Machine-learned interatomic potentials for alloys and alloy phase diagrams

Machine-learned multi-system surrogate models for materials prediction

(2019) Chandramouli Nyshadham et al.   npj Computational Materials

Moment tensor potentials as a promising tool to study diffusion processes

(2019) I.I. Novoselov et al.   COMPUTATIONAL MATERIALS SCIENCE

Data-driven learning and prediction of inorganic crystal structures

(2018) Volker L. Deringer et al.   FARADAY DISCUSSIONS

Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials

(2018) Gabriele C. Sosso et al.   MOLECULAR SIMULATION

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

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

Data-driven learning and prediction of inorganic crystal structures

(2018) Volker L. Deringer et al.   FARADAY DISCUSSIONS

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

(2018) Daniele Dragoni et al.   PHYSICAL REVIEW MATERIALS

Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning

(2018) Miguel A. Caro et al.   CHEMISTRY OF MATERIALS

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

(2018) Felix C. Mocanu et al.   JOURNAL OF PHYSICAL CHEMISTRY B

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

(2018) Konstantin Gubaev et al.   COMPUTATIONAL MATERIALS SCIENCE

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

(2018) Francesco Maresca et al.   npj Computational Materials

Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials

(2017) S. T. John et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Machine learning unifies the modeling of materials and molecules

(2017) Albert P. Bartók et al.   Science Advances

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

(2017) Conrad W. Rosenbrock et al.   npj Computational Materials

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

(2016) Gerardo Andrés Cisneros et al.   CHEMICAL REVIEWS

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

Comparing molecules and solids across structural and alchemical space

(2016) Sandip De et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Crystal structure representations for machine learning models of formation energies

(2015) Felix Faber et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Prediction of the material with highest known melting point fromab initiomolecular dynamics calculations

(2015) Qi-Jun Hong et al.   PHYSICAL REVIEW B

Next generation interatomic potentials for condensed systems

(2014) Christopher Michael Handley et al.   EUROPEAN PHYSICAL JOURNAL B

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

(2014) Dawei Feng et al.   JOURNAL OF MATERIALS SCIENCE

Accuracy and transferability of Gaussian approximation potential models for tungsten

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

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

(2013) Katja Hansen et al.   Journal of Chemical Theory and Computation

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

(2013) Monica Pozzo et al.   PHYSICAL REVIEW B

On representing chemical environments

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

Accelerating materials property predictions using machine learning

(2013) Ghanshyam Pilania et al.   Scientific Reports

Ground-state characterizations of systems predicted to exhibitL11orL13crystal structures

(2012) Lance J. Nelson et al.   PHYSICAL REVIEW B

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

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

(2012) Matthew J. L. Mills et al.   THEORETICAL CHEMISTRY ACCOUNTS

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

(2010) Jiří Sopoušek et al.   JOURNAL OF ALLOYS AND COMPOUNDS

Efficient Sampling of Atomic Configurational Spaces

(2010) Lívia B. Pártay et al.   JOURNAL OF PHYSICAL CHEMISTRY B

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

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

Algorithm for generating derivative structures

(2008) Gus L. W. Hart et al.   PHYSICAL REVIEW B