Nature Computational Science - A physics-infused heterogeneous graph neural network has been developed to address challenges in designing complex nanomaterials with spatially varying compositions....

Graph neural networks built on physically motivated representations enable gradient-based optimization of complex upconverting nanoparticle heterostructures, revealing photophysical design rules and a...

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Samuel Blau, Berkeley Lab Video Recording Slides (pptx, pdf)

Scientists will finally be able to simulate the chemistry that drives our bodies, our environment, and our technologies.

Machine learning (ML) models hold the promise of transforming atomic simulations by delivering quantum chemical accuracy at a fraction of the computational cost. Realization of this potential would en...

YouTube video by Coordinated Science Laboratory

Accurate modeling of long-range forces is critical in atomistic simulations, as they play a central role in determining the properties of materials and chemical systems. However, standard machine lear...

Zichi et al., (2024). RNMC: kinetic Monte Carlo implementations for complex reaction networks. Journal of Open Source Software, 9(104), 7244, https://doi.org/10.21105/joss.07244

Hydrolysis is a fundamental family of chemical reactions where water facilitates the cleavage of bonds. The process is ubiquitous in biological and chemical systems, owing to water's remarkable versat...

Scaling has been critical in improving model performance and generalization in machine learning. It involves how a model's performance changes with increases in model size or input data, as well as ho...