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White Paper

Using artificial intelligence and machine learning to enhance materials modeling

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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