(* = corresponding author, † = equal contributions)
2020
N. Artrith*,
“Learning What Makes Catalysts Good”, Matter (Cell Press) 3 (2020) 985-986
.
T. Morawietz* and N. Artrith*,
“Machine Learning-Accelerated Quantum Mechanics-Based Atomistic Simulations for Industrial Applications”, J. Comput. Aided Mol. Des. (2020) Open Access DOI: https://doi.org/10.1007/s10822-020-00346-6
.
A.M. Cooper, J. Kästner, A. Urban, N. Artrith*,
“Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide”,
npj Comput Mater 6 (2020) 54. Open
Access
.
The database can be obtained from the
Materials Cloud repository
.
N. Artrith*, Zhexi Lin, Jingguang G. Chen,
“Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol
Reforming using Machine Learning”,
ACS Catal. 10 (2020) 9438–9444 (Letter)
.
(preprint)
D.H. Kwon*
“The Impact of Surface Structure Transformations on the Performance of Li-Excess Cation-Disordered Rocksalt Cathodes”,
Cell Reports Physical Science 1 (2020) 100187. Open Access
.
B. Ouyang
“Effect of Fluorination on Lithium Transport and Short‐Range Order in Disordered‐Rocksalt‐Type Lithium‐Ion Battery Cathodes”,
Adv. Energy Mater. 10 (2020) 1903240
.
2019 and before