Machine learning enabled design of sustainable alloys considering real world constraints
Abstract
Designing sustainable alloys must reconcile high performance with real-world constraints, including feedstock scarcity and variability, processing and energy requirements, regulatory thresholds, and economic limitations. Addressing these constraints reaches far beyond today's conventional performance-focused frameworks. It needs to consider not only complex property-processing-performance trade-offs but also sustainable production pathways. Here, we propose a physics-informed ensemble (PIE) learning framework that integrates fundamental structure-property relationships with processing routes and sustainability criteria for mechanism-based alloy design. It predicts properties from composition-microstructure relationships and infers processing routes under sustainability constraints, establishing an interdisciplinary approach to identifying sustainable alloy solutions. We validate the approach for Mg alloys, a lightweight yet energy-intensive metallic material class, considering performance, cost, and carbon emissions. Trained on a benchmark dataset, the PIE model systematically explores plausible alloy compositions and microstructures to construct a digital library of millions of candidate alloys. From this space, we identify two sustainable Mg alloy solutions that overcome the conventional performance-cost-emission trade-off: a rare-earth-containing alloy increases yield and tensile strength by 72.8 percent and 61.1 percent, respectively, over alloys from the same family at comparable cost and emissions; and a rare-earth-free alloy reduces cost and carbon emissions by 40.4 percent and 88.9 percent, respectively, relative to its primary-feedstock counterpart, without compromising mechanical performance. This work introduces a general, physics-informed, and quantifiable framework for designing sustainable alloys under real-world constraints.
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BibTeX
@misc{li2026machine,
title = {{Machine learning enabled design of sustainable alloys considering real world constraints}},
author = {Quan Li and Zhilong Wang and Lei Shi and Baojing Feng and Changhui Liu and Jie Wang and Jingya Wang and Weisen Zheng and Matic JovicevicKlug and Ziyuan Rao and Jinjin Li and Dierk Raabe and Xiaoqin Zeng},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.35946},
eprint = {2610.35946},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.35946},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Machine learning enabled design of sustainable alloys considering real world constraints
AU - Quan Li
AU - Zhilong Wang
AU - Lei Shi
AU - Baojing Feng
AU - Changhui Liu
AU - Jie Wang
AU - Jingya Wang
AU - Weisen Zheng
AU - Matic JovicevicKlug
AU - Ziyuan Rao
AU - Jinjin Li
AU - Dierk Raabe
AU - Xiaoqin Zeng
PY - 2026
DA - 2026/10/23
PB - arXiv
DO - 10.48550/arxiv.2610.35946
UR - https://arxiv.org/abs/2610.35946
AB - Designing sustainable alloys must reconcile high performance with real-world constraints, including feedstock scarcity and variability, processing and energy requirements, regulatory thresholds, and economic limitations. Addressing these constraints reaches far beyond today's conventional performance-focused frameworks. It needs to consider not only complex property-processing-performance trade-offs but also sustainable production pathways. Here, we propose a physics-informed ensemble (PIE) learning framework that integrates fundamental structure-property relationships with processing routes and sustainability criteria for mechanism-based alloy design. It predicts properties from composition-microstructure relationships and infers processing routes under sustainability constraints, establishing an interdisciplinary approach to identifying sustainable alloy solutions. We validate the approach for Mg alloys, a lightweight yet energy-intensive metallic material class, considering performance, cost, and carbon emissions. Trained on a benchmark dataset, the PIE model systematically explores plausible alloy compositions and microstructures to construct a digital library of millions of candidate alloys. From this space, we identify two sustainable Mg alloy solutions that overcome the conventional performance-cost-emission trade-off: a rare-earth-containing alloy increases yield and tensile strength by 72.8 percent and 61.1 percent, respectively, over alloys from the same family at comparable cost and emissions; and a rare-earth-free alloy reduces cost and carbon emissions by 40.4 percent and 88.9 percent, respectively, relative to its primary-feedstock counterpart, without compromising mechanical performance. This work introduces a general, physics-informed, and quantifiable framework for designing sustainable alloys under real-world constraints.
ER -