Tuplewise Material Representation Based Machine Learning for Accurate Band Gap Prediction

Abstract

The open-access material databases allowed us to approach scientific questions from a completely new perspective with machine learning methods. Here, on the basis of open-access databases, we focus on the classical band gap problem for predicting accurately the band gap of a crystalline compound using a machine learning approach with newly developed tuplewise graph neural networks (TGNN), which is devised to automatically generate input representation of crystal structures in tuple types and to exploit crystal-level properties as one of the input features. Our method brings about a highly accurate prediction of the band gaps at hybrid functionals and GW approximation levels for multiple material data sets without heavy computational cost. Furthermore, to demonstrate the applicability of our prediction model, we provide a data set of GW band gaps for 45835 materials predicted by TGNN posing higher accuracy than standard density functional theory calculations.

Deep Learning Optical Spectroscopy Based on Experimental Database: Potential Applications to Molecular Design

“… 26 Lin and co-workers used a DL method to predict the bandgap of configurationally hybridized graphene and boron nitride, 21 and Chang and co-workers used a tuplewise material representation to predict the band gap of organic–inorganic perovskite, 2D materials, and binary and ternary inorganic materials. 27 Jiang and co-workers reported DL models that could predict infrared and ultraviolet absorption spectra from the conformations of a molecule using a theoretical database that was generated by molecular dynamics simulations and DFT calculations. 23 , 28 Rinke and co-workers reported a DL method to predict molecular excitation spectra based on the QM7b and QM9 data sets of small organic molecules containing up to 17 C, N, O, S, and halogen atoms.…”

“…DL based on big data has attracted substantial attention because it can potentially solve problems via direct learning from data without well-defined rules, empirical laws, or theories. , In chemistry, DL has proven promising for predicting various properties of molecules and materials, − optimizing reactions and retrosynthesis, − and designing new molecules including drugs , and materials. − Additionally, DL has been effectively used in computational chemistry. , Nakata and Shimazaki reported a large-scale electronic structure database (PubChemQC) and recently, using the PubChemQC database, Lee and co-workers reported a random forest model to predict the highest oscillator strength and the corresponding excitation energy of molecules . Lin and co-workers used a DL method to predict the bandgap of configurationally hybridized graphene and boron nitride, and Chang and co-workers used a tuplewise material representation to predict the band gap of organic–inorganic perovskite, 2D materials, and binary and ternary inorganic materials . Jiang and co-workers reported DL models that could predict infrared and ultraviolet absorption spectra from the conformations of a molecule using a theoretical database that was generated by molecular dynamics simulations and DFT calculations. , Rinke and co-workers reported a DL method to predict molecular excitation spectra based on the QM7b and QM9 data sets of small organic molecules containing up to 17 C, N, O, S, and halogen atoms .…”

Deep learning modeling strategy for material science: from natural materials to metamaterials

“…For example, the co-crystal graph network [102] can achieve high-precision prediction of different data in different eutectic spaces and has strong robustness and generalization. Tuples GNN (TGNN) [103] can accurately predict the band gap of crystal materials, and has shown good performance in the band gap prediction of four open material databases. Embedding an encoder-decoder in the orbital graph CNN [104] can learn the element characteristics, interactions between orbitals and topological features of crystal materials for discovering new materials.…”

Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in The Upstream of Development of Novel Energy Technologies

“…determine directly solar cell performance. Machine learning is now more and more often used to help design better solar cells and specifically materials for solar cells and other optoelectronic applications [12][13][14][15]65,66]. It is used to predict better active materials, to optimize device performance or even optimize fabrication processes [15].…”