Machine learning for metallurgy I. A neural-network potential for Al-Cu

Performance and Cost Assessment of Machine Learning Interatomic Potentials

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

Atomistic mechanism and probability determination of the cutting of Guinier-Preston zones by edge dislocations in dilute Al-Cu alloys

(2020) Bin Wu et al.   Physical Review Materials

Materials Cloud, a platform for open computational science

(2020) Leopold Talirz et al.   Scientific Data

AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance

(2020) Sebastiaan P. Huber et al.   Scientific Data

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

(2019) Félix Musil et al.   Journal of Chemical Theory and Computation

Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials

(2019) Andreas Singraber et al.   Journal of Chemical Theory and Computation

Active learning of uniformly accurate interatomic potentials for materials simulation

(2019) Linfeng Zhang et al.   PHYSICAL REVIEW MATERIALS

Parallel Multistream Training of High-Dimensional Neural Network Potentials

(2019) Andreas Singraber et al.   Journal of Chemical Theory and Computation

Physically informed artificial neural networks for atomistic modeling of materials

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

Effective cluster interactions and pre–precipitate morphology in binary Al-based alloys

(2019) O.I. Gorbatov et al.   ACTA MATERIALIA

Temperature-dependent nucleation kinetics of Guinier-Preston zones in Al–Cu alloys: An atomistic kinetic Monte Carlo and classical nucleation theory approach

(2019) Hiroshi Miyoshi et al.   ACTA MATERIALIA

Atomistic dislocation core energies and calibration of non-singular discrete dislocation dynamics

(2019) Yi Hu et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Strategies for improving the sustainability of structural metals

(2019) Dierk Raabe et al.   NATURE

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

(2019) Noam Bernstein et al.   npj Computational Materials

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

(2018) Giulio Imbalzano et al.   JOURNAL OF CHEMICAL PHYSICS

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

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

(2018) Daniele Dragoni et al.   PHYSICAL REVIEW MATERIALS

Atomistic modeling of fracture

(2018) Predrag Andric et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

An atomistic investigation of the interaction of dislocations with Guinier-Preston zones in Al-Cu alloys

(2018) G. Esteban-Manzanares et al.   ACTA MATERIALIA

Coherent Precipitation and Strengthening in Compositionally Complex Alloys: A Review

(2018) Qing Wang et al.   Entropy

Screw dislocation structure and mobility in body centered cubic Fe predicted by a Gaussian Approximation Potential

(2018) Francesco Maresca et al.   npj Computational Materials

Precision and efficiency in solid-state pseudopotential calculations

(2018) Gianluca Prandini et al.   npj Computational Materials

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

(2017) Felix A. Faber et al.   Journal of Chemical Theory and Computation

Neural network potential for Al-Mg-Si alloys

(2017) Ryo Kobayashi et al.   PHYSICAL REVIEW MATERIALS

AiiDA: automated interactive infrastructure and database for computational science

(2016) Giovanni Pizzi et al.   COMPUTATIONAL MATERIALS SCIENCE

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Gaussian approximation potentials: A brief tutorial introduction

(2015) Albert P. Bartók et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Amorphous Al–Cu alloy nanowires decorated with carbon spheres synthesised from waste engine oil

(2015) A.B. Suriani et al.   JOURNAL OF ALLOYS AND COMPOUNDS

Understanding Anharmonicity in fcc Materials: From its Origin toab initioStrategies beyond the Quasiharmonic Approximation

(2015) A. Glensk et al.   PHYSICAL REVIEW LETTERS

First principles phonon calculations in materials science

(2015) Atsushi Togo et al.   SCRIPTA MATERIALIA

The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies

(2015) Scott Kirklin et al.   npj Computational Materials

Pseudopotentials periodic table: From H to Pu

(2014) Andrea Dal Corso   COMPUTATIONAL MATERIALS SCIENCE

Accuracy and transferability of Gaussian approximation potential models for tungsten

(2014) Wojciech J. Szlachta et al.   PHYSICAL REVIEW B

Breakdown of the Arrhenius Law in Describing Vacancy Formation Energies: The Importance of Local Anharmonicity Revealed byAb initioThermodynamics

(2014) A. Glensk et al.   Physical Review X

An Atomistic-Based Hierarchical Multiscale Examination of Age Hardening in an Al-Cu Alloy

(2013) Chandra Veer Singh et al.   METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

(2012) Shyue Ping Ong et al.   COMPUTATIONAL MATERIALS SCIENCE

Coupled quantum–continuum analysis of crack tip processes in aluminum

(2011) A.K. Nair et al.   JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS

Interatomic potential for the Al-Cu system

(2011) F. Apostol et al.   PHYSICAL REVIEW B

Extended-Connectivity Fingerprints

(2010) David Rogers et al.   Journal of Chemical Information and Modeling

Quantitative prediction of solute strengthening in aluminium alloys

(2010) Gerard Paul M. Leyson et al.   NATURE MATERIALS

First principles impurity diffusion coefficients

(2009) M. Mantina et al.   ACTA MATERIALIA

QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials

(2009) Paolo Giannozzi et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Ab initioup to the melting point: Anharmonicity and vacancies in aluminum

(2009) B. Grabowski et al.   PHYSICAL REVIEW B

Prediction of Dislocation Cores in Aluminum from Density Functional Theory

(2008) C. Woodward et al.   PHYSICAL REVIEW LETTERS