Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of ML-IAPs based on four local environment descriptors -Behler-Parrinello symmetry functions, smooth overlap of atomic positions (SOAP), the Spectral Neighbor Analysis Potential (SNAP) bispectrum components, and moment tensors -using a diverse data set generated using high-throughput density functional theory (DFT) calculations. The data set comprising bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) is chosen to span a range of crystal structures and bonding. All descriptors studied show excellent performance in predicting energies and forces far surpassing that of classical IAPs, as well as predicting properties such as elastic constants and phonon dispersion curves. We observe a general trade-off between accuracy and the degrees of freedom of each model, and consequently computational cost. We will discuss these trade-offs in the context of model selection for molecular dynamics and other applications.

Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

In this work, we present a comprehensive investigation of the elastic properties (the full elastic tensor, bulk, shear and Young's moduli, and Poisson's ratio) of 23 well-known ceramic alkali superionic conductor electrolytes (SICEs) using first principles calculations. We find that the computed elastic moduli are in good agreement with experimental data (wherever available) and chemical bonding nature. The anion species and structural framework have a significant influence on the elastic properties, and the relative elastic moduli of the various classes of SICEs follow the order thiophosphate < antiperovskite < phosphate < NASICON < garnet < perovskite. Within the same framework structure, we observe that Na SICEs are softer than their Li analogs. We discuss the implications of these findings in the context of fabrication, battery operation, and enabling a Li metal anode. The data computed in this work will also serve as a useful reference for future experiments as well as theoretical modeling of SICEs for rechargeable alkali-ion batteries.

In this work, we performed a first-principles investigation of the phase stability, dopant formation energy and Na+ conductivity of pristine and doped cubic Na3PS4 (c-Na3PS4). We show that pristine c-Na3PS4 is an extremely poor Na ionic conductor, and the introduction of Na+ excess is the key to achieving reasonable Na+ conductivities. We studied the effect of aliovalent doping of M4+ for P5+ in c-Na3PS4, yielding Na3+x M x P1–x S4 (M = Si, Ge, and Sn with x = 0.0625; M = Si with x = 0.125). The formation energies in all the doped structures with dopant concentration of x = 0.0625 are found to be relatively low. Using ab initio molecular dynamics simulations, we predict that 6.25% Si-doped c-Na3PS4 has a Na+ conductivity of 1.66 mS/cm, in excellent agreement with previous experimental results. Remarkably, we find that Sn4+ doping at the same concentration yields a much higher predicted Na+ conductivity of 10.7 mS/cm, though with a higher dopant formation energy. A higher Si4+ doping concentration of x = 0.125 also yields a significant increase in Na+ conductivity with an even higher dopant formation energy. Finally, topological and van Hove correlation function analyses suggest that the channel volume and correlation in Na+ motions may play important roles in enhancing Na+ conductivity in this structure.

All-solid-state sodium-ion batteries are promising candidates for large-scale energy storage applications. The key enabler for an all-solid-state architecture is a sodium solid electrolyte that exhibits high Na+ conductivity at ambient temperatures, as well as excellent phase and electrochemical stability. In this work, we present a first-principles-guided discovery and synthesis of a novel Cl-doped tetragonal Na3PS4 (t-Na3−xPS4−xClx) solid electrolyte with a room-temperature Na+ conductivity exceeding 1 mS cm−1. We demonstrate that an all-solid-state TiS2/t-Na3−xPS4−xClx/Na cell utilizing this solid electrolyte can be cycled at room-temperature at a rate of C/10 with a capacity of about 80 mAh g−1 over 10 cycles. We provide evidence from density functional theory calculations that this excellent electrochemical performance is not only due to the high Na+ conductivity of the solid electrolyte, but also due to the effect that “salting” Na3PS4 has on the formation of an electronically insulating, ionically conducting solid electrolyte interphase.