Machine learning Hubbard parameters with equivariant neural networks

M Uhrin, A Zadoks, L Binci, N Marzari… - npj Computational …, 2025 - nature.com
Density-functional theory with extended Hubbard functionals (DFT+ U+ V) provides a robust
framework to accurately describe complex materials containing transition-metal or rare-earth …

Stability and Speciation of Hydrated Magnetite {111} Surfaces from Ab Initio Simulations with Relevance for Geochemical Redox Processes

AS Katheras, K Karalis, M Krack… - … science & technology, 2023 - ACS Publications
Magnetite is a common mixed Fe (II, III) iron oxide in mineral deposits and the product of
(anaerobic) iron corrosion. In various Earth systems, magnetite surfaces participate in …

Hubbard U through polaronic defect states

S Falletta, A Pasquarello - npj Computational Materials, 2022 - nature.com
Since the preliminary work of Anisimov and co-workers, the Hubbard corrected DFT+ U
functional has been used for predicting properties of correlated materials by applying on-site …

Predicting structure-dependent Hubbard U parameters via machine learning

G Cai, Z Cao, F **e, H Jia, W Liu, Y Wang, F Liu… - Materials …, 2024 - iopscience.iop.org
DFT+ U is a widely used treatment in the density functional theory (DFT) to deal with
correlated materials that contain open-shell elements, whereby the quantitative and …

High-Performance Pd2Cu2 Cluster Supported on CeO2(110) for the Electroreduction of CO2

P Liu, H Zhu, B Li, C Wu, S Jia, B Suo… - The Journal of …, 2024 - ACS Publications
Copper and palladium exhibit excellent catalytic performance for the electrochemical
reduction of CO2 (CO2RR). Here, a Pd x Cu4–x (x= 2, 3) cluster was supported on CeO2 …

In‐Plane Twinning Defects in Hexagonal GeSb2Te4

JJ Wang, HM Zhang, XD Wang, L Lu… - Advanced Materials …, 2022 - Wiley Online Library
Abstract Ge–Sb–Te (GST) alloys are an important family of phase‐change materials
employed in non‐volatile memories and neuromorphic devices. Conventional memory cells …

Improving supervised machine learning for materials science

S Gong - 2022 - dspace.mit.edu
Despite the widespread applications of machine learning models in materials science, in
many cases the performance of machine learning models is not sufficiently accurate enough …