4.8 Article

Machine-learned interatomic potentials for alloys and alloy phase diagrams

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

Atomic permutationally invariant polynomials for fitting molecular force fields

Alice E. A. Allen et al.

Summary: The approach combines machine learned potentials with empirical force field terms to bridge the gap between transferable empirical force fields and machine learned potentials. By introducing body order decomposition and iterative fitting schemes, the aPIP force field achieves high accuracy and transferability on small organic molecules.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2021)

Article Chemistry, Physical

Machine-learned multi-system surrogate models for materials prediction

Chandramouli Nyshadham et al.

NPJ COMPUTATIONAL MATERIALS (2019)

Article Materials Science, Multidisciplinary

Moment tensor potentials as a promising tool to study diffusion processes

I. I. Novoselov et al.

COMPUTATIONAL MATERIALS SCIENCE (2019)

Article Materials Science, Multidisciplinary

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

Konstantin Gubaev et al.

COMPUTATIONAL MATERIALS SCIENCE (2019)

Article Materials Science, Multidisciplinary

Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning

Evgeny Podryabinkin et al.

PHYSICAL REVIEW B (2019)

Article Physics, Multidisciplinary

A robust algorithm for k-point grid generation and symmetry reduction

Gus L. W. Hart et al.

JOURNAL OF PHYSICS COMMUNICATIONS (2019)

Article Materials Science, Multidisciplinary

Improving accuracy of interatomic potentials: more physics or more data? A case study of silica

Ivan S. Novikov et al.

MATERIALS TODAY COMMUNICATIONS (2019)

Article Chemistry, Physical

Data-driven learning and prediction of inorganic crystal structures

Volker L. Deringer et al.

FARADAY DISCUSSIONS (2018)

Article Physics, Multidisciplinary

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

Volker L. Deringer et al.

PHYSICAL REVIEW LETTERS (2018)

Article Chemistry, Physical

Data-driven learning and prediction of inorganic crystal structures

Volker L. Deringer et al.

FARADAY DISCUSSIONS (2018)

Article Materials Science, Multidisciplinary

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

Daniele Dragoni et al.

PHYSICAL REVIEW MATERIALS (2018)

Article Chemistry, Physical

Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential

Felix C. Mocanu et al.

JOURNAL OF PHYSICAL CHEMISTRY B (2018)

Article Chemistry, Physical

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

Francesco Maresca et al.

NPJ COMPUTATIONAL MATERIALS (2018)

Article Chemistry, Physical

Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials

S. T. John et al.

JOURNAL OF PHYSICAL CHEMISTRY B (2017)

Article Multidisciplinary Sciences

Machine learning unifies the modeling of materials and molecules

Albert P. Bartok et al.

SCIENCE ADVANCES (2017)

Article Chemistry, Physical

Discovering the building blocks of atomic systems using machine learning: application to grain boundaries

Conrad W. Rosenbrock et al.

NPJ COMPUTATIONAL MATERIALS (2017)

Article Physics, Fluids & Plasmas

Constant-pressure nested sampling with atomistic dynamics

Robert J. N. Baldock et al.

PHYSICAL REVIEW E (2017)

Article Materials Science, Multidisciplinary

Active learning of linearly parametrized interatomic potentials

Evgeny V. Podryabinkin et al.

COMPUTATIONAL MATERIALS SCIENCE (2017)

Article Materials Science, Multidisciplinary

Robustness of the cluster expansion: Assessing the roles of relaxation and numerical error

Andrew H. Nguyen et al.

PHYSICAL REVIEW B (2017)

Article Chemistry, Physical

Extracting Crystal Chemistry from Amorphous Carbon Structures

Volker L. Deringer et al.

CHEMPHYSCHEM (2017)

Review Chemistry, Multidisciplinary

Modeling Molecular Interactions in Water: From Pairwise to Many Body Potential Energy Functions

Gerardo Andres Cisneros et al.

CHEMICAL REVIEWS (2016)

Article Mathematics, Interdisciplinary Applications

MOMENT TENSOR POTENTIALS: A CLASS OF SYSTEMATICALLY IMPROVABLE INTERATOMIC POTENTIALS

Alexander V. Shapeev

MULTISCALE MODELING & SIMULATION (2016)

Article Chemistry, Physical

Comparing molecules and solids across structural and alchemical space

Sandip De et al.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2016)

Article Materials Science, Multidisciplinary

Determining pressure-temperature phase diagrams of materials

Robert J. N. Baldock et al.

PHYSICAL REVIEW B (2016)

Article Materials Science, Multidisciplinary

Efficient generation of generalized Monkhorst-Pack grids through the use of informatics

Pandu Wisesa et al.

PHYSICAL REVIEW B (2016)

Article Chemistry, Physical

Crystal structure representations for machine learning models of formation energies

Felix Faber et al.

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)

Article Materials Science, Multidisciplinary

Prediction of the material with highest known melting point from ab initio molecular dynamics calculations

Qi-Jun Hong et al.

PHYSICAL REVIEW B (2015)

Article Physics, Condensed Matter

Next generation interatomic potentials for condensed systems

Christopher Michael Handley et al.

EUROPEAN PHYSICAL JOURNAL B (2014)

Article Materials Science, Multidisciplinary

Thermodynamic properties of silver-palladium alloys determined by a solid state electrochemical method

Dawei Feng et al.

JOURNAL OF MATERIALS SCIENCE (2014)

Article Materials Science, Multidisciplinary

Accuracy and transferability of Gaussian approximation potential models for tungsten

Wojciech J. Szlachta et al.

PHYSICAL REVIEW B (2014)

Article Chemistry, Physical

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

Katja Hansen et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)

Article Materials Science, Multidisciplinary

Melting curve of face-centered-cubic nickel from first-principles calculations

Monica Pozzo et al.

PHYSICAL REVIEW B (2013)

Article Materials Science, Multidisciplinary

On representing chemical environments

Albert P. Bartok et al.

PHYSICAL REVIEW B (2013)

Article Multidisciplinary Sciences

Accelerating materials property predictions using machine learning

Ghanshyam Pilania et al.

SCIENTIFIC REPORTS (2013)

Article Materials Science, Multidisciplinary

Ground-state characterizations of systems predicted to exhibit L11 or L13 crystal structures

Lance J. Nelson et al.

PHYSICAL REVIEW B (2012)

Article Physics, Multidisciplinary

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Matthias Rupp et al.

PHYSICAL REVIEW LETTERS (2012)

Article Chemistry, Physical

Polarisable multipolar electrostatics from the machine learning method Kriging: an application to alanine

Matthew J. L. Mills et al.

THEORETICAL CHEMISTRY ACCOUNTS (2012)

Article Chemistry, Physical

Experimental determination of phase equilibria and reassessment of Ag-Pd system

Jiri Sopousek et al.

JOURNAL OF ALLOYS AND COMPOUNDS (2010)

Article Chemistry, Physical

Efficient Sampling of Atomic Configurational Spaces

Livia B. Partay et al.

JOURNAL OF PHYSICAL CHEMISTRY B (2010)

Article Physics, Multidisciplinary

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

Albert P. Bartok et al.

PHYSICAL REVIEW LETTERS (2010)

Article Materials Science, Multidisciplinary

Theoretical investigation of bulk ordering and surface segregation in Ag-Pd and other isoelectornic alloys

A. V. Ruban et al.

PHYSICAL REVIEW B (2007)

Article Chemistry, Physical

Enthalpies of mixing of metallic systems relevant for lead-free soldering: Ag-Pd and Ag-Pd-Sn

C Luef et al.

JOURNAL OF ALLOYS AND COMPOUNDS (2005)

Article Materials Science, Multidisciplinary

Partial atomic volume and partial molar enthalpy of formation of the 3d metals in the palladium-based solid solutions

M Ellner

METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE (2004)

Article Physics, Multidisciplinary

Optimization of effective atom centered potentials for London dispersion forces in density functional theory

OA von Lilienfeld et al.

PHYSICAL REVIEW LETTERS (2004)

Article Chemistry, Physical

Classical and quasiclassical spectral analysis of CH5+ using an ab initio potential energy surface

A Brown et al.

JOURNAL OF CHEMICAL PHYSICS (2003)