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.

PHIN OS and PHIN Atomic engine provide a powerful platform for performing high-throughput phonon calculations. Its modular design, active-learning based engine, and efficient task orchestration make it possible to explore phonon properties across diverse material classes with accuracy and speed beyond what is possible with a DFT-only approach. The computed phonon band structures and DOS are in good agreement with DFT-based reports, demonstrating that PHIN Atomic and PHIN OS are powerful tools for reliable high-throughput phonon calculations. The results presented here capture the essential vibrational features of MAX phases, NiTi shape memory alloys, and Mg₃Bi₂ thermoelectric, underscoring the utility of PHIN OS for high-throughput materials discovery, characterization, and design.

Over the past year, we have reduced the data requirements 99% and the time by 97%

Cropping allows us to generate training data from complex simulations to ensure the accuracy of 1000+ atom simulations...

Understanding and analyzing a system’s response to various inputs is central to any scientific or engineering R&D. Designing a new steel...

Digitizing materials development requires materials models that can predict materials behavior accurately, quickly and cheaply. Digital...

Digitizing materials development requires the accurate prediction of materials properties at lower costs than an equivalent experiment . . .

Materials innovation is slow and complex, often taking decades. We started PHIN to bring trustworthy AI models to revolutionize materials development through a new paradigm of digital simulation.