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Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques

https://doi.org/10.1103/physrevb.93.115104
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Machine learning stability and band gap of lead-free halide double perovskite materials for perovskite solar cells
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Machine learning-aided discovery of bismuth-based transition metal oxide double perovskites for solar cell applications
inferred← citeshttps://doi.org/10.1016/j.xcrp.2023.101555openalex-snapshot
Enhancing prediction accuracy of physical band gaps in semiconductor materials
inferred← citeshttps://doi.org/10.1016/j.xcrp.2025.103093openalex-snapshot
Searching for high-performance thermoelectric materials via an advanced machine learning framework
inferred← citeshttps://doi.org/10.1021/acs.chemmater.0c00768openalex-snapshot
Quantum Chemistry-Informed Active Learning to Accelerate the Design and Discovery of Sustainable Energy Storage Materials
inferred← citeshttps://doi.org/10.1021/acs.chemmater.1c02040openalex-snapshot
Discovery of Energy Storage Molecular Materials Using Quantum Chemistry-Guided Multiobjective Bayesian Optimization
inferred← citeshttps://doi.org/10.1021/acs.chemmater.1c02644openalex-snapshot
Environmental Stability of Crystals: A Greedy Screening
inferred← citeshttps://doi.org/10.1021/acs.chemmater.3c00214openalex-snapshot
The Intermetallic Reactivity Database: Compiling Chemical Pressure and Electronic Metrics toward Materials Design and Discovery
inferred← citeshttps://doi.org/10.1021/acs.chemmater.3c01131openalex-snapshot
Accurate Prediction of HSE06 Band Structures for a Diverse Set of Materials Using Δ-Learning
inferred← citeshttps://doi.org/10.1021/acs.chemmater.7b00156openalex-snapshot
Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning
inferred← citeshttps://doi.org/10.1021/acs.chemmater.7b02027openalex-snapshot
Mining Materials Design Rules from Data: The Example of Polymer Dielectrics
inferred← citeshttps://doi.org/10.1021/acs.chemmater.7b05304openalex-snapshot
Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons
inferred← citeshttps://doi.org/10.1021/acs.chemmater.8b00686openalex-snapshot
Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene
inferred← citeshttps://doi.org/10.1021/acs.chemmater.9b01519openalex-snapshot
Data-Driven Discovery of Photoactive Quaternary Oxides Using First-Principles Machine Learning
inferred← citeshttps://doi.org/10.1021/acs.chemmater.9b02044openalex-snapshot
Design Rules for High Oxygen-Ion Conductivity in Garnet-Type Oxides
inferred← citeshttps://doi.org/10.1021/acs.chemrev.0c00749openalex-snapshot
Machine Learning for Electronically Excited States of Molecules
inferred← citeshttps://doi.org/10.1021/acs.chemrev.3c00708openalex-snapshot
Machine Learning Aided Design and Optimization of Thermal Metamaterials
inferred← citeshttps://doi.org/10.1021/acs.chemrev.8b00728openalex-snapshot
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.inorgchem.1c01366openalex-snapshot
AB2X6 Compounds and the Stabilization of Trirutile Oxides
inferred← citeshttps://doi.org/10.1021/acs.inorgchem.3c00233openalex-snapshot
Data Mining and Graph Network Deep Learning for Band Gap Prediction in Crystalline Borate Materials
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00698openalex-snapshot
Prediction of Molecular Electronic Transitions Using Random Forests
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00699openalex-snapshot
Thermochemical Data Fusion Using Graph Representation Learning
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c01897openalex-snapshot
Automatic Prediction of Band Gaps of Inorganic Materials Using a Gradient Boosted and Statistical Feature Selection Workflow
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c02083openalex-snapshot
Coarse-Grained Crystal Graph Neural Networks for Reticular Materials Design
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00977openalex-snapshot
Feature Engineering for Materials Chemistry—Does Size Matter?
inferred← citeshttps://doi.org/10.1021/acs.jctc.2c00822openalex-snapshot
Performance of Screened-Exchange Functionals for Band Gaps and Lattice Constants of Crystals
inferred← citeshttps://doi.org/10.1021/acs.jctc.5c00701openalex-snapshot
Introducing Virtual Points in Equivariant Networks by Extending Atom Representation for Effective Prediction
inferred← citeshttps://doi.org/10.1021/acs.jctc.5c01798openalex-snapshot
Machine Learning for Accelerated Metal Oxide Thermochemical Predictions
inferred← citeshttps://doi.org/10.1021/acs.jctc.9b00322openalex-snapshot
Large-Scale Benchmark of Exchange–Correlation Functionals for the Determination of Electronic Band Gaps of Solids
inferred← citeshttps://doi.org/10.1021/acs.jpca.0c07802openalex-snapshot
Tuplewise Material Representation Based Machine Learning for Accurate Band Gap Prediction
inferred← citeshttps://doi.org/10.1021/acs.jpca.7b02882openalex-snapshot
Importance of the Kinetic Energy Density for Band Gap Calculations in Solids with Density Functional Theory
inferred← citeshttps://doi.org/10.1021/acs.jpca.7b08750openalex-snapshot
Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships
inferred← citeshttps://doi.org/10.1021/acs.jpca.8b02842openalex-snapshot
Machine-Learning Energy Gaps of Porphyrins with Molecular Graph Representations
inferred← citeshttps://doi.org/10.1021/acs.jpcc.1c02890openalex-snapshot
Machine Learning-Based Screening of Highly Stable and Active Ternary Pt Alloys for Oxygen Reduction Reaction
inferred← citeshttps://doi.org/10.1021/acs.jpcc.4c03848openalex-snapshot
Monte Carlo Explicitly Correlated Second-Order Many-Body Green’s Function Calculations of Semiconductor Band Gaps
inferred← citeshttps://doi.org/10.1021/acs.jpcc.7b07421openalex-snapshot
An Empirical, yet Practical Way To Predict the Band Gap in Solids by Using Density Functional Band Structure Calculations
inferred← citeshttps://doi.org/10.1021/acs.jpcc.9b03925openalex-snapshot
General Atomic Neighborhood Fingerprint for Machine Learning-Based Methods
inferred← citeshttps://doi.org/10.1021/acs.jpcc.9b11768openalex-snapshot
Machine Learning for Predicting the Band Gaps of ABX 3 Perovskites from Elemental Properties
inferred← citeshttps://doi.org/10.1021/acs.jpclett.0c02271openalex-snapshot
Voxelized Atomic Structure Potentials: Predicting Atomic Forces with the Accuracy of Quantum Mechanics Using Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1021/acs.jpclett.2c03073openalex-snapshot
A Critical Review of Machine Learning Techniques on Thermoelectric Materials
inferred← citeshttps://doi.org/10.1021/acs.jpclett.3c00665openalex-snapshot
A Unified View of Vibrational Spectroscopy Simulation through Kernel Density Estimations
inferred← citeshttps://doi.org/10.1021/acs.jpclett.5c00173openalex-snapshot
Efficient and Accurate Prediction of Double Perovskite Quasiparticle Band Gaps via Machine Learning and a Descriptor
inferred← citeshttps://doi.org/10.1021/acs.jpclett.8b00124openalex-snapshot
Predicting the Band Gaps of Inorganic Solids by Machine Learning
inferred← citeshttps://doi.org/10.1021/acs.jpclett.8b01707openalex-snapshot
Metallic Metal–Organic Frameworks Predicted by the Combination of Machine Learning Methods and Ab Initio Calculations
inferred← citeshttps://doi.org/10.1021/acs.jpclett.9b02232openalex-snapshot
Phillips-Inspired Machine Learning for Band Gap and Exciton Binding Energy Prediction
inferred← citeshttps://doi.org/10.1021/acs.jpclett.9b02517openalex-snapshot
Machine Learning Protocol for Surface-Enhanced Raman Spectroscopy
inferred← citeshttps://doi.org/10.1021/acsaem.1c01223openalex-snapshot
Machine Learning-Aided Materials Design Platform for Predicting the Mechanical Properties of Na-Ion Solid-State Electrolytes
inferred← citeshttps://doi.org/10.1021/acsami.9b02174openalex-snapshot
Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia
inferred← citeshttps://doi.org/10.1021/acscatal.9b04186openalex-snapshot
Machine Learning for Catalysis Informatics: Recent Applications and Prospects
inferred← citeshttps://doi.org/10.1021/acsmaterialslett.1c00204openalex-snapshot
Catalyze Materials Science with Machine Learning
inferred← citeshttps://doi.org/10.1021/acsomega.1c01517openalex-snapshot
Prediction and Classification of Formation Energies of Binary Compounds by Machine Learning: An Approach without Crystal Structure Information
inferred← citeshttps://doi.org/10.1021/acsphyschemau.1c00017openalex-snapshot
Feature Blending: An Approach toward Generalized Machine Learning Models for Property Prediction
inferred← citeshttps://doi.org/10.1021/jacs.2c04741openalex-snapshot
Data-Driven Enhancement of ZT in SnSe-Based Thermoelectric Systems
inferred← citeshttps://doi.org/10.1038/s41467-018-06322-xopenalex-snapshot
Deep neural networks for accurate predictions of crystal stability
inferred← citeshttps://doi.org/10.1038/s41467-019-13297-wopenalex-snapshot
Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
inferred← citeshttps://doi.org/10.1038/s41467-020-17112-9openalex-snapshot
Identifying domains of applicability of machine learning models for materials science
inferred← citeshttps://doi.org/10.1038/s41467-021-26921-5openalex-snapshot
Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data
inferred← citeshttps://doi.org/10.1038/s41467-022-28122-0openalex-snapshot
Representing individual electronic states for machine learning GW band structures of 2D materials
inferred← citeshttps://doi.org/10.1038/s41467-024-53748-7openalex-snapshot
Unsupervised representation learning of Kohn–Sham states and consequences for downstream predictions of many-body effects
inferred← citeshttps://doi.org/10.1038/s41524-017-0056-5openalex-snapshot
Machine learning in materials informatics: recent applications and prospects
inferred← citeshttps://doi.org/10.1038/s41524-018-0081-zopenalex-snapshot
A strategy to apply machine learning to small datasets in materials science
inferred← citeshttps://doi.org/10.1038/s41524-018-0129-0openalex-snapshot
Active learning for accelerated design of layered materials
inferred← citeshttps://doi.org/10.1038/s41524-019-0177-0openalex-snapshot
Identifying Pb-free perovskites for solar cells by machine learning
inferred← citeshttps://doi.org/10.1038/s41524-019-0221-0openalex-snapshot
Recent advances and applications of machine learning in solid-state materials science
inferred← citeshttps://doi.org/10.1038/s41524-019-0239-3openalex-snapshot
Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition
inferred← citeshttps://doi.org/10.1038/s41524-021-00493-wopenalex-snapshot
Topological representations of crystalline compounds for the machine-learning prediction of materials properties
inferred← citeshttps://doi.org/10.1038/s41524-021-00564-yopenalex-snapshot
Predicting thermoelectric properties from chemical formula with explicitly identifying dopant effects
inferred← citeshttps://doi.org/10.1038/s41524-023-01185-3openalex-snapshot
Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets
inferred← citeshttps://doi.org/10.1038/s41524-025-01833-wopenalex-snapshot
Automated workflow for accurate high-throughput GW calculations using plane waves
inferred← citeshttps://doi.org/10.1038/s41570-021-00278-1openalex-snapshot
Molecular excited states through a machine learning lens
inferred← citeshttps://doi.org/10.1038/s41598-018-34201-4openalex-snapshot
Machine learning assisted optimization of electrochemical properties for Ni-rich cathode materials
inferred← citeshttps://doi.org/10.1038/s41598-020-64918-0openalex-snapshot
Modelling and synthesis of Magnéli Phases in ordered titanium oxide nanotubes with preserved morphology
inferred← citeshttps://doi.org/10.1038/s41598-021-94275-5openalex-snapshot
Discovery of novel Li SSE and anode coatings using interpretable machine learning and high-throughput multi-property screening
inferred← citeshttps://doi.org/10.1038/s41598-022-19205-5openalex-snapshot
A universal similarity based approach for predictive uncertainty quantification in materials science
inferred← citeshttps://doi.org/10.1038/s41598-023-31928-7openalex-snapshot
Descriptor engineering in machine learning regression of electronic structure properties for 2D materials
inferred← citeshttps://doi.org/10.1038/s41598-024-69194-wopenalex-snapshot
Putting error bars on density functional theory
inferred← citeshttps://doi.org/10.1038/s41598-024-73551-0openalex-snapshot
Predicting refractive index of inorganic compounds using machine learning
inferred← citeshttps://doi.org/10.1038/s41598-025-11505-wopenalex-snapshot
Ultrafast core-to-core luminescence in BaF2 - LaF3 single crystals
inferred← citeshttps://doi.org/10.1038/s43246-021-00194-3openalex-snapshot
A geometric-information-enhanced crystal graph network for predicting properties of materials
inferred← citeshttps://doi.org/10.1038/s43246-023-00373-4openalex-snapshot
Band gap predictions of double perovskite oxides using machine learning
inferred← citeshttps://doi.org/10.1038/srep19375openalex-snapshot
Machine learning bandgaps of double perovskites
inferred← citeshttps://doi.org/10.1039/c6cp06187gopenalex-snapshot
Exploring experimental fitness landscapes for chemical synthesis and property optimization
inferred← citeshttps://doi.org/10.1039/c7sc01247kopenalex-snapshot
Predicting electronic structure properties of transition metal complexes with neural networks
inferred← citeshttps://doi.org/10.1039/c7sc03961aopenalex-snapshot
Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure
inferred← citeshttps://doi.org/10.1039/c8me00012copenalex-snapshot
Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery
inferred← citeshttps://doi.org/10.1039/c8ra02958jopenalex-snapshot
Discovery of zirconium dioxides for the design of better oxygen-ion conductors using efficient algorithms beyond data mining
inferred← citeshttps://doi.org/10.1039/c8tc05554hopenalex-snapshot
Band gap and band alignment prediction of nitride-based semiconductors using machine learning
inferred← citeshttps://doi.org/10.1039/c9cp02803jopenalex-snapshot
NMR shifts in aluminosilicate glasses via machine learning
inferred← citeshttps://doi.org/10.1039/c9ra07755copenalex-snapshot
Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity
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