New Opportunities for Data-Driven Chemistry and Materials Science Through Automation

In recent years, many protocols in computational materials science have been automated and made available within software packages (primarily Python-based).^[1] This ranges from the automation of simple heuristics (oxidation states, coordination environments)^[2] to the automation of protocols, including multiple DFT and post-processing tools such as (an)harmonic phonon computations or bonding analysis^[3]. Such developments also shorten the time frames of projects after such developments have been made available and open new possibilities.

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^[4] or for predicting thermal properties relevant for thermal management in electronics.^[5] Combining high-throughput ab initio 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.^[6]

In this talk, I will show our latest efforts to link automation with data-driven chemistry and materials science.

References
[1] J. George, Trends Chem. 2021, 3, 697–699.
[2] D. Waroquiers, J. George, M. Horton, S. Schenk, K. A. Persson, G.-M. Rignanese, X. Gonze, G. Hautier, Acta Cryst B 2020, 76, 683–695.
[3] J. George, G. Petretto, A. Naik, M. Esters, A. J. Jackson, R. Nelson, R. Dronskowski, G.-M. Rignanese, G. Hautier, ChemPlusChem 2022, 87, e202200123.
[4] K. Ueltzen, A. Naik, C. Ertural, P. Benner, J. George, Article in Preparation 2023.
[5] A. A. Naik, C. Ertural, N. Dhamrait, P. Benner, J. George, Sci Data 2023, 10, 610.
[6] C. Ertural, V. L. Deringer, J. George, Article in Preparation 2023.