BetaCitation Atlas

RESEARCH PAPER · MRS Communications · 2019

A Bayesian framework for materials knowledge systems

Surya R. Kalidindi

Read original paper ↗

See where this research leads.

29 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

A Bayesian framework for materials knowledge systemsOriginalData Infrastructure Elements in Support of Accelerated Materials Innovation: ELA, PyMKS, and MATIN21Design space visualization for guiding investments in biodegradable and sustainably sourced materials7Feature engineering of material structure for AI-based materials knowledge systems70Mining the Correlations Between Optical Micrographs and Mechanical Properties of Cold-Rolled HSLA Steels Using Machine Learning Approaches26Reduced-Order Models for Ranking Damage Initiation in Dual-Phase Composites Using Bayesian Neural Networks10A machine learning framework for the temporal evolution of microstructure during static recrystallization of polycrystalline materials simulated by cellular automaton37Biomaterialomics: Data-Driven Pathways to Next-Generation Biomaterials3Recurrent localization networks applied to the Lippmann-Schwinger equation10Materials graph ontology23Reconstructing random heterogeneous media through differentiable optimization34Machine learning approaches for feature engineering of the crystal structure: Application to the prediction of the formation energy of cubic compounds17Reduced Order Multiscale Simulation of Diffuse Damage in Concrete13Bayesian Optimization in Materials Science: A Survey1Bayesian Calibration of Multiple Coupled Simulation Models for Metal Additive Manufacturing: A Bayesian Network Approach17Efficient Exploration of Microstructure-Property Spaces via Active Learning17Biomaterialomics: Data science-driven pathways to develop fourth-generation biomaterials134Self-supervised optimization of random material microstructures in the small-data regime19Establishing reduced-order process-structure linkages from phase field simulations of dendritic grain growth during solidification6Graph neural networks for efficient learning of mechanical properties of polycrystals65Voxelized Representations of Atomic Systems for Machine Learning Applications2Gaussian process autoregression models for the evolution of polycrystalline microstructures subjected to arbitrary stretching tensors12A new framework for the assessment of model probabilities of the different crystal plasticity models for lamellar grains in α+β Titanium alloys11Voxelized atomic structure framework for materials design and discovery7Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery31High-Throughput Experiments and Machine Learning Strategies for Efficient Exploration of Additively Manufactured Inconel 62529Optimizing machine learning yield functions using query-by-committee for support vector classification with a dynamic stopping criterion11A brief review of machine learning-assisted Mg alloy design, processing, and property predictions38Physics descriptors enhanced Bayesian learning method for permeability of random media under sparse data4Rapid Development of Metal Additive Manufacturing Using Artificial Intelligence/Machine Learning and High-Throughput Material Testing18

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

29 direct citing papers saved.

  1. Data Infrastructure Elements in Support of Accelerated Materials Innovation: ELA, PyMKS, and MATIN

    Integrating materials and manufacturing innovation · 2019-10-10

    Surya R. Kalidindi, Ali Khosravani, Berkay Yucel, Apaar Shanker, Aleksandr L. Blekh

    Read original paper ↗ ·

  2. Design space visualization for guiding investments in biodegradable and sustainably sourced materials

    MRS Communications · 2020-01-14

    James S. Peerless, Emre Sevgen, Stephen D. Edkins, Jason Koeller, Edward Kim, Yoolhee Kim, Astha Gargt, Erin Antono, Julia Ling

    Read original paper ↗ ·

  3. Feature engineering of material structure for AI-based materials knowledge systems

    Journal of Applied Physics · 2020-07-28

    Surya R. Kalidindi

    Read original paper ↗ ·

  4. Mining the Correlations Between Optical Micrographs and Mechanical Properties of Cold-Rolled HSLA Steels Using Machine Learning Approaches

    Integrating materials and manufacturing innovation · 2020-09-01

    Berkay Yucel, Sezen Yucel, A. Ray, Lode Duprez, Surya R. Kalidindi

    Read original paper ↗ ·

  5. Reduced-Order Models for Ranking Damage Initiation in Dual-Phase Composites Using Bayesian Neural Networks

    JOM · 2020-10-13

    Aditya Venkatraman, David Montes de Oca Zapiain, Surya R. Kalidindi

    Read original paper ↗ ·

  6. A machine learning framework for the temporal evolution of microstructure during static recrystallization of polycrystalline materials simulated by cellular automaton

    Computational Materials Science · 2020-11-12

    Sepideh Hashemi, Surya R. Kalidindi

    Read original paper ↗ ·

  7. Biomaterialomics: Data-Driven Pathways to Next-Generation Biomaterials

    SSRN Electronic Journal · 2021-01-01

    Bikramjit Basu, N. H. Gowtham, Yang Xiao, Surya R. Kalidindi, Kam W. Leong

    Read original paper ↗ ·

  8. Recurrent localization networks applied to the Lippmann-Schwinger equation

    Computational Materials Science · 2021-03-07

    Conlain Kelly, Surya R. Kalidindi

    Read original paper ↗ ·

  9. Materials graph ontology

    Materials Letters · 2021-04-09

    Sven P. Voigt, Surya R. Kalidindi

    Read original paper ↗ ·

  10. Reconstructing random heterogeneous media through differentiable optimization

    Computational Materials Science · 2021-06-01

    Paul Seibert, Marreddy Ambati, Alexander Raßloff, Markus Kästner

    Read original paper ↗ ·

  11. Machine learning approaches for feature engineering of the crystal structure: Application to the prediction of the formation energy of cubic compounds

    Physical Review Materials · 2021-06-02

    Prathik R. Kaundinya, Kamal Kumar Choudhary, Surya R. Kalidindi

    Read original paper ↗ ·

  12. Reduced Order Multiscale Simulation of Diffuse Damage in Concrete

    Materials · 2021-07-08

    Giao Vu, Fabian Diewald, Jithender Jaswant Timothy, Christoph Gehlen, Günther Meschke

    Read original paper ↗ ·

  13. Bayesian Optimization in Materials Science: A Survey

    arXiv (Cornell University) · 2021-07-29

    Lars Kotthoff, Hud Wahab, Patrick Alfred Johnson

    Read original paper ↗ ·

  14. Bayesian Calibration of Multiple Coupled Simulation Models for Metal Additive Manufacturing: A Bayesian Network Approach

    ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering · 2021-08-31

    Jiahui Ye, Mohamad Mahmoudi, Kübra Karayağız, Luke Johnson, Raiyan Seede, İbrahim Karaman, Raymundo Arróyave, Alaa H. Elwany

    Read original paper ↗ ·

  15. Efficient Exploration of Microstructure-Property Spaces via Active Learning

    Frontiers in Materials · 2022-02-14

    Lukas Morand, Norbert Link, Tarek Iraki, Johannes Dornheim, Dirk Helm

    Read original paper ↗ ·

  16. Biomaterialomics: Data science-driven pathways to develop fourth-generation biomaterials

    Acta Biomaterialia · 2022-02-22

    Bikramjit Basu, N. H. Gowtham, Yang Xiao, Surya R. Kalidindi, Kam W. Leong

    Read original paper ↗ ·

  17. Self-supervised optimization of random material microstructures in the small-data regime

    npj Computational Materials · 2022-03-21

    Maximilian Rixner, Phaedon‐Stelios Koutsourelakis

    Read original paper ↗ ·

  18. Establishing reduced-order process-structure linkages from phase field simulations of dendritic grain growth during solidification

    Computational Materials Science · 2022-08-03

    Jiajun Zhao, Junjie Li, Xiaobing Hu, Yujian Wang, Yiming Chen, Feng He, Zhijun Wang, Zhanglong Zhao, Jincheng Wang

    Read original paper ↗ ·

  19. Graph neural networks for efficient learning of mechanical properties of polycrystals

    Computational Materials Science · 2022-11-12

    Jonathan M. Hestroffer, Marie‐Agathe Charpagne, Marat I. Latypov, Irene J. Beyerlein

    Read original paper ↗ ·

  20. Voxelized Representations of Atomic Systems for Machine Learning Applications

    Challenges and advances in computational chemistry and physics · 2023-01-01

    Matthew C. Barry, Satish Kumar, Surya R. Kalidindi

    Read original paper ↗ ·

  21. Gaussian process autoregression models for the evolution of polycrystalline microstructures subjected to arbitrary stretching tensors

    International Journal of Plasticity · 2023-01-19

    Sepideh Hashemi, Surya R. Kalidindi

    Read original paper ↗ ·

  22. A new framework for the assessment of model probabilities of the different crystal plasticity models for lamellar grains in α+β Titanium alloys

    Modelling and Simulation in Materials Science and Engineering · 2023-04-18

    Aditya Venkatraman, Soumya Mohan, V. Roshan Joseph, David L. McDowell, Surya R. Kalidindi

    Read original paper ↗ ·

  23. Voxelized atomic structure framework for materials design and discovery

    Computational Materials Science · 2023-08-24

    Matthew C. Barry, Jacob R. Gissinger, Michael Chandross, Kristopher E. Wise, Surya R. Kalidindi, Satish Kumar

    Read original paper ↗ ·

  24. Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery

    Patterns · 2023-11-01

    Xiaoning Qian, Byung-Jun Yoon, Raymundo Arróyave, Xiaofeng Qian, Edward R. Dougherty

    Read original paper ↗ ·

  25. High-Throughput Experiments and Machine Learning Strategies for Efficient Exploration of Additively Manufactured Inconel 625

    Acta Materialia · 2024-01-01

    Zachary S. Courtright, Aditya Venkatraman, Berkay Yucel, Venkata Surya Karthik Adapa, Abel A. Diaz, Surya R. Kalidindi

    Read original paper ↗ ·

  26. Optimizing machine learning yield functions using query-by-committee for support vector classification with a dynamic stopping criterion

    Computational Mechanics · 2024-02-12

    Ronak Shoghi, Lukas Morand, Dirk Helm, Alexander Hartmaier

    Read original paper ↗ ·

  27. A brief review of machine learning-assisted Mg alloy design, processing, and property predictions

    Journal of Materials Research and Technology · 2024-05-01

    Yanhui Cheng, Lifei Wang, Chaoyang Yang, Yunli Bai, Hongxia Wang, Weili Cheng, Hanuma Reddy Tiyyagura, Alexander A. Komissarov, Kwang Seon Shin

    Read original paper ↗ ·

  28. Physics descriptors enhanced Bayesian learning method for permeability of random media under sparse data

    Engineering Applications of Artificial Intelligence · 2025-01-06

    Hang Qi, Xiaofei Guan, Qing Chen, Zhengwu Jiang, Fang Liu, Jieqiong Zhang, Hehua Zhu

    Read original paper ↗ ·

  29. Rapid Development of Metal Additive Manufacturing Using Artificial Intelligence/Machine Learning and High-Throughput Material Testing

    Annual Review of Materials Research · 2025-04-04

    Venkata Surya Karthik Adapa, Surya R. Kalidindi, Christopher Saldaña

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