Papers citing Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation
Inference Without Ground Truth: A Conceptual Theory of Validation in Materials AI
Journal of Artificial Intelligence for Materials Science · 2022
Michael Turner, Daniel Brooks, Olivia Grant
Read original paper ↗ ·Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials
Communications in computer and information science · 2022
Markus Eisenbach, Mariia Karabin, Massimiliano Lupo Pasini, Junqi Yin
Read original paper ↗ ·Atomic structure generation from reconstructing structural fingerprints
Machine Learning Science and Technology · 2022
Victor Fung, Shuyi Jia, Jiaxin Zhang, Sirui Bi, Junqi Yin, Panchapakesan Ganesh
Read original paper ↗ ·Application of SMILES to Cheminformatics and Generation of Optimum SMILES Descriptors Using CORAL Software
Challenges and advances in computational chemistry and physics · 2023
Andrey Andreevich Toropov, Alla P. Toropova
Read original paper ↗ ·Conceptual Limits of Analogy-Based Reasoning in Generative Materials Models
Journal of Artificial Intelligence for Materials Science · 2023
Carlos Ramirez, Elena Torres, Pablo Ortega, Sofia Mendes
Read original paper ↗ ·Generative Models for Materials Science — Conceptual Capabilities and Scientific Limits: A Review Study
Journal of Artificial Intelligence for Materials Science · 2023
Laura Meier, Daniel Fischer, Thomas Braun, Stefan Koch
Read original paper ↗ ·Representation Compression and Scientific Loss in Materials AI
Journal of Artificial Intelligence for Materials Science · 2023
Claire Dupont, J. Martin
Read original paper ↗ ·The Illusion of Novelty in Generative Materials Models
Journal of Artificial Intelligence for Materials Science · 2023
Lucas Andrade, Mariana Lopes
Read original paper ↗ ·UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · 2023
D. Torbunov, Yi Huang, Haiwang Yu, Jin Huang, Shinjae Yoo, Meifeng Lin, Brett Viren, Yihui Ren
Read original paper ↗ ·Physics guided deep learning for generative design of crystal materials with symmetry constraints
npj Computational Materials · 2023
Yong Zhao, Edirisuriya M. Dilanga Siriwardane, Zhenyao Wu, Nihang Fu, Mohammed Al‐Fahdi, Ming Hu, Jianjun Hu
Read original paper ↗ ·Representations of Materials for Machine Learning
Annual Review of Materials Research · 2023
James R. Jr. Damewood, Jessica Karaguesian, Jaclyn R. Lunger, Aik Rui Tan, Mingrou Xie, Jiayu Peng, Rafael Gómez‐Bombarelli
Read original paper ↗ ·Generative artificial intelligence and its applications in materials science: Current situation and future perspectives
Journal of Materiomics · 2023
Yue Liu, Zhengwei Yang, Zhenyao Yu, Zitu Liu, Dahui Liu, Hailong Lin, Mingqing Li, Shuchang Ma, Maxim Avdeev, Siqi Shi
Read original paper ↗ ·Inverse Design of Phononic Crystal with Desired Transmission via a Gradient-Descent Approach
Chinese Physics Letters · 2023
Yuhang 宇航 Wei 魏, Dahai 达海 He 贺
Read original paper ↗ ·Prediction of microstructure evolution at the atomic scale by deep generative model in combination with recurrent neural networks
Acta Materialia · 2023
Kohei Sase, Yasushi Shibuta
Read original paper ↗ ·Element selection for functional materials discovery by integrated machine learning of elemental contributions to properties
npj Computational Materials · 2023
Andrij Vasylenko, Dmytro Antypov, Vladimir V. Gusev, Michael W. Gaultois, Matthew Stephen Dyer, Matthew J. Rosseinsky
Read original paper ↗ ·X-ray Diffraction Data Analysis by Machine Learning Methods—A Review
Applied Sciences · 2023
Vasile-Adrian Surdu, Romuald Győrgy
Read original paper ↗ ·An evolutionary variational autoencoder for perovskite discovery
Frontiers in Materials · 2023
Ericsson Tetteh Chenebuah, Michel Nganbe, Alain Tchagang
Read original paper ↗ ·Guided diffusion for inverse molecular design
Nature Computational Science · 2023
Tomer Weiss, Eduardo Mayo Yanes, Sabyasachi Chakraborty, Luca Cosmo, Alex M. Bronstein, Renana Gershoni‐Poranne
Read original paper ↗ ·Networked Test System Attack Detection Based on Deep Generative Models
International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · 2023
Haidong Wu
Read original paper ↗ ·Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery
Patterns · 2023
Xiaoning Qian, Byung-Jun Yoon, Raymundo Arróyave, Xiaofeng Qian, Edward R. Dougherty
Read original paper ↗ ·Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells
Journal of Chemical Information and Modeling · 2023
Arsen Sultanov, Jean‐Claude Crivello, Tabea Rebafka, Nataliya Sokolovska
Read original paper ↗ ·Generative adversarial networks and diffusion models in material discovery
Digital Discovery · 2023
Michael Alverson, Sterling G. Baird, Ryan Murdock, Sin-Hang Enoch Ho, Jeremy A. Johnson, Taylor D. Sparks
Read original paper ↗ ·A Conceptual Framework for Detecting Scientific Illusions in Generative Materials Outputs
Journal of Artificial Intelligence for Materials Science · 2024
Alejandro Torres, Miguel Fernandez
Read original paper ↗ ·AI: Catalyst for Drug Discovery and Development
Interdisciplinary biotechnological advances · 2024
Khushbu Nailwal, Sumit Durgapal, Khushboo Dasauni, Tapan Kumar Nailwal
Read original paper ↗ ·Dismai-Bench: benchmarking and designing generative models using disordered materials and interfaces
Digital Discovery · 2024
Adrian Xiao Bin Yong, Tianyu Su, Elif Ertekin
Read original paper ↗ ·Explainable AI for Interpretable Structure–Property Maps in Polymer Blend Systems
Journal of Artificial Intelligence for Materials Science · 2024
Claire Martin, Julien Robert, Sophie Bernard
Read original paper ↗ ·Inverse design of semiconductor materials with deep generative models
Journal of Materials Chemistry A · 2024
Chenglong Qin, Jinde Liu, Shiyin Ma, Jiguang Du, Jiang Gang, Liang L. Zhao
Read original paper ↗ ·Machine Learning-Driven Optimization of Transport Layers in MAPbI₃ Perovskite Solar Cells for Enhanced Performance
IEEE Access · 2024
Velpuri Leela Devi, Piyush Kuchhal, Debasis De, Abhinav Sharma, Neeraj Kumar Shukla, Mona Aggarwal
Read original paper ↗ ·Recent Advances in Machine Learning-Accelerated Materials Discovery — From Descriptors to Autonomous Experiments
Journal of Artificial Intelligence for Materials Science · 2024
Nguyen Thanh Huy, Le Thi Bich, Pham Quang Minh
Read original paper ↗ ·Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design
ACS Engineering Au · 2024
Markus J. Buehler
Read original paper ↗ ·Micromechanics-based deep-learning for composites: Challenges and future perspectives
European Journal of Mechanics - A/Solids · 2024
Mohsen Mirkhalaf, I.B.C.M. Rocha
Read original paper ↗ ·Applications of generative adversarial networks in materials science
Materials Genome Engineering Advances · 2024
Yuan Jiang, Jinshan Li, Xianglin Yang, Ruihao Yuan
Read original paper ↗ ·Perspectives on the promise and perils of generative AI in academia
Human Resource Development International · 2024
Robert Mayfield Yawson
Read original paper ↗ ·Solving inverse problems using normalizing flow prior: Application to optical spectra
Physical review. B./Physical review. B · 2024
Jun H. Park, Juyeob Lee, Jungseek Hwang
Read original paper ↗ ·A guide to discovering next-generation semiconductor materials using atomistic simulations and machine learning
Computational Materials Science · 2024
Arun Mannodi‐Kanakkithodi
Read original paper ↗ ·Foundations of Generative AI
Advances in computational intelligence and robotics book series · 2024
Tina Babu, Rekha R Nair, P M Ebin
Read original paper ↗ ·AI for science in electrochemical energy storage: A multiscale systems perspective on transportation electrification
Nexus · 2024
Shuangqi Li, H. Oliver Gao, Fengqi You
Read original paper ↗ ·Democratizing Microreactor Technology for Accelerated Discoveries in Chemistry and Materials Research
Micromachines · 2024
Tomomi Satō, Koji Masuda, Chikako Sano, Keiji Matsumoto, Hidetoshi Numata, Seiji Munetoh, Toshihiro Kasama, Ryo Miyake
Read original paper ↗ ·Machine learning-guided design, synthesis, and characterization of atomically dispersed electrocatalysts
Current Opinion in Electrochemistry · 2024
Sirui Li, Hanguang Zhang, Edward F. Holby, Piotr Zelenay, Wilton J. M. Kort-Kamp
Read original paper ↗ ·Borides as promising M2AX phase materials with high elastic modulus using machine learning and optimization
Journal of Materials Informatics · 2024
Ashwin Mhadeshwar, Trupti Mohanty, Taylor D. Sparks
Read original paper ↗ ·A deep generative modeling architecture for designing lattice-constrained perovskite materials
npj Computational Materials · 2024
Ericsson Tetteh Chenebuah, Michel Nganbe, Alain Tchagang
Read original paper ↗ ·Accelerated discovery of eutectic compositionally complex alloys by generative machine learning
npj Computational Materials · 2024
Ziqiang Chen, Y. H. Shang, Xiaodi Liu, Yong Gang Yang
Read original paper ↗ ·The Legal Paradigm of Generative AI in Malaysia and India: Problems and Prospects
Venue not supplied by source · 2024
Saslina Kamaruddin, Niteesh Kumar Uphaday, Hayyum Suleikha Selamat, Nadia Nabila Mohd Saufi, Wan Rosalili Wan Rosli, Ani Munirah Mohamad
Read original paper ↗ ·From Data to Discovery: Recent Trends of Machine Learning in Metal–Organic Frameworks
JACS Au · 2024
Junkil Park, Honghui Kim, Yeonghun Kang, Yunsung Lim, Jihan Kim
Read original paper ↗ ·Machine-Learned Models for Power Magnetic Material Characteristics
IEEE Transactions on Power Electronics · 2024
Paweł Leszczyński, K. Kutorasiński, Marcin Szewczyk, Jarosław Pawłowski
Read original paper ↗ ·Introduction to Generative AI in Cybersecurity
Advances in human and social aspects of technology book series · 2024
Azeem Khan, Noor Zaman Jhanjhi, Ghassan A. A. Abdulhabeb, Sayan Kumar Ray, Wei Wei Goh
Read original paper ↗ ·Plasmonics Meets Perovskite Photovoltaics: Innovations and Challenges in Boosting Efficiency
Molecules · 2024
Chen Wang, Xiaodan Wang, Bin Luo, Xiaohao Shi, Xiangqian Shen
Read original paper ↗ ·Adaptive AI Alignment: Established Resources for Aligning Machine Learning with Human Intentions and Values in Changing Environments
Machine Learning and Knowledge Extraction · 2024
Stephen Fox
Read original paper ↗ ·Machine learning in advancing anode materials for Lithium-Ion batteries – A review
Inorganic Chemistry Communications · 2024
Mohammad Aminul Islam, Zahid Ahsan, Mustafizur Rahman, Md. Ibrahim Abdullah, Masud Rana, Nayem Hossain, Mohammad Assaduzzaman Chowdhury
Read original paper ↗ ·Data Generation for Machine Learning Interatomic Potentials and Beyond
Chemical Reviews · 2024
Maksim Kulichenko, Benjamin T. Nebgen, Nicholas Lubbers, Justin S. Smith, Kipton M. Barros, Alice E. A. Allen, Adela Habib, Emily Shinkle, Nikita S. Fedik, Ying Wai Li, Richard A. Messerly, Sergei Tretiak
Read original paper ↗ ·