The Perfect Convergence of Physics and AI:
Designing Predictable Future Materials
물리 법칙과 인공지능의 융합 예측 가능한 미래 소재를 설계하다
Our Core Capabilities
Full-Stack PFM Technology
We predict microstructure evolution perfectly through 7 proprietary Full-Stack PFM codes covering the entire lifecycle from sintering to failure.
Neuro-Symbolic AI (PINO)
Accelerating simulation speeds by over 10,000x using PINO technology, combining the rigor of physical laws with the ultra-fast computation of AI.
Autonomous Evolution
Building a 'Grey-box Evolving Thermodynamics' system where the physics engine autonomously calibrates errors and evolves by absorbing real-time field data.
Lab-to-Field Deployment
Going beyond theoretical research, we provide 'Portable Palantir' solutions—lightweight models deployable on edge devices in actual manufacturing sites.
Major Research Areas
Phase-Field Simulation
Experience the microstructure evolution predicted by our Full-Stack PFM engine. Scroll down to control the simulation time-lapse.
Global Impact
We aim to shift the paradigm of material development from 'experience' to 'data & physics', establishing national data sovereignty and global standards.
Material Metaverse
Realizing a 'Material Metaverse' that transcends physical spatiotemporal limits through infinite virtual testing.
Fabless-Foundry Ecosystem
Creating a collaborative ecosystem linking design (Lab) and manufacturing (Foundry) to innovate R&D efficiency and drastically shorten development cycles.
Technological Sovereignty
Breaking reliance on foreign software by providing an independent material analysis platform capable of operating in extreme environments and closed networks.
Zero-Waste R&D
Drastically reducing experimental waste by replacing trial-and-error with virtual simulations, setting a standard for sustainable eco-friendly material industries.
Missing Link Discovery
Bridging the 'Meso-scale' gap between the atomic (Quantum) and macroscopic (Continuum) worlds to solve the missing link in materials science.
Global Standardization
Leaping as a global hub for computational materials science by deploying an open-source Physics-ML framework used by researchers worldwide.