Employing Machine Learning to investigate properties of molecules and periodic systems
||Employing Machine Learning to investigate properties of molecules and periodic systems|
||SNIC Small Compute|
||Rodrigo Pereira de Carvalho <email@example.com>|
||2020-01-01 – 2020-11-01|
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 have been employing machine learning to show the possibility to discover new materials , calculate physical properties without the high computational cost of prior techniques  and even to by-pass the solution of Quantum Mechanic equations . 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.
To this date, we have been developing an original database of organic energy materials based on high quality DFT calculations for molecular and periodic systems. So far, we have an impressive number of ~12000 molecular structures and ~100 periodic systems (related to energy applications). These results have been used to create neural networks models to predict key properties for energy applications, developing an Artificial Intelligence approach to design new potential electrodes for battery technologies.
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).