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Electroencephalography (EEG) offers millisecond temporal resolution, but inferring underlying neural sources is a severely ill-posed spatial inverse problem. While deep learning has advanced spatial reconstruction, current architectures face a critical dilemma: frame-by-frame models discard vital temporal context, wher...
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespread and impactful. This success is due largely to the existence of a well-developed and established physics-based modeling framework, ranging...
DensIP is introduced, a physics-based model of intermolecular interactions that uses machine-learned electron densities and only four universal parameters that outperforms state-of-the-art general-purpose MLFFs for long-range interactions and can be applied to molecules as large as drug ligands.
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