Highly accurate ab initio molecular dynamics (MD) methods are the gold standard for studying molecular mechanisms in the condensed phase, however, they are too expensive to capture many key properties that converge slowly with respect to simulation length and time scales. Machine learning (ML) approaches which reach the accuracy of ab initio simulation, and which are, at the same time, sufficiently affordable hold the key to bridging this gap. In this work we present a robust ML potential for the EC:EMC binary solvent, a key component of liquid electrolytes in rechargeable Li-ion batteries. We identify the necessary ingredients needed to successfully model this liquid mixture of organic molecules. In particular, we address the challenge posed by the separation of scale between intra- and inter-molecular interactions, which is a general issue in all condensed phase molecular systems.

Open access to research data is increasingly important for accelerating research. Grant authorities therefore request detailed plans for how data is managed in the projects they finance. We have recently developed such a plan for the EU−H2020 BIG‐MAP project—a cross‐disciplinary project targeting disruptive battery‐material discoveries. Essential for reaching the goal is extensive sharing of research data across scales, disciplines and stakeholders, not limited to BIG‐MAP and the European BATTERY 2030+ initiative but within the entire battery community. The key challenges faced in developing the data management plan for such a large and complex project were to generate an overview of the enormous amount of data that will be produced, to build an understanding of the data flow within the project and to agree on a roadmap for making all data FAIR (findable, accessible, interoperable, reusable). This paper describes the process we followed and how we structured the plan.

Classical parallel-tempering Monte Carlo simulations in the isothermal–isobaric ensemble were carried out for the (H₂O)₂₀ and Ar(H₂O)₂₀ clusters, over a wide range of temperatures (30–1000 K) and pressures (3 kPa–10 GPa) in order to study their thermodynamic properties and structural changes. The TIP4P/ice water model is employed for the water–water interactions, while both semiempirical and ab initio-based potentials are used to model the interaction between the rare-gas atoms and the water molecules. Temperature–pressure phase diagrams for these cluster systems were constructed by employing a two-dimensional multiple-histogram method. Structural changes were detected by analyzing the heat capacity landscape and the Pearson correlation coefficient profile for the interaction energy and volume. Those at high pressure correspond to solid-to-solid transitions and are found to be related to clathrate-like cages around the Ar atom. It is also shown that the formation and thermodynamic stability of such structures are determined by the intermolecular interaction between the rare-gas atoms and the host water molecules.