Janine George1,2
Federal Institute for Materials Research and Testing (BAM)1,Friedrich-Schiller-Universität Jena2
Janine George1,2
Federal Institute for Materials Research and Testing (BAM)1,Friedrich-Schiller-Universität Jena2
In recent years, many protocols in computational materials science have been automated and made available within software packages (primarily Python-based).<sup>[1]</sup> This ranges from the automation of simple heuristics (oxidation states, coordination environments)<sup>[2]</sup> to the automation of protocols, including multiple DFT and post-processing tools such as (an)harmonic phonon computations or bonding analysis<sup>[3]</sup>. Such developments also shorten the time frames of projects after such developments have been made available and open new possibilities.<br/><br/>For example, we can now easily make data-driven tests of well-known rules and heuristics or develop quantum chemistry-based materials descriptors for machine learning approaches. These tests and descriptors can have applications related to magnetic ground state predictions of materials relevant for spintronic applications<sup>[4]</sup> or for predicting thermal properties relevant for thermal management in electronics.<sup>[5]</sup> Combining high-throughput <i>ab initio</i> computations with fitting, fine-tuning machine learning models and predictions of such models within complex workflows is also possible and promises further acceleration in the field.<sup>[6] </sup><br/><br/>In this talk, I will show our latest efforts to link automation with data-driven chemistry and materials science.<br/><br/><b>References</b><br/>[1] J. George, <i>Trends Chem.</i> <b>2021</b>, <i>3</i>, 697–699.<br/>[2] D. Waroquiers, J. George, M. Horton, S. Schenk, K. A. Persson, G.-M. Rignanese, X. Gonze, G. Hautier, <i>Acta Cryst B</i> <b>2020</b>, <i>76</i>, 683–695.<br/>[3] J. George, G. Petretto, A. Naik, M. Esters, A. J. Jackson, R. Nelson, R. Dronskowski, G.-M. Rignanese, G. Hautier, <i>ChemPlusChem</i> <b>2022</b>, <i>87</i>, e202200123.<br/>[4] K. Ueltzen, A. Naik, C. Ertural, P. Benner, J. George, <i>Article in Preparation</i> <b>2023</b>.<br/>[5] A. A. Naik, C. Ertural, N. Dhamrait, P. Benner, J. George, <i>Sci Data</i> <b>2023</b>, <i>10</i>, 610.<br/>[6] C. Ertural, V. L. Deringer, J. George, <i>Article in Preparation</i> <b>2023</b>.