Here we introduce a new paradigm in PHIN-OS, composable design space search that enables using state of the art Bayesian Optimization (BO) into any atomic scale simulation workflow. The functionality reduces the cost of materials design by 97+% by efficiently navigating vast design spaces.

Case study of the nudged elastic band (NEB) workflow in PHIN-OS to predict Li diffusion in an LFP cathode.

We use PHIN-OS to simulate the Horiuti–Polanyi mechanism for acetylene hydrogenation, a widely used process for purifying industrial hydrocarbon streams. The case study validates a reproducible digital workflow that can subsequently be used to rapidly develop novel, cost-effective catalysts.

By simulating silicon vacancy energy, surface energy, and melting temperature, we showcase the ability of MLIPs to simulate real properties relevant to semiconductor development. We show that fine-tuning in PHIN-atomic is necessary to accurately simulate the properties of real materials and is a significant improvement over pretrained models.

Battery simulations have traditionally required significant expertise and manual oversight to define simulation workflows and ensure accuracy. We show how both problems are addressed with PHIN-OS and PHIN-atomic. We leverage these tools to simulate the formation of solid electrolyte interphases, which remains a grand challenge in battery research.