Noise is usually associated with disorder, but it can also generate large-scale order. Here, the authors show that three distinct systems, spanning soft matter and stochastic optimization, self-organi...

The PropMolFlow model uses flow matching to efficiently generate chemically valid molecules in three dimensions with targeted properties, enabling accelerated discovery of molecules useful in material...

PropMolFlow is a flow-matching method for property-guided molecule generation that matches diffusion model performance while generating stable, valid structures more quickly and enabling the discovery...

Alt: a close up of a person's hand holding a marker that says sharpie

Commentary: Many materials' properties and phase boundaries are generally not well known under extreme pressure and temperature conditions. This is a consequence of the scarcity of experimental inform...

Generative models for materials, especially inorganic crystals, hold potential to transform the theoretical prediction of novel compounds and structures. Advancement in this field depends critically o...

Breakthrough offers way to develop AI to match flocking birds and schooling fish

🔬 Here comes the lab’s first neurobiology paper, made possible by the amazing work of Dr. Jiyeon H., grad student Asit Pal, and collaborators, especially Andre Fenton (lead PI on the paper), Hans A. H...

Long-term memory formation transiently activates Ca2+-calmodulin kinase IIα (CaMKII) and atypical protein kinase C isoform iota/lambda (PKC𝜄/λ), whereas persistent activation of the other atypical PKC...

Self-organization through noisy interactions is ubiquitous across physics, mathematics, and machine learning, yet how long-range structure emerges from local noisy dynamics remains poorly understood. ...

Stability is a fundamental requirement for both biological and engineered neural circuits, yet it is surprisingly difficult to guarantee in the presence of recurrent interactions. Standard linear dyna...

A principled approach to dynamic divisive normalization in recurrent neural networks ensuring unconditional stability and improved trainability.