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 …

Recent years have witnessed the fast development of machine learning potentials (MLPs)
and their widespread applications in chemistry, physics, and material science. By fitting …

Predicting spectra and related properties such as the dielectric function of crystalline
materials based on machine learning has a huge, hitherto unexplored, technological …

In recent years, contrastive learning has gained widespread adoption in machine learning
applications to physical systems primarily due to its distinctive cross-modal capabilities and …

Gut microbiota plays a crucial role in human health and can be influenced by environmental
factors. While past studies have examined the impact of the environment on gut microbiota …

Abstract Machine learning can enable a computational framework to learn from data,
thereby enhancing decision-making for targeted properties. Based on the significance of …

Machine learning interatomic potentials (MLIPs) have become increasingly effective at
approximating quantum mechanical calculations at a fraction of the computational cost …

Achieving higher operational voltages, faster charging, and broader temperature ranges for
Li-ion batteries necessitates advancements in electrolyte engineering. However, the …

There has been an ongoing race for the past couple of years to develop the best universal
machine learning interatomic potential. This rapid growth has driven researchers to create …

Machine learning interatomic potentials (MLIPs) are changing atomistic simulations in
chemistry and materials science. Yet, building a single, universal MLIP--capable of …