TWIN, a MACE-based implicit-solvent MLP trained solely on ab initio and experimental data, transfers across drugs, peptides and proteins with near-DFT accuracy at ~100 imes lower cost.
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In: Protein Simulations
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A tailored quantum multi-programming workflow for the LUCJ ansatz enables parallel circuit execution with SQD/ext-SQD post-processing that mitigates cross-talk, yielding ethanol energies within 0.001 kcal/mol of classical HCI references.
xDRESP dynamically fits atom-centered multipoles of arbitrary order to the QM/MM electrostatic potential, accurately reproducing ESP and molecular multipoles and tracking charge flow in an SN2 reaction.
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Transferable Implicit Solvent Machine Learning Potential for Drugs and Proteins Approaching Ab Initio Accuracy
TWIN, a MACE-based implicit-solvent MLP trained solely on ab initio and experimental data, transfers across drugs, peptides and proteins with near-DFT accuracy at ~100 imes lower cost.
-
A Quantum Multi-Programming Framework to Maximize Quantum Resources for the LUCJ Ansatz
A tailored quantum multi-programming workflow for the LUCJ ansatz enables parallel circuit execution with SQD/ext-SQD post-processing that mitigates cross-talk, yielding ethanol energies within 0.001 kcal/mol of classical HCI references.
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