8 citations · 15 across the 8 of their papers we have counts for
3 papers · 1 filter
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
Thermodynamic Interpolation: A generative approach to molecular thermodynamics and kinetics
Selma Moqvist, Weilong Chen, Mathias Schreiner +2
Using normalizing flows and reweighting, Boltzmann Generators enable equilibrium sampling from a Boltzmann distribution, defined by an energy function and thermodynamic state. In t…
Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics
Mathias Schreiner, Ole Winther, Simon Olsson
Computing properties of molecular systems rely on estimating expectations of the (unnormalized) Boltzmann distribution. Molecular dynamics (MD) is a broadly adopted technique to ap…