Publications, see
All Free Open Access
Just Online Publications (2025):
K Boonpalit, N. Artrith*, “Mechanistic Insights into the Oxygen Evolution Reaction on Nickel-Doped Barium Titanate via Machine Learning-Accelerated Simulations” arXiv preprint 2412.15452 (2025):
In Won Yeu, Annika Stuke, Jon López-Zorrilla, James M Stevenson, David R Reichman, Richard A Friesner, Alexander Urban, N. Artrith*, “Scalable Training of Neural Network Potentials for Complex Interfaces Through Data Augmentation” arXiv preprint arXiv:2412.05773 (2025)
J He, S Qi, H Wang, J Ma, N. Artrith*, “Highly Antioxidative Lithium Salt Enables High-Voltage Ether Electrolyte for Lithium Metal Battery” ACS Applied Energy Materials 8 , 343–354 (2025):
An Niza El Aisnadaa, Kajjana Boonpalit, Robin van der Kruit, Koen M Draijer, Jon Lopez-Zorrilla, Masahiro Miyauchi, Akira Yamaguchi, N. Artrith* “Cost-Effective Strategy of Enhancing Machine Learning Potentials by Transfer Learning from a Multicomponent Data Set on ænet-PyTorch”, The Journal of Physical Chemistry C 129, 658–669 (2025):
R Jacobs, D Morgan, S Attarian, J Meng, C Shen, Z Wu, C Xie, JH Yang, N. Artrith, et al, “A Practical Guide to Machine Learning Interatomic Potentials–Status and Future” Current opinion in solid state materials science 35, 101214 (2025) :
Selected Publications (2024):
C Cao, MR Carbone, C Komurcuoglu, S Jagriti S., K Sun, H Guo, N. Artrith, et al, “Atomic Insights into the Oxidative Degradation Mechanisms of Sulfide Solid Electrolytes” Cell Reports Physical Science, 5, 101909 (2024):
Hendrik P Rodenburg, Florian Stainer, Koen M Draijer, Hailan Ni, Jonas Spychala, N. Artrith, H Martin R Wilkening, Peter Ngene, “Superprotonic Conductivity in Hexagonal and Tetragonal Cesium Hydroxide Hydrate”, Advanced Functional Materials, 2412219 (2024):
Marta Perxés, Christopher R O'Connor, Koen M Draijer, Nienke L Visser, N. Artrith, Christian Reece, Petra de Jongh, Jessi ES van der Hoeven, “In Situ Analysis of Gas Dependent Redistribution Kinetics in Bimetallic Au-Pd Nanoparticles”, Journal of Materials Chemistry A 12, 32760 (2024) :
Selected Publications (2023):
J Lopez-Zorrilla, XM Aretxabaleta, IW Yeu, I Etxebarria, H Manzano, N. Artrith* “ænet-PyTorch: a GPU-supported implementation for machine learning atomic potentials training” J. Chem. Phys. 158, 164105 (2025):
H Guo, MR Carbone, C Cao, N. Artrith*, et al, “Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes” Nature Scientific Data 10 , 349 (2023):
AS Johnson, D Perez-Salinas, KM Siddiqui, S Kim, S Choi, K Volckaert, N. Artrith, et al, “Ultrafast X-ray imaging of the light-induced phase transition in VO2” Nature Physics , 19, 215–220 (2023).
HP Rodenburg, A Mutschke, L Ngamwongwan, V Gulino, V Kyriakou, N. Artrith, P. Ngene “Mixed hydride-electronic conductivity in Rb2CaH4 and Cs2CaH4” Solid state ionics 403, 116384 (2023):
L Kong, J Li, L Sun, H Yang, H Hao, C Chen, N Artrith, JAG Torres, Z Lu, N. Artrith, et al, “Overcoming the size limit of first principles molecular dynamics simulations with an in-distribution substructure embedding active learner”
arXiv preprint 2311.08177 (2023) :
V Gharakhanyan, M Aalto, A Alsoulah, N. Artrith, A Urban, “Constructing and Compressing Global Moment Descriptors from Local Atomic Environments” arXiv preprint 2310.05386:
Selected Publications (2022):
N. Artrith*, J.A. Garrido Torres, A. Urban and M.S. Hybertsen*, "Data-driven approach to parameterize SCAN+U for an accurate description of 3d transition metal oxide thermochemistry", Phys. Rev. Materials, 6, 035003 (2022): ; Preprint:
S.R. Denny, Z. Lin, W.N. Porter, N. Artrith*, and J.G. Chen*, "Machine Learning Prediction and Experimental Verification of Pt-Modified Nitride Catalysts for Ethanol Reforming with Reduced Precious Metal Loading" Applied Catalysis B: Environmental (IF = 19.503), 312, 121380 (2022):
Selected Publications 2021:
N. Artrith*, K.T. Butler, F.X. Coudert, S. Han, O. Isayev, A. Jain, and A. Walsh, "Best practices in machine learning for chemistry", Nature Chemistry, 13, 505–508 (2021):
J.A. Garrido Torres, V. Gharakhanyan, N. Artrith, T.H. Eegholm, A. Urban, Augmenting Zero-Kelvin Quantum Mechanics with Machine Learning for the Prediction of Chemical Reactions at High Temperatures, Nature Communications 12 (1), 1-9 (2021):
M.S. Chen, T. Morawietz, H. Mori, T.E. Markland*, and N. Artrith*, "AENET-LAMMPS and AENET-TINKER: Interfaces for Accurate and Efficient Molecular Dynamics Simulations with Machine Learning Potentials", J. Chem. Phys. 155, 074801 (2021): ; Preprint
A.M. Miksch, T. Morawietz, J. Kästner, A. Urban, and N. Artrith*,
Mach. Learn.: Sci. Technol. 2, 031001 (2021):
H. Guo, Q. Wang, A. Stuke, A. Urban, and N. Artrith*,
"Accelerated Atomistic Modeling of Solid-State Battery Materials with Machine Learning",
Frontiers in Energy Research, 9, 695902 (2021):