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Liquid electrolytes are one of the most important components of Li-ion batteries, which are a critical technology of the modern world. However, we still lack the computational tools required to accurately calculate key properties of these materials (viscosity and ionic diffusivity) from first principles necessary to support improved designs. In this work, we report a machine learning-based force field for liquid electrolyte simulations, which bridges the gap between the accuracy of range-separated hybrid density functional theory and the efficiency of classical force fields. Predictions of material properties made with this force field are quantitatively accurate compared to experimental data. Our model uses the QRNN deep neural network architecture, which includes both long-range interactions and global charge equilibration. The training data set is composed solely of non-periodic density functional theory (DFT), allowing the practical use of an accurate theory (here, ωB97X-D3BJ/def2-TZVPD), which would be prohibitively expensive for generating large data sets with periodic DFT. In this report, we focus on seven common carbonates and LiPF 6 , but this methodology has very few assumptions and can be readily applied to any liquid electrolyte system. This provides a promising path forward for large-scale atomistic modeling of many important battery chemistries.

Pyridoxal 5'-phosphate (PLP)-dependent enzymes have been extensively studied for their ability to fine-tune PLP cofactor electronics to promote a wide array of chemistries. Neutron crystallography offers a straightforward approach to studying the electronic states of PLP and the electrostatics of enzyme active sites, responsible for the reaction specificities, by enabling direct visualization of hydrogen atom positions. Here we report a room-temperature joint X-ray/neutron structure of aspartate aminotransferase (AAT) with pyridoxamine 5'-phosphate (PMP), the cofactor product of the first half reaction catalyzed by the enzyme. Between PMP N SB and catalytic Lys258 Nζ amino groups an equally shared deuterium is observed in an apparent low-barrier hydrogen bond (LBHB). Density functional theory calculations were performed to provide further evidence of this LBHB interaction. The structural arrangement and the juxtaposition of PMP and Lys258, facilitated by the LBHB, suggests active site preorganization for the incoming ketoacid substrate that initiates the second half-reaction., Competing Interests: There are no conflicts of interest to declare., (This journal is © The Royal Society of Chemistry.)