A comprehensive Ai-driven framework for predictive design of grained materials: Case study on nanoglass
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Machine Learning in Materials Science · Injection Molding Process and Properties · Mineral Processing and Grinding
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (61)
Show source-provided references
1. Discovering and understanding materials through computation10.1038/s41563-021-01015-1 ↗
2. Modeling process–structure–property relationships in metal additive manufacturing: a review on physics-driven versus data-driven approaches10.1088/2515-7639/abca7b ↗
3. Artificial intelligence and machine learning in design of mechanical materials10.1039/D0MH01451F ↗
4. Machine learning in additive manufacturing: a review10.1007/s11837-020-04155-y ↗
5. Machine-learning-assisted de novo design of organic molecules and polymers: opportunities and challenges
6. Machine learning for glass science and engineering: a review10.1016/j.jnoncrysol.2019.04.039 ↗
7. Machine learning for composite materials10.1557/mrc.2019.32 ↗
8. Machine learning for molecular and materials science10.1038/s41586-018-0337-2 ↗
9. Materials informatics10.1007/s10845-018-1392-0 ↗
10. Emerging materials intelligence ecosystems propelled by machine learning10.1038/s41578-020-00255-y ↗
11. Random forests10.1023/A:1010933404324 ↗
12. Support-vector networks10.1023/A:1022627411411 ↗
13. Machine learning-assisted discovery of strong and conductive Cu alloys: data mining from discarded experiments and physical features10.1016/j.matdes.2020.109248 ↗
14. Predicting the Young’s Modulus of Silicate Glasses using High-Throughput Molecular Dynamics Simulations and Machine Learning
15. Machine learning directed search for ultraincompressible, superhard materials10.1021/jacs.8b02717 ↗
16. A machine learning based plasticity model using proper orthogonal decomposition10.1016/j.cma.2020.113008 ↗
17. A machine learning approach to fracture mechanics problems10.1016/j.actamat.2020.03.016 ↗
18. Exploring elastoplastic constitutive law of microstructured materials through artificial neural network—A mechanistic-Based data-Driven approach10.1115/1.4047208 ↗
19. D.A. Forsyth, J.L. Mundy, V. di Gesú, R. Cipolla, Shape, contour and grouping in computer vision, 1681 (1999). https://doi.org/10.1007/3-540-46805-6.10.1007/3-540-46805-6 ↗
20. S. Amari, M.A. Arbib, eds., Competition and cooperation in neural nets, 45 (1982). https://doi.org/10.1007/978-3-642-46466-9.10.1007/978-3-642-46466-9 ↗
21. Generative adversarial nets
22. M. Mirza, S. Osindero, Conditional Generative Adversarial Nets, (2014). https://arxiv.org/abs/1411.1784v1 (accessed December 15, 2024).
23. Learning structured output representation using deep conditional generative models
24. Auto-encoding variational bayes
25. Topology design with conditional generative adversarial networks
26. Artificial intelligence design algorithm for nanocomposites optimized for shear crack resistance10.1088/2399-1984/ab36f0 ↗
27. De novo composite design based on machine learning algorithm10.1016/j.eml.2017.10.001 ↗
28. Accelerated search and design of stretchable graphene Kirigami using machine learning10.1103/PhysRevLett.121.255304 ↗
29. Deep generative design: integration of topology optimization and generative models10.1115/1.4044229 ↗
30. IH-GAN: a conditional generative model for implicit surface-based inverse design of cellular structures10.1016/j.cma.2022.115060 ↗
31. DiffMat: data-driven inverse design of energy-absorbing metamaterials using diffusion model10.1016/j.cma.2024.117440 ↗
32. Univariate conditional variational autoencoder for morphogenic pattern design in frontal polymerization-based manufacturing10.1016/j.cma.2025.117848 ↗
33. I.J. Beyerlein, S. Xu, J. LLorca, J.A. El-Awady, J.R. Mianroodi, B. Svendsen, Alloy design for mechanical properties: conquering the length scales, (2019). https://arxiv.org/abs/1904.04569v1 (accessed December 22, 2024).
34. Computational homogenization of polycrystals10.1016/bs.aams.2018.07.001 ↗
35. M. Hasan, P. Acar, Microstructure-sensitive stochastic design of polycrystalline materials for quasi-isotropic properties, Doi.Org/10.2514/1.J061788 60 (2022) 6869–6880. https://doi.org/10.2514/1.J061788.10.2514/1.J061788 ↗
36. Advanced deep learning framework for multi-scale prediction of mechanical properties from microstructural features in polycrystalline materials10.1016/j.cma.2025.117844 ↗
37. Structure and Properties of Nanoglasses10.1002/adem.201800404 ↗
38. Modified atomic structure in a Pd Fe Si nanoglass10.1016/0022-3093(89)90007-0 ↗
39. Nanoglasses: a new kind of noncrystalline materials10.3762/bjnano.4.61 ↗
40. Nanostructured solids – From nano-glasses to quantum transistors10.1016/j.nantod.2014.02.008 ↗
41. Microstructure formation of metallic nanoglasses: insights from molecular dynamics simulations10.1016/j.actamat.2017.12.014 ↗
42. Composition and grain size effects on the structural and mechanical properties of CuZr nanoglasses10.1063/1.4891450 ↗
43. Influence of grain size and composition, topology and excess free volume on the deformation behavior of Cu-Zr nanoglasses10.3762/bjnano.6.56 ↗
44. Atomistic simulations of nanoindentation on nanoglasses: effects of grain size and gradient microstructure on the mechanical properties10.1016/j.intermet.2022.107782 ↗
45. Influence of topological structure and chemical segregation on the thermal and mechanical properties of Pd–Si nanoglasses10.1016/j.actamat.2020.03.021 ↗
46. Chord-length distribution function for two-phase random media10.1103/PhysRevE.47.2950 ↗
47. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales10.1016/j.cpc.2021.108171 ↗
48. Process-structure-mechanical property relationships in Cu-Zr nanoglass: insights from molecular dynamics10.1016/j.mechmat.2025.105274 ↗
49. Computational microstructure characterization and reconstruction: review of the state-of-the-art techniques10.1016/j.pmatsci.2018.01.005 ↗
50. Application of chord length distributions and principal component analysis for quantification and representation of diverse polycrystalline microstructures10.1016/j.matchar.2018.09.020 ↗
51. Large-scale 3D random polycrystals for the finite element method: generation, meshing and remeshing10.1016/j.cma.2011.01.002 ↗
52. An algorithm for least-squares estimation of nonlinear parameters10.1137/0111030 ↗
53. A method for the solution of certain non-linear problems in least squares10.1090/qam/10666 ↗
54. $I$-Divergence geometry of probability distributions and minimization problems10.1214/aop/1176996454 ↗
55. Optuna: a Next-generation Hyperparameter Optimization Framework
56. Adam: a Method for Stochastic Optimization
57. Tensile strength prediction of corroded steel plates by using machine learning approach
58. Prediction of Mechanical Properties of Highly Functional Lightweight Fiber-Reinforced Concrete Based on Deep Neural Network and Ensemble Regression Trees Methods10.3390/ma15196740 ↗
59. Extraction of mechanical properties of materials through deep learning from instrumented indentation10.1073/pnas.1922210117 ↗
60. StressNet - Deep learning to predict stress with fracture propagation in brittle materials
61. Divergence Measures Based on the Shannon Entropy10.1109/18.61115 ↗