Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide

Performance and Cost Assessment of Machine Learning Interatomic Potentials

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sGDML: Constructing accurate and data efficient molecular force fields using machine learning

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Active learning of uniformly accurate interatomic potentials for materials simulation

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De novo exploration and self-guided learning of potential-energy surfaces

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SchNet – A deep learning architecture for molecules and materials

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Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics

(2018) Volker L. Deringer et al.   Journal of Physical Chemistry Letters

Data-Driven Learning of Total and Local Energies in Elemental Boron

(2018) Volker L. Deringer et al.   PHYSICAL REVIEW LETTERS

Thermodynamic limit for synthesis of metastable inorganic materials

(2018) Muratahan Aykol et al.   Science Advances

Accelerating high-throughput searches for new alloys with active learning of interatomic potentials

(2018) Konstantin Gubaev et al.   COMPUTATIONAL MATERIALS SCIENCE

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

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Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Uncovering Large-Scale Conformational Change in Molecular Dynamics without Prior Knowledge

(2016) Ryan L. Melvin et al.   Journal of Chemical Theory and Computation

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

(2016) Anubhav Jain et al.   JOURNAL OF MATERIALS RESEARCH

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

Taking the Human Out of the Loop: A Review of Bayesian Optimization

(2016) Bobak Shahriari et al.   PROCEEDINGS OF THE IEEE

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

Design and synthesis of the superionic conductor Na10SnP2S12

(2016) William D. Richards et al.   Nature Communications

MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories

(2015) Robert T. McGibbon et al.   BIOPHYSICAL JOURNAL

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

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

What are the true clusters?

(2015) Christian Hennig   PATTERN RECOGNITION LETTERS

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Molecular dynamics simulation of amorphous HfO2for resistive RAM applications

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On representing chemical environments

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Perspective on density functional theory

(2012) Kieron Burke   JOURNAL OF CHEMICAL PHYSICS

RMC_POT: A computer code for reverse monte carlo modeling the structure of disordered systems containing molecules of arbitrary complexity

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High-k (k=30) amorphous hafnium oxide films from high rate room temperature deposition

(2011) Flora M. Li et al.   APPLIED PHYSICS LETTERS

Gate Oxides Beyond SiO2

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

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Two faces of active learning

(2010) Sanjoy Dasgupta   THEORETICAL COMPUTER SCIENCE

PACKMOL: A package for building initial configurations for molecular dynamics simulations

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