Physically informed graph neural networks for prediction of optical properties of solid materials

Despite the success of machine learning (ML) to predict complex materials properties, current ML methods are purely data-driven approaches that do not incorporate physical principles. These methods often suffer from interpretation difficulties, required large training sets, and probable poor generalization outside the observational domain. We here develop a physically informed graph neural network (GNN) to predict the frequency-dependent dielectric function of solid crystals, from which we can calculate all optical properties. The accurate prediction of optical properties using first-principles methods such as density functional theory (DFT) can be a computationally tedious task and becomes almost impossible for large systems. The dielectric function is a high-dimensional, complex-valued, and tensorial target output which presents additional difficulties to ML models. We augment our GNN with a learning bias which penalizes the model for predicting unphysical features in the dielectric function such as bandgaps, resonance frequencies, resonance amplitudes, etc. Our model is trained and validated on a database of DFT-calculated dielectric spectra for a pool of 17,805 different materials obtained from the JARVIS-DFT database [1]. The physical consistency achieved by our physically informed GNN model makes it more generalizable outside the training domain, and thus more reliable to screen new functional materials of arbitrary compositional and structural diversity.



[1] Choudhary, Kamal, Qin Zhang, Andrew CE Reid, Sugata Chowdhury, Nhan Van Nguyen, Zachary Trautt, Marcus W. Newrock, Faical Yannick Congo, and Francesca Tavazza. "Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms." Scientific data 5, no. 1 (2018): 1-12.