BetaCitation Atlas

RESEARCH PAPER · Communications Materials · 2021

Predicting synthesizability of crystalline materials via deep learning

Ali Davariashtiyani, Zahra Kadkhodaie, Sara Kadkhodaei

Read original paper ↗

See where this research leads.

43 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

Predicting synthesizability of crystalline materials via deep learningOriginalMetallicity in Quasar Broad-line Regions at Redshift ∼ 641Perovskite synthesizability using graph neural networks80Composable Infrastructures for an Academic Research Environment: Lessons Learned4Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions79Searching guidelines for scalable and controllable design of multifunctional materials and hybrid interfaces: Status and perspective3Into the Unknown: How Computation Can Help Explore Uncharted Material Space65Machine learned synthesizability predictions aided by density functional theory30Process-Informatics-Assisted Preparation of Lithium Titanate Crystals with Various Sizes and Morphologies5Machine learning-inspired battery material innovation49Materials synthesizability and stability prediction using a semi-supervised teacher-student dual neural network22Model Entropy and Scientific Information Loss in Compressed Representations of Materials0Drug Design and Disease Diagnosis: The Potential of Deep Learning Models in Biology70Machine‐Learning‐Assisted Determination of the Global Zero‐Temperature Phase Diagram of Materials109Functional Material Systems Enabled by Automated Data Extraction and Machine Learning33Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions38Accelerating the prediction of stable materials with machine learning104AI-enabled materials discovery for advanced ceramic electrochemical cells28Formation energy prediction of crystalline compounds using deep convolutional network learning on voxel image representation23Predicting synthesis recipes of inorganic crystal materials using elementwise template formulation15Deep Learning-Ready Voxel Representation of Protein-Ligand Complexes from an Enhanced PBDbind v.2020 Dataset1Machine learning for efficient grazing-exit x-ray absorption near edge structure spectroscopy analysis: Bayesian optimization approach4NanoConstruct: A web application builder of ellipsoidal nanoparticles for the investigation of their crystal growth, stability, and the calculation of atomistic descriptors12Synthesizability of materials stoichiometry using semi-supervised learning21From Design to Device: Challenges and Opportunities in Computational Discovery of p -Type Transparent Conductors18Predicting Crystalline Material Properties with AI: Bridging Molecular to Particle Scales6Impact of data bias on machine learning for crystal compound synthesizability predictions16Crystal synthesizability prediction using contrastive positive unlabeled learning6Computation-guided exploration of the reaction parameter space of N , N -dimethylformamide hydrolysis6Data-driven exploration of layered double hydroxide crystals exhibiting high fluoride ion adsorption properties and chemical stability2Defining "Novel Material" in Generative Models: A Boundary between Chemical Validity and Synthesizability0SynCoTrain: a dual classifier PU-learning framework for synthesizability prediction9Lean CNNs for Mapping Electron Charge Density Fields to Material Properties7Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models28Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models1Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models33Predicting the synthesizability of inorganic crystals by bridging crystal graphs and phonon dynamics0Artificial intelligence for post-moore field-effect transistors: a review and perspective1Hybrid pathways for methane production: Merging thermodynamic insights with machine learning4Synthesis-Aware Materials Redesign via Large Language Models12Informatics for thermodynamics: Stability to synthesizability0Bridging theory and experiment in materials discovery: Machine-learning-assisted prediction of synthesizable structures3Beyond structure: revolutionising materials discovery via AI-driven synthesis protocol-property relationships0Enhancing Generalization in Synthesizability Prediction of Structurally Dissimilar Materials0

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

43 direct citing papers saved.

  1. Metallicity in Quasar Broad-line Regions at Redshift ∼ 6

    The Astrophysical Journal · 2022-01-31

    Shu Wang, Linhua Jiang, Yue Guo Shen, Luis C. Ho, Marianne Vestergaard, Eduardo Bañados, Chris J. Willott, Jin Lei Wu, Siwei Zou, Jinyi Yang, Feige Wang, Xiaohui Fan, Xue-Bing Wu

    Read original paper ↗ ·

  2. Perovskite synthesizability using graph neural networks

    npj Computational Materials · 2022-04-20

    Geun Ho Gu, Jidon Jang, Juhwan Noh, Aron Walsh, Yousung Jung

    Read original paper ↗ ·

  3. Composable Infrastructures for an Academic Research Environment: Lessons Learned

    IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) · 2022-05-01

    Lance Long, Timothy Bargo, Luc Renambot B, Maxine D. Brown, Andrew E. Johnson

    Read original paper ↗ ·

  4. Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions

    Chemistry of Materials · 2022-08-05

    Haoyan Huo, Christopher J. Bartel, Tanjin He, Amalie Trewartha, Alexander Dunn, Bin Ouyang, Anubhav Jain, Gerbrand Ceder

    Read original paper ↗ ·

  5. Searching guidelines for scalable and controllable design of multifunctional materials and hybrid interfaces: Status and perspective

    Frontiers in Chemical Engineering · 2022-08-31

    Fernando G. Echeverrigaray, Fernando Álvarez

    Read original paper ↗ ·

  6. Into the Unknown: How Computation Can Help Explore Uncharted Material Space

    Journal of the American Chemical Society · 2022-10-07

    Austin M. Mroz, Víctor Posligua, Andrew Tarzia, Emma Helen Wolpert, Kim E. Jelfs

    Read original paper ↗ ·

  7. Machine learned synthesizability predictions aided by density functional theory

    Communications Materials · 2022-10-12

    Andrew S. Lee, Suchismita Sarker, James E. Saal, Logan T. Ward, Christopher K. H. Borg, Apurva Mehta, Christopher Wolverton

    Read original paper ↗ ·

  8. Process-Informatics-Assisted Preparation of Lithium Titanate Crystals with Various Sizes and Morphologies

    Industrial & Engineering Chemistry Research · 2022-12-27

    Daigo Kaneko, Hiromasa Kaneko, Fumitaka Hayashi, Kohei Fukaishi, Tetsuya Yamada, Katsuya Teshima

    Read original paper ↗ ·

  9. Machine learning-inspired battery material innovation

    Energy Advances · 2023-01-01

    Man‐Fai Ng, Yongming Sun, Zhi Wei Seh

    Read original paper ↗ ·

  10. Materials synthesizability and stability prediction using a semi-supervised teacher-student dual neural network

    Digital Discovery · 2023-01-01

    Daniel Gleaves, Nihang Fu, Edirisuriya M. Dilanga Siriwardane, Yong Zhao, Jianjun Hu

    Read original paper ↗ ·

  11. Model Entropy and Scientific Information Loss in Compressed Representations of Materials

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

    Sanjay Kulkarni, Meenal Joshi, Rohan Patil, Aniket Deshmukh

    Read original paper ↗ ·

  12. Drug Design and Disease Diagnosis: The Potential of Deep Learning Models in Biology

    Current Bioinformatics · 2023-02-27

    Sarojini Sreeraman, Kannan Mayuri, Raja Babu Singh Kushwah, Vickram Sundaram, Alaguraj Veluchamy, Thirunavukarasou Anand, Konda Mani Saravanan

    Read original paper ↗ ·

  13. Machine‐Learning‐Assisted Determination of the Global Zero‐Temperature Phase Diagram of Materials

    Advanced Materials · 2023-03-22

    Jonathan Schmidt, Noah Hoffmann, Hai‐Chen Wang, Pedro Borlido, Pedro J. M. A. Carriço, Tiago F. T. Cerqueira, Silvana Botti, Miguel A. L. Marques

    Read original paper ↗ ·

  14. Functional Material Systems Enabled by Automated Data Extraction and Machine Learning

    Advanced Functional Materials · 2023-06-05

    Payam Kalhor, Nicole Jung, Stefan Bräse, Christof Wöll, Manuel M. Tsotsalas, Pascal Friederich

    Read original paper ↗ ·

  15. Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions

    npj Computational Materials · 2023-08-25

    Evan R. Antoniuk, Gowoon Cheon, George Wang, Daniel Bernstein, William Cai, Evan J. Reed

    Read original paper ↗ ·

  16. Accelerating the prediction of stable materials with machine learning

    Nature Computational Science · 2023-11-09

    Sean D. Griesemer, Yi Xia, Chris M. Wolverton

    Read original paper ↗ ·

  17. AI-enabled materials discovery for advanced ceramic electrochemical cells

    Energy and AI · 2023-11-09

    Idris Temitope Bello, Ridwan Taiwo, Oladapo Christopher Esan, Adesola Habeeb Adegoke, Ahmed Olanrewaju Ijaola, Zheng Li, Siyuan Zhao, Chen Wang, Zongping Shao, Meng Ni

    Read original paper ↗ ·

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

    Communications Materials · 2023-12-08

    Ali Davariashtiyani, Sara Kadkhodaei

    Read original paper ↗ ·

  19. Predicting synthesis recipes of inorganic crystal materials using elementwise template formulation

    Chemical Science · 2023-12-08

    Seong-Min Kim, Juhwan Noh, Geun Ho Gu, Shuan Chen, Yousung Jung

    Read original paper ↗ ·

  20. Deep Learning-Ready Voxel Representation of Protein-Ligand Complexes from an Enhanced PBDbind v.2020 Dataset

    ChemRxiv · 2023-12-11

    Matheus Müller Pereira da Silva, Isabella Alvim Guedes, Fábio Lima Custódio, Laurent E. Dardenne

    Read original paper ↗ ·

  21. Machine learning for efficient grazing-exit x-ray absorption near edge structure spectroscopy analysis: Bayesian optimization approach

    Machine Learning Science and Technology · 2024-04-23

    Cafer Tufan Cakir, Can Bogoclu, Franziska Emmerling, Christina Streli, Ana Guilherme Buzanich, Martin Radtke

    Read original paper ↗ ·

  22. NanoConstruct: A web application builder of ellipsoidal nanoparticles for the investigation of their crystal growth, stability, and the calculation of atomistic descriptors

    Computational and Structural Biotechnology Journal · 2024-05-25

    Panagiotis D. Kolokathis, Dimitrios Zouraris, Evangelos Voyiatzis, Nikolaos K. Sidiropoulos, Andreas Tsoumanis, Georgia Melagraki, Kaido Tämm, Iseult Lynch, Antreas Afantitis

    Read original paper ↗ ·

  23. Synthesizability of materials stoichiometry using semi-supervised learning

    Matter · 2024-06-01

    Jidon Jang, Juhwan Noh, Lan Zhou, Geun Ho Gu, John M. Gregoire, Yousung Jung

    Read original paper ↗ ·

  24. From Design to Device: Challenges and Opportunities in Computational Discovery of p -Type Transparent Conductors

    PRX Energy · 2024-08-05

    Rachel Woods‐Robinson, Monica Morales‐Masis, Geoffroy Hautier, Andrea Crovetto

    Read original paper ↗ ·

  25. Predicting Crystalline Material Properties with AI: Bridging Molecular to Particle Scales

    Industrial & Engineering Chemistry Research · 2024-10-15

    Weiye Chen, Muyang Li, Tuo Yao, Jian Liu, Shengzhe Jia, Zhenguo Gao, Junbo Gong

    Read original paper ↗ ·

  26. Impact of data bias on machine learning for crystal compound synthesizability predictions

    Machine Learning Science and Technology · 2024-11-15

    Ali Davariashtiyani, Busheng Wang, Samad Hajinazar, Eva Zurek, Sara Kadkhodaei

    Read original paper ↗ ·

  27. Crystal synthesizability prediction using contrastive positive unlabeled learning

    Computer Physics Communications · 2024-12-07

    Tao Sun, Jianmei Yuan

    Read original paper ↗ ·

  28. Computation-guided exploration of the reaction parameter space of N , N -dimethylformamide hydrolysis

    Digital Discovery · 2025-01-01

    Ignas Pakamorė, Ross Stewart Forgan

    Read original paper ↗ ·

  29. Data-driven exploration of layered double hydroxide crystals exhibiting high fluoride ion adsorption properties and chemical stability

    CrystEngComm · 2025-01-01

    Fumitaka Hayashi, Ryuki Harada, Hiroaki Sugitani, Hiromasa Kaneko, Tien Quang Nguyen, Mongkol Tipplook, Tetsuya Yamada, Michihisa Koyama, Katsuya Teshima

    Read original paper ↗ ·

  30. Defining "Novel Material" in Generative Models: A Boundary between Chemical Validity and Synthesizability

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

    Khaled Mahfouz, Rania Abdelaziz, Sherif Adel, Dina Mostafa, Tamer Nabil, Reem Saad

    Read original paper ↗ ·

  31. SynCoTrain: a dual classifier PU-learning framework for synthesizability prediction

    Digital Discovery · 2025-01-01

    Sasan Amariamir, Janine George, Philipp Benner

    Read original paper ↗ ·

  32. Lean CNNs for Mapping Electron Charge Density Fields to Material Properties

    Integrating materials and manufacturing innovation · 2025-01-27

    Pranoy Ray, Kamal Kumar Choudhary, Surya R. Kalidindi

    Read original paper ↗ ·

  33. Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

    Angewandte Chemie International Edition · 2025-02-13

    Seong-Min Kim, Joshua Schrier, Yousung Jung

    Read original paper ↗ ·

  34. Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

    Angewandte Chemie · 2025-02-13

    Seong-Min Kim, Joshua Schrier, Yousung Jung

    Read original paper ↗ ·

  35. Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models

    Nature Communications · 2025-07-15

    Zhilong Song, Shuaihua Lu, Ming‐Gang Ju, Qionghua Zhou, Jinlan Wang

    Read original paper ↗ ·

  36. Predicting the synthesizability of inorganic crystals by bridging crystal graphs and phonon dynamics

    Chinese Physics B · 2025-09-02

    Mei Ma, Wei Ma, Le Gao, Zongguo Wang, Hao 昊 Liu 刘

    Read original paper ↗ ·

  37. Artificial intelligence for post-moore field-effect transistors: a review and perspective

    Materials Today Electronics · 2025-09-05

    Xiang Li, Nan Jiang, Wenhan Zhou, Xiaoyi Zhang, Yang Hu, Shuo Wang, Huipu Wang, Hengze Qu, Haibo Zeng, Shengli Zhang

    Read original paper ↗ ·

  38. Hybrid pathways for methane production: Merging thermodynamic insights with machine learning

    Journal of Cleaner Production · 2025-09-17

    Azita Etminan, Peter J. Holliman, Peyman Karimi, Majid Majd, Ian Mabbett, Mary Larimi, Ciarán Martin, Anna R. L. Carter

    Read original paper ↗ ·

  39. Synthesis-Aware Materials Redesign via Large Language Models

    Journal of the American Chemical Society · 2025-10-06

    Jaehwan Choi, Seong-Min Kim, Yousung Jung

    Read original paper ↗ ·

  40. Informatics for thermodynamics: Stability to synthesizability

    Elsevier eBooks · 2026-01-01

    Krishna Rajan

    Read original paper ↗ ·

  41. Bridging theory and experiment in materials discovery: Machine-learning-assisted prediction of synthesizable structures

    Physical review. B./Physical review. B · 2026-01-12

    YU Xin, P. Liu, Zhuohang Xie, Wenhui Mi, Pengyue Gao, Hong Jian Zhao, Jian Lv, Yanchao Wang, Yanming Ma

    Read original paper ↗ ·

  42. Beyond structure: revolutionising materials discovery via AI-driven synthesis protocol-property relationships

    Journal of Physics Materials · 2026-05-15

    G. Lambard

    Read original paper ↗ ·

  43. Enhancing Generalization in Synthesizability Prediction of Structurally Dissimilar Materials

    Journal of Chemical Information and Modeling · 2026-07-09

    Seongmin Kim, S Kwon, Jiwoo Choi, Yousung Jung

    Read original paper ↗ ·
Citation Atlas · A map of research, not a ranking.