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Initiative for Computational Catalysis, Flatiron Institute, 160 5th Avenue, New York, NY 10010, USA
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Widely used simulations of liquid water are surprisingly inconsistent, with errors exceeding 20% in key properties, but this work provides a robust, reproducible benchmark.
Optimizing the nodal surface in DMC calculations closes the gap with CCSD(T) benchmarks for hydrogen-bonded systems, offering a more accurate and efficient computational approach.
Achieve sub-chemical accuracy in predicting the lattice energies of complex molecular crystals, including drug-like molecules, at DFT-level computational cost, unlocking high-throughput polymorph screening.
Forget generic MLIPs: MACE-POLAR-1 nails long-range electrostatics, slashing errors in protein-ligand binding and crystal energies.
Achieve transition-metal chemical accuracy in DFT calculations for catalytic reactions on metal surfaces by using a new framework for designing non-self-consistent density functional approximations.
