Quantum ESPRESSO
- (2017) P Giannozzi et al. JOURNAL OF PHYSICS-CONDENSED MATTER
- Control of piezoelectricity in amino acids by supramolecular packing
- (2017) Sarah Guerin et al. NATURE MATERIALS
- Chemistry with semi-classical electrons: reaction trajectories auto-generated by sub-atomistic force fields
- (2017) Chen Bai et al. Chemical Science
- ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
- (2017) J. S. Smith et al. Chemical Science
- ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
- (2017) Justin S. Smith et al. Scientific Data
- An Empirical Polarizable Force Field Based on the Classical Drude Oscillator Model: Development History and Recent Applications
- (2016) Justin A. Lemkul et al. CHEMICAL REVIEWS
- eReaxFF: A Pseudoclassical Treatment of Explicit Electrons within Reactive Force Field Simulations
- (2016) Md Mahbubul Islam et al. Journal of Chemical Theory and Computation
- Magnetism and Bond Order in Diatomic Molecules Described by Semiclassical Electrons
- (2016) Solen Ekesan et al. JOURNAL OF PHYSICAL CHEMISTRY B
- Exchange potentials for semi-classical electrons
- (2016) Judith Herzfeld et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Mathematical modeling and physical reality in noncovalent interactions
- (2015) Peter Politzer et al. JOURNAL OF MOLECULAR MODELING
- Non-adiabatic dynamics modeling framework for materials in extreme conditions
- (2015) Hai Xiao et al. MECHANICS OF MATERIALS
- Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network
- (2015) S. Alireza Ghasemi et al. PHYSICAL REVIEW B
- Pointillist rendering of electron charge and spin density suffices to replicate trends in atomic properties
- (2015) Solen Ekesan et al. PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- Transferable pseudoclassical electrons for aufbau of atomic ions
- (2014) Solen Ekesan et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- On representing chemical environments
- (2013) Albert P. Bartók et al. PHYSICAL REVIEW B
- Natural polarizability and flexibility via explicit valency: The case of water
- (2012) Seyit Kale et al. JOURNAL OF CHEMICAL PHYSICS
- A beginner's guide to the modern theory of polarization
- (2012) Nicola A. Spaldin JOURNAL OF SOLID STATE CHEMISTRY
- Nonadiabatic Study of Dynamic Electronic Effects during Brittle Fracture of Silicon
- (2012) Patrick L. Theofanis et al. PHYSICAL REVIEW LETTERS
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- (2012) Matthias Rupp et al. PHYSICAL REVIEW LETTERS
- Maximally localized Wannier functions: Theory and applications
- (2012) Nicola Marzari et al. REVIEWS OF MODERN PHYSICS
- Lewis-inspired representation of dissociable water in clusters and Grotthuss chains
- (2011) Seyit Kale et al. JOURNAL OF BIOLOGICAL PHYSICS
- Atom-centered symmetry functions for constructing high-dimensional neural network potentials
- (2011) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- Effect of the damping function in dispersion corrected density functional theory
- (2011) Stefan Grimme et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu
- (2010) Stefan Grimme 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
- The dynamics of highly excited electronic systems: Applications of the electron force field
- (2009) Julius T. Su et al. JOURNAL OF CHEMICAL PHYSICS
- Polarization effects in molecular mechanical force fields
- (2009) Piotr Cieplak et al. JOURNAL OF PHYSICS-CONDENSED MATTER
- QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials
- (2009) Paolo Giannozzi et al. JOURNAL OF PHYSICS-CONDENSED MATTER
- 970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13
- (2009) Lorenz C. Blum et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- Dielectric properties of solids in the regular and split-charge equilibration formalisms
- (2009) Razvan A. Nistor et al. PHYSICAL REVIEW B
- Vibrational subsystem analysis: A method for probing free energies and correlations in the harmonic limit
- (2008) H. Lee Woodcock et al. JOURNAL OF CHEMICAL PHYSICS
- Origin and control of superlinear polarizability scaling in chemical potential equalization methods
- (2008) G. Lee Warren et al. JOURNAL OF CHEMICAL PHYSICS
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now