利用經驗規律來尋找形成固溶體合金早已成為材料設計的重要步驟。但是，現有的經驗規律預測的準確率很低，尤其是在諸如高熵合金等元素種類較多的情況下。

來自美國能源部兩個國家實驗室（NETL&ORNL）的研究團隊（Zongrui Pei， Junqi Yin， Michael C. Gao， etc），通過機器學習獲得的積極結果認識到：用經驗規律預測合金固溶體這條路其實是可以很準確的，而之前的經驗規律的局限性在于，沒有選取到最重要的物理量作為參數。作者利用機器學習提供的物理量，構建了新的經驗規律，并用其預測了三組高熵固溶體的形成，分別對應于最具代表性的晶體結構（FCC、BCC、HCP），得到了跟計算相圖高度一致的結果。新的經驗規律應用于高熵合金固溶體形成的預測，將非常直觀而高效。

該文近期發表于npj Computational Materials 6： 50 （2020），英文標題與摘要如下。

Machine-learning informed prediction of high-entropy solid solution formation： Beyond the Hume-Rothery rules

Zongrui Pei，JunqiYin，Jeffrey A. Hawk， David E. Alman&Michael C. Gao， npj

The empirical rules for the prediction of solid solution formation proposed so far in the literature usually have very compromised predictability. Some rules with seemingly good predictability were， however， tested using small data sets. Based on an unprecedented large dataset containing 1252 multicomponent alloys， machine-learning methods showed that the formation of solid solutions can be very accurately predicted （93%）。 The machine-learning results help identify the most important features， such as molar volume， bulk modulus， and melting temperature. As such a new thermodynamics-based rule was developed to predict solid–solution alloys. The new rule is nonetheless slightly less accurate （73%） but has roots in the physical nature of the problem. The new rule is employed to predict solid solutions existing in the three blocks， each of which consists of 9 elements. The predictions encompass face-centered cubic （FCC）， body-centered cubic （BCC）， and hexagonal closest packed （HCP） structures in a high throughput manner. The validity of the prediction is further confirmed by CALculations of PHAse Diagram （CALPHAD） calculations with high consistency （94%）。 Since the new thermodynamics-based rule employs only elemental properties， applicability in screening for solid solution high-entropy alloys is straightforward and efficient.

責任編輯:pj