High Entropy Alloys (HEAs), Multi-principal Component Alloys (MCA), or Compositionally Complex Alloys (CCAs) are alloys that contain multiple principal alloying elements. While many HEAs have been shown to have unique properties, their discovery has been largely done through costly and time-consuming trial-and-error approaches, with only an infinitesimally small fraction of the entire possible composition space having been explored. In this work, the exploration of the HEA composition space is framed as a Continuous Constraint Satisfaction Problem (CCSP) and solved using a novel Constraint Satisfaction Algorithm (CSA) for the rapid and robust exploration of alloy thermodynamic spaces. The algorithm is used to discover regions in the HEA Composition-Temperature space that satisfy desired phase constitution requirements. The algorithm is demonstrated against a new (TCHEA1) CALPHAD HEA thermodynamic database. The database is first validated by comparing phase stability predictions against experiments and then the CSA is deployed and tested against design tasks consisting of identifying not only single phase solid solution regions in ternary, quaternary and quinary composition spaces but also the identification of regions that are likely to yield precipitation-strengthened HEAs.
Machine Learning Potentials for Alloys: A Detailed Workflow to Predict Phase Diagrams and Benchmark Accuracy
organization= Department of Materials Science；Engineering, Texas A\&M University , city= College Station , postcode= 77843 , state= TX , country= USA；organization= J. Mike Walker '66 Department of Mechanical Engineering, Texas A\&M University , city= College Station , postcode= 77843 , state= TX , country= USA；organization= Wm Michael Barnes '64 Department of Industrial；Systems Engineering, Texas A\&M University , city= College Station , postcode= 77843 , state= TX , country= USA
Information Entropy Based Crystal Structure Prediction of Chemically Disordered Alloys via Graph Convolutional Neural Networks
Indian Institute of Engineering Science and Technology, Shibpur（印度工程科学与技术学院，希布尔）；Indian Institute of Technology (BHU), Varanasi（印度理工学院（贝拿勒斯印度教大学），瓦拉纳西）
Exploration of the Microstructure Space in TiAlZrN Ultra-Hard Nanostructured Coatings
Ti $_{1-x-y}$ Al $_{x}$ Zr $_{y}$ N立方合金在25-70\% Al成分范围内由于高温下的亚稳相变途径而具有高的时效硬化能力。因此，它们是超硬纳米涂层材料的理想候选者。越来越多的证据表明，这种效果与弹性化学场诱导的相分离成成分分离的纳米立方氮化物相有关。在这里，我们研究了在1200 $^\circ$ C的调幅区域内的伪三元系的微观结构的演变，通过使用弹性化学相场模型。我们的模拟表明，氮化物纳米畴之间的弹性相互作用不仅极大地影响微观结构的形态，而且局部化学相平衡。在富铝区域的组合物空间中，我们进一步观察到富AlN相转变成其平衡纤锌矿晶体结构的发病。这项工作指出了这些氮化物系统可能获得的广泛的微观结构，并且它们的定制可能导致过渡金属氮化物基涂层材料的性能的显着改善。
Lowering the Exponential Wall: Accelerating High-Entropy Alloy Catalysts Screening using Local Surface Energy Descriptors from Neural Network Potentials
Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges
organization= Department of Materials Science；Engineering, Texas A\&M University , city= College Station , postcode= 77843 , state= TX , country= USA；organization= J. Mike Walker '66 Department of Mechanical Engineering, Texas A\&M University , city= College Station , postcode= 77843 , state= TX , country= USA；organization= Wm Michael Barnes '64 Department of Industrial；Systems Engineering, Texas A\&M University , city= College Station , postcode= 77843 , state= TX , country= USA
Compositional phase stability in medium-entropy and high-entropy Cantor-Wu alloys from an ab initio all-electron, Landau-type theory and atomistic modelling
Experimentally validated and empirically compared machine learning approach for predicting yield strength of additively manufactured multi-principal element alloys from Co-Cr-Fe-Mn-Ni system
From Phase Prediction to Phase Design: A ReAct Agent Framework for High-Entropy Alloy Discovery
Luxembourg Institute of Science and Technology (LIST)（卢森堡科学与技术研究院）；Department of Physics and Materials Science, University of Luxembourg（卢森堡大学物理与材料科学系）
The hidden sustainability bottleneck in high-entropy alloy design
University of Lorraine, Mines Nancy（洛林大学，南锡矿业学院）；LORIA, University of Lorraine – INRIA - CNRS（洛林大学-法国国家信息与自动化研究所-法国国家科学研究中心，洛林研究实验室与计算机应用）；Institut Jean Lamour, Université de Lorraine – CNRS（让·拉穆尔研究所，洛林大学-法国国家科学研究中心）；Department of Gaseous Electronics, Jožef Stefan Institute（约瑟夫·斯特凡研究所，气体电子系）；EMINES, University Mohamed VI（穆罕默德六世大学，EMINES）；Hanoi University of Science and Technology（河内科学技术大学）；Kyiv Polytechnic Institute（基辅理工学院）；Department of Physics, Aristotle University Thessaloniki（萨洛尼卡亚里士多德大学，物理系）；Department of Chemical and Materials Engineering, University of Alberta（阿尔伯塔大学，化学与材料工程系）；Galatasaray Üniversitesi（加拉塔萨雷大学）；Faculty of Physics, ITMO University（ITMO大学，物理学院）；Sustainable Minerals Institute, The University of Queensland（昆士兰大学，可持续矿产研究所）；New Uzbekistan University（新乌兹别克斯坦大学）；University of Lorraine, CNRS, CREGU, GeoRessources（洛林大学，法国国家科学研究中心，CREGU，GeoRessources）；Jožef Stefan International Postgraduate School（约瑟夫·斯特凡国际研究生院）；Max-Planck-Institut für Nachhaltige Materialien（马克斯·普朗克可持续材料研究所）
Favorable Interfacial Chemomechanics Enables Stable Cycling of High Li-Content Li-In/Sn Anodes in Sulfide Electrolyte Based Solid-State Batteries
固态电池（SSB）可以在电池安全性和能量密度方面提供范式转变。然而，这一前景取决于将高性能电极与最先进的固体电解质集成的能力。例如，锂（Li）金属，最高能量密度的阳极候选者，遭受导致电池故障的严重界面化学机械问题。In/Sn的Li合金是有吸引力的替代品，但它们的开发主要限于低容量（低Li含量）和富In的Li $_x$ In（x $\leq$ 0.5）。在这里，基本的电化学机械行为的Li-In和Li-Sn合金的不同Li化学计量是解开硫化物电解质为基础的SSB。通过控制合成和制造技术开发的金属间化合物电极显示出令人印象深刻的（电）化学稳定性，其中Li $_6$ PS $_5$ Cl作为固体电解质，并且在电化学Li插入/脱嵌期间在最佳堆叠压力下保持近乎完美的界面接触。通过计算和电化学研究的结合，揭示了它们在锂迁移势垒随组成的有趣变化及其对观察到的锂循环过电位的影响。合金的稳定界面化学力学允许在相对高的电流密度（1 mA cm $^{-2}$）和容量（1 mAh cm $^{-2}$）下的长期无枝晶Li循环（>1000 h），如对于Li $_{13}$ In $_3$和Li $_{17}$ Sn $_4$所证明的，与低Li含量类似物相比，从容量和成本考虑，这是更期望的。这些认识对锂离子电池用高容量Li-In/Sn合金阳极的开发具有指导意义。
