A. Urban

activity

20152022

most citedEfficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species

345 citations · 807 across the 6 of their papers we have counts for

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physics.comp-ph2020

Efficient Training of ANN Potentials by Including Atomic Forces via Taylor Expansion and Application to Water and a Transition-Metal Oxide

April M. Cooper, Johannes Kästner, Alexander Urban +1

Artificial neural network (ANN) potentials enable the efficient large-scale atomistic modeling of complex materials with near first-principles accuracy. For molecular dynamics simu…

physics.comp-ph2018

An L $_{0}$ L $_{1}$ -norm compressive sensing paradigm for the construction of sparse predictive lattice models using mixed integer quadratic programming

Wenxuan Huang, Alexander Urban, Penghao Xiao +6

First-principles based lattice models allow the modeling of ab initio thermodynamics of crystalline mixtures for applications such as the construction of phase diagrams and the ide…

Efficient Training of ANN Potentials by Including Atomic Forces via Taylor Expansion and Application to Water and a Transition-Metal Oxide

An L0​L1​-norm compressive sensing paradigm for the construction of sparse predictive lattice models using mixed integer quadratic programming

An L $_{0}$ L $_{1}$ -norm compressive sensing paradigm for the construction of sparse predictive lattice models using mixed integer quadratic programming