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RESEARCH PAPER · Carbon · 2020

Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN

Yuan Dong, Dawei Li, Chi Zhang, Chuhan Wu, Hong Wang, Ming Xin, Jianlin Jack Cheng, Jian Lin

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42 direct citing works

Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GANOriginal3‑D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning125A machine learning workflow for 4D printing: understand and predict morphing behaviors of printed active structures37Ground state charge density prediction in C-BN nanoflakes using rotation equivariant feature-free artificial neural networks3Data-driven algorithms for inverse design of polymers100Generative deep learning as a tool for inverse design of high entropy refractory alloys50Generative models for inverse design of inorganic solid materials38Intrinsically low lattice thermal conductivity of monolayer hexagonal aluminum nitride (h-AlN) from first-principles: A comparative study with graphene36Machine learning assisted rediscovery of methane storage and separation in porous carbon from material literature25Machine learning-assisted systematical polymerization planning: case studies on reversible-deactivation radical polymerization35Inverse machine learning framework for optimizing lightweight metamaterials186The data-intensive scientific revolution occurring where two-dimensional materials meet machine learning62RANGE-GAN: Design Synthesis Under Constraints Using Conditional Generative Adversarial Networks14Combining Generative Adversarial Network and First-Principles Based Microkinetics for Heterogeneous Catalyst Design0Inverse Design of Two-Dimensional Airfoils Using Conditional Generative Models and Surrogate Log-Likelihoods36Design of architectured composite materials with an efficient, adaptive artificial neural network-based generative design method49Inverse design of two-dimensional materials with invertible neural networks69Dispersion relation prediction and structure inverse design of elastic metamaterials via deep learning95Fabrication, characteristics, and applications of boron nitride and their composite nanomaterials102Machine Learning Guided Synthesis of Flash Graphene110Deep Generative Models in Engineering Design: A Review356Text Mining for Energy Materials3Deep learning in light–matter interactions19Heterogeneous catalyst design by generative adversarial network and first-principles based microkinetics23Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Design7Assessing Deep Generative Models in Chemical Composition Space34Machine learning accelerates the materials discovery142Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118Explainable machine learning for carbon dioxide adsorption on porous carbon41Engineering an AI-based forward-reverse platform for the design of cross-ribosome binding sites of a transcription factor biosensor18Improved Transformer-Based Target Matching of Terahertz Broadband Reflective Metamaterials With Monolayer Graphene17Uncovering stress fields and defects distributions in graphene using deep neural networks5Methods, progresses, and opportunities of materials informatics72Predicting mechanical properties of defective h-BN nanosheets using Data-Driven models12Comparing forward and inverse design paradigms: A case study on refractory high-entropy alloys18A machine learning method to quantitatively predict alpha phase morphology in additively manufactured Ti-6Al-4V62Molecular generation strategy and optimization based on A2C reinforcement learning in de novo drug design17Advances of machine learning in materials science: Ideas and techniques105Investigating the effect of textural properties on CO2 adsorption in porous carbons via deep neural networks using various training algorithms49De novo molecule design towards biased properties via a deep generative framework and iterative transfer learning8Materials discovery with extreme properties via reinforcement learning-guided combinatorial chemistry22Artificial intelligence generates novel 3D printing formulations52Deep learning in two-dimensional materials: Characterization, prediction, and design68Data-driven deep learning prediction of boron-doped graphene work function1Advancing Architectural Design Through Generative Adversarial Network Deep Learning Technology6Data-Driven Multi-Objective Inverse Design of Bcc Lattice Metamaterials with Tunable Weight Vectors0AI-empowered digital design of zeolites: Progress, challenges, and perspectives15Design and optimization of a mechanical metamaterial featuring dual tunability in auxeticity and bandgap modulation14Recent advances in machine learning guided mechanical properties prediction and design of two-dimensional materials16Programmable Piezoelectricity of 2D Hexagonal Boron Nitride via Defect Engineering2Inverse design of mechanical metamaterials balancing manufacturability and compactness: A case study on lattice cells6Machine learning in polymer science: A new lens for physical and chemical exploration35GAN-driven discovery of low-cost non-noble metallic electrocatalysts for glycerol electroreduction3EnzymeTuning: a Deep-learning-based Toolbox for Optimizing Enzyme-constrained Metabolic Modeling with Enhanced Proteome Abundance Prediction1Predictive Approaches for 3D ‐Printing: Methods and Approaches for Polymeric Materials5Machine learning predictive models of CO2 adsorption in sustainable waste-derived activated carbon9The role of AI in the advancement of 2D materials for biosensing and nanoelectronics4Machine learning-assisted advances in graphene and 2D materials0EnzymeTuning improves enzyme-constrained metabolic modeling and proteome abundance prediction through deep learning1AI-driven predictive and inverse design framework for defect engineering in hexagonal boron nitride monolayers1Artificial intelligence-driven inverse design of catalytic materials0

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  7. Intrinsically low lattice thermal conductivity of monolayer hexagonal aluminum nitride (h-AlN) from first-principles: A comparative study with graphene

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  14. Inverse Design of Two-Dimensional Airfoils Using Conditional Generative Models and Surrogate Log-Likelihoods

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  18. Fabrication, characteristics, and applications of boron nitride and their composite nanomaterials

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  19. Machine Learning Guided Synthesis of Flash Graphene

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  22. Deep learning in light–matter interactions

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  23. Heterogeneous catalyst design by generative adversarial network and first-principles based microkinetics

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