Climate Change and Materials Criticality challenges are driving urgent responses from global governments. These global responses drive policy to achieve sustainable, resilient, clean solutions with Advanced Materials (AdMats) for industrial supply chains and economic prosperity. The research landscape comprising industry, academe, and government identified a critical path to accelerate the Green Transition far beyond slow conventional research through Digital Technologies that harness Artificial Intelligence, Smart Automation and High Performance Computing through Materials Acceleration Platforms, MAPs. In this perspective, following the short paper, a broad overview about the challenges addressed, existing projects and building blocks of MAPs will be provided while concluding with a review of the remaining gaps and measures to overcome them.

Dendrite formation and growth remains a major obstacle toward high‐performance all solid‐state batteries using Li metal anodes. The ceramic Li_(1+x)Al_(x)Ti_(2−x)(PO₄)₃ (LATP) solid‐state electrolyte shows a higher than expected stability against electrochemical decomposition despite a bulk electronic conductivity that exceeds a recently postulated threshold for dendrite‐free operation. Here, transmission electron microscopy, atom probe tomography, and first‐principles based simulations are combined to establish atomistic structural models of glass‐amorphous LATP grain boundaries. These models reveal a nanometer‐thin complexion layer that encapsulates the crystalline grains. The distinct composition of this complexion constitutes a sizable electronic impedance. Rather than fulfilling macroscopic bulk measures of ionic and electronic conduction, LATP might thus gain the capability to suppress dendrite nucleation by sufficient local separation of charge carriers at the nanoscale.

Poly(ethylene oxide) (PEO)-based polymers are common hosts in solid polymer electrolytes (SPEs) for high-power energy devices. Molecular simulations have provided valuable molecular insights into structures and ion transport mechanisms of PEO-based SPEs. The calculation of thermodynamic and kinetic properties rely crucially on the dependability of the molecular force fields describing inter- and intra-molecular interactions with the target system. In this work, we reparametrized atomic partial charges for the widely applied optimized potentials for liquid simulations (OPLS) force field of PEO. The revised OPLS force field, OPLS^R, improves the calculations of density, thermal expansion coefficient, and the phase transition of the PEO system. In particular, OPLS^R greatly enhances the accuracy of the calculated dielectric constant of PEO, which is critical for simulating polymer electrolytes. The reparameterization method was further applied to SPE system of PEO/LiTFSI with O:Li ratio of 16:1. Based on the reparametrized partial charges, we applied separate charge-scaling factors for PEO and Li salts. The charge-rescaled OPLS^R model significantly improves the resulting kinetics of Li⁺ transport while maintaining the accurate description of coordination structures within PEO-based SPE. The proposed OPLS^R force field can benefit the future simulation studies of SPE systems.

Machine-learning interatomic potentials, such as Gaussian Approximation Potentials (GAPs), constitute a powerful class of surrogate models to computationally involved first-principles calculations. At a similar predictive quality but significantly reduced cost, they could leverage otherwise barely tractable extensive sampling as in global surface structure determination (SSD). This efficiency is jeopardized though, if an a priori unknown structural and chemical search space as in SSD requires an excessive number of first-principles data for the GAP training. To this end, we present a general and data-efficient iterative training protocol that blends the creation of new training data with the actual surface exploration process. Demonstrating this protocol with the SSD of low-index facets of rutile IrO₂ and RuO₂, the involved simulated annealing on the basis of the refining GAP identifies a number of unknown terminations even in the restricted sub-space of (1 × 1) surface unit cells. Particularly in an O-poor environment, some of these, then metal-rich terminations, are thermodynamically most stable and are reminiscent of complexions as discussed for complex ceramic materials.