Speaker: Pascal Friederich
Title: Machine Learning for Accelerated Materials Discovery
Abstract: Machine learning can accelerate the design of new molecules and materials in multiple ways, e.g. by learning from large amounts of (simulated or experimental) data to predict molecular or materials properties faster, or even by interfacing machine learning algorithms for autonomous decision-making directly with automated high-throughput experiments. In this talk, I will give a brief overview of our research activities on graph neural networks for materials property prediction [1,2], machine learning accelerated atomistic simulations [3,4], as well as on using machine learning methods for decision-making processes in automated materials science and chemistry labs [5].
[1] Reiser et al., Software Impacts 2021, https://www.sciencedirect.com/science/article/pii/S266596382100035X
[2] Reiser et al., arXiv:2208.09481
[3] Friederich et al., Nature Materials 2021, https://www.nature.com/articles/s41563-020-0777-6
[4] Li et al., Chemical Science 2021, https://pubs.rsc.org/en/content/articlehtml/2021/sc/d0sc05610c
[5] Luo et al., Angewandte Chemie 2022, https://onlinelibrary.wiley.com/doi/full/10.1002/anie.202200242