Foundation models for all matter and all matters
Our foundation models are trained on quantum mechanics and verified on materials across the periodic table, addressing challenges across industries, from energy storage to synthetic fuels, carbon capture and beyond.
Energy Storage
Resolve questions about cathode, anode, electrolyte, and SEI composition, performance, and cross-talk behind safety, lifetime, and rate capability to screen ion transport, diffusion barriers, and electrolyte decomposition in simulation so only the chemistries that earn it reach the lab.
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Catalysis
Engineer PGM-free, earth-abundant catalysts without the guesswork by modeling reaction pathways, surface chemistry, and thermodynamics across thermo- and electro- catalysts, hydrogenation, and oil & gas processes to focus synthesis on the candidates most likely to hold activity, selectivity, and cost.
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Separations
Identify the polymer membranes or metal-organic frameworks (MOFs) that achieve high selectivity and rejection by modeling binding, solvation, transport, and selectivity from critical-mineral recovery to product purification, before a single campaign scales.
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Semiconductors
Predict how electronic structure, defect chemistry, and thermal properties drive device performance and yield across process parameters and next-generation materials to narrow choices before a fabrication run.
Explore →PHIN Materials Awarded Federal Funding to Accelerate Catalyst Innovation via AI
We are excited to deploy our AI agents and simulation technology with PNNL, WashU, and Lectrolyst to reduce the time to develop new industrial catalysts from decades to months.
Read the case study →Battery Simulations with PHIN
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.
Read the case study →Catalyst Simulations With PHIN
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.
Read the case study →Li diffusion in LFP
Case study of the nudged elastic band (NEB) workflow in PHIN-OS to predict Li diffusion in an LFP cathode.
Read the case study →Phonon Calculations With PHIN
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.
Read the case study →Semiconductor Simulations With PHIN
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.
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