Design of new descriptors.
Aiming at both, the transferability of our model and the reduction of the training data set,
we introduce 2 different classes of descriptors, based on fundamental chemical and physical properties: Atom Types
[1] and Atom Probes [2] (figure). The main difference from previous models is that our descriptors are based on
the chemical character of the atoms which consist the skeleton of the materials and not their general structural
characteristics. With this bottom up approach we go one step down in the size of the descriptors employing chemical intuition. References
[1] "A Universal Machine Learning Algorithm for Large Scale Screening of Materials", G. S. Fanourgakis, K. Gkagkas, E. Tylianakis and G. Froudakis, Journal of the American Chemical Society 142 (8), 3814-3822 (2020)
[2] "A robust Machine Learning algorithm for the prediction of methane adsorption in nanoporous materials", G. S. Fanourgakis, K. Gkagkas, E. Tylianakis, M. Klontzas, G. Froudakis, The Journal of Physical Chemistry A 123, 6080-6087 (2019)