Predicting oxidation and spin states by high-dimensional neural networks: Applications to lithium manganese oxide spinels

Machine Learning for Molecular Simulation

(2020) Frank Noé et al.   Annual Review of Physical Chemistry

Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

(2020) Michael G. Taylor et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization

(2020) Jon Paul Janet et al.   ACS Central Science

Covalency competition dominates the water oxidation structure–activity relationship on spinel oxides

(2020) Yuanmiao Sun et al.   Nature Catalysis

From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5

(2019) Marco Eckhoff et al.   Journal of Chemical Theory and Computation

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

(2019) Volker L. Deringer et al.   ADVANCED MATERIALS

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

(2019) K. T. Schütt et al.   Nature Communications

Machine-Learning Energy Gaps of Porphyrins with Molecular Graph Representations

(2018) Zheng Li et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network

(2018) Jon Paul Janet et al.   Journal of Physical Chemistry Letters

Inverse molecular design using machine learning: Generative models for matter engineering

(2018) Benjamin Sanchez-Lengeling et al.   SCIENCE

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships

(2017) Jon Paul Janet et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Predicting electronic structure properties of transition metal complexes with neural networks

(2017) Jon Paul Janet et al.   Chemical Science

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Concentration-Dependent Proton Transfer Mechanisms in Aqueous NaOH Solutions: From Acceptor-Driven to Donor-Driven and Back

(2016) Matti Hellström et al.   Journal of Physical Chemistry Letters

Neural network molecular dynamics simulations of solid–liquid interfaces: water at low-index copper surfaces

(2016) Suresh Kondati Natarajan et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

How van der Waals interactions determine the unique properties of water

(2016) Tobias Morawietz et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm

(2015) Michael Gastegger et al.   Journal of Chemical Theory and Computation

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

(2015) Matthias Rupp et al.   Journal of Physical Chemistry Letters

Li-ion battery materials: present and future

(2015) Naoki Nitta et al.   Materials Today

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

Machine learning of molecular electronic properties in chemical compound space

(2013) Grégoire Montavon et al.   NEW JOURNAL OF PHYSICS

A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges

(2012) Tobias Morawietz et al.   JOURNAL OF CHEMICAL PHYSICS

Neural network potentials for metals and oxides - First applications to copper clusters at zinc oxide

(2012) Nongnuch Artrith et al.   PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

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

High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide

(2011) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Determination of manganese valence states in (Mn3+, Mn4+) minerals by electron energy-loss spectroscopy

(2010) S. Zhang et al.   AMERICAN MINERALOGIST

Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

(2010) Geoffroy Hautier et al.   CHEMISTRY OF MATERIALS

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

Challenges for Rechargeable Li Batteries†

(2009) John B. Goodenough et al.   CHEMISTRY OF MATERIALS

Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool

(2009) Alexander Stukowski   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Atomic-resolution imaging of oxidation states in manganites

(2009) M. Varela et al.   PHYSICAL REVIEW B

Jahn–Teller distortion and electronic structure of LiMn2O4

(2008) C.Y. Ouyang et al.   JOURNAL OF ALLOYS AND COMPOUNDS

Interpretation of electron diffusion coefficient in organic and inorganic semiconductors with broad distributions of states

(2008) Juan Bisquert   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Metadynamics Simulations of the High-Pressure Phases of Silicon Employing a High-Dimensional Neural Network Potential

(2008) Jörg Behler et al.   PHYSICAL REVIEW LETTERS