Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

We consider the problem of generating periodic materials with deep models. While
symmetry-aware molecule generation has been studied extensively, periodic materials …

Supervised machine learning approaches have been increasingly used in accelerating
electronic structure prediction as surrogates of first-principle computational methods, such …

Periodic material or crystal property prediction using machine learning has grown popular in
recent years as it provides a computationally efficient replacement for classical simulation …

Lack of rigorous reproducibility and validation are significant hurdles for scientific
development across many fields. Materials science, in particular, encompasses a variety of …

Crystal structures are characterized by atomic bases within a primitive unit cell that repeats
along a regular lattice throughout 3D space. The periodic and infinite nature of crystals …

Recently, the remarkable capabilities of large language models (LLMs) have been
illustrated across a variety of research domains such as natural language processing …

Crystalline materials, with their symmetrical and periodic structures, possess a diverse array
of properties and have been widely used in various fields, ranging from electronic devices to …

Physical simulations of fluids are crucial for understanding fluid dynamics across many
applications, such as weather prediction and engineering design. While high-resolution …

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 …