Deep neural network predicts molecular wavefunctions in atomic orbital basis from which quantum properties are derived at force-field efficiency.
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TDGL simulations of thermal quenches in chiral superfluid 3He-A report excitation of collective modes via PSD and damping-dependent Kibble-Zurek scaling with nu≈1/2 and z transitioning from 1 to 2.
Proposes TSCG hierarchical representation and Transformer propagator for universal coarse-grained protein MD with claimed 10k-20k times acceleration over all-atom MD while preserving statistical properties.
citing papers explorer
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Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions
Deep neural network predicts molecular wavefunctions in atomic orbital basis from which quantum properties are derived at force-field efficiency.
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Excitation of Collective Modes in a Chiral Superfluid by Thermal Quench
TDGL simulations of thermal quenches in chiral superfluid 3He-A report excitation of collective modes via PSD and damping-dependent Kibble-Zurek scaling with nu≈1/2 and z transitioning from 1 to 2.
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Towards a Universal Foundation Model for Protein Dynamics: A Multi-Chain Tree-Structured Framework with Transformer Propagators
Proposes TSCG hierarchical representation and Transformer propagator for universal coarse-grained protein MD with claimed 10k-20k times acceleration over all-atom MD while preserving statistical properties.