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Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of 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 of problems in the physical sciences, including 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 as a starting or “foundation” model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.
Synthesis of Li ion battery materials via ball milling has been a huge area of growth, leading to new high-capacity electrode materials, such as a number of promising disordered rocksalt (DRS) phases. In prior work, it was generally assumed that the synthesis was facilitated simply by local heating effects during the milling process. In this work, we show that ball milling Li2MoO4 leads to a phase transformation to the high pressure spinel polymorph and we report electrochemical data for this phase. This observation of the formation of a high pressure polymorph shows that local heating effects alone cannot explain the phase transformation observed (phenakite to spinel) and so indicates the importance of other effects. In particular, we propose that when the milling balls collide with the material, the resulting shockwaves exert a localised pressure effect, in addition to local heating. To provide further support for this, we additionally report ball milling results for a number of case studies (Li2MnO3, Li2SnO3, Nb2O5) which reinforces the conclusion that local heating alone cannot explain the phase transformations observed. The work presented thus provides greater fundamental understanding of milling as a synthetic pathway and suggests potential strategies to prepare such samples without milling (e.g., doping to create internal chemical pressure). In addition, we suggest that further research is needed into the effect of the use of milling as a route to smaller particles, since we believe that such milling may also be affecting the surface structure of the particles through the influence of the shockwaves generated.
