Data-Driven Material Design: Harnessing High-Throughput Simulations and AI
From first-principles calculations to machine learning-driven materials discovery, computational methods enhance our understanding of material behavior under different conditions. Furthermore, these modern computational tools allow scientists to explore vast design spaces more efficiently. Namely, by simulating material properties before synthesis, researchers can rapidly screen potential candidates, optimize structures, and uncover novel materials that might not have been feasible through traditional experimentation alone. As the power of computation continues to grow, the role of computational tools in solving complex materials science challenges will only expand, accelerating innovation and transforming the way materials are understood, discovered, and developed. I will present examples from our research that illustrate how we integrate high-throughput computing with machine learning (ML) and artificial intelligence (AI) to tackle complex challenges in materials science [1, 2]. I will first discuss recent progress in the development of automated computational workflows that support large-scale screening of materials for targeted properties, such as high electro-conversion or stability against electrochemical dissolution. These frameworks also allow us to develop large databases of relevant materials properties. When combined with modern ML tools, these databases can be used to train surrogate ML-models capable of screening millions of candidate chemistries to identify the ones with optimal reactivity or stability. Furthermore, I will illustrate the use of interpretable ML in materials research that aims to enhance explainability of predictive ML models, enabling understanding of the underlying factors influencing design decisions. Lastly, I will present our current work on inverse material design, where AI methods—particularly generative pretrained transformers—are used to predict new material candidates based on desired properties, pushing the boundaries of materials innovation. [1] M. Davis, W. Kort-Kamp, E. F. Holby, P. Zelenay, and I. Matanovic, Computational Screening of Transition Metal-Nitrogen-Carbon Materials as Electrocatalysts for CO 2 Reduction. Electrochimica Acta 510, 145357(2025). [2] M. Davis, W. Kort-Kamp, I. Matanovic, P. Zelenay and E. F. Holby, Design of Amine-Functionalized Materials for Direct Air Capture Using Integrated High-Throughput Calculations and Machine Learning, accepted in Communications Chemistry (2025).