Machine learning Hubbard parameters with equivariant neural networks
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 …
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
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 …
(anaerobic) iron corrosion. In various Earth systems, magnetite surfaces participate in …
Hubbard U through polaronic defect states
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 …
functional has been used for predicting properties of correlated materials by applying on-site …
Predicting structure-dependent Hubbard U parameters via machine learning
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 …
correlated materials that contain open-shell elements, whereby the quantitative and …
High-Performance Pd2Cu2 Cluster Supported on CeO2(110) for the Electroreduction of CO2
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 …
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 …
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 …
many cases the performance of machine learning models is not sufficiently accurate enough …