T. Sewell

collaborators

2 papers

cs.CE2021

MD-inferred neural network monoclinic finite-strain hyperelasticity models for $β$ -HMX: Sobolev training and validation against physical constraints

Nikolaos N. Vlassis, Puhan Zhao, Ran Ma +2

We present a machine learning framework to train and validate neural networks to predict the anisotropic elastic response of the monoclinic organic molecular crystal $β$ -HMX in the…

cond-mat.mtrl-sci2021

Heat-current filtering for Green-Kubo and Helfand-moment molecular dynamics predictions of thermal conductivity: Application to the organic crystal $β$ -HMX

Andrey Pereverzev, Tommy Sewell

The closely related Green-Kubo and Helfand moment approaches are applied to obtain the thermal conductivity tensor of $β$ -1,3,5,7-tetranitro-1,3,5,7-tetrazoctane ( $β$ -HMX) at $T=30…

MD-inferred neural network monoclinic finite-strain hyperelasticity models for β-HMX: Sobolev training and validation against physical constraints

Heat-current filtering for Green-Kubo and Helfand-moment molecular dynamics predictions of thermal conductivity: Application to the organic crystal β-HMX

MD-inferred neural network monoclinic finite-strain hyperelasticity models for $β$ -HMX: Sobolev training and validation against physical constraints

Heat-current filtering for Green-Kubo and Helfand-moment molecular dynamics predictions of thermal conductivity: Application to the organic crystal $β$ -HMX