Compact atomic descriptors enable accurate predictions via linear models

Origins of structural and electronic transitions in disordered silicon

(2021) Volker L. Deringer et al.   NATURE

Performance and Cost Assessment of Machine Learning Interatomic Potentials

(2020) Yunxing Zuo et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Continuous and optimally complete description of chemical environments using Spherical Bessel descriptors

(2020) Emir Kocer et al.   AIP Advances

Partitioning of sulfur between solid and liquid iron under Earth’s core conditions: Constraints from atomistic simulations with machine learning potentials

(2020) Zhigang Zhang et al.   GEOCHIMICA ET COSMOCHIMICA ACTA

Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids

(2020) Christoph Scherer et al.   Journal of Chemical Theory and Computation

Practical Deep-Learning Representation for Fast Heterogeneous Catalyst Screening

(2020) Geun Ho Gu et al.   Journal of Physical Chemistry Letters

On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

(2020) Jonathan Vandermause et al.   npj Computational Materials

Descriptors representing two- and three-body atomic distributions and their effects on the accuracy of machine-learned inter-atomic potentials

(2020) Ryosuke Jinnouchi et al.   JOURNAL OF CHEMICAL PHYSICS

Hydration free energies from kernel-based machine learning: Compound-database bias

(2020) Clemens Rauer et al.   JOURNAL OF CHEMICAL PHYSICS

Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH3SO3H and H2O2 in Phenol

(2020) Kevin Rossi et al.   Journal of Chemical Theory and Computation

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

Atom-density representations for machine learning

(2019) Michael J. Willatt et al.   JOURNAL OF CHEMICAL PHYSICS

A novel approach to describe chemical environments in high-dimensional neural network potentials

(2019) Emir Kocer et al.   JOURNAL OF CHEMICAL PHYSICS

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

(2019) Jiang Wang et al.   ACS Central Science

Representations and descriptors unifying the study of molecular and bulk systems

(2019) Kevin Rossi et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information

(2018) Oliver T. Unke et al.   JOURNAL OF CHEMICAL PHYSICS

Building machine learning force fields for nanoclusters

(2018) Claudio Zeni et al.   JOURNAL OF CHEMICAL PHYSICS

Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts

(2018) Benjamin Meyer et al.   Chemical Science

MoleculeNet: a benchmark for molecular machine learning

(2018) Zhenqin Wu et al.   Chemical Science

Machine learning hydrogen adsorption on nanoclusters through structural descriptors

(2018) Marc O. J. Jäger et al.   npj Computational Materials

SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates

(2018) Runhai Ouyang et al.   PHYSICAL REVIEW MATERIALS

Chemical shifts in molecular solids by machine learning

(2018) Federico M. Paruzzo et al.   Nature Communications

Representations in neural network based empirical potentials

(2017) Ekin D. Cubuk et al.   JOURNAL OF CHEMICAL PHYSICS

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

(2015) A.P. Thompson et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Big Data of Materials Science: Critical Role of the Descriptor

(2015) Luca M. Ghiringhelli et al.   PHYSICAL REVIEW LETTERS

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Compressive sensing as a paradigm for building physics models

(2013) Lance J. Nelson et al.   PHYSICAL REVIEW B

Neural network interatomic potential for the phase change material GeTe

(2012) Gabriele C. Sosso et al.   PHYSICAL REVIEW B

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS

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