  Machine-learned force fields have transformed the atomistic modelling of
materials by enabling simulations of ab initio quality on unprecedented time
and length scales. However, they are currently limited by: (i) the significant
computational and human effort that must go into development and validation of
potentials for each particular system of interest; and (ii) a general lack of
transferability from one chemical system to the next. Here, using the
state-of-the-art MACE architecture we introduce a single general-purpose ML
model, trained on a public database of 150k inorganic crystals, that is capable
of running stable molecular dynamics on molecules and materials. We demonstrate
the power of the MACE-MP-0 model - and its qualitative and at times
quantitative accuracy - on a diverse set problems in the physical sciences,
including the properties of solids, liquids, gases, chemical reactions,
interfaces and even the dynamics of a small protein. The model can be applied
out of the box and as a starting or "foundation model" for any atomistic system
of interest and is thus a step towards democratising the revolution of ML force
fields by lowering the barriers to entry.
