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RESEARCH PAPER · The Journal of Physical Chemistry Letters · 2019

Accelerated Data-Driven Accurate Positioning of the Band Edges of MXenes

Avanish Mishra, Swanti Satsangi, Arunkumar Chitteth Rajan, Hiroshi Mizuseki, Kwang‐Ryeol Lee, Abhishek Kumar Singh

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Accelerated Data-Driven Accurate Positioning of the Band Edges of MXenesOriginalCoupling the High-Throughput Property Map to Machine Learning for Predicting Lattice Thermal Conductivity106Phillips-Inspired Machine Learning for Band Gap and Exciton Binding Energy Prediction55Robust random forest based non-fullerene organic solar cells efficiency prediction71Rational Design of Flexible Two-Dimensional MXenes with Multiple Functionalities396Machine Learning for Catalysis Informatics: Recent Applications and Prospects613Two-dimensional MXenes: From morphological to optical, electric, and magnetic properties and applications914Guided patchwork kriging to develop highly transferable thermal conductivity prediction models22Accelerated prediction of Vickers hardness of Co- and Ni-based superalloys from microstructure and composition using advanced image processing techniques and machine learning78A Statistical Approach for the Rapid Prediction of Electron Relaxation Time Using Elemental Representatives39Predicting band gaps and band-edge positions of oxide perovskites using DFT and machine learning2Development of Vickers hardness prediction models via microstructural analysis and machine learning50Advancing Physical Chemistry with Machine Learning73Accelerated Discovery of Thermoelectric Materials Using Machine Learning4Machine-learning-assisted low dielectric constant polymer discovery49Machine Learning Aided Design of Polymer with Targeted Band Gap Based on DFT Computation57Machine learning in materials science: From explainable predictions to autonomous design281Molecular excited states through a machine learning lens200The data-intensive scientific revolution occurring where two-dimensional materials meet machine learning62Accelerated Discovery of the Valley-Polarized Quantum Anomalous Hall Effect in MXenes31Unraveling surface functionalization of Cr2B2T2 (T = OH, O, Cl, H) MBene by first-principles calculations30Feature Blending: An Approach toward Generalized Machine Learning Models for Property Prediction19Hyperconverged autonomous organic reaction infrastructure (HAORI) driven by SpecSNN, for low dielectric constant polymer research2Symbolic regression in materials science via dimension-synchronous-computation41Recent advance on machine learning of MXenes for energy storage and conversion45Data‐Driven Materials Innovation and Applications187A simple descriptor for magnetic classification of 2D MXene materials9Recent Advancement in Rational Design Modulation of MXene: A Voyage from Environmental Remediation to Energy Conversion and Storage44Predicting band gaps and band-edge positions of oxide perovskites using density functional theory and machine learning45Transition of electrochemical measurement to machine learning in the perspective of two-dimensional materials8Highly interpretable machine learning framework for prediction of mechanical properties of nickel based superalloys13Current progresses in two-dimensional MXene-based framework: prospects from superficial synthesis to energy conversion and storage applications57Retrospective insights into recent MXene-based catalysts for CO 2 electro/photoreduction: how far have we gone?45Prediction of ideal strength by machine learning18Band Alignment of 2D Material–Water Interfaces13Machine learning assisted screening of MXenes pseudocapacitive materials36MXenes and their interfaces for the taming of carbon dioxide & nitrate: A critical review70Transition metal carbide‐based photocatalysts for artificial photosynthesis36Advancements in High‐Throughput Screening and Machine Learning Design for 2D Ferromagnetism: A Comprehensive Review10Scope of machine learning in materials research—A review230Mxenes for Zn-based energy storage devices: Nano-engineering and machine learning31Regressive Multi-Scale BiGRU Model for Work Function Prediction of MXene Materials With Oppositional Learning-Based Optimization3Unlocking the potential of Ti 3 C 2 electrodes: a data-driven capacitance prediction study10Unraveling the photocatalytic potential of transition metal sulfide and selenide monolayers for overall water splitting and photo-corrosion inhibition46Machine Learning Assisted Screening of MXene with Superior Anchoring Effect in Al–S Batteries34Recent advances in the use of MXenes for photoelectrochemical sensors106Combined approach to capture the evolution of oxidation of Nickel based superalloys using data driven approaches7Janus ScYCBr2 MXene as a Promising Thermoelectric Material40Full prediction of band potentials in semiconductor materials11MXene Property Prediction via Graph Contrastive Learning3Heteroatom doping in 2D MXenes for energy storage/conversion applications65Machine Learning-Assisted Screening of Hexagonal 2D van der Waals Bilayers for Photocatalysis9Enhanced solar harvesting efficiency in nanostructured MXene monolayers based on scandium and yttrium30First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes71Advancing SERS Applications of 2D Materials through the Interplay of Rational Design and Structure‐Property Relationships17Advancements in MXene transformation: Nanoarchitectonics for biomedical and energy applications23MXene Material Property Prediction via Transfer Learning with Graph Neural Networks1MXgap: A MXene Learning Tool for Bandgap Prediction19Machine Learning Integrating Surface Features and Crystal Similarity for Exploring 2D Materials1Machine-Learning-Enabled High-Throughput MXene Property Prediction0Unveiling Process–Structure Mapping with a Deep Variational Autoencoder0Nb 2 CBr 2 MXene Monolayer as a Novel Material: A First Principle Study6Artificial intelligence transforming materials research: possible road ahead for India1Machine learning aided design of reversible MXene electrocatalysts for Li–air batteries0Recent progress in density functional theory and machine learning for predicting MXene properties2Design and synthesis of Ti3C2 and Ti3C2Tx MXenes for sensing, energy, and electromagnetic interference shielding: Advancing multifunctional applications with machine learning-assisted material design1ϒ-scheme heterojunction photocatalyst: Polarization-driven charge separation beyond interfacial limitations0MXenes From Fundamentals to Market: Structural Characteristics, Emerging Synthesis Routes, Applications, and AI‐Accelerated Innovations0

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  2. Phillips-Inspired Machine Learning for Band Gap and Exciton Binding Energy Prediction

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  3. Robust random forest based non-fullerene organic solar cells efficiency prediction

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  4. Rational Design of Flexible Two-Dimensional MXenes with Multiple Functionalities

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  7. Guided patchwork kriging to develop highly transferable thermal conductivity prediction models

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  8. Accelerated prediction of Vickers hardness of Co- and Ni-based superalloys from microstructure and composition using advanced image processing techniques and machine learning

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  9. A Statistical Approach for the Rapid Prediction of Electron Relaxation Time Using Elemental Representatives

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  12. Advancing Physical Chemistry with Machine Learning

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  19. Accelerated Discovery of the Valley-Polarized Quantum Anomalous Hall Effect in MXenes

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  20. Unraveling surface functionalization of Cr2B2T2 (T = OH, O, Cl, H) MBene by first-principles calculations

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  21. Feature Blending: An Approach toward Generalized Machine Learning Models for Property Prediction

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  24. Recent advance on machine learning of MXenes for energy storage and conversion

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  26. A simple descriptor for magnetic classification of 2D MXene materials

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  41. Regressive Multi-Scale BiGRU Model for Work Function Prediction of MXene Materials With Oppositional Learning-Based Optimization

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