The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing
the field of materials engineering thanks to their power to predict material properties, design …

Since the discovery of deep eutectic solvents (DESs) in 2003, significant progress has been
made in the field, specifically advancing aspects of their preparation and physicochemical …

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 …

It is a long‐standing challenge to break the tradeoffs between different mechanical property
indicators such as the strength versus toughness in the design of lightweight lattice …

We report a series of deep learning models to solve complex forward and inverse design
problems in molecular modeling and design. Using both diffusion models inspired by …

Practical applications of mechanical metamaterials often involve solving inverse problems
aimed at finding microarchitectures that give rise to certain properties. The limited resolution …

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 …

Transformer neural networks show promising capabilities, in particular for uses in materials
analysis, design, and manufacturing, including their capacity to work effectively with human …

Additively manufactured metamaterials are architectured cellular materials that can be
engineered through structural innovations to achieve unusual mechanical and …