Events
October 2024: D3OM2S Annual Review
January 2024: Y. Chi (CMU), R. Jayan (CMU), and J. Simmons (AFRL) co-organized minisymposium on “The Intersection of Computational Imaging and Materials Science” at IS&T Electronic Imaging Symposium
July 2023: CMU/RX/AFOSR Joint Workshop at the Intersection of Materials Science and Machine Learning
Publications
Gourley, A.; Quirarte, G.; Malen, J.; Beuth, J.; Reeja-Jayan B. WC-Ni cemented carbide laser powder bed fusion parameter selection informed by thermomechanical modeling. Advanced Materials, under review (2025).
Zadan, M.; Hu, Y.; Lipp, J.; Lewis, N.; Shah, D.; Islam, M. F.; Nepal, D.; Grasinger, M.; Dayal, K.; Tabor, C.; Majidi, C. MXene-Coated Liquid Metal Nanodroplets. Langmuir, under review (2024).
Efimov, T.; Dong, H.; Shah, M.; Simmons, J.; Donegan, S.; Chi, Y. Leveraging Multimodal Diffusion Models to Accelerate Imaging with Side Information, Computer Vision and Pattern Recognition, (2025), arXiv:2410.05143
Varley, Z.; Rohrer, G. S.; De Graef, M.; Dynamic sampling for scanned microscopy modalities. Materials Characterization 2024, 211, 113865.
Gourley, A.; Kaufman, J.; Aman, B.; Schwalbach, E.; Beuth, J.; Rueschhoff, L.; Reeja-Jayan, B. Spreading anomaly semantic segmentation and 3D reconstruction of binder jet additive manufacturing powder bed images. The International Journal of Advanced Manufacturing Technology 2024, 134, 3139-3151.
Ferguson, K.; Gillman, A.; Hardin, J.; Kara, L. B. Scalar Field Prediction on Meshes Using Interpolated Multiresolution Convolutional Neural Networks. Journal of Applied Mechanics 2024, 91, 101002.
Abdullah, A.; Ferguson, K.; Drummy, L.; Kara, L. B.; Bockstaller, M. R. Evaluation of Information Content in 2D and 3D Microstructural Characterization of Brush Particle-Based Hybrid Materials. Electronic Imaging 2024, 36, 1-7.
D. A. Boiko, R. MacKnight, B. Kline, G. Gomes "Autonomous chemical research with large language models", Nature 2024, 624, 570-578.
Katelyn Jones, William D. Musinski, Adam L. Pilchak, Reji John, Paul A. Shade, Anthony D. Rollett, Elizabeth A. Holm, “Predicting Fatigue Crack Growth Metrics from Fractographs: Towards Fractography by Computer Vision”, Int. J. Fatigue, 2023, 177 107915.
Dong, H., Donegan, S., Shah, M., & Chi, Y. A lightweight transformer for faster and robust EBSD data collection. Scientific Reports, 2023 13, 21253.
Dhriti Nepal, Saewon Kang, Katarina M Adstedt, Krishan Kanhaiya, Michael R Bockstaller, L Catherine Brinson, Markus J Buehler, Peter V Coveney, Kaushik Dayal, Jaafar A El-Awady, Luke C Henderson, David L Kaplan, Sinan Keten, Nicholas A Kotov, George C Schatz, Silvia Vignolini, Fritz Vollrath, Yusu Wang, Boris I Yakobson, Vladimir V Tsukruk, Hendrik Heinz, Hierarchically structured bioinspired nanocomposites. Nature Materials, 2023, 22, 18-35.
C. E. Krill III, E. A. Holm, J. M. Dake, R. Cohn, K. Holikov, F. Andorfer, Extreme Abnormal Grain Growth: Connecting Mechanisms to Microstructural Outcomes, 2023. 53:1.
R. Cohn, Computer vision and deep learning for microstructural modeling and automated characterization of materials, PhD thesis, Carnegie Mellon University, 2022.
K. Choudhary, B. DeCost, C. Chen, A. Jain, F. Tavazza, R. Cohn, C. WooPark, A. Choudhary, A. Agrawal, S. J. L. Billinge, E. Holm, S. P. Ong, C. Wolverton, Recent Advances and Applications of Deep Learning Methods in Materials Science, npj Comput. Mater. 2022, 8, 59, 1-26.
Cohn, R., Anderson, I., Prost, T., Tiarks, J., White, E., & Holm, E. Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data. JOM, 2021, 73, 2159–2172.
Cohn, R., & Holm, E. Unsupervised Machine Learning Via Transfer Learning and k-Means Clustering to Classify Materials Image Data. Integrating Materials and Manufacturing Innovation, 2020, 10, 231–244.
Presentations
Grasinger, M. Joint Chemical Engineering and Manufacturing Engineering Graduate Seminar, Brigham Young University, (2024).
Grasinger, M. Micromechanics of Composites with High Surface Energy Inclusions. AMSE DESS, (2024).
Bockstaller, M. R. Facile Design Strategies for Hyperuniform Polymer Hybrid Materials, American Conference on Neutron Scattering, Oak Ridge National Laboratory, 2024.
Rollett, A. D. Computational Tools for Advancing Materials Maturity Level in Additive Manufacturing, Invited lecture at MS&T 2024, Pittsburgh, PA.
Jones, K.; Shade, P.; John, R.; Golden, P.; Holm, E.; Rollett, A. Using Unsupervised Learning to Cluster Fatigue Life Based on Ti64 Fatigue Fracture Surface Characteristics. MS&T 2024, Pittsburgh, PA.
Rollett, A. D. Experience at CMU with Metals Additive Manufacturing, Qual. & Cert., Co-Design, Seminar at Imperial College, 2024, London, UK
Majidi, C. Liquid Metal – Polymer Composite Films for Soft Electron-ics and Thermal Management,” ACS Central Regional Meeting, (2024).
Majidi, C. Departmental Seminar, Mechanical & Aerospace Engineering, University of Virginia, (2024).
Bockstaller, M. R. Predicting PPP-Relations in Polymer Hybrid Materials Using ML-Regression Methods, Sandia National Laboratory, Albuquerque, 2024.
Jayan, R. Yes, You can Microwave Metails & Other Tales at the Interface of Materials and Electromagnetics, Seminar, Materials Science Engineering, Boston University, 2024.
Varley, Z.; De Graef, M.; Rohrer, G.; Shah, M.; Uchic, M. Patch-wise Canonical Correlation Analysis in SEM: Advancing 3D Serial Sectioning Image Registration. Accelerating Discovery for Mechanical Behavior of Materials, 2024, Cleveland, OH.
Chi, Y. Score-based Diffusion Models: Data Generation and Inverse Problems, Keynote at IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2024, London, UK.
Varley, Z.; De Graef, M.; Rohrer, G. EBSD Geometry Calibration Through SE(3) Lie Group Optimization, MS&T 2024, Pittsburgh, PA.
Majidi, C. Departmental Seminar, Mechanical Engineering, Vanderbilt University, (2024).
Gourley, A.; Kaufman, J.; Aman, B.; Schwalbach, E.; Beuth, J.; Rueschhoff, L.; Reeja-Jayan, B. Identification of Binder Jet Spreading Anomalies Through Semantic Segmentation. MS&T 2024, Pittsburgh, PA.
Chi, Y. Solving Inverse Problems with Generative Priors: From Low-rank to Diffusion Models, Keynote at NIST/IEEE Conference on Computational Imaging Using Synthetic Apertures (CISA), 2024, Boulder, CO.
Gourley, A.; Kaufman, J.; Aman, B.; Schwalbach, E.; Beuth, J.; Rueschhoff, L.; Reeja-Jayan, B. Identification of Binder Jet Spreading Anomalies Through Semantic Segmentation. Accelerating Discovery for Mechanical Behavior of Materials, 2024, Cleveland, OH.
Ferguson, K.; Kara, L. B.; Abdullah, A.; Bockstaller, M. R.; Drummy, L.; Harper, E.; Gillman, A. Machine Learning Enhanced Prediction of Properties of Nanoparticle Hybrid Materials. Accelerating Materials Science with AI, 2024, Baltimore, MD.
Majidi, C. Macromolecules Innovation Institute, Virginia Tech, (2024).
Dayal, K. Transforming Materials and Structures, SIAM Mathematics and Materials Meeting, (2024).
Jones, K.; Shade, P.; Rollett, A. D. “Using Unsupervised Learning to Cluster Fatigue Life Based on Small Crack Characteristics” Electronic Imaging, Burlingame, CA, 16 Jan. 2024.
Varley, Z. “Local entropy key points for materials science image collation”, IS&T Electronic Imaging 2024.
Dong, H. “Machine Learning for Scientific Imaging”, IS&T Electronic Imaging, 2024.
Abdullah, A., Drummy, L.; Bockstaller, M. R. Evaluation of Information Content in 2D and 3D Microstructural Characterization of Brush Particle-Based Hybrid Materials, IS&T Electronic Imaging, 2024.
Gourley, A.; Neopane, O. Lunch & Learn, Carnegie Mellon University, December 2023.
Jayan, R. B.; Singh, A. Machine Learning and Materials: Getting it all to Work Together, Keynote Speaker, Air Force Research Laboratory (AFRL)/Air Force Office of Scientific Research (AFOSR), Joint Workshop at the Intersection of Materials Science and Machine Learning, 2023.
Gourley, A. Laser Powder Bed Fusion of Tungsten Carbide-Nickel Geometries Leveraging Thermomechanical Modeling, Oct. 2nd, 2023.
Gourley, A. Adaptive Experimentation for Ceramic AM: Binder Jet Parameter Selection and Powder Spreadability. Air Force Research Laboratory, Jan. 9th, 2024.
Jones, K.; Musinski, W. D.; Pilchak, A. L.; John, R.; Shade, P. A.; Rollett, A. D.; Holm, E. A. “Predicting Fatigue Crack Growth Metrics from Fractographs: Towards Fractography by Computer Vision”, ASM Pittsburgh Young Members Night, Dec. 2023, Invited talk.
Jones, K; Shade, P.; Rollett, A. D. “Using Unsupervised Learning to Cluster Fatigue Life Based on Small Crack Characteristics” Pacific Rim International Conference on Advance Materials and Processing, Jeju Island, Korea, 21 Nov. 2023.
Jones, Katelyn, “Using Unsupervised Learning to Cluster Fatigue Life Based on Small Crack Characteristics” Materials Science & Technology, Columbus, OH, 03 Oct. 2023.
Jones, Katelyn, Shade, Paul, et al. “Using Unsupervised Learning to Cluster Fatigue Life Based on Small Crack Characteristics” Gordon Research Seminar (GRS) and Gordon Research Conference (GRC), Stone Hill College, 8 July 2023, Invited Conference Presentation at GRS and Poster Presentation at GRC.
Chi, Y. Provable Learning from Data with Priors: from Low-rank to Diffusion Models, Keynote at Conference on the Mathematics of Deep Learning (DeepMath), 2023, Baltimore, MD.
Bockstaller, M. R.; Abdullah, A.; Zhao, Y.; Lee, J.; Wang, Z.; Matyjaszewski, K.; Ferguson, K.; Kara, L. B.; Harper, E.; Drummy, L. Elucidation of chain dispersity effect on structure and mechanical properties of brush particle solids. APS March Meeting 2023, W05.
Abdullah, A.; Lee, J.; Zhao, Y.; Wang, Z.; Bockstaller, M. Molecular Weight Dispersity as Design Parameter to Enable Brush Particle Hybrid Materials with Enhanced Fracture Toughness and Inorganic Content. APS March Meeting 2022, D18. 010
Fiorino, A.; Neopane, O.; Singh, A. Gaussian Processes for Episodic Experimental Design, ICML2022 Workshop on Adaptive Experimental Design and Active Learning in the Real World.
Gourley, A.; Reeja-Jayan, B.; Beuth, J. Modeling and Monitoring of Thermal Accumulation During Laser Powder Bed Fusion of Cemented Carbides, MS&T 2022, Oct. 2022, Pittsburgh, PA.
Cohn, R.; Holm, E. A. Cloud-native deep learning approach for predicting microstructural evolution in materials processing simulations (Keynote), 10th International Conference on Multiscale Materials Modeling (MMM10), Baltimore, MD, 2022.
Cohn, R.; Holm, E. A. High-throughput Machine Learning Experiments with Graph Neural Networks for Predicting Abnormal Grain Growth in Polycrystalline Materials, Materials Science & Technology 2022 (MS&T22), Pittsburgh, PA, 2022.
Jones, K.; Holm, E. A.; Rollett, A. D. Multimodal Data of Fatigue Fracture Surfaces for Analysis in a CNN, Materials Science & Technology 2022 (MS&T22), Pittsburgh, PA, 2022.
Jones, K.; Shade, P.; Musinski, W.; John, R.; Rollett, A. D.; Holm, E. A. Combining Multimodal Data of Fatigue Fracture Surfaces for Analysis in a CNN, 19th U.S. National Congress on Theoretical and Applied Mechanics (USNCTAM2022), Austin, TX, June 2022.
Cohn, R.; Holm, E. A. AMPIS: Automated Materials Particle Instance Segmentation, First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM2022), Pittsburgh, PA, 2022.
Jones, K.; Rollett, A. D.; Holm, E. A. Combining Multimodal Data of Fatigue Fracture Surfaces for Analysis in a CNN, First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM2022), Pittsburgh, PA, 2022.
Cohn, R.; Holm, E. A. Neural Message Passing for Prediction of Abnormal Grain Growth in Monte Carlo Simulations of Materials Processing, First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM2022), Pittsburgh, PA, 2022.
Jones, K.; Holm, E. A.; Rollett, A. D. Combining Multimodal Data of Fatigue Fracture Surfaces for Analysis in a CNN, TMS Annual Meeting 2022, Anaheim, CA, 2022.
Cohn, R.; Holm, E. A. Neural Message Passing for Prediction of Abnormal Grain Growth in Monte Carlo Simulations of Polycrystalline Materials, TMS Annual Meeting 2022, Anaheim, CA, 2022.
Holm, E. A. Making the most of what we’ve got: Designing microstructural data sets for AI applications (Invited), TMS Annual Meeting 2022, Anaheim, CA, 2022.
Holm, E. A. Computer vision in materials science (Plenary), Workshop on AI for Design and Manufacturing (ADAM), 36th AAAI Conference on Artificial Intelligence (AAAI22), virtual, February 2022.
Holm, E. A.; Daly, S. Computer vision for materials science (Online Course), TMS Online Course: Artificial Intelligence in Materials Science and Engineering, virtual, November 2021.
Jones, K.; Shade, P.; Musinski, W.; John, R.; Pilchak, A.; Rollett, A. D.; Holm, E. Discovering the structural signature of fatigue crack growth rate using computer vision and machine learning, Materials Science & Technology 2021 (MS&T21), Columbus, OH 2021.
Holm, E. A. New roles for data science in materials science (Plenary), SIAM Conference on Mathematical Aspects of Materials Science (MS21), virtual, May 2021.
Holm, E. A.; Lei, B.; Jones, K.; Cohn, R.; Gao, N.; Data science approaches for microstructure-property connections in structural materials (Invited), TMS Annual Meeting 2021, virtual, March 2021.
Jones, K.; Musinski, W.; Pilchak, A.; John, R.; Shade, P.; Rollett, A.D.; Holm, E. Discovering the structural signature of fatigue crack growth rate using computer vision and machine learning, TMS Annual Meeting 2021, virtual, March 2021.
Cohn, R.; Shah, M.; Pilchak, A.; Payton, E.; Rollett, A. D.; Holm, E. Machine learning approach to understanding abnormal grain growth, TMS Annual Meeting 2021, virtual, March 2021.
Holm, E. A. Computer vision and machine learning for image data in materials science (Invited), IS&T Electronic Imaging 2021, virtual, January 2021.
Holm, E. A. Computer vision and machine learning for microstructural image data (Invited), Materials Science & Technology 2020 (MS&T20), virtual, November 2020.
Jones, K.; Holm, E.; Rollett, A. D.; Identifying Crack Initiation Sites with CNNs, Materials Science & Technology 2020 (MS&T20), virtual, November 2020.
Holm, E. A. MRS/ASM Webinar: Machine Learning for Microstructural Data (Host), October 2020. 489 attendees from 37 countries.
Holm, E. A. Microstructural characterization and analysis using computer vision and machine learning (Invited), International Materials Applications & Technologies (IMAT2020), virtual, October 2020.
Monardo, V.; Iyer, A.; Donegan, S.; Graef, M.D.; Chi, Y. Plug-And-Play Image Reconstruction Meets Stochastic Variance-Reduced Gradient Methods, 2021 IEEE International Conference on Image Processing (ICIP), 2021, pp. 2868-2872, doi: 10.1109/ICIP42928.2021.9506021.
Varley, Z.; Rohrer, G.; De Graef, M. (Apr. 3-6, 2022) Semi-supervised Dynamic Sampling for 3D Electron Backscatter Diffraction, AIM 2022 Conference, Pittsburgh, PA, United States
Varley, Z.; Rohrer, G.; De Graef, M. (Oct. 9-12, 2022) Sparse Sampling for 3D Electron Backscatter Diffraction, MS&T 2022 Conference, Pittsburgh, PA, United States