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RESEARCH PAPER · Scientific Reports · 2022

Moving closer to experimental level materials property prediction using AI

Dipendra Jha, Vishu Gupta, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal

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Moving closer to experimental level materials property prediction using AIOriginalA deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures44Machine Learning Spectroscopy Based on Group Contribution and Molecule Contribution Methods0Which Deep Learning Framework Should I Use: A Comparative Study For Deep Regression Modeling3Dynamic Material Characterization Through In-Situ Electrical Resistivity Measurements of High Temperature Transient Liquid Phase Sinter Alloys0Machine Learning Spectroscopy Using a 2-Stage, Generalized Constituent Contribution Protocol14MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction36Improving deep learning model performance under parametric constraints for materials informatics applications12AI for Learning Deformation Behavior of a Material: Predicting Stress-Strain Curves 4000x Faster Than Simulations5Pre-Activation based Representation Learning to Enhance Predictive Analytics on Small Materials Data7Center-environment deep transfer machine learning across crystal structures: from spinel oxides to perovskite oxides60An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems18What Counts as Knowledge in AI-Driven Materials Science? A Philosophical and Practical Reframing0Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets90Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning70Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks8Chapter 4: Physically informed deep learning networks for simulating microstructure evolution of 3D polycrystals0JARVIS-Leaderboard: a large scale benchmark of materials design methods65In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back78Machine Learning and Small Data-Guided Optimization of Silica Shell Morphology on Gold Nanorods10Accurate formation enthalpies of solids using reaction networks4Evaluation of machine learning models for the accelerated prediction of density functional theory calculated 19F chemical shifts based on local atomic environments6MD-HIT: Machine learning for material property prediction with dataset redundancy control53Combining Transfer Learning and Representation Learning to Improve Predictive Analytics on Small Materials Data2Hybrid-LLM-GNN: integrating large language models and graph neural networks for enhanced materials property prediction21Integrating DFG spectroscopy with machine learning for direct and accurate gas pressure diagnosis from spectral imagery1An Artificial Intelligence Approach to Evaluating the Sound Absorption Performance of Materials: A Systematic Review0Machine Learning in nanoarchitectonics24Artificial Intelligence for Materials Discovery, Development, and Optimization171EMERGING STRATEGIES IN PHASE-TUNED PEROVSKITE MATERIALS FOR EFFICIENT HYDROGEN EVOLUTION: A MACHINE LEARNING PERSPECTIVE0AI Driven Discovery and Design of Smart Materials0Chemical mechanism prediction unravels multiple macromolecular outcomes from free-radical reactions0Machine learning electronic structure methods0Application of artificial intelligence in predicting and optimizing material properties3Dynamic embedding representation for graph neural networks to enhance materials property prediction with limited datasets0Crystal Quotient Graph and Periodic Message Passing Enhancing Material Property Prediction0

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35 direct citing papers saved.

  1. A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures

    Journal of Intelligent Manufacturing · 2022-10-14

    Yuwei Mao, Hui Zhi Lin, Christina Xuan Yu, Roger Frye, Darren Beckett, Kevin Anderson, Lars Jacquemetton, Fred Carter, Zhangyuan Gao, Wei‐keng Liao, Alok Choudhary, Kornel F. Ehmann, Ankit Agrawal

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  2. Machine Learning Spectroscopy Based on Group Contribution and Molecule Contribution Methods

    Research Square · 2022-10-18

    Jinming Fan, Chao Qian, Shaodong Zhou

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  3. Which Deep Learning Framework Should I Use: A Comparative Study For Deep Regression Modeling

    International Conference on Computational Science and Computational Intelligence (CSCI) · 2022-12-01

    Vishu Gupta, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal

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  4. Dynamic Material Characterization Through In-Situ Electrical Resistivity Measurements of High Temperature Transient Liquid Phase Sinter Alloys

    ˜The œminerals, metals & materials series · 2023-01-01

    Gilad Nave, P. McCluskey

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  5. Machine Learning Spectroscopy Using a 2-Stage, Generalized Constituent Contribution Protocol

    Research · 2023-01-01

    Jinming Fan, Chao Qian, Shaodong Zhou

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  6. MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction

    Journal of Chemical Information and Modeling · 2023-03-27

    Vishu Gupta, Kamal Kumar Choudhary, Yuwei Mao, Kewei Wang, Francesca Tavazza, Carelyn E. Campbell, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal

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  7. Improving deep learning model performance under parametric constraints for materials informatics applications

    Scientific Reports · 2023-06-05

    Vishu Gupta, Alec Peltekian, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal

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  8. AI for Learning Deformation Behavior of a Material: Predicting Stress-Strain Curves 4000x Faster Than Simulations

    Unknown journal · 2023-06-18

    Yuwei Mao, Shahriyar Keshavarz, Vishu Gupta, Andrew C. E. Reid, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal

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  9. Pre-Activation based Representation Learning to Enhance Predictive Analytics on Small Materials Data

    Unknown journal · 2023-06-18

    Vishu Gupta, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal

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  10. Center-environment deep transfer machine learning across crystal structures: from spinel oxides to perovskite oxides

    npj Computational Materials · 2023-06-22

    Yihang Li, Ruijie Zhu, Yuanqing Wang, Lingyan Feng, Yi Liu

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  11. An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems

    npj Computational Materials · 2023-06-23

    Yuwei Mao, Mahmudul Hasan, Arindam Paul, Vishu Gupta, Kamal Kumar Choudhary, Francesca Tavazza, Wei‐keng Liao, Alok Choudhary, Pınar Acar, Ankit Agrawal

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  12. What Counts as Knowledge in AI-Driven Materials Science? A Philosophical and Practical Reframing

    Journal of Artificial Intelligence for Materials Science · 2024-01-01

    Maria Hernandez, Carlos Vega

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  13. Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

    npj Computational Materials · 2024-01-02

    Vishu Gupta, Kamal Kumar Choudhary, Brian DeCost, Francesca Tavazza, Carelyn E. Campbell, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal

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  14. Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning

    npj Computational Materials · 2024-01-10

    Hajime Shimakawa, Akiko Kumada, Masahiro Sato

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  15. Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks

    Journal of Cheminformatics · 2024-02-16

    Vishu Gupta, Youjia Li, Alec Peltekian, Muhammed Nur Talha Kilic, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal

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  16. Chapter 4: Physically informed deep learning networks for simulating microstructure evolution of 3D polycrystals

    Unknown journal · 2024-04-30

    Ashley Lenau, USDOE National Nuclear Security Administration (NNSA), The Ohio State Univ., Columbus, OH (United States), Reeju Pokharel, Alexander Scheinker, Stephen R. Niezgoda

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  17. JARVIS-Leaderboard: a large scale benchmark of materials design methods

    npj Computational Materials · 2024-05-07

    Kamal Kumar Choudhary, Daniel Wines, Kangming Li, Kevin F. Garrity, Vishu Gupta, Aldo Humberto Romero, Jaron T. Krogel, Kayahan Saritas, Addis Fuhr, Panchapakesan Ganesh, Paul R. C. Kent, Keqiang Yan, Yuchao Lin, Shuiwang Ji, Ben J. Blaiszik, Patrick Reiser, Pascal Friederich, Ankit Agrawal, Pratyush Tiwary, Eric R. Beyerle, Peter Minch, Trevor David Rhone, Ichiro Takeuchi, Robert B. Wexler, Arun Mannodi‐Kanakkithodi, Elif Ertekin, Avanish Mishra, Nithin Mathew, Mitchell A. Wood, Andrew Rohskopf, Jason R. Hattrick-Simpers, Shih‐Han Wang, Luke E. K. Achenie, Hongliang Xin, Maureen E. Williams, Adam J. Biacchi, Francesca Tavazza

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  18. In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

    Advanced Materials · 2024-05-25

    Abdulrahman Aldossary, Jorge A. Campos-Gonzalez-Angulo, Sergio Pablo‐García, Shi Xuan Leong, Ella Miray Rajaonson, Luca Thiede, Gary Tom, Andrew Wang, Davide Avagliano, Alán Aspuru‐Guzik

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  19. Machine Learning and Small Data-Guided Optimization of Silica Shell Morphology on Gold Nanorods

    Chemistry of Materials · 2024-08-22

    Akhlak U. Mahmood, Melanie M. Ghelardini, Joseph B. Tracy, Yaroslava G. Yingling

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  20. Accurate formation enthalpies of solids using reaction networks

    npj Computational Materials · 2024-10-14

    Rasmus Fromsejer, Bjørn Maribo‐Mogensen, Georgios M. Kontogeorgis, Xiaodong Liang

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  21. Evaluation of machine learning models for the accelerated prediction of density functional theory calculated 19F chemical shifts based on local atomic environments

    Artificial Intelligence Chemistry · 2024-10-17

    Sophia Li, Emma Wang, Leia Pei, Sourodeep Deb, Prashanth Prabhala, Sai Hruday Reddy Nara, Raina Panda, Shiven Eltepu, Marx Akl, Larry McMahan, Edward Njoo

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  22. MD-HIT: Machine learning for material property prediction with dataset redundancy control

    npj Computational Materials · 2024-10-18

    Qin Li, Nihang Fu, Sadman Sadeed Omee, Jianjun Hu

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  23. Combining Transfer Learning and Representation Learning to Improve Predictive Analytics on Small Materials Data

    International Conference on Machine Learning and Applications · 2024-12-18

    Vishu Gupta, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal

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  24. Hybrid-LLM-GNN: integrating large language models and graph neural networks for enhanced materials property prediction

    Digital Discovery · 2024-12-18

    Youjia Li, Vishu Gupta, Muhammed Nur Talha Kilic, Kamal Kumar Choudhary, Daniel Wines, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal

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  25. Integrating DFG spectroscopy with machine learning for direct and accurate gas pressure diagnosis from spectral imagery

    Optics Express · 2025-04-28

    Alireza Raheemi Bahambari, Alireza Khorsandi, Iraj Jabbari

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  26. An Artificial Intelligence Approach to Evaluating the Sound Absorption Performance of Materials: A Systematic Review

    Düzce Üniversitesi Bilim ve Teknoloji Dergisi · 2025-04-30

    Oya Babacan

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  27. Machine Learning in nanoarchitectonics

    Advances in Colloid and Interface Science · 2025-05-13

    Bogdan V. Parakhonskiy, Junnan Song, André G. Skirtach

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  28. Artificial Intelligence for Materials Discovery, Development, and Optimization

    ACS Nano · 2025-07-25

    Benediktus Madika, Aditi Saha, Chaeyul Kang, Batzorig Buyantogtokh, Joshua Agar, Chris M. Wolverton, Peter W. Voorhees, P. B. Littlewood, Sergei V. Kalinin, Seungbum Hong

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  29. EMERGING STRATEGIES IN PHASE-TUNED PEROVSKITE MATERIALS FOR EFFICIENT HYDROGEN EVOLUTION: A MACHINE LEARNING PERSPECTIVE

    FUDMA Journal of Sciences · 2025-08-14

    Reuben Abraham Solomon, Oluwatosin Mary Kayode

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  30. AI Driven Discovery and Design of Smart Materials

    Journal of Mines Metals and Fuels · 2025-10-09

    Mahesh Ashok Bote, Naeem Shaikh, Shruti Pandit, A. C. Bhagali, Azhar Mulla

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  31. Chemical mechanism prediction unravels multiple macromolecular outcomes from free-radical reactions

    Reaction Chemistry & Engineering · 2025-11-10

    Shahriar Hojjati Emami

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  32. Machine learning electronic structure methods

    Elsevier eBooks · 2026-01-01

    Tobias Henkes, Pavlo O. Dral, Carolin Müller

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  33. Application of artificial intelligence in predicting and optimizing material properties

    Engineering Research Express · 2026-01-21

    Kawaljit Singh Randhawa

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  34. Dynamic embedding representation for graph neural networks to enhance materials property prediction with limited datasets

    Materials Research Express · 2026-03-12

    Vishu Gupta, Kamal Kumar Choudhary, Youjia Li, Muhammed Nur Talha Kilic, Daniel Wines, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal

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  35. Crystal Quotient Graph and Periodic Message Passing Enhancing Material Property Prediction

    IEEE Transactions on Emerging Topics in Computational Intelligence · 2026-04-29

    Weiwei Zhang, Lixin Tang, Meiling Xu

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