Conspectus Humans are continually bombarded with massive amounts of data. To deal with
this influx of information, we use the concept of attention in order to perceive the most …

We report a flexible multi-modal mechanics language model, MeLM, applied to solve
various nonlinear forward and inverse problems, that can deal with a set of instructions …

Machine learning (ML) has emerged as an indispensable methodology to describe,
discover, and predict complex physical phenomena that efficiently help us learn underlying …

We report a flexible language-model-based deep learning strategy, applied here to solve
complex forward and inverse problems in protein modeling, based on an attention neural …

The chemical industry is facing ever-increasing challenges for developing novel products
and processes capable of reducing environmental impacts and curbing resource depletion …

Recent successes of foundation models in artificial intelligence have prompted the
emergence of large-scale chemical pre-trained models. Despite the growing interest in large …

If the promise of generative modeling techniques is realized, it may fundamentally change
how we carry out molecular simulation. The suite of techniques and models collectively …

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature.
Because hierarchy gives rise to unique properties and functions, many have sought …

Molecular mechanics (MM) potentials have long been a workhorse of computational
chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety …

Abstract Machine learning techniques have been widely applied in many fields of chemistry,
physics, biology, and materials science. One of the most fruitful applications is machine …