This white paper explains how combining physics-based models and artificial intelligence can accelerate new material development. Physics-based models such as density functional theory and molecular dynamics simulate material behavior using fundamental physical laws, providing deep mechanistic insight but often at high computational cost. In contrast, machine learning models rely on experimental data to make fast, data-driven predictions without explicitly encoding physical principles. The paper discusses strategies for integrating these approaches to balance accuracy, speed, and scalability. By leveraging the strengths of both methods, organizations can improve prediction quality, reduce experimentation, and more efficiently explore complex materials design spaces.

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