2026-10-16T01:09:28Z https://data.mendeley.com/oai

oai:data.mendeley.com/jxrp8x4z2d.1 2026-09-14T09:33:01Z

li, kefan Molecular dynamics dataset of cyclic mechanical responses for machine-learning-based gradient design of TiNi shape memory alloys Mendeley Data This dataset supports the study entitled “Predicting cyclic mechanical responses and optimizing gradient structures of TiNi shape memory alloys via combined molecular dynamics simulation and machine learning”. It contains 78,208 ordered sequence samples derived from molecular dynamics simulations of nanograined TiNi shape memory alloys with different grain-size distributions and gradient architectures. The dataset includes structural descriptors, including minimum grain size, maximum grain size, average grain size, gradient coefficient, and grain-boundary volume fraction; loading conditions, including temperature and maximum strain amplitude; state variables, including cycle number, loading–unloading state, and instantaneous strain; and the corresponding instantaneous stress. The samples are identified by MD Model ID and arranged chronologically to preserve the path-dependent characteristics of cyclic deformation. The data were used for group cross-validation, cyclic stress–strain response prediction, virtual screening, Pareto analysis, functional stability evaluation, and SHAP interpretation. The dataset can be used to reproduce the machine-learning analyses and facilitate further research on the data-driven design of gradient nanograined TiNi shape memory alloys. Materials Science Artificial Intelligence Mechanical Engineering Computational Materials Science Zhaoyang, Hou Dataset doi:10.17632/jxrp8x4z2d.1 oai:data.mendeley.com/jxrp8x4z2d.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/jxrp8x4z2d 2026-09-14T09:33:01Z