NpjComput.Mater.：多組分合金權衡關係的破譯：從頭算框架

海歸學者發起的公益學術平臺

分享資訊，整合資源

交流學術，偶爾風月

耐火多主元素合金（MPEAs）因其優良的力學效能而在高溫應用領域引起了廣泛的關注。這些合金通常由幾種高熔點元素組成，儘管它們在室溫下具有高屈服強度，但在高溫應用中受限於低拉伸延展性和急劇的韌脆轉變。探索具有更優異強度和延性組合的新組分，是提升耐火MPEAs力學效能的關鍵挑戰，這同時也為採用計算機輔助設計探索這些材料的巨大組分空間開闢了新的機會。近年來，許多工作引入了計算方法來識別和表徵具有定製效能的合金，如機器學習（ML）、基於密度泛函理論（DFT）的從頭算等。針對合金的從頭算模擬，常見的方法包括使用特殊的準隨機結構（SQS）、相干勢近似（CPA）和機器學習原子間勢（MLIPs），但這些方法都存在各自的缺陷。

Fig. 1 | Flowchart of the materials design loop
using Bayesian multi-objective optimization.

來自奧地利萊奧本材料研究中心的Franco Moitzi等人，提出了一種整合先進從頭算技術的貝葉斯多目標最佳化框架，透過一種簡單的解析模型來分析趨勢，成功地應用於描述難熔MPEAs的固溶強化和延展性。該框架結合了CPA和MLIPs兩種方法，同時引入了一個簡單模型，可以準確捕捉難熔合金整個組分空間中與強化和塑性有關所有量的濃度依賴性質。作者對三組分和四組分合金進行了強度和延性的多目標最佳化，並將這些結果用來驗證模型，並擴充套件到了更大的合金。該研究為破解難熔MPEAs的傳統強度-延性難題提供了重要的研究思路。

Fig. 11 | Ductility index, D, and CRSS, τy evaluated for various alloys using the VBAmodel.

作者所提出的框架是通用的，可以擴充套件到其他感興趣的材料和特性，能實現在整個組成空間內對帕累托最優 MPEA 進行預測和可處理的高通量篩選。該文近期發表於npj Computational Materials10: 152 (2024)，英文標題與摘要如下，點選左下角“閱讀原文”可以自由獲取論文PDF。

Ab initio framework for deciphering trade-off relationships in multi-component alloys

Franco Moitzi, Lorenz Romaner, Andrei V. Ruban, Max Hodapp & Oleg E. Peil

While first-principles methods have been successfully applied to characterize individual properties of multi-principal element alloys (MPEA), their use in searching for optimal trade-offs between competing properties is hampered by high computational demands. In this work, we present a framework to explore Pareto-optimal compositions by integrating advanced ab initio-based techniques into a Bayesian multi-objective optimization workflow, complemented by a simple analytical model providing straightforward analysis of trends. We benchmark the framework by applying it to solid solution strengthening and ductility of refractory MPEAs, with the parameters of the strengthening and ductility models being efficiently computed using a combination of the coherent-potential approximation method, accounting for finite-temperature effects, and actively-learned moment-tensor potentials parameterized with ab initio data. Properties obtained from ab initio calculations are subsequently used to extend predictions of all relevant material properties to a large class of refractory alloys with the help of the analytical model validated by the data and relying on a few element-specific parameters and universal functions that describe bonding between elements. Our findings offer crucial insights into the traditional strength-vs-ductility dilemma of refractory MPEAs. The proposed framework is versatile and can be extended to other materials and properties of interest, enabling a predictive and tractable high-throughput screening of Pareto-optimal MPEAs over the entire composition space.