Results 111 to 120 of about 15,495,898 (198)

Choosing the right molecular machine learning potential. [PDF]

Chem Sci, 2021
Pinheiro M, Ge F, Ferré N, Dral PO, Barbatti M.   +4 more
europepmc   +1 more source

Homogeneous and inhomogeneous screening benchmarks for machine-learning interatomic potentials

Wei Chen, Gian‐Marco Rignanese
openalex   +1 more source

Machine-learning interatomic potential for temperature-dependent properties of Nb2AlC MAX phase as a bond coat [PDF]

Hayoung Son, Hyokyeong Kim, Jungdo Park, Seungtaek Lee, Jiwoong Kim   +4 more
openalex   +1 more source

Proton Transport on Graphamine: A Deep-Learning Potential Study. [PDF]

J Phys Chem C Nanomater Interfaces
Ananthabhotla LY, Achar SK, Johnson JK.
europepmc   +1 more source

Machine learning potential for interacting dislocations in the presence of free surfaces. [PDF]

Sci Rep, 2022
Lanzoni D, Rovaris F, Montalenti F.
europepmc   +1 more source

Enhancing the Efficiency and Flexibility of AutoMeKin: Integrating ORCA and Machine-Learning Potentials

Omar Rodríguez López, Emilio Martı́nez-Núñez, Berta Fernández, Saulo A. Vázquez, Saulo A. Vázquez   +4 more
openalex   +1 more source

Chromatin Profiling Reveals Distinct Male and Female Trajectories for Developmental Learning Potential. [PDF]

Dev Neurobiol
Kunzelman GW, Batistuzzo A, London SE.
europepmc   +1 more source

Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential. [PDF]

ACS Cent Sci, 2023
Axelrod S, Shakhnovich E, Gómez-Bombarelli R.   +2 more
europepmc   +1 more source

Defect-limited thermal transport in AlN using pretrained machine-learning interatomic potentials

Minseok Moon, Wonjun Choi, Seungwu Han, Youngho Kang   +3 more
openalex   +1 more source

An automated framework for exploring and learning potential-energy surfaces. [PDF]

Nat Commun
Liu Y, Morrow JD, Ertural C, Fragapane NL, Gardner JLA, Naik AA, Zhou Y, George J, Deringer VL.   +8 more
europepmc   +1 more source