Dynamic exchange-correlation functional for bandgap optimization - reparametrization and machine learning - Dissertation abstract

Aug 5, 2026

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This dissertation explores predicting the physical properties of solids using first-principles methods, centered on Density Functional Theory (DFT) and enhanced by machine learning. DFT leverages electronic density to model electron–electron interactions with a practical balance of accuracy and computational efficiency. The study focuses on tuning SCAN semilocal density functional parameters to improve electronic bandgap predictions, reducing discrepancies with experiment. A dynamic XC functional (d-SCAN) is developed by adjusting inner parameters to match bandgaps of semiconductors and insulators, clarifying links between SCAN parameters and electronic behavior. Results identify metallicity and bonding as key exchange parameters affecting both bandgaps and related dielectric properties. For doped materials, d-SCAN shows improved bandgap opening, while ML models reliably predict parameter values using key material descriptors.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages134

Reading Time5 h 38 min

AccessFree to download

Acknowledgments

Introduction

Background and Motivation

Problem Statement

Limitations of Current Methods

Research Objectives

Outline of the Dissertation

Methods

Ab initio Methods

Density Functional Theory

Graduate Theses, Dissertations, and Problem Reports 2024 Dynamic exchange-correlation functional for bandgap optimization: reparametrization and machine learning Viviana Faride Dovale Farelo West Virginia University Follow this and additional works at: [https://researchrepository.wvu.edu/etd](https://researchrepository.wvu.edu/etd) Part of the Condensed Matter Physics Commons Recommended Citation Dovale Farelo, Viviana Faride, "Dynamic exchange-correlation functional for bandgap optimization: reparametrization and machine learning" (2024) . Graduate Theses, Dissertations, and Problem Reports. 12615. [https://researchrepository.wvu.edu/etd/12615](https://researchrepository.wvu.edu/etd/12615) This Dissertation is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s) . You are free to use this Dissertation in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Dissertation has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact [researchrepository@mail.wvu.edu](researchrepository@mail.wvu.edu). Dynamic exchange-correlation functional for bandgap optimization: reparametrization and machine learning Viviana Faride Dovale Farelo Dissertation submitted to the Eberly College of Arts and Sciences at West Virginia University in partial fulfillment of requirements for the degree of Doctor of Philosophy in Physics Aldo Humberto Romero, Ph.D. , Chair Paul Cassak, Ph.D. Matthew Johnson, Ph.D. Srinjoy Das, Ph.D. Department of Physics and Astronomy Morgantown, West Virginia 2024 Keywords: DFT, SCAN, exchange, correlation, machine learning. Copyright 2024 Viviana Faride Dovale Farelo Abstract Dynamic exchange-correlation functional for bandgap optimization: reparametrization and machine learning Viviana Faride Dovale Farelo This dissertation explores predicting the physical properties of solids using firstprinciples methods, with a focus on Density Functional Theory (DFT) . DFT uses the electronic density within a material to predict its properties, simplifying the treatment of electron-electron interactions and allowing the study of realistic systems with a balanced treatment between accuracy and computational efficiency. Additionally, machine learning (ML) is employed to create correlations between some physical properties of solidsand other properties or parameters that are more difficult to calculate. The main problem addressed in this study is adjusting the parameters in the Strongly Constrained and Appropriately Normed (SCAN) semilocal density functional to accurately predict the electronic properties of various solid materials. SCAN is an exchangecorrelation (XC) functional used in DFT to handle the quantum mechanical exchange and correlation effects in electron-electron interactions. The electronic nature of solids (metal, semiconductor, or insulator) is defined by the value of the electronic bandgap. Although SCAN improves electronic bandgap predictions compared to other XC functionals, it still falls short of experimental values. The goal is to develop a dynamic XC functional, called d-SCAN, focused on predicting electronic bandgaps by tuning the inner parameters to match experimental bandgap values of various semiconductors and insulators. This aims to provide insights into the relationship between SCAN parameters and the material’s electronic behavior. Key findings are that the current SCAN XC functional cannot always match the experimental bandgaps of some materials. The two main exchange par

FAQs

What problem does this dissertation address?

It addresses how to adjust parameters in the SCAN semilocal density functional so electronic properties—especially electronic bandgaps—match experimental values more accurately for different solids.

What is d-SCAN and what is its goal?

d-SCAN is a dynamic exchange-correlation functional designed to predict electronic bandgaps by tuning SCAN inner parameters to fit experimental bandgaps across semiconductors and insulators.

Which factors and features most influence SCAN parameter tuning and ML prediction?

The study finds two main exchange parameters, metallicity and bonding, strongly influence bandgap accuracy. For the ML model, covalency, space group, and bond strength are identified as the top key features.

Aug 5, 2026

Upload by Oliver