Abstract: For the study of molecules and materials, conventional theoretical and experimental spectroscopies are well established in the natural sciences, but they are slow and expensive. Our objective is to launch a new era of artificial intelligence (AI) enhanced spectroscopy that learns from the plethora of already available experimental and theoretical spectroscopy data. Once trained, the AI can make predictions of spectra instantly and at no further cost. In this new paradigm, AI spectroscopy would complement conventional theoretical and experimental spectroscopy to greatly accelerate the spectroscopic analysis of materials, make predictions for novel and hitherto uncharacterized materials, and discover entirely new materials.
In this presentation, I will introduce the two approaches we have used to learn spectroscopic properties: kernel ridge regression (KRR) and deep neural networks (NN). The models are trained and validated on data generated by accurate state-of-the art quantum chemistry computations for diverse subsets of the GBD-13 and GBD-17 molecular datasets [1,2]. The molecules are represented by a simple, easily attainable numerical description based on nuclear charges and cartesian coordinates [3,4]. The complexity of the molecular descriptor [4] turns out to be crucial for the learning success, as I will demonstrate for KRR. I will then show, how we can learn spectra (i.e. continuous target quantities) with NNs. We design and test three different NN architectures: multilayer perceptron (MLP) [5], convolutional neural network (CNN) and deep tensor neural network (DTNN) [6]. Already the MLP is able to learn spectra, but the learning quality improves significantly for the CNN and reaches its best performance for the DTNN. Both CNN and DTNN capture even small nuances in the spectral shape.
* This work was performed in collaboration with A. Stuke, K. Ghosh, L. Himanen, M. Todorovic, and A. Vehtari
[1] L. C. Blum et al., J. Am. Chem. Soc. 131, 8732 (2009)
[2] R. Ramakrishnan et al., Scientific Data 1, 140022 (2014)
[3] M. Rupp et al., Phys. Rev. Lett. 108, 058301 (2012)
[4] H. Huo and M. Rupp, arXiv:1704.06439
[5] G. Montavon et al., New J. Phys. 15, 095003 (2013)
[6] K. T. Schutt et al., Nat. Comm. 8, 13890 (2017)
Speaker: Patrick Rinke
Affiliation: Professor of Physics, Aalto University
Place of Seminar: University of Helsinki