Accelerating Multiobjective Collaborative Optimization Of Doped Thermoelectric Materials Via Artificial Intelligence
ASecurity
**arXiv ID:** 2504.08258 **Authors:** Yuxuan Zeng, Wenhao Xie, Wei Cao, Tan Peng, Yue Hou, Ziyu Wang, Jing Shi **Published:** 2025-04-11T05:10:18Z **Abstract:** The thermoelectric performance of materials exhibits complex nonlinear dependencies on both elemental types and their proportions, rendering traditional trial-and-error approaches inefficient and time-consuming for material discovery. In this work, we present a deep learning model capable of accurately predicting thermoelectric proper...
Installs into .claude/skills of the current project.
Are you the author of Accelerating Multiobjective Collaborative Optimization Of Doped Thermoelectric Materials Via Artificial Intelligence?
Add the live security badge to your README. It updates with every re-scan.
[](https://www.skillsdirectory.com/skills/hiyenwong-accelerating-multiobjective-collaborative-optimiza)
# Accelerating Multi-Objective Collaborative Optimization of Doped Thermoelectric Materials via Artificial Intelligence
**arXiv ID:** 2504.08258
**Authors:** Yuxuan Zeng, Wenhao Xie, Wei Cao, Tan Peng, Yue Hou, Ziyu Wang, Jing Shi
**Published:** 2025-04-11T05:10:18Z
**Abstract:**
The thermoelectric performance of materials exhibits complex nonlinear dependencies on both elemental types and their proportions, rendering traditional trial-and-error approaches inefficient and time-consuming for material discovery. In this work, we present a deep learning model capable of accurately predicting thermoelectric properties of doped materials directly from their chemical formulas, achieving state-of-the-art performance. To enhance interpretability, we further incorporate sensitivity analysis techniques to elucidate how physical descriptors affect the thermoelectric figure of merit (zT). Moreover, we establish a coupled framework that integrates a surrogate model with a multi-objective genetic algorithm to efficiently explore the vast compositional space for high-performance candidates. Experimental validation confirms the discovery of a novel thermoelectric material with superior $zT$ values in the medium-temperature regime.
## Skill Description
This skill is generated from the arXiv paper: Accelerating Multi-Objective Collaborative Optimization of Doped Thermoelectric Materials via Artificial Intelligence (2504.08258).
## How to Use
[To be filled in by the user or by future automation]
## References
- [arXiv:2504.08258](http://arxiv.org/abs/2504.08258v1)
