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RESEARCH PAPER · Journal of Materials Informatics · 2021

Generative models for inverse design of inorganic solid materials

Litao Chen, Wentao Zhang, Zhiwei Nie, Shunning Li, Feng Ming Pan

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42 direct citing works

Generative models for inverse design of inorganic solid materialsOriginalInformatics is fueling new materials discovery10Acoustic Lens Design Using Machine Learning0From High-Throughput Computation to Autonomous Discovery: A Review of Closed-Loop Data Infrastructures in Materials Engineering0Inverse Design of Materials by Machine Learning137Recent progress in generative adversarial networks applied to inversely designing inorganic materials: A brief review50Assessing Deep Generative Models in Chemical Composition Space34The Illusion of Novelty in Generative Materials Models0A deep autoencoder based approach for the inverse design of an acoustic-absorber36A scalable crystal representation for reverse engineering of novel inorganic materials using deep generative models1Generative artificial intelligence and its applications in materials science: Current situation and future perspectives293Inverse design and AI/Deep generative networks in food design: A comprehensive review59A scalable crystal representation for reverse engineering of novel inorganic materials using deep generative models10Recent advances in the interface structure prediction for heteromaterial systems15Scope of machine learning in materials research—A review230Toward Community-Agreed Metrics for Generative Materials Models: A Position on Inverse Design Reporting0Discovery of magnesium-aluminum alloys by generative model and automatic differentiation approach2Reducing Overpotential of Lithium–Oxygen Batteries by Diatomic Metal Catalyst Orbital Matching Strategy13AI for dielectric capacitors16Deep Learning Methods for Microstructural Image Analysis: The State-of-the-Art and Future Perspectives31Influence of alloying elements on microstructure, mechanical properties and corrosion behaviour of hypoeutectic Sn-6.5wt%Zn-0.5 wt%X (X = Ag, Al, Cu) lead-free solders4Optimizing Spectral Properties of Cesium Tungsten Bronze Films Doped with Silver Nanowires Based on the Machine Learning Method19Machine learning descriptors for crystal materials: applications in Ni-rich layered cathode and lithium anode materials for high-energy-density lithium batteries20Machine learning applied to the design and optimization of polymeric materials: A review51Objective Functions Shape Materials Futures: A Conceptual Study of Optimization Targets in AI-Driven Design0Why Diffusion Models Fail for Inverse Materials Design: Mode Collapse, Compositional Violation, and Stability Loss0AI-Driven Inverse Design of Materials: Past, Present, and Future78Inverse design of high-performance piezoelectric semiconductors via advanced crystal representation and large language models1Research on the Application of Deep Learning and VR Technology in Youth Physical Education Teaching1Data-driven electrochemical behavior prediction for refractory high-entropy alloys by global and focused learning5Machine learning-driven mass transfer modeling and inverse design for nanofiltration membranes7Dreaming Up Novel Quantum Dyes using Inverse Machine Learning in MatFlow0A survey of AI-supported materials informatics7Artificial Intelligence as a Co-Scientist in Materials Science: From Pattern Recognition to Self-Driving Laboratories0Uncertainty guided inverse design of polymers0Sensitivity and Performance Analysis of an AI Model for Virtual Prototyping of Brazed Ceramic–Metal Composite Materials0Interlocked Woven COFs as Molecular Metamaterials with Snap-through and Jamming Mechanics2Discovery of interpretable T c descriptors in conventional superconductors guided by symbolic regression1Biomacromolecule–MOF Composites for Intracellular Delivery: From Empirical Construction to Rational Design and AI‐Assisted Screening1

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  1. Informatics is fueling new materials discovery

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  2. Acoustic Lens Design Using Machine Learning

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  3. From High-Throughput Computation to Autonomous Discovery: A Review of Closed-Loop Data Infrastructures in Materials Engineering

    Journal of Computational and Data-Driven Materials Engineering · 2022-01-01

    Ravi Kumar, Neha Sharma, Aniket Deshmukh

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  4. Inverse Design of Materials by Machine Learning

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  5. Recent progress in generative adversarial networks applied to inversely designing inorganic materials: A brief review

    Computational Materials Science · 2022-07-01

    Rahma Jabbar, Rahma Jabbar, Rateb Jabbar, Rateb Jabbar, S. Kamoun

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  6. Assessing Deep Generative Models in Chemical Composition Space

    Chemistry of Materials · 2022-10-19

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  7. The Illusion of Novelty in Generative Materials Models

    Journal of Artificial Intelligence for Materials Science · 2023-01-01

    Lucas Andrade, Mariana Lopes

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  8. A deep autoencoder based approach for the inverse design of an acoustic-absorber

    Engineering With Computers · 2023-02-15

    K. Mahesh, S. Kumar Ranjith, R. S. Mini

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  9. A scalable crystal representation for reverse engineering of novel inorganic materials using deep generative models

    ChemRxiv · 2023-03-21

    Abhishek Tewari, Rochan Bajpai, Atharva Shukla, Janish Kumar

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  10. Generative artificial intelligence and its applications in materials science: Current situation and future perspectives

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    Yue Liu, Zhengwei Yang, Zhenyao Yu, Zitu Liu, Dahui Liu, Hailong Lin, Mingqing Li, Shuchang Ma, Maxim Avdeev, Siqi Shi

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  11. Inverse design and AI/Deep generative networks in food design: A comprehensive review

    Trends in Food Science & Technology · 2023-06-03

    Mahmoud Al-Sarayreh, Mariza Gomes Reis, Alistair J. Carr, Marlon Martins dos Reis

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  12. A scalable crystal representation for reverse engineering of novel inorganic materials using deep generative models

    Computational Materials Science · 2023-09-22

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  13. Recent advances in the interface structure prediction for heteromaterial systems

    Journal of Materials Informatics · 2023-10-18

    Jili Li, Yefei Li

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  14. Scope of machine learning in materials research—A review

    Applied Surface Science Advances · 2023-11-28

    Md Hosne Mobarak, Mariam Akter Mimona, Md. Aminul Islam, Nayem Hossain, Fatema Tuz Zohura, Ibnul Imtiaz, Md Israfil Hossain Rimon

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  15. Toward Community-Agreed Metrics for Generative Materials Models: A Position on Inverse Design Reporting

    Journal of Computational and Data-Driven Materials Engineering · 2024-01-01

    Sven Larsson, Erik Johansson, Anna Nilsson

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  16. Discovery of magnesium-aluminum alloys by generative model and automatic differentiation approach

    Modelling and Simulation in Materials Science and Engineering · 2024-03-28

    Shuwei Cheng, Zhelin Li, Hongfei Zhang, Xiaohong Yan, Shibing Chu

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  17. Reducing Overpotential of Lithium–Oxygen Batteries by Diatomic Metal Catalyst Orbital Matching Strategy

    The Journal of Physical Chemistry Letters · 2024-05-15

    Aixiang Mao, Jing Li, Jing Li, Jiahui Li, Jiahui Li, Honglai Liu, Cheng Lian

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  18. AI for dielectric capacitors

    Energy storage materials · 2024-07-01

    Run‐Lin Liu, Jian Wang, Zhong-Hui Shen, Yang Shen

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  19. Deep Learning Methods for Microstructural Image Analysis: The State-of-the-Art and Future Perspectives

    Integrating materials and manufacturing innovation · 2024-07-15

    Khaled Alrfou, Tian Zhao, Amir Kordijazi

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  20. Influence of alloying elements on microstructure, mechanical properties and corrosion behaviour of hypoeutectic Sn-6.5wt%Zn-0.5 wt%X (X = Ag, Al, Cu) lead-free solders

    Journal of Materials Science Materials in Electronics · 2024-08-01

    Jesús Rafael González Parra, Omar Novelo-Peralta, G.A. Lara-Rodríguez, I.A. Figueroa, Arturo Barba‐Pingarrón, Miguel Ángel Hernández

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  21. Optimizing Spectral Properties of Cesium Tungsten Bronze Films Doped with Silver Nanowires Based on the Machine Learning Method

    The Journal of Physical Chemistry C · 2024-09-11

    Zeming He, Wenxi Xie, Siyang Zhang, Yin Gao, Ashraf Y. Elnaggar, Juanna Ren, Islam H. El Azab, Zeinhom Mohamed El-Bahy, Ming Yang, Hang Zhang, Zhanhu Guo

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  22. Machine learning descriptors for crystal materials: applications in Ni-rich layered cathode and lithium anode materials for high-energy-density lithium batteries

    Journal of Materials Informatics · 2024-10-28

    Ruiqi Zhang, Fangchao Rong, Genming Lai, Guangfeng Wu, Yaokun Ye, Jiaxin Zheng

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  23. Machine learning applied to the design and optimization of polymeric materials: A review

    Next Materials · 2024-12-14

    Sudarsan M. Pai, Karim Ali Shah, Sruthi Sunder, Rodrigo Queiroz Albuquerque, Christian Brütting, Holger Ruckdäschel

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  24. Objective Functions Shape Materials Futures: A Conceptual Study of Optimization Targets in AI-Driven Design

    Journal of Artificial Intelligence for Materials Science · 2025-01-01

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  25. Why Diffusion Models Fail for Inverse Materials Design: Mode Collapse, Compositional Violation, and Stability Loss

    Journal of Computational and Data-Driven Materials Engineering · 2025-01-01

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  26. AI-Driven Inverse Design of Materials: Past, Present, and Future

    Chinese Physics Letters · 2025-01-31

    Xu Han, Xin-De Wang, Meng-Yuan 孟圆 Xu 徐, Feng Zhen, Bo-Wen 博文 Yao 姚, Peng-Jie Guo, Ze-Feng Gao, Zhong-Yi Lu

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  27. Inverse design of high-performance piezoelectric semiconductors via advanced crystal representation and large language models

    Applied Physics Letters · 2025-03-01

    Chen Zhang, Siyuan Lv, Haojie Gong, Qianxi Cheng, Jianxiu Guo, Zheng Duanmu, Hang Xiao

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  28. Research on the Application of Deep Learning and VR Technology in Youth Physical Education Teaching

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  29. Data-driven electrochemical behavior prediction for refractory high-entropy alloys by global and focused learning

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  30. Machine learning-driven mass transfer modeling and inverse design for nanofiltration membranes

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  31. Dreaming Up Novel Quantum Dyes using Inverse Machine Learning in MatFlow

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  32. A survey of AI-supported materials informatics

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  33. Artificial Intelligence as a Co-Scientist in Materials Science: From Pattern Recognition to Self-Driving Laboratories

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  34. Uncertainty guided inverse design of polymers

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  35. Sensitivity and Performance Analysis of an AI Model for Virtual Prototyping of Brazed Ceramic–Metal Composite Materials

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  36. Interlocked Woven COFs as Molecular Metamaterials with Snap-through and Jamming Mechanics

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  37. Discovery of interpretable T c descriptors in conventional superconductors guided by symbolic regression

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  38. Biomacromolecule–MOF Composites for Intracellular Delivery: From Empirical Construction to Rational Design and AI‐Assisted Screening

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