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

collaborators

12 papers

cond-mat.mtrl-sci20221 cited

AI-Aided Mapping of the Structure-Composition-Conductivity Relationships of Glass-Ceramic Lithium Thiophosphate Electrolytes

Haoyue Guo, Qian Wang, Alexander Urban +1

Lithium thiophosphates (LPS) with the composition (LiS)(PS) are among the most promising prospective electrolyte materials for solid-state batteries (SSBs),…

cond-mat.mtrl-sci2021

Strategies for the Construction of Machine-Learning Potentials for Accurate and Efficient Atomic-Scale Simulations

April M. Miksch, Tobias Morawietz, Johannes Kästner +2

Recent advances in machine-learning interatomic potentials have enabled the efficient modeling of complex atomistic systems with an accuracy that is comparable to that of conventio…

cond-mat.mtrl-sci2020

Potential and pH dependence of the buried interface of membrane-coated electrocatalysts

Jianzhou Qu, Alexander Urban

Semipermeable silica membranes are attractive as protective coatings for metal electrocatalysts such as platinum but their impact on the catalytic properties has not been fully und…

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…

cond-mat.dis-nn20193 cited

Atomic-scale factors that control the rate capability of nanostructured amorphous Si for high-energy-density batteries

Nongnuch Artrith, Alexander Urban, Yan Wang +1

Nanostructured Si is the most promising high-capacity anode material to substantially increase the energy density of Li-ion batteries. Among the remaining challenges is its low rat…

physics.comp-ph2018

An LL-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…