Universal interatomic potentials for advanced materials modeling - lab-cosmo/upet

PET-MAD is a fast and lightweight universal machine-learning potential, trained on a small but diverse dataset, that delivers near-quantum accuracy in atomistic simulations for both organic and inorga...

A python package for the prediction of chemical shieldings of organic solids and beyond. - lab-cosmo/shiftml

The use of machine learning to estimate the energy of a group of atoms, and the forces that drive them to more stable configurations, have revolutionized the fields of computational chemistry and...

Molecular dynamics (MD) simulation predicts the trajectory of atoms by solving Newton's equation of motion with a numeric integrator. Due to physical constraints, the time step of the integrator need ...

The Trotter factorization of the Liouville propagator is used to generate new reversible molecular dynamics integrators. This strategy is applied to derive reve

Most atomistic machine learning (ML) models rely on a locality ansatz and decompose the energy into a sum of short-ranged, atom-centered contributions. This lea

Computing representations for atomistic machine learning - metatensor/featomic

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