Optimization and validation of a deep learning CuZr atomistic potential: Robust applications for crystalline and amorphous phases with near-DFT accuracy

Active learning of uniformly accurate interatomic potentials for materials simulation

(2019) Linfeng Zhang et al.   PHYSICAL REVIEW MATERIALS

Physically informed artificial neural networks for atomistic modeling of materials

(2019) G. P. Purja Pun et al.   Nature Communications

Iterative-Learning Strategy for the Development of Application-Specific Atomistic Force Fields

(2019) Tran Doan Huan et al.   Journal of Physical Chemistry C

Fast, accurate, and transferable many-body interatomic potentials by symbolic regression

(2019) Alberto Hernandez et al.   npj Computational Materials

Development of a semi-empirical potential suitable for molecular dynamics simulation of vitrification in Cu-Zr alloys

(2019) M. I. Mendelev et al.   JOURNAL OF CHEMICAL PHYSICS

Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron

(2018) Daniele Dragoni et al.   PHYSICAL REVIEW MATERIALS

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning

(2018) Hongxiang Zong et al.   npj Computational Materials

Machine Learning a General-Purpose Interatomic Potential for Silicon

(2018) Albert P. Bartók et al.   Physical Review X

Accurate force field for molybdenum by machine learning large materials data

(2017) Chi Chen et al.   PHYSICAL REVIEW MATERIALS

Glass-forming ability, thermal stability of B2 CuZr phase, and crystallization kinetics for rapidly solidified Cu–Zr–Zn alloys

(2016) D.Y. Wu et al.   JOURNAL OF ALLOYS AND COMPOUNDS

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

High tensile plasticity and strength of a CuZr-based bulk metallic glass composite

(2016) Zhiliang Ning et al.   MATERIALS & DESIGN

Atomsk: A tool for manipulating and converting atomic data files

(2015) Pierre Hirel   COMPUTER PHYSICS COMMUNICATIONS

Thermal stability of B2 CuZr phase, microstructural evolution and martensitic transformation in Cu–Zr–Ti alloys

(2015) K.K. Song et al.   INTERMETALLICS

First principles phonon calculations in materials science

(2015) Atsushi Togo et al.   SCRIPTA MATERIALIA

The Microstructural Evolution and Mechanical Properties of Zr-Based Metallic Glass under Different Strain Rate Compressions

(2015) Tao-Hsing Chen et al.   Materials

A database to enable discovery and design of piezoelectric materials

(2015) Maarten de Jong et al.   Scientific Data

Neural network-based approaches for building high dimensional and quantum dynamics-friendly potential energy surfaces

(2014) Sergei Manzhos et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Microstructural Evolution and Mechanical Behaviour of Metastable Cu–Zr–Co Alloys

(2014) S. Pauly et al.   JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY

Representing potential energy surfaces by high-dimensional neural network potentials

(2014) J Behler   JOURNAL OF PHYSICS-CONDENSED MATTER

Error Estimates for Solid-State Density-Functional Theory Predictions: An Overview by Means of the Ground-State Elemental Crystals

(2013) K. Lejaeghere et al.   CRITICAL REVIEWS IN SOLID STATE AND MATERIALS SCIENCES

Six-dimensional potential energy surface of the dissociative chemisorption of HCl on Au(111) using neural networks

(2013) TianHui Liu et al.   Science China-Chemistry

Correlation between the microstructures and the deformation mechanisms of CuZr-based bulk metallic glass composites

(2013) K. K. Song et al.   AIP Advances

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

(2013) Anubhav Jain et al.   APL Materials

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

Modified embedded atom method potential for Al, Si, Mg, Cu, and Fe alloys

(2012) B. Jelinek et al.   PHYSICAL REVIEW B

Tensile properties of ZrCu-based bulk metallic glasses at ambient and cryogenic temperatures

(2011) L.S. Huo et al.   JOURNAL OF NON-CRYSTALLINE SOLIDS

Highly optimized embedded-atom-method potentials for fourteen fcc metals

(2011) H. W. Sheng et al.   PHYSICAL REVIEW B

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

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

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

(2009) L. Martínez et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

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

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

Development of suitable interatomic potentials for simulation of liquid and amorphous Cu–Zr alloys

(2009) M.I. Mendelev et al.   PHILOSOPHICAL MAGAZINE

Atomic Level Structure in Multicomponent Bulk Metallic Glass

(2009) Y. Q. Cheng et al.   PHYSICAL REVIEW LETTERS

Cu-Zr (Copper-Zirconium)

(2008) H. Okamoto   JOURNAL OF PHASE EQUILIBRIA AND DIFFUSION