  Accurate and fast prediction of materials properties is central to the
digital transformation of materials design. However, the vast design space and
diverse operating conditions pose significant challenges for accurately
modeling arbitrary material candidates and forecasting their properties. We
present MatterSim, a deep learning model actively learned from large-scale
first-principles computations, for efficient atomistic simulations at
first-principles level and accurate prediction of broad material properties
across the periodic table, spanning temperatures from 0 to 5000 K and pressures
up to 1000 GPa. Out-of-the-box, the model serves as a machine learning force
field, and shows remarkable capabilities not only in predicting ground-state
material structures and energetics, but also in simulating their behavior under
realistic temperatures and pressures, signifying an up to ten-fold enhancement
in precision compared to the prior best-in-class. This enables MatterSim to
compute materials' lattice dynamics, mechanical and thermodynamic properties,
and beyond, to an accuracy comparable with first-principles methods.
Specifically, MatterSim predicts Gibbs free energies for a wide range of
inorganic solids with near-first-principles accuracy and achieves a 15 meV/atom
resolution for temperatures up to 1000K compared with experiments. This opens
an opportunity to predict experimental phase diagrams of materials at minimal
computational cost. Moreover, MatterSim also serves as a platform for
continuous learning and customization by integrating domain-specific data. The
model can be fine-tuned for atomistic simulations at a desired level of theory
or for direct structure-to-property predictions, achieving high data efficiency
with a reduction in data requirements by up to 97%.
