Machine learning interatomic potentials (MLIP) are powerful tools for using large-scale molecular dynamics (MD) to evaluate material properties, including the performance of solid-state electrolytes ...

Molecular machine learning (ML) underpins critical workflows in drug discovery, material science, and catalyst optimization by rapidly predicting molecular interactions and properties. For instance, ...

The advent of machine learning interatomic potentials (MLPs) is revolutionising the field of computational materials science, enabling simulations of large systems and complex material properties with ...

Machine learning is a multibillion-dollar business with seemingly endless potential, but it poses some risks. Here's how to avoid the most common machine learning mistakes. Machine learning technology ...

A research group has developed SPACIER, an advanced polymer material design tool that integrates machine learning with molecular simulations. As a proof of concept, the group successfully synthesized ...

The ability to anticipate what comes next has long been a competitive advantage -- one that's increasingly within reach for developers and organizations alike, thanks to modern cloud-based machine ...