Seminar: Combining theory, experiment and AI in materials design: new opportunities for sustainable world

Breakthrough discoveries of novel materials with advanced functionalities enable materials science to participate in a transition towards a sustainable society and contribute to a solution of UN Sustainable Development Goals (SDG). Unfortunately, it still takes up to 20 years and longer to design materials. To reduce the time, we need to find non-conventional paths and develop approaches that go beyond state-of-the-art materials design paradigms. In this talk we demonstrate capabilities of state-of-the-art theoretical simulations combined with experiment, artificial intelligence (AI) and machine learning (ML) to disclose materials functionalities attractive for sustainable applications. We review recent advances in theoretical description of materials properties, strengthened with AI/ML tools [1] and advanced visual exploration [2]. Focusing on solid state spin qubits, we present results of systematic theoretical exploration of point defects in wide band gap semiconductors [3]. We identify materials systems with properties attractive for quantum computing and communications [4] and present a strategy of using SiC defects qubits to design quantum reservoir computing (QRC) systems, a new disruptive technology with a potential to achieve qualitative improvements in speed and reduction in power consumption – two or more orders of magnitude – compared to classical machine learning systems. Next, we demonstrate the capability of a collaboration between experiment and theory to discover materials with in high-pressure high-temperature (HPHT) synthesis at TPa compression and temperature above 2000 K [5] and show that theory guided decompression allows to quench phases with advanced functionalities to ambient conditions [6].

[1] B. Mukhamedov, F. Tasnádi and I. A. Abrikosov, Mater. Des. 253, 113865 (2025); H. Levämäki, et al., NPJ Comp. Mater. 8, 17 (2022).

[2] M. Bykov, et al., Phys. Rev. Lett. 126, 175501 (2021); D. Laniel, et al., Nature Chem. 15, 641 (2023).

[3] V. Ivády, et al., Npj Comp. Mater. 4,76 (2018); J. Davidsson, et al., Comput. Phys. Commun 269, 108091 (2021).

[4] O. Bulancea-Lindvall, et al., Phys. Rev. B 108, 224106 (2023); J. Davidsson, et al., Npj Comp. Mater. 10, 109 (2024).

[5] L. Dubrovinsky, et al., Nature 605, 274 (2022).

[6] D. Laniel, et al., Adv. Mater. (2023); Adv. Funct. Mater. 2416892 (2024).