Despite their widespread success in various domains, Transformer networks have yet to
perform well across datasets in the domain of 3D atomistic graphs such as molecules even …

Despite rapid adoption and deployment of large language models (LLMs), the internal
computations of these models remain opaque and poorly understood. In this work, we seek …

Active crystals are highly ordered structures that emerge from the self-organization of motile
objects, and have been widely studied in synthetic, and bacterial, active matter. Whether …

We present an approach to mitigating the risks of malicious image editing posed by large
diffusion models. The key idea is to immunize images so as to make them resistant to …

We present a general-purpose solver for convex quadratic programs based on the
alternating direction method of multipliers, employing a novel operator splitting technique …

In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good
representations by encouraging them to be invariant under meaningful transformations …

Electronic flat-band materials host quantum states characterized by a quenched kinetic
energy. These flat bands are often conducive to enhanced electron correlation effects and …

Equivariant Transformers such as Equiformer have demonstrated the efficacy of applying
Transformers to the domain of 3D atomistic systems. However, they are still limited to small …

Deep graph neural networks are extensively utilized to predict chemical reactivity and
molecular properties. However, because of the complexity of chemical space, such models …

Bayesian optimization (BO) has been leveraged for guiding autonomous and high-
throughput experiments in materials science. However, few have evaluated the efficiency of …