1. Microstructure Characterization and Reconstruction in Python: MCRpy (this opens in a new tab) | Paul Seibert et al. |
2. CrabNet for Explainable Deep Learning in Materials Science: Bridging the Gap Between Academia and Industry (this opens in a new tab) | Anthony Yu-Tung Wang et al. |
3. A Comparison of Statistically Equivalent and Realistic Microstructural Representative Volume Elements for Crystal Plasticity Models (this opens in a new tab) | Fatemeh Azhari et al. |
4. Random Generation of Lattice Structures with Short-Range Order (this opens in a new tab) | Lauren T. W. Fey, Irene J. Beyerlein |
5. A Machine Learning Strategy for Race-Tracking Detection During Manufacturing of Composites by Liquid Moulding (this opens in a new tab) | Joaquín Fernández-León et al. |
6. On the Prediction of Uniaxial Tensile Behavior Beyond the Yield Point of Wrought and Additively Manufactured Ti-6Al-4V (this opens in a new tab) | Maria J. Quintana, Andrew J. Temple, D. Gary Harlow, Peter C. Collins |
7. Ontopanel: A Tool for Domain Experts Facilitating Visual Ontology Development and Mapping for FAIR Data Sharing in Materials Testing (this opens in a new tab) | Yue Chen et al. |
8. Compound Knowledge Graph-Enabled AI Assistant for Accelerated Materials Discovery (this opens in a new tab) | Kareem S. Aggour et al. |
9. Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design (this opens in a new tab) | Yuwei Mao et al. |
10. Bi-directional Scan Pattern Effects on Residual Stresses and Distortion in As-built Nitinol Parts: A Trend Analysis Simulation Study (this opens in a new tab) | Medad C. C. Monu et al. |
11. PRISMS-Plasticity TM: An Open-Source Rapid Texture Evolution Analysis Pipeline (this opens in a new tab) | Mohammadreza Yaghoobi, John E. Allison, Veera Sundararaghavan |
12. Consistent Quantification of Precipitate Shapes and Sizes in Two and Three Dimensions Using Central Moments (this opens in a new tab) | Felix Schleifer et al. |
13. Quantitative Benchmarking of Acoustic Emission Machine Learning Frameworks for Damage Mechanism Identification (this opens in a new tab) | C. Muir et al. |
14. Computational Alloy Design for Process-Related Uncertainties in Powder Metallurgy (this opens in a new tab) | T. T. Molla, A. Atthapreyangkul, G. B. Schaffer |
15. Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Toward ML-Assisted Advanced Manufacturing (this opens in a new tab) | Scott Howland et al. |
16. On the Fidelity of the Scaling Laws for Melt Pool Depth Analysis During Laser Powder Bed Fusion (this opens in a new tab) | M. Naderi, J. Weaver, D. Deisenroth, N. Iyyer, R. McCauley |
17. A Novel Methodology for the Thermographic Cooling Rate Measurement during Powder Bed Fusion of Metals Using a Laser Beam (this opens in a new tab) | David L. Wenzler et al. |
18. A Framework for the Optimal Selection of High-Throughput Data Collection Workflows by Autonomous Experimentation Systems (this opens in a new tab) | Rohan Casukhela, Sriram Vijayan, Joerg R. Jinschek, Stephen R. Niezgoda |
19. Quantifying Dynamic Signal Spread in Real-Time High-Energy X-ray Diffraction (this opens in a new tab) | Daniel P. Banco et al. |
20. Temperature-Dependent Material Property Databases for Marine Steels—Part 3: HSLA-80 (this opens in a new tab) | Jennifer K. Semple, Daniel H. Bechetti, Wei Zhang, Charles R. Fisher |
21. 3D Minimum Channel Width Distribution in a Ni-Base Superalloy (this opens in a new tab) | Moritz Müller, Bernd Böttger, Felix Schleifer, Michael Fleck, Uwe Glatzel |
22. Feature Engineering for Microstructure–Property Mapping in Organic Photovoltaics (this opens in a new tab) | Sepideh Hashemi et al. |
23. Calcium-Treated Steel Cleanliness Prediction Using High-Dimensional Steelmaking Process Data (this opens in a new tab) | Stephano Piva et al. |
24. Physics-Informed Machine Learning and Uncertainty Quantification for Mechanics of Heterogeneous Materials (this opens in a new tab) | B. V. S. S. Bharadwaja et al. |
25. Computational Efficient Modeling of Supersolidus Liquid Phase Sintering in Multi-component Alloys for ICME Applications (this opens in a new tab) | Tesfaye T. Molla |