Abstract In present study, the relations between hardness and elemental descriptors in the multi-component alloys (MACs) are particularly uncovered via machine learning (ML) and first-principles calculations. The RBF neural network is utilized to efficiently train a large database which allows an acceptable accuracy for identifying the overall role of elemental features for target properties. Detailed information from ML predictions indicates the critical element of Al and its significant advantages for the hardness in a model Al–Cr–Fe–Ni system. Investigations on elastic properties using first-principles calculations provide relations between physical quantities and match well with ML model. Furthermore, the Al 1.2 CrFeNi alloy was selected as an example, experimentally synthesized and properties-identified, to provide a strong validation in this case. Combined with some other reported alloys in this system, the prediction of the developed model can achieve above 90%. Further experimental analysis found that a dual phase microstructure presents in the Al 1.2 CrFeNi alloy, leading to a superior work hardening ability compared with the reported high performance alloys. This study offers reliable composition-properties features and encourages more researches using this integrated approach to guide for tuning novel MACs.

中文翻译：

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多组分合金硬度的建模和预测：结合机器学习、第一原理和实验研究

摘要 在目前的研究中，通过机器学习 (ML) 和第一性原理计算，特别揭示了多组分合金 (MAC) 中硬度与元素描述符之间的关系。RBF 神经网络用于有效地训练大型数据库，该数据库允许在识别目标属性的元素特征的整体作用时具有可接受的准确性。ML 预测的详细信息表明了 Al 的关键元素及其对模型 Al-Cr-Fe-Ni 系统硬度的显着优势。使用第一性原理计算对弹性特性的研究提供了物理量之间的关系，并与 ML 模型很好地匹配。此外，选择 Al 1.2 CrFeNi 合金作为示例，通过实验合成和性能鉴定，在这种情况下提供强有力的验证。结合该系统中其他一些报道的合金，所开发模型的预测可以达到 90% 以上。进一步的实验分析发现，Al 1.2 CrFeNi 合金中存在双相微观结构，与报道的高性能合金相比，具有优异的加工硬化能力。这项研究提供了可靠的组合特性特征，并鼓励更多的研究使用这种集成方法来指导调整新的 MAC。