Data in support of "Design and Selection of High Entropy Alloys for Hardmetal Matrix Applications using a Coupled Machine Learning and CALPHAD Methodology"

Abstract: This study aimed to utilise a combined Machine Learning (ML) and CALculations of PHAse Diagrams (CALPHAD) methodology to design hardmetal matrix phases for metal forming applications that could serve as the basis for carbide reinforcement. The vast compositional space that High Entropy Alloys (HEAs) occupy, offers a promising avenue to satisfy the application design criteria of wear resistance and ductility. To efficiently explore this space, random forest ML models are constructed and trained from publicly available experimental HEA databases to make phase constitution and hardness predictions. Interrogation of the ML models constructed revealed accuracies > 78.7% and mean absolute error of 66.1 HV for phase and hardness predictions. Six promising alloy compositions, extracted from the ML predictions and CALPHAD calculations, were experimentally fabricated and tested. The hardness predictions are found to be systematically under and over predicted depending on the alloy microstructure. In parallel, the phase classification models were found to lack sensitivity towards additional intermetallic phase formation. Despite the discrepancies identified between ML and experimental results, the fabricated compositions showed promise for further experimental evaluation. These discrepancies were believed to be directly associated with the available databases but importantly have highlighted several avenues for both ML and database development.

摘要：本研究旨在结合机器学习（Machine Learning, ML）与相图计算（CALPHAD）方法，设计可作为碳化物增强体基础、适用于金属成型应用的硬质合金基体相。高熵合金（High Entropy Alloys, HEAs）所覆盖的广阔成分空间，为满足耐磨性与延展性的应用设计要求提供了极具潜力的路径。为高效探索该成分空间，本研究基于公开可用的高熵合金实验数据库构建并训练随机森林机器学习模型，以开展相组成与硬度的预测。对所构建的机器学习模型的分析结果表明，其相组成与硬度预测的准确率均高于78.7%，硬度预测的平均绝对误差为66.1 HV。研究人员从机器学习预测结果与相图计算结果中筛选出6种极具潜力的合金成分，并开展了实验制备与性能测试。结果发现，硬度预测值会根据合金微观组织出现系统性偏高或偏低的情况。与此同时，相分类模型对额外生成的金属间化合物相缺乏识别敏感性。尽管机器学习预测结果与实验结果之间存在上述偏差，但所制备的合金成分仍具备进一步实验评估的潜力。经分析，该偏差与现有公开数据库的局限性直接相关，但重要的是，本研究同时为机器学习与数据库开发指明了若干优化方向。

搜集汇总

背景与挑战

背景概述

该数据集支持一项高熵合金设计研究，结合机器学习与CALPHAD方法，包含6种实验合金成分及其性能测试数据，用于开发硬质合金基体相。数据集可公开共享，模型预测精度达78.7%，但存在系统性偏差，揭示了数据库改进需求。