Accurate prediction of grain boundary structures and energetics in CdTe: a machine-learning potential approach

Neural-network interatomic potential for grain boundary structures and their energetics in silicon

(2020) T. Yokoi et al.   Physical Review Materials

Halide-Induced Step Faceting and Dissolution Energetics from Atomistic Machine Learned Potentials on Cu(100)

(2020) Mitchell C. Groenenboom et al.   Journal of Physical Chemistry C

Neural network atomic potential to investigate the dislocation dynamics in bcc iron

(2020) Hideki Mori et al.   Physical Review Materials

Application of machine learning potentials to predict grain boundary properties in fcc elemental metals

(2020) Takayuki Nishiyama et al.   Physical Review Materials

Revisiting the structures and energies of silicon 〈110〉 symmetric tilt grain boundaries

(2019) Liang Wang et al.   JOURNAL OF MATERIALS RESEARCH

Stabilization and self-passivation of symmetrical grain boundaries by mirror symmetry breaking

(2019) Ji-Sang Park   PHYSICAL REVIEW MATERIALS

Machine-learning-based interatomic potential for phonon transport in perfect crystalline Si and crystalline Si with vacancies

(2019) Hasan Babaei et al.   Physical Review Materials

Passivating Grain Boundaries in Polycrystalline CdTe

(2019) Chuan-Jia Tong et al.   Journal of Physical Chemistry C

Anharmonic thermodynamics of vacancies using a neural network potential

(2019) Anton S. Bochkarev et al.   Physical Review Materials

Predicting phase behavior of grain boundaries with evolutionary search and machine learning

(2018) Qiang Zhu et al.   Nature Communications

Grain boundary phases in bcc metals

(2018) T. Frolov et al.   Nanoscale

A correlative investigation of grain boundary crystallography and electronic properties in CdTe thin film solar cells

(2017) Guillaume Stechmann et al.   SOLAR ENERGY MATERIALS AND SOLAR CELLS

Ab initiocalculations of grain boundaries in bcc metals

(2016) Daniel Scheiber et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Neural network molecular dynamics simulations of solid–liquid interfaces: water at low-index copper surfaces

(2016) Suresh Kondati Natarajan et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Symmetric tilt boundaries in body-centered cubic tantalum

(2016) Eric N. Hahn et al.   SCRIPTA MATERIALIA

Structural and electronic properties of Σ7 grain boundaries in α -Al 2 O 3

(2015) Hannes Guhl et al.   ACTA MATERIALIA

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Stability and electronic structure of the low-Σgrain boundaries in CdTe: a density functional study

(2015) Ji-Sang Park et al.   NEW JOURNAL OF PHYSICS

First principles phonon calculations in materials science

(2015) Atsushi Togo et al.   SCRIPTA MATERIALIA

Grain-Boundary-Enhanced Carrier Collection in CdTe Solar Cells

(2014) Chen Li et al.   PHYSICAL REVIEW LETTERS

Direct Imaging of Cl- and Cu-Induced Short-Circuit Efficiency Changes in CdTe Solar Cells

(2014) Jonathan D. Poplawsky et al.   Advanced Energy Materials

A refined parameterization of the analytical Cd–Zn–Te bond-order potential

(2013) Donald K. Ward et al.   JOURNAL OF MOLECULAR MODELING

High-dimensional neural network potentials for metal surfaces: A prototype study for copper

(2012) Nongnuch Artrith 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

Interatomic potentials for strontium titanate: An assessment of their transferability and comparison with density functional theory

(2008) Nicole A. Benedek et al.   PHYSICAL REVIEW B

Restoring the Density-Gradient Expansion for Exchange in Solids and Surfaces

(2008) John P. Perdew et al.   PHYSICAL REVIEW LETTERS