Topic: Predictive Materials Informatics for Extreme Conditions

The reliable design and deployment of materials in extreme environments—such as ultrahigh temperatures, intense radiation fields, high pressures, and corrosive atmospheres—are critical to advanced applications in aerospace, nuclear energy, defense, and other strategic sectors. However, conventional empirical and experimental approaches often fail to capture the complex, multi-scale phenomena that govern materials behavior under such harsh conditions. Recent advances in materials informatics—driven by machine learning, high-throughput computation, and data-centric modeling—are transforming this landscape. These approaches enable predictive insights, accelerate materials discovery, and support rational design strategies that go beyond traditional trial-and-error methods.

This Special Issue, Predictive Materials Informatics for Extreme Conditions, brings together cutting-edge research on data-driven methodologies for understanding, predicting, and optimizing materials performance in extreme environments. Topics include novel algorithms, multi-scale and multi-physics modeling frameworks, integrative approaches that couple experiments with informatics, and applications to critical material systems. By bridging fundamental materials science with advanced computational intelligence, this collection aims to advance predictive capabilities and design principles essential for the next generation of high-performance materials.

Materials informatics, machine learning, extreme environments, predictive modeling, data-driven materials design, multi-scale simulations