Development of high conductivity solid-state electrolytes for lithium ion batteries has proceeded rapidly in recent years, but incorporating these new materials into high-performing batteries has proven difficult. Interfacial resistance is now the limiting factor in many systems, but the exact mechanisms of this resistance have not been fully explained - in part because experimental evaluation of the interface can be very difficult. In this work, we develop a computational methodology to examine the thermodynamics of formation of resistive interfacial phases. The predicted interfacial phase formation is well correlated with experimental interfacial observations and battery performance. We calculate that thiophosphate electrolytes have especially high reactivity with high voltage cathodes and a narrow electrochemical stability window. We also find that a number of known electrolytes are not inherently stable but react in situ with the electrode to form passivating but ionically conducting barrier layers. As a reference for experimentalists, we tabulate the stability and expected decomposition products for a wide range of electrolyte, coating, and electrode materials including a number of high-performing combinations that have not yet been attempted experimentally.

Large-scale simulations with complex electron interactions remain one of the greatest challenges for atomistic modelling. Although classical force fields often fail to describe the coupling between electronic states and ionic rearrangements, the more accurate ab initio molecular dynamics suffers from computational complexity that prevents long-time and large-scale simulations, which are essential to study technologically relevant phenomena. Here we present the Crystal Hamiltonian Graph Neural Network (CHGNet), a graph neural network-based machine-learning interatomic potential (MLIP) that models the universal potential energy surface. CHGNet is pretrained on the energies, forces, stresses and magnetic moments from the Materials Project Trajectory Dataset, which consists of over 10 years of density functional theory calculations of more than 1.5 million inorganic structures. The explicit inclusion of magnetic moments enables CHGNet to learn and accurately represent the orbital occupancy of electrons, enhancing its capability to describe both atomic and electronic degrees of freedom. We demonstrate several applications of CHGNet in solid-state materials, including charge-informed molecular dynamics in Li_xMnO₂, the finite temperature phase diagram for Li_xFePO₄ and Li diffusion in garnet conductors. We highlight the significance of charge information for capturing appropriate chemistry and provide insights into ionic systems with additional electronic degrees of freedom that cannot be observed by previous MLIPs.

To close the gap between the rates of computational screening and experimental realization of novel materials ^1,2 , we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 36 compounds from a set of 57 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials synthesis and motivates further integration of computations, historical knowledge and robotics.

The rapidly expanding field of nonaqueous multivalent intercalation batteries offers a promising way to overcome safety, cost, and energy density limitations of state-of-the-art Li-ion battery technology. We present a critical and rigorous analysis of the increasing volume of multivalent battery research, focusing on a wide range of intercalation cathode materials and the mechanisms of multivalent ion insertion and migration within those frameworks. The present analysis covers a wide variety of material chemistries, including chalcogenides, oxides, and polyanions, highlighting merits and challenges of each class of materials as multivalent cathodes. The review underscores the overlap of experiments and theory, ranging from charting the design metrics useful for developing the next generation of MV-cathodes to targeted in-depth studies rationalizing complex experimental results. On the basis of our critical review of the literature, we provide suggestions for future multivalent cathode studies, including a strong emphasis on the unambiguous characterization of the intercalation mechanisms.