Search query: band gap prediction

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abstract: The bandgap often plays an important role in functional materials applications. For example, optoelectronic materials are generally wide bandgap semiconductors, while thermoelectric materials are narrow bandgap semiconductor materials. Therefore, predicting the bandgap rapidly and accurately for a given class of materials structures has great scientific importance for the functional materials applications. However, considering that the method of obtaining high precision band gaps based on first principles high throughput calculations is time consuming and inefficient, and it is also not realistic to systematically measure a large number of material system band gaps. Machine learning methods based the statistics may be a promising alternative. This paper designs an ensemble learning model for effectively and accurately predicting bandgap values. Based on the calculated band gap values of diamond like structures in thermoelectric materials, on the one hand, single component substitution strategy was used to generate large quantities of similar compounds, and the repetitive structures was filtered out by using the structural repeatability examination technique, resulting in 356 unique material structures. On the other hand, in combination with machine learning techniques, an efficient band gap prediction model was constructed, and by which the band gap values of 50 similar material systems are predicted and verified. As is the result of the experiment, this prediction model has 77.73% accuracy. It is enough robustness and stability to be widely used in thermoelectric materials application scenarios which require large band gap prediction.
author_names: Yonglin Xu

Xiangmeng Wang

Xin Li

Lili Xi

Jianyue Ni

Wenhao Zhu

Wu Zhang

Jiong Yang
title: New materials band gap prediction based on the high throughput calculation and the machine learning
year: 2019

author_names: Bikash Patra

Subrata Jana

Lucian A Constantin

Prasanjit Samal
title: Efficient band gap prediction of semiconductors and insulators from a semilocal exchange correlation functional
venue: Physical Review B
year: 2019

abstract: Machine learning models are capable of capturing the structure property relationship from a dataset of computationally demanding ab initio calculations. Over the past two years, the Organic Materials Database (OMDB) has hosted a growing number of calculated electronic properties of previously synthesized organic crystal structures. The complexity of the organic crystals contained within the OMDB, which have on average 82 atoms per unit cell, makes this database a challenging platform for machine learning applications. In this paper, the focus is on predicting the band gap which represents one of the basic properties of a crystalline materials. With this aim, a consistent dataset of 12 500 crystal structures and their corresponding DFT band gap are released, freely available for download at this https URL. An ensemble of two state of the art models reach a mean absolute error (MAE) of 0.388 eV, which corresponds to a percentage error of 13% for an average band gap of 3.05 eV. Finally, the trained models are employed to predict the band gap for 260 092 materials contained within the Crystallography Open Database (COD) and made available online so that the predictions can be obtained for any arbitrary crystal structure uploaded by a user.
author_names: Bart Olsthoorn

R Matthias Geilhufe

Stanislav S Borysov

Alexander V Balatsky
title: Band Gap Prediction for Large Organic Crystal Structures with Machine Learning
venue: Advanced Quantum Technologies
year: 2019

abstract: Abstract We present a novel approach to apply machine learning techniques to build a more robust prediction model for band gap energies (BG E) of chalcopyrites, a class of materials for energy applications in the fields of solar energy, photocatalysis, and thermoelectrics. Guided by knowledge from domain experts and by previous works on the field, we aim to accelerate the discovery of new solar materials. Our objectives are two folds: (i) Identify the optimal set of features that best describes a given predicted variable. (ii) Boost prediction accuracy via applying various regression algorithms. Ordinary Least Square, Partial Least Square and Lasso regressions, combined with well adjusted feature selection techniques are applied and tested to predict the band gap energy of chalcopyrites materials. Compared to the results reported in Zeng et al. (2002) Suh et al. (1999, 2004) and Dey et al. (2014) our approach shows that learning and using only a subset of relevant features can improve the prediction accuracy by about 40
author_names: Fadoua Khmaissia

Hichem Frigui

Mahendra Sunkara

Jacek B Jasinski

Alejandro Martinez Garcia

Tom Pace

Madhu Menon
title: Accelerating band gap prediction for solar materials using feature selection and regression techniques
year: 2018

abstract: An important property with any new material is the band gap. Standard density functional theory methods grossly underestimate band gaps. This is known as the band gap problem. Here, we show that the hybrid B3PW91 density functional returns band gaps with a mean absolute deviation (MAD) from experiment of 0.22 eV over 64 insulators with gaps spanning a factor of 500 from 0.014 to 7 eV. The MAD is 0.28 eV over 70 compounds with gaps up to 14.2 eV, with a mean error of 0.03 eV. To benchmark the quality of the hybrid method, we compared the hybrid method to the rigorous GW many body perturbation theory method. Surprisingly, the MAD for B3PW91 is about 1.5 times smaller than the MAD for GW. Furthermore, B3PW91 is 3 4 orders of magnitude faster computationally. Hence, B3PW91 is a practical tool for predicting band gaps of materials before they are synthesized and represents a solution to the band gap prediction problem.
author_names: Jason M Crowley

Jamil Tahir-Kheli

William A Goddard
title: Resolution of the Band Gap Prediction Problem for Materials Design.
venue: The journal of physical chemistry letters
year: 2016

abstract: Lappeenranta University of Technology LUT School of Engineering Science Technical Physics
author_names: Vadim Sotskov
title: Band gap prediction for inorganic crystals with machine learning
year: 2018

abstract: This paper provides a detailed comparison of the two scale homogenization method and of the Bloch Floquet theory for the determination of band gaps in locally resonant metamaterials. A medium composed by a stiff matrix with soft inclusions with 2D periodicity is considered and the equivalent mass density of the homogenized medium is explicitly obtained both for in plane and out of plane wave propagation through two scale asymptotic expansion. The intervals of frequency where the effective mass is negative identify the band gaps of the material. The Bloch Floquet problem is then considered and, through an asymptotic analysis, its is shown that it leads to the same prediction of the band gaps. The results are confirmed by some examples and the limits of the asymptotic approach are explicitly given and numerically verified.
author_names: Claudia Comi

Jean-Jacques Marigo
title: Homogenization Approach and Bloch Floquet Theory for Band Gap Prediction in 2D Locally Resonant Metamaterials
venue: Journal of Elasticity
year: 2019

abstract: In this paper, a Multilayer Perceptron artificial neural network is modeled to estimate complete photonic band gaps (C PBGs) of bi dimensional photonic crystals. Unit cells of square lattice photonic crystals, composed of two silicon round rods and embedded in air, have been designed by an artificial immune network algorithm, and their geometries have been stored in a database along with their C PGBs. Tests using unknown unit cells assess and demonstrate the C PBG predicting capability of the modeled Multilayer Perceptron.
author_names: Adriano da Silva Ferreira

H E H Figueroa

Gilliard Nardel Malheiros Silveira
title: Complete band gap prediction of 2D photonic crystals by using multilayer perceptron
venue: 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON)
year: 2017

abstract: Abstract We report a comprehensive and systematic study of two halide perovskites by using density functional theory calculations. Two structurally different perovskites have been studied, which are cubic CH3NH3PbI3 (MAPI) and HC(NH2)2PbI3 (FAPI) We have used twenty four exchange correlation functionals, ranging from three LDA functionals, ten GGA functionals, seven MGGA and four hybrids among others have been tested, in order to determine the accuracy of these methods for the prediction of band gaps. Moreover, we have studied several possibilities to tackle the calculations of perovskites. That is, we have tested Numerical Atomic Orbitals with All Electron Relativistic calculations and compared to Plane Wave framework with pseudopotentials and relativistic corrections. Moreover, we have studied different k points set, pseudopotentials types, with and without cell optimizations, with and without dispersion corrections and with and without Spin Orbit coupling. The results show that PBE and RPBE exhibit a good compromise between the accuracy of the results and computational demands.
author_names: Noemi Hernandez-Haro

Joaquin Ortega-Castro

Yaroslav B Martynov

Rashid G Nazmitdinov

Antonio Frontera
title: DFT prediction of band gap in organic inorganic metal halide perovskites: An exchange correlation functional benchmark study
year: 2019

abstract: A novel nonempirical scaling correction method is developed to tackle the challenge of band gap prediction in density functional theory. For finite systems the scaling correction largely restores the straight line behavior of electronic energy at fractional electron numbers. The scaling correction can be generally applied to a variety of mainstream density functional approximations, leading to significant improvement in the band gap prediction. In particular, the scaled version of a modified local density approximation predicts band gaps with an accuracy consistent for systems of all sizes, ranging from atoms and molecules to solids. The scaled modified local density approximation thus provides a useful tool to quantitatively characterize the size dependent effect on the energy gaps of nanostructures.
author_names: Xiao Zheng

Aron J Cohen

Paula Mori-Sanchez

Xiangqian Hu

Weitao Yang
title: Improving band gap prediction in density functional theory from molecules to solids.
venue: Physical review letters
year: 2011