Physics AI for materials

Design better materials.Start from within.

Connect atomic interactions, evolving defects and material performance. Turn that understanding into faster predictions for composition, processing and engineering design.

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From defects to design

The difference starts inside.

The same composition can become very different materials.

Processing changes atomic arrangements, defects and grains. These internal changes shape how a material deforms and accumulates damage—and create opportunities to design it better.

Connect the inside to the design →
  1. Atoms

    Atoms

    Interactions set the energy

    Learn how atomic layers interact and move.

  2. Defects

    Defects

    Small structures, collective behavior

    Follow dislocations, twins and interfaces as they evolve.

  3. Microstructure

    Microstructure

    Processing leaves an internal history

    See how grains reorganize during deformation.

  4. Performance

    Performance

    The response becomes a design input

    Connect evolving states to material behavior and better choices.

Physics AI. Built for materials.

Turn physical knowledge into reusable prediction.

High-fidelity simulations reveal material behavior. AI learns its patterns. Together, they turn repeated calculations into reusable models for exploring materials and accelerating engineering workflows.

Material structureMaterial & physics data
Physics AI
Predicted material stressPrediction & solver use

Design materials

Connect composition and processing to performance.

Test virtually

Explore deformation and damage before the next experiment.

Accelerate simulation

Use surrogates directly or alongside FEM.

Resolve the physics. Accelerate prediction.

Numerical computing and learned models play complementary roles: understanding mechanisms, building data and making the next prediction faster.

High-fidelity computing

Resolve defects, transport and microstructure with numerical methods and GPU parallelism.

Phase field · FEM · Defect dynamics · GPU

Physics AI models

Learn reusable field and evolution predictions from data, physical laws or both.

PDCO · MPTT · Evolution surrogates

Faster predictions. Wider possibilities.

From evolving microstructures to large meshes, turn computational advances into room for more design exploration.

≈140×

Faster evolution prediction

64-step phase-field prediction · A100 versus single-core CPU FEM.

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0.24%

Phase-field prediction error

Mean endpoint field error over three initial states, relative to FEM.

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27.48M

Volume mesh points

MPTT prediction across a full three-dimensional Ahmed case.

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See the material. See the possibility.

Explore what Physics AI and numerical computing make visible—from atomic interactions to fields inside a material.

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MPTT

Predict inside complex structures.

Connect local interfaces, changing meshes and large-scale fields.

PDCO

Learn directly from physical laws.

Learn predictions from physical laws when simulation samples are costly.

Evolution

Follow defects as they change.

Explore moving boundaries and their coupled physical states.

NN-MGSF / DFD

Take atomic information further.

Bring atomic-layer interactions into collective defect evolution.

What will your next material do?

Bring your composition, processing or performance challenge. Let’s connect it to a materials computing approach.

Let’s talk materials →