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.
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:
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