Abstract
A survey of the contributions to the Journal of Chemical Physics' Special Topic on Software for Atomistic Machine Learning.
Guest Editorial: Special Topic on Software for Atomistic Machine Learning
Related papers
Ranked by semantic similarity — how closely each paper's abstract matches this one (100% = near-identical topic).
- Guest Editorial: Special Topic on Data-enabled Theoretical Chemistry (2018)73% match
- Introduction to machine learning potentials for atomistic simulations (2024)67% match
- An Atomistic Machine Learning Package for Surface Science and Catalysis (2019)66% match
- Atomistic Simulations for Reactions and Spectroscopy in the Era of Machine Learning -- Quo Vadis? (2022)66% match
- Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019)66% match
- Machine learning at the atomic-scale (2020)64% match
- Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems (2020)64% match
- Accelerating Atomistic Simulations with Piecewise Machine Learned Ab Initio Potentials at Classical Force Field-like Cost (2020)63% match