most citedA Novel Approach to Describe Chemical Environments in High Dimensional Neural Network Potentials

49 citations · 64 across the 4 of their papers we have counts for

collaborators

5 papers

cond-mat.mtrl-sci202012 cited

Distribution of Topological Types in Grain-Growth Microstructures

Emanuel A. Lazar, Jeremy K. Mason, Robert D. MacPherson +1

An open question in studying normal grain growth concerns the asymptotic state to which microstructures converge. In particular, the distribution of grain topologies is unknown. We…

cs.CG2019

Statistical Topology of Bond Networks with Applications to Silica

Benjamin Schweinhart, David Rodney, Jeremy Mason

Whereas knowledge of a crystalline material's unit cell is fundamental to understanding the material's properties and behavior, there are not obvious analogues to unit cells for di…

cond-mat.mtrl-sci20193 cited

Basis functions on the grain boundary space: Theory

Jeremy K. Mason, Srikanth Patala

With the increasing availability of experimental and computational data concerning the properties and distribution of grain boundaries in polycrystalline materials, there is a corr…

physics.comp-ph2019

Continuous and Optimally Complete Description of Chemical Environments Using Spherical Bessel Descriptors

Emir Kocer, Jeremy K. Mason, Hakan Erturk

Recently, machine learning potentials have been advanced as candidates to combine the high-accuracy of quantum mechanical simulations with the speed of classical interatomic potent…

physics.comp-ph201949 cited

A Novel Approach to Describe Chemical Environments in High Dimensional Neural Network Potentials

Emir Kocer, Jeremy K. Mason, Hakan Erturk

A central concern of molecular dynamics simulations are the potential energy surfaces that govern atomic interactions. These hypersurfaces define the potential energy of the system…