Combining data science with quantum chemistry: Industrial applications in Healthcare and Electronics
Presenter: (Jan) Gerit Brandenburg, Senior Scientist at Merck
New technologies are made possible by new molecules and materials, and until recently those could only be discovered experimentally. However, approaches based on the fundamental laws of quantum mechanics are now integrated into many design initiatives in academia and industry, underpinning efforts such as the Materials Genome initiative or the computational crystal structure prediction [1, 2]. To infer relevant properties from complementary computer-generated and experimental data, modern Machine Learning (ML) approaches are well suited. [3,4]
The different driving forces for academic developments and industrial applications will be discussed and use-cases on how to bring both sides together are shown. A particular focus is the impact of ML-enhanced In Silico structure prediction in pharmaceutical development and the application of simulated electronic spectra for automatically identifying and quantifying impurities in OLED production.
References:
[1] Annu. Rev. Mater. Res. 49, 1 (2019) doi:10.1146/annurev-matsci-070218-010143
[2] Molecular Crystal Structure Prediction, Elsevier Australia (2017) eBook ISBN: 9780128098363
[3] J. Chem. Phys. 154, 61101 (2021) doi:10.1063/5.0041008
[4] Nat Commun 12, 3927 (2021) doi: 10.1038/s41467-021-24119-3