Computational Materials Science and Data-Driven Design focus on using modeling, simulation, and advanced data analytics to understand, predict, and optimize material behavior across length and time scales. This session explores how computational approaches accelerate materials discovery by reducing reliance on trial-and-error experimentation and enabling informed decision-making throughout the material development lifecycle. By integrating physics-based models with data-centric methods, researchers can rapidly identify promising materials and processing routes for targeted applications.

Computational techniques allow scientists to probe atomic-scale interactions, microstructural evolution, and macroscopic properties with high precision. Methods such as density functional theory, molecular dynamics, and phase-field modeling provide insight into structure–property relationships that govern material performance. These tools support rational design strategies that improve efficiency, reliability, and functionality. As digital approaches become increasingly central to materials innovation, Materials Science Conference platforms highlight computational research as a key enabler of next-generation material systems.

A major theme of the session is the integration of data-driven methodologies with traditional modeling frameworks. High-throughput simulations generate large datasets that, when combined with machine learning and statistical analysis, reveal hidden trends and correlations. These insights enable rapid screening of material compositions and structures, significantly shortening development timelines. Closely related to this paradigm is Data-Driven Materials Design, which leverages data analytics to guide material selection, optimization, and performance prediction.

The session also examines digital workflows that connect simulation, experimentation, and manufacturing. Computational tools support virtual prototyping, allowing researchers to evaluate material behavior under varied conditions before physical testing. This approach reduces cost, minimizes waste, and improves scalability. Integration of computational outputs with experimental validation ensures accuracy and builds confidence in predictive models.

Uncertainty quantification and model validation are critical aspects addressed in this session. Understanding model limitations and variability ensures that predictions are robust and transferable to real-world applications. Advances in multiscale modeling link atomic-level behavior to component-level performance, supporting comprehensive design strategies across engineering systems.

Sustainability and efficiency considerations further underscore the importance of computational materials science. Digital design approaches reduce resource consumption by optimizing materials for longevity, recyclability, and performance efficiency. By enabling informed decision-making early in the design process, computational methods support environmentally responsible material development. Through the convergence of modeling, data science, and engineering insight, this session demonstrates how computational approaches are reshaping the future of materials innovation.