Post hoc calibration turns uncertainty estimates from decorative numbers into useful decision signals for chemistry ML.
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Active learning turns costly surface-hopping into a push-button workflow.
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How a smart feature and a pinch of phase-fixing let machine learning predict nonadiabatic couplings like a pro.
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A new AI-powered protocol learns excited states with accuracy that finally makes it practical.
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New Software Unveils Hidden Patterns in Nonadiabatic Dynamics.
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MD simulations can be 10x faster by replacing files with socket communication.
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Effortlessly achieve rotationally invariant machine learning of vectors with the rotate-predict-rotate (RPR) method.
(more…) A quantum sampled molecular
dataset ups the challenge of training ML models.
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