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RESEARCH PAPER · Frontiers in Materials · 2022

Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation

Addis Fuhr, Bobby G. Sumpter

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Deep Generative Models for Materials Discovery and Machine Learning-Accelerated InnovationOriginalInference Without Ground Truth: A Conceptual Theory of Validation in Materials AI0Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials4Atomic structure generation from reconstructing structural fingerprints10Application of SMILES to Cheminformatics and Generation of Optimum SMILES Descriptors Using CORAL Software2Conceptual Limits of Analogy-Based Reasoning in Generative Materials Models0Generative Models for Materials Science — Conceptual Capabilities and Scientific Limits: A Review Study0Representation Compression and Scientific Loss in Materials AI0The Illusion of Novelty in Generative Materials Models0UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation188Physics guided deep learning for generative design of crystal materials with symmetry constraints111Representations of Materials for Machine Learning132Generative artificial intelligence and its applications in materials science: Current situation and future perspectives293Inverse Design of Phononic Crystal with Desired Transmission via a Gradient-Descent Approach6Prediction of microstructure evolution at the atomic scale by deep generative model in combination with recurrent neural networks23Element selection for functional materials discovery by integrated machine learning of elemental contributions to properties15X-ray Diffraction Data Analysis by Machine Learning Methods—A Review100An evolutionary variational autoencoder for perovskite discovery11Guided diffusion for inverse molecular design85Networked Test System Attack Detection Based on Deep Generative Models0Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery31Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells8Generative adversarial networks and diffusion models in material discovery88A Conceptual Framework for Detecting Scientific Illusions in Generative Materials Outputs0AI: Catalyst for Drug Discovery and Development0Dismai-Bench: benchmarking and designing generative models using disordered materials and interfaces5Explainable AI for Interpretable Structure–Property Maps in Polymer Blend Systems0Inverse design of semiconductor materials with deep generative models26Machine Learning-Driven Optimization of Transport Layers in MAPbI₃ Perovskite Solar Cells for Enhanced Performance23Recent Advances in Machine Learning-Accelerated Materials Discovery — From Descriptors to Autonomous Experiments0Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design81Micromechanics-based deep-learning for composites: Challenges and future perspectives38Applications of generative adversarial networks in materials science41Perspectives on the promise and perils of generative AI in academia8Solving inverse problems using normalizing flow prior: Application to optical spectra0A guide to discovering next-generation semiconductor materials using atomistic simulations and machine learning14Foundations of Generative AI14AI for science in electrochemical energy storage: A multiscale systems perspective on transportation electrification17Democratizing Microreactor Technology for Accelerated Discoveries in Chemistry and Materials Research10Machine learning-guided design, synthesis, and characterization of atomically dispersed electrocatalysts14Borides as promising M2AX phase materials with high elastic modulus using machine learning and optimization6A deep generative modeling architecture for designing lattice-constrained perovskite materials29Accelerated discovery of eutectic compositionally complex alloys by generative machine learning43The Legal Paradigm of Generative AI in Malaysia and India: Problems and Prospects2From Data to Discovery: Recent Trends of Machine Learning in Metal–Organic Frameworks90Machine-Learned Models for Power Magnetic Material Characteristics7Introduction to Generative AI in Cybersecurity1Plasmonics Meets Perovskite Photovoltaics: Innovations and Challenges in Boosting Efficiency17Adaptive AI Alignment: Established Resources for Aligning Machine Learning with Human Intentions and Values in Changing Environments2Machine learning in advancing anode materials for Lithium-Ion batteries – A review17Data Generation for Machine Learning Interatomic Potentials and Beyond175A brief overview of deep generative models and how they can be used to discover new electrode materials8Artificial intelligence for personalized nanomedicine; from material selection to patient outcomes159A review of large language models and autonomous agents in chemistry268Kolmogorov–Arnold Network Made Learning Physics Laws Simple15Machine Learning Advances in High-Entropy Alloys: A Mini-Review25Applications of machine learning in high-entropy alloys: a review of recent advances in design, discovery, and characterization40Artificial Intelligence and Generative Models for Materials Discovery: A Review14Efficient Prediction of Quasi-Phase Equilibrium in KKS Phase Field Model via Grey Wolf-Optimized Neural Network0Molecular representation learning: cross-domain foundations and future Frontiers27Navigating materials design spaces with efficient Bayesian optimization: a case study in functionalized nanoporous materials0Unlocking new horizons, challenges of integrating machine learning to energy conversion and storage research9Generative AI designs the next generation of smart materials from pixels to products14Advancing PFAS Remediation through Physics-Based Modeling of 2D Materials: Recent Progress, Challenges, and Opportunities16Mechanical property prediction and configuration effect exploration of particulate reinforced metal matrix composites via an interpretable deep learning approach20Finite element analysis, machine learning, and digital twins for soft robots: state-of-arts and perspectives41Multimodal foundation models for material property prediction and discovery42Inverse design of high-performance piezoelectric semiconductors via advanced crystal representation and large language models1Artificial Intelligence in Gas Sensing: A Review130445High-throughput screening for novel medical materials: machine learning-enabled approaches0THE EFFECTIVENESS OF HEALTHCARE INTERVENTIONS FOR HIGH-INTENSITY INTERVAL TRAINING UNDER GENERATIVE ARTIFICIAL INTELLIGENCE TECHNOLOGY3Towards an automated workflow in materials science for combining multi-modal simulation and experimental information using data mining and large language models11Employing Modern Technologies in Drawing Inspiration from Nature to Develop Form in Industrial Products0Deep learning-driven medical image analysis for computational material science applications6Inverse design of catalytic active sites via interpretable topology-based deep generative models22Applications of generative artificial intelligence in battery research: Current status and prospects15Enhancing Computational Design by integrating Visual Programming Language with Natural Language Processing Models0Generative Design for Engineering Applications: A State-of-the-Art Review18Machine learning for 2D material–based devices13A physics-informed machine learning framework for accelerated discovery of single-phase B2 multi-principal element intermetallics17AI Design for High Entropy Alloys: Progress, Challenges and Future Prospects16Machine learning-driven materials discovery: Unlocking next-generation functional materials – A review50A survey of AI-supported materials informatics7Data-driven optimization of boron-doped graphene work function for enhanced anode materials0Metal oxide candidates for thermochemical water splitting obtained with a generative diffusion model2Artificial intelligence/machine learning of polymer materials and discovery chemistry5Artificial intelligence transforming materials research: possible road ahead for India1EcoCurrentNet an integrated DNN-CatBoost model for predicting optoelectronic material performance under varying conditions1Simulation-conditioned generative modeling for biologically realistic pattern prediction0Predicting and designing properties of twelve alloy families using artificial neural networks and generative adversarial networks1Conceptual Foundations of Scientific Evaluation for Generative Materials AI: A Review Study0Generative AI for Sustainable Problem Solving in I5.00Harnessing generative AI for efficient organic materials discovery in low-data regimes1Next-gen VOC sensing: AI-driven non-invasive detection0Smart Nanoelectronics: Integrating AI and ML in Materials Design and Nanoscale Device Optimisation0Predicting Materials Using Variational Autoencoders: A Systematic Literature Review8Mechanically active polymeric adhesives (MAPAs) for tissue regeneration4Machine Learning-Assisted Polymer and Polymer Composite Design for Additive Manufacturing13CTGAN-based multi-objective optimization of rotary tillage blade design for coastal saline alkaline soils0Future-oriented precision creation: recent advances in the intelligent design and synthesis of fine chemicals1AI-powered open-source infrastructure for accelerating materials discovery and advanced manufacturing26A latent space approach to multi-material topology optimization2AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design0Machine Learning as a Catalyst in Advanced Materials Research and Development0Artificial Intelligence and the Discovery of Antibiotics: Reinventing with Opportunities, Challenges, and Clinical Translation14Generative Modeling of Entangled Polymers with a Distance-Based Variational Autoencoder0Machine learning drives a new paradigm in inorganic solid-state electrolytes research2Artificial intelligence and machine learning in optoelectronics and energy storage: From materials design to device applications0Differential attention transformer-enhanced graph neural network for accurate material property prediction3Algebraic language models for inverse design of metamaterials via diffusion transformers7AI-augmented materials design for additive manufacturing: From tacit expertise to scalable knowledge systems0Fast and Flexible 3D Molecular Design Framework for Novel Organic Optoelectronic Materials0A Generative AI Approach to Inverse Design for Continuous Pharmaceutical Manufacturing0Autonomous laboratories for sustainable nanomaterials discovery0Role of TiO2 nanostructures in non-invasive monitoring of VOC biomarkers0Strategies and Multilayer Architectures for Decoupling Conductivity–Transparency Trade-Off0A composition generation framework embedded with domain knowledge for chemically disordered inorganic compounds0Data-Driven Reduction of Rail Pad Dynamic Stiffness Testing: Benchmarking a cGAN Against Gaussian Process Kriging0Civil engineering in the age of artificial intelligence: state of the art of breakthrough, innovation, and intelligent design0

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