MACE-MP-0 is a general-purpose atomistic ML force field trained on public data that enables stable simulations of diverse chemical systems with qualitative and sometimes quantitative accuracy, serving as a starting point for fine-tuning.
Ab initio surface chemistry with chemical accuracy
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Open-source parallel implementations of RCCSDT, RCCSDT(Q), RCCSDTQ and UCCSDT in PySCF achieve near-ideal scaling to ~3000 cores and extend canonical high-order CC to systems with ~100 electrons in 450 orbitals.
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A foundation model for atomistic materials chemistry
MACE-MP-0 is a general-purpose atomistic ML force field trained on public data that enables stable simulations of diverse chemical systems with qualitative and sometimes quantitative accuracy, serving as a starting point for fine-tuning.
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High-performance parallel implementation of high-order coupled-cluster theories
Open-source parallel implementations of RCCSDT, RCCSDT(Q), RCCSDTQ and UCCSDT in PySCF achieve near-ideal scaling to ~3000 cores and extend canonical high-order CC to systems with ~100 electrons in 450 orbitals.