Machine learning interatomic potentials (MLIPs) are one of the main techniques in the
materials science toolbox, able to bridge ab initio accuracy with the computational efficiency …

Renowned for their high porosity and structural diversity, metal–organic frameworks (MOFs)
are a promising class of materials for a wide range of applications. In recent decades, with …

The design of functional materials with desired properties is essential in driving
technological advances in areas like energy storage, catalysis, and carbon capture1–3 …

Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from
energy storage to solvents, where they have been touted as “designer solvents” as they can …

Scientific machine learning (ML) aims to develop generalizable models, yet assessments of
generalizability often rely on heuristics. Here, we demonstrate in the materials science …

Date: October 18, 2024 Correspondence: L. Barroso-Luque (lbluque@ meta. com), CL
Zitnick (zitnick@ meta. com), Z. Ulissi (zulissi@ meta. com) Code: https://github. com/FAIR …

Machine learning-based interatomic potentials enable accurate materials simulations on
extended time-and length scales. ML potentials based on the atomic cluster expansion …

We introduce Orb, a family of universal interatomic potentials for atomistic modelling of
materials. Orb models are 3-6 times faster than existing universal potentials, stable under …

The generation of equilibrium samples of molecular systems has been a long-standing
problem in statistical physics. Boltzmann Generators are a generative machine learning …

With the emergence of Foundational Machine Learning Interatomic Potential (FMLIP)
models trained on extensive datasets, transferring data between different ML architectures …