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white paper

Using artificial intelligence and machine learning to enhance materials modeling

People in a conference room talking with a CAD software over it
There are three significant concerns with materials design automation. There is insufficient data because scientists are conducting fewer expensive, time-consuming tests. Second, data-driven solutions and a statistical mindset do not correspond to the differential equations and complex relationships found in nature. Lastly, there are no methods for performing in-depth exploration on a subset of a chemical space.

Simcenter Culgi handles data scarcity with physical-chemistry expertise, transforms domain knowledge into valuable insights using AI methods, and screens chemical space to narrow down potential candidates.

Simcenter Culgi uses one-of-kind advantages to address and resolve typical challenges seen in smart materials modeling

AI/ML models may be missing essential data in the later engineering and production stages, causing time-to-market delays. Simcenter Culgi adds critical automation to the design and development stages, increasing trust in data-driven approaches that address complex physical-chemistry constraints and objectives.

The AI/ML in Simcenter Culgi can:

  • Reduce computations to focus on a crucial chemical space

  • Replace physical testing with AI, based on chemistry-informed virtual simulations

  • Develop new digitally upgraded materials using a range of preliminary lab data

  • Settle OEM manufacturing and logistics regulations for faster time-to-market

Download and read the white paper for more information.

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