Synergistic Fusion of Multi-Source Knowledge via Evidence Theory for High-Entropy Alloy Discovery

TL;DR

This paper introduces a novel framework that combines computational data and scientific literature using evidence theory to predict high-entropy alloy stability, improving discovery efficiency and interpretability.

Contribution

It presents a systematic method integrating multi-source knowledge and element substitutability with Dempster-Shafer theory for HEA prediction, outperforming single-source models.

Findings

Superior phase stability prediction accuracy in cross-validation

Robustness in predictions with missing key elements

Enhanced interpretability of alloy formation factors

Abstract

Discovering novel high-entropy alloys (HEAs) with desirable properties is challenging due to the vast compositional space and complex phase formation mechanisms. Efficient exploration of this space requires a strategic approach that integrates heterogeneous knowledge sources. Here, we propose a framework that systematically combines knowledge extracted from computational material datasets with domain knowledge distilled from scientific literature using large language models (LLMs). A central feature of this approach is the explicit consideration of element substitutability, identifying chemically similar elements that can be interchanged to potentially stabilize desired HEAs. Dempster-Shafer theory, a mathematical framework for reasoning under uncertainty, is employed to model and combine substitutabilities based on aggregated evidence from multiple sources. The framework predicts the phase stability of candidate HEA compositions and is systematically evaluated on both quaternary alloy systems, demonstrating superior performance compared to baseline machine learning models and methods reliant on single-source evidence in cross-validation experiments. By leveraging multi-source knowledge, the framework retains robust predictive power even when key elements are absent from the training data, underscoring its potential for knowledge transfer and extrapolation. Furthermore, the enhanced interpretability of the methodology offers insights into the fundamental factors governing HEA formation. Overall, this work provides a promising strategy for accelerating HEA discovery by integrating computational and textual knowledge sources, enabling efficient exploration of vast compositional spaces with improved generalization and interpretability.