耐火多主元素合金（MPEAs）因其优良的力学性能而在高温应用领域引起了广泛的关注。这些合金通常由几种高熔点元素组成，尽管它们在室温下具有高屈服强度，但在高温应用中受限于低拉伸延展性和急剧的韧脆转变。
Fig. 1 | Flowchart of the materials design loop using Bayesian multi-objective optimization.
探索具有更优异强度和延性组合的新组分，是提升耐火MPEAs力学性能的关键挑战，这同时也为采用计算机辅助设计探索这些材料的巨大组分空间开辟了新的机会。
来自奥地利莱奥本材料研究中心的Franco Moitzi等，提出了一种集成先进从头算技术的贝叶斯多目标优化框架，通过一种简单的解析模型来分析趋势，成功地应用于描述难熔MPEAs的固溶强化和延展性。
该框架结合了CPA和MLIPs两种方法，同时引入了一个简单模型，可以准确捕捉难熔合金整个组分空间中与强化和塑性有关所有量的浓度依赖性质。
Fig. 5 | Elastic modulus C44 and misfit volumes for various equimolar alloys.
作者对三组分和四组分合金进行了强度和延性的多目标优化，并将这些结果用来验证模型，并扩展到了更大的合金。该研究为破解难熔MPEAs的传统强度-延性难题提供了重要的研究思路。
Fig. 6 | Parity plots showing strength and ductility calculated directly and from the virtual bond approximation for various alloys from the MoNbTi and MoNbTiTa system.
作者所提出的框架是通用的，可以扩展到其他感兴趣的材料和特性，能实现在整个组成空间内对帕累托最优 MPEA 进行预测和可处理的高通量筛选。该文近期发布于npj Computational Materials10: 152 (2024)。
Fig. 7 | Comparsion between predicted and experimental strength for various alloys.
Editorial Summary
Refractory multi-principal element alloys (MPEAs) have gained significant interest for high-temperature applications due to their excellent mechanical properties. These alloys consist of several high-melting-point elements, and although they exhibit high yield strength at room temperature, their application in high-temperature environments is limited by low tensile ductility and a sharp brittle-to-ductile transition.
The quest for discovering new compositions of refractory MPEAs with
improved mechanical properties remains an important challenge, which also opens
new opportunities for computer-guided design for the efficient exploration of
the vast composition space of these materials. In recent years, many works have
introduced computational methods to identify and characterize alloys with
tailored properties, such as machine learning (ML) and density functional
theory (DFT) based ab initiocalculations. For ab initio alloy modeling, the common approaches include special
quasi-random structures (SQS), coherent potential approximation (CPA) and machine-learning
interatomic potentials (MLIPs). However, these approaches usually have their
own limitations.
Fig. 9 | Cell size convergence. Convergence of surface energies with respect to the number of layers for pure Mo, equimolarMoNb,MoNbTi, and MoNbTa, along with corresponding results for an SQS cell sized 4 × 4 x 10. All values are obtained with MTPs.
Franco Moitzi et al. from the Materials Center Leoben, Austria, proposed a framework 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, and successfully applied the framework for describing solid solution strengthening and ductility of refractory MPEAs. The framework combines two approaches, i.e., CPA and MLIPs, and introduces a simple model that accurately captures the concentration-dependent properties related to strengthening and plasticity across the entire compositional space of a wide range of refractory alloys. The authors conducted multi-objective optimization of strength and ductility for ternary and quaternary alloys, and used the results to validate the model before extending it to more complex alloys.
Fig. 10 | Feasible regions and correlations between materials parameters for MoNbTi and MoNbTiTa.
This study offers 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.
Fig. 11 | Ductility index, D, and CRSS, τy evaluated for various alloys using the VBA model.
原文Abstract及其翻译
Ab initio framework for deciphering trade-off relationships in multi-component alloys (用于破译多组分合金中权衡关系的从头算框架)
Franco Moitzi, Lorenz Romaner, Andrei V. Ruban, Max Hodapp & Oleg E. Peil
Abstract 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.
摘要 虽然第一性原理方法已经成功应用于表征多主元素合金（MPEA）的独立属性，但它们在寻找竞争性质之间的最优权衡方面的应用上却受到了高计算量的阻碍。在这项工作中，我们提出了一个框架，通过将先进的基于从头计算的技术集成到贝叶斯多目标优化工作流中，来探索帕累托最优组分，并辅以一个简单的分析模型来提供直接的趋势分析。我们将该框架应用于难熔MPEAs的固溶强化和延展性，并以此为基准，结合考虑有限温度效应的相干势近似方法和从头算数据参数化的主动学习矩张量势，有效地计算强化和延展性模型的参数。通过借助由数据验证的分析模型，并依靠一些元素特有的参数和描述元素之间键合的通用函数，从头计算中获得的性质随后被用于扩展一大类难熔合金的所有相关材料性质的预测。我们的研究结果为破解难熔MPEAs的传统强度-延性难题提供了重要见解。所提出的框架是多功能的，可以扩展到其他感兴趣的材料和特性，能实现在整个组成空间内对帕累托最优 MPEA 进行预测和可处理的高通量筛选。
