Topic: Active learning for data-efficient optimisation of materials and processes

Abstract: The arrival of materials science data infrastructures in the past decade has ushered in the era of data-driven materials science based on artificial intelligence (AI) algorithms, which has facilitated breakthroughs in materials optimisation and design. Of particular interest are active learning algorithms, where datasets are collected on-the-fly in the search for optimal solutions. We encoded such a probabilistic algorithm into the Bayesian Optimization Structure Search (BOSS) Python tool for materials optimisation [1].

BOSS builds N-dimensional surrogate models for materials’ energy or property landscapes to infer global optima, allowing us to conduct targeted materials engineering. The models are iteratively refined by sequentially sampling materials data with high information content. This creates compact and informative datasets. We utilised this approach for computational density functional theory studies of molecular surface adsorbates [2], thin film growth [3], solid-solid interfaces [4] and molecular conformers [5]. With experimental colleagues, we applied BOSS to accelerate the development of novel materials with targeted properties, and to optimise materials processing [7]. With recent multi-objective and multi-fidelity implementations for active learning, BOSS can make use of different information sources to help us discover optimal solutions faster in both academic and industrial settings.

[1] npj Comput. Mater., 5, 35 (2019)

[2] Beilstein J. Nanotechnol. 11, 1577-1589 (2020), Adv. Func. Mater., 31, 2010853 (2021) [3] Adv. Sci. 7, 2000992 (2020) [4] ACS Appl. Mater. Interfaces 14 (10), 12758-12765 (2022) [5] J. Chem. Theory Comput. 17, 1955 (2020) [6] MRS Bulletin 47, 29-37 (2022) [7] ACS Sustainable Chem. Eng. 10, 9469 (2022)

Bio: Milica Todorović is an Associate Professor in Materials Engineering at the Department of Mechanical and Materials Engineering, University of Turku. She gained an MSci in Physics at University College London, followed by a DPhil in Materials Science from Merton College at the University of Oxford. She went on to specialise in development and high performance computing applications of large-scale first principles calculations at the National Institute for Materials Science, Japan, and scanning probe microscopy simulations at Universidad Autonoma de Madrid before settling in Finland. Her research focuses on interfacing artificial intelligence algorithms with first principles simulations of materials with the aim to optimise material functionality.