Na Vacancy-Driven Phase Transformation and Fast Ion Conduction in W-Doped Na3SbS4 from Machine Learning Force Fields
Sign Up to like & getrecommendations! Published in 2024 at "Chemistry of Materials"
DOI: 10.1021/acs.chemmater.4c00936
Abstract: Solid-state sodium batteries require effective electrolytes that conduct at room temperature. The Na3PnCh4 (Pn = P, Sb; Ch = S, Se) family has been studied for their high Na ion conductivity. The population of Na… read more here.
Keywords: learning force; machine learning; ion; diffusion ... See more keywords
Machine Learning Force Field-Aided Cluster Expansion Approach to Phase Diagram of Alloyed Materials.
Sign Up to like & getrecommendations! Published in 2024 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.4c00463
Abstract: First-principles approaches based on density functional theory (DFT) have played important roles in the theoretical study of multicomponent alloyed materials. Considering the highly demanding computational cost of direct DFT-based sampling of the configurational space, it… read more here.
Keywords: learning force; disorder; machine learning; alloyed materials ... See more keywords
Machine Learning Force Field for Optimization of Isolated and Supported Transition Metal Particles
Sign Up to like & getrecommendations! Published in 2025 at "Journal of Chemical Theory and Computation"
DOI: 10.1021/acs.jctc.4c01606
Abstract: Computational modeling is an integral part of catalysis research. With it, new methodologies are being developed and implemented to improve the accuracy of simulations while reducing the computational cost. In particular, specific machine-learning techniques have… read more here.
Keywords: field optimization; learning force; machine; machine learning ... See more keywords
Constant-Potential Machine Learning Force Field for the Electrochemical Interface.
Sign Up to like & getrecommendations! Published in 2025 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.5c00784
Abstract: Better understanding and prediction of the electrochemical interface require large-scale atomistic simulations. Machine learning force fields (MLFFs) have proven to be an effective approach. However, current MLFFs typically do not account for the effect of… read more here.
Keywords: learning force; electrochemical interface; machine learning; constant potential ... See more keywords
Insights into Lithium Sulfide Glass Electrolyte Structures and Ionic Conductivity via Machine Learning Force Field Simulations.
Sign Up to like & getrecommendations! Published in 2024 at "ACS applied materials & interfaces"
DOI: 10.1021/acsami.4c00618
Abstract: Sulfide-based solid electrolytes (SEs) are important for advancing all-solid-state batteries (ASSBs), primarily due to their high ionic conductivities and robust mechanical stability. Glassy SEs (GSEs) comprising mixed Si and P glass formers are particularly promising… read more here.
Keywords: lithium; learning force; sulfide; conductivity ... See more keywords
Capturing Dynamic Core Reconstruction and Ligand Desorption of Atomically Precise Ag Nanoclusters with Machine Learning Force Field.
Sign Up to like & getrecommendations! Published in 2025 at "Journal of the American Chemical Society"
DOI: 10.1021/jacs.5c15207
Abstract: Atomically precise silver nanoclusters (NCs) protected by alkynyl ligands represent an emerging class of electrocatalysts demonstrating high activity and selectivity in reactions, such as CO2 electroreduction. However, their dynamic structural evolution mechanisms under electrochemical operating… read more here.
Keywords: learning force; capturing dynamic; machine learning; force field ... See more keywords
Bridging deep learning force fields and electronic structures with a physics-informed approach
Sign Up to like & getrecommendations! Published in 2024 at "npj Computational Materials"
DOI: 10.1038/s41524-025-01668-5
Abstract: This work presents a physics-informed neural network approach bridging deep-learning force field and electronic structure simulations, illustrated through twisted two-dimensional large-scale material systems. The deep potential molecular dynamics model is adopted as the backbone, and… read more here.
Keywords: deep learning; learning force; physics informed; bridging deep ... See more keywords
Energy-free machine learning force field for aluminum
Sign Up to like & getrecommendations! Published in 2017 at "Scientific Reports"
DOI: 10.1038/s41598-017-08455-3
Abstract: We used the machine learning technique of Li et al. (PRL 114, 2015) for molecular dynamics simulations. Atomic configurations were described by feature matrix based on internal vectors, and linear regression was used as a… read more here.
Keywords: machine; machine learning; free machine; learning force ... See more keywords