Employing Machine Learning to investigate properties of molecules and periodic systems
Title: Employing Machine Learning to investigate properties of molecules and periodic systems
DNr: SNIC 2018/7-79
Project Type: SNIC Small Compute
Principal Investigator: Rodrigo Pereira de Carvalho <rodrigo.carvalho@physics.uu.se>
Affiliation: Uppsala universitet
Duration: 2018-12-03 – 2020-01-01
Classification: 10304
Keywords:

Abstract

Nowadays, it is a common sense on science that the interplay of Artificial Intelligence (more specifically by means of machine learning techniques) and first principles calculations can lead us to a breakthrough on materials design. Recent works has been employing machine learning to show the possibility to discover new materials [1], calculate physical properties without the high computational cost of prior techniques [2] and even to by-pass the solution of Quantum Mechanic equations [3]. For molecular systems, we can produce a high-accurate fingerprints to serve as inputs for machine learning, but for periodic systems (solids, surfaces, etc) we face some difficulties, besides new approachs has been developed [2,4]. The specific goals for this project are: (I) Apply the machine learning machinery to molecular systems to improve ab initio calculations. (ii) Benchmark a few options to represent periodic systems (specially aimed to electrodes). (iii) Interplay of our machinery with DFT to investigate properties of selected materials. 1. Balachandran, Prasanna V., et al. Physical Review Materials 2.4 (2018) 2. Faber, Felix, et al. International Journal of Quantum Chemistry 115.16 (2015) 3. Brockherde, Felix, et al. Nature communications 8.1 (2017) 4. Huo, Haoyan, and Matthias Rupp. "Unified representation for machine learning of molecules and crystals." preprint (2017).