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RESEARCH PAPER · Patterns · 2021

Predicting energy and stability of known and hypothetical crystals using graph neural network

Shubham Pandey, Jiaxing Qu, Vladan Stevanović, Peter C. St. John, Prashun Gorai

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Predicting energy and stability of known and hypothetical crystals using graph neural networkOriginalData-centric approach to improve machine learning models for inorganic materials16Review of computational approaches to predict the thermodynamic stability of inorganic solids196Graph-based machine learning beyond stable materials and relaxed crystal structures10Investigation of the Pb–Fe–Se Ternary System and the Synthesis of a Ternary Pb1–xFexSe Phase3graphenv: a Python library for reinforcement learning on graph search spaces2Graph neural networks for materials science and chemistry860Accelerating the evaluation of crucial descriptors for catalyst screening via message passing neural network11Upper-Bound Energy Minimization to Search for Stable Functional Materials with Graph Neural Networks27Graph Neural Networks for Periodic Crystals: A Systematic Review of Representational Trade-Offs and Hidden Assumptions0Phonon properties calculation of inorganic perovskite CsSnX3 (X=Cl, Br, I) in cubic phase using density functional theory (DFT)4Graph Neural Network Guided Evolutionary Search of Grain Boundaries in 2D Materials24Graph Comparison of Molecular Crystals in Band Gap Prediction Using Neural Networks22Accelerating the prediction of stable materials with machine learning104Formation energy prediction of crystalline compounds using deep convolutional network learning on voxel image representation23A Critical Examination of "State-of-the-Art" Claims in Materials GNNs: Benchmark Artifacts and Unstated Limitations0Probabilistic prediction of material stability: integrating convex hulls into active learning10The Case against Ignoring Long-Range Interactions in Graph-Based Property Prediction for Polar Crystals0Machine-Learning-Assisted Construction of Ternary Convex Hull Diagrams13How do physicochemical properties contribute to inhibitory activity of promising peptides against Zika Virus NS3 protease?2Accelerating defect predictions in semiconductors using graph neural networks39The Stability Prediction and Epitaxial Growth of Boron Nitride Nanodots on Different Substrates4Methods and applications of machine learning in computational design of optoelectronic semiconductors22Graph representation of multi-dimensional materials2Computational Insights into Phase Equilibria between Wide-Gap Semiconductors and Contact Materials4Statistically Derived Proxy Potentials Accelerate Geometry Optimization of Crystal Structures2Unleashing the power of artificial intelligence in phonon thermal transport: Current challenges and prospects12Fairness and Diversity in Recommender Systems: A Survey116A guide to discovering next-generation semiconductor materials using atomistic simulations and machine learning14Using Graph Neural Networks to Predict Positions of the Absorption Maxima of a Number of Dyes1State-of-the-art review on various applications of machine learning techniques in materials science and engineering26Charting the chemical space of Zintl phases with graph neural networks and bonding insights2Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration36Prediction of Thermodynamic Properties of C60-Based Fullerenols Using Machine Learning3Systematic softening in universal machine learning interatomic potentials145Defect modeling in semiconductors: the role of first principles simulations and machine learning17Which plays a key role in sampling-based large-scale GNNs, sampling or iteration?0Application and prospects of artificial intelligence in condensed matter structural chemistry0Why GNNs Fail for Metastable Phase Formation Energies: A Failure Mode Analysis0Unified Graph-Based Interatomic Potential for Perovskite Structure Optimization4Representation learning of crystal materials using Graph Neural Networks: Passive symmetry challenges and advances0An Informatic Elucidation of Transition Metal Chalcogenide-Based Materials for Energy Storage and Harvesting0Artificial intelligence in nanotechnology: current applications and future perspectives0

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  2. Review of computational approaches to predict the thermodynamic stability of inorganic solids

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  3. Graph-based machine learning beyond stable materials and relaxed crystal structures

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  4. Investigation of the Pb–Fe–Se Ternary System and the Synthesis of a Ternary Pb1–xFexSe Phase

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  5. graphenv: a Python library for reinforcement learning on graph search spaces

    The Journal of Open Source Software · 2022-09-05

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  6. Graph neural networks for materials science and chemistry

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  7. Accelerating the evaluation of crucial descriptors for catalyst screening via message passing neural network

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    Hieu Anh Doan, Chenyang Li, Logan T. Ward, Mingxia Zhou, Larry A. Curtiss, Rajeev S. Assary

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  8. Upper-Bound Energy Minimization to Search for Stable Functional Materials with Graph Neural Networks

    JACS Au · 2022-12-31

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  9. Graph Neural Networks for Periodic Crystals: A Systematic Review of Representational Trade-Offs and Hidden Assumptions

    Journal of Computational and Data-Driven Materials Engineering · 2023-01-01

    Maria Gonzalez, Javier Ruiz, Lucia Torres, Elena Ruiz

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  10. Phonon properties calculation of inorganic perovskite CsSnX3 (X=Cl, Br, I) in cubic phase using density functional theory (DFT)

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    Elsyifa Fitria Rahmani, Hasniah Aliah, Pina Pitriana

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  11. Graph Neural Network Guided Evolutionary Search of Grain Boundaries in 2D Materials

    ACS Applied Materials & Interfaces · 2023-04-11

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  12. Graph Comparison of Molecular Crystals in Band Gap Prediction Using Neural Networks

    ACS Omega · 2023-10-16

    Takuya Taniguchi, Mayuko Hosokawa, Toru Asahi

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  13. Accelerating the prediction of stable materials with machine learning

    Nature Computational Science · 2023-11-09

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  14. Formation energy prediction of crystalline compounds using deep convolutional network learning on voxel image representation

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  15. A Critical Examination of "State-of-the-Art" Claims in Materials GNNs: Benchmark Artifacts and Unstated Limitations

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  16. Probabilistic prediction of material stability: integrating convex hulls into active learning

    Materials Horizons · 2024-01-01

    Andrew C. Novick, Diana Cai, Quan Nguyen, Roman Garnett, Ryan Prescott Adams, Eric S. Toberer

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  17. The Case against Ignoring Long-Range Interactions in Graph-Based Property Prediction for Polar Crystals

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

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  18. Machine-Learning-Assisted Construction of Ternary Convex Hull Diagrams

    Journal of Chemical Information and Modeling · 2024-01-25

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  19. How do physicochemical properties contribute to inhibitory activity of promising peptides against Zika Virus NS3 protease?

    Journal of Molecular Modeling · 2024-01-30

    Débora Feliciano Savino, João Vitor Silva, Soraya da Silva Santos, Felipe Rebello Lourenço, Jeanine Giarolla

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  20. Accelerating defect predictions in semiconductors using graph neural networks

    APL Machine Learning · 2024-03-01

    Md. Habibur Rahman, Prince Gollapalli, Panayotis Manganaris, Satyesh Kumar Yadav, Ghanshyam Pilania, Brian DeCost, Kamal Kumar Choudhary, Arun Mannodi‐Kanakkithodi

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  21. The Stability Prediction and Epitaxial Growth of Boron Nitride Nanodots on Different Substrates

    Molecules · 2024-03-15

    Muhamad Jalu Purnomo, Yosi Febrita, Okto Dinaryanto, Wojciech Gierlotka, Ing‐Song Yu

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  22. Methods and applications of machine learning in computational design of optoelectronic semiconductors

    Science China Materials · 2024-03-19

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  23. Graph representation of multi-dimensional materials

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  24. Computational Insights into Phase Equilibria between Wide-Gap Semiconductors and Contact Materials

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  25. Statistically Derived Proxy Potentials Accelerate Geometry Optimization of Crystal Structures

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  26. Unleashing the power of artificial intelligence in phonon thermal transport: Current challenges and prospects

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  27. Fairness and Diversity in Recommender Systems: A Survey

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    Yuying Zhao, Yu Wang, Yunchao Liu, Xueqi Cheng, Charų C. Aggarwal, Tyler Derr

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  28. A guide to discovering next-generation semiconductor materials using atomistic simulations and machine learning

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  29. Using Graph Neural Networks to Predict Positions of the Absorption Maxima of a Number of Dyes

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  31. Charting the chemical space of Zintl phases with graph neural networks and bonding insights

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  32. Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration

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  33. Prediction of Thermodynamic Properties of C60-Based Fullerenols Using Machine Learning

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  38. Why GNNs Fail for Metastable Phase Formation Energies: A Failure Mode Analysis

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  39. Unified Graph-Based Interatomic Potential for Perovskite Structure Optimization

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  40. Representation learning of crystal materials using Graph Neural Networks: Passive symmetry challenges and advances

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  41. An Informatic Elucidation of Transition Metal Chalcogenide-Based Materials for Energy Storage and Harvesting

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