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

Discovering novel high-entropy alloys (HEAs) with desirable properties is challenging due to the vast compositional space and the complexity of phase formation mechanisms. Efficiently exploring this space requires a strategic approach that integrates diverse knowledge sources. This study proposes a framework that systematically combines knowledge extracted from computational material datasets with domain knowledge distilled from scientific literature using large language models. A central feature includes explicitly considering element substitutability and identifying chemically similar elements that can be potentially interchanged to stabilize desired HEAs. Dempster--Shafer theory, a mathematical framework for reasoning under uncertainty, is employed to model and integrate substitutability based on aggregated evidence from multiple sources. The framework predicts the phase stability of candidate HEA compositions and is systematically evaluated on quaternary alloy systems, demonstrating superior performance over baseline machine learning models and methods that rely on single-source evidence in cross-validation experiments. By leveraging multi-source knowledge, the framework retains strong 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 unveils fundamental factors governing HEA formation. Overall, this study presents 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.

With the established methodological foundation, we apply the proposed method to quaternary alloy datasets, evaluating its predictive performance in terms of accuracy and interpretability. Experiments are conducted considering four computational datasets of quaternary alloys:

$\mathcal{D}_{0.9Tm}$ and $\mathcal{D}_{1350K}$: These datasets include \emph{all possible quaternary} alloys generated from a set of 26 elements: Fe, Co, Ir, Cu, Ni, Pt, Pd, Rh, Au, Ag, Ru, Os, Si, As, Al, Re, Mn, Ta, Ti, W, Mo, Cr, V, Hf, Nb, and Zr. The stability of these alloys---defined as whether they form an HEA phase---is predicted using methods proposed by Chen \emph{et al.} at two different temperatures: $0.9\,T_m$ (approximately $90\%$ of the melting temperature $T_m$ of the alloy) and $1350\,( K)$. These predictions are obtained via a high-throughput computational workflow, which employs a regular-solution model using binary interaction parameters derived from \textit{ab initio} density functional theory (DFT) to compute and compare Gibbs free energies of solid solutions against competing intermetallic phases.

$\mathcal{D}_{Mag}$ and $\mathcal{D}_{T_C}$: These datasets comprise 5,968 equiatomic quaternary high-entropy alloys (HEAs), each formed by selecting four elements from a set of 21 transition metals: Fe, Co, Ir, Cu, Ni, Pt, Pd, Rh, Au, Ag, Ru, Os, Tc, Re, Mn, Ta, W, Mo, Cr, V, and Nb. Their magnetizations ($\mathcal{D}_{Mag}$) and Curie temperatures ($\mathcal{D}_{T_C}$) in the body-centered cubic (BCC) phase are computed using the Korringa--Kohn--Rostoker coherent approximation method. These datasets are derived from an original pool of $147,630$ equiatomic quaternary HEAs.