广大师生：

华南理工大学材料学院“海外优青分论坛”于2023年12月启动，旨在面向全球邀请拥有不同学术背景的青年才俊，围绕国际科学前沿、热点研究领域以及行业产业的技术问题等展开探讨和交流。通过这个平台，互相启迪、开拓视野，增强国际交流与合作，促进双方共同发展。

一、论坛时间

2023年12月22日10:45-12:00

二、地点

华南理工大学五山校区8号楼201会议室

三、论坛议程

欢迎广大师生参加！

 华南理工大学材料学院

                                           2023年12月20日

报告摘要：

Co and Ni are two major elements in high-temperature structural alloys that include superalloys for turbine engines and hard metals for cutting tools. The recent development of complex concentrated alloys (CCAs), loosely defined as alloys without a single principal element (e.g., CoNiFeMn), offers additional opportunities for designing new alloys through extensive composition and structure modifications. Solid-solution strengthening and stacking fault energy engineering are two of the most important strengthening mechanisms in Co- and Ni-based alloys and CCAs. While studied for decades, the potency and quantitative materials properties of these mechanisms remain elusive.

Solid-solution strengthening originates from stress field interactions between dislocations and solutes of various species in the alloy. These stress fields can be engineered by composition modification in 3d transition metal-based CCAs, and therefore, a wide range of alloys with promising mechanical strength may be designed. We reported the experimental and computational validation of newly developed theories for solid-solution strengthening in 3d (MnFeCoNi) alloys. The strengthening effects of Al, Ti, V, Cr, Cu, and Mo as alloying elements are quantified by coupling the Labusch-type strengthening model and experimental measurements. We further proposed the utilization of “mechanism maps” and “elemental mover’s distance” as treasure maps for decision-making in the material design. We design and develop a Python package for high-throughput calculations of solid-solution strengthening for complex alloys, aiming to accelerate the materials discovery for the research community.

Stacking fault energy engineering can enable novel deformation mechanisms and exceptional strength in face-centered cubic (FCC) materials such as austenitic TRIP/TWIP steels and CoNi-based superalloys exhibiting local phase transformation strengthening via Suzuki segregation. To further investigate the driving force of segregation, we elucidate the electronic origin of the segregation of 3d, 4d, and 5d elements in the Co- and Ni-alloys. In addition, using Co-Ni binary alloy as the model system, we predicted the Co segregation at arbitrary alloy composition at finite temperatures for the first time. Co segregation was found to be within the innermost plane of the intrinsic stacking fault, leading to a decrease in stacking fault energy. We further proposed a new first-principles method to study stacking fault energy in disordered FCC Co and Ni alloys with and without solute segregation as a function of bulk alloy composition and temperature. This thermodynamic proof further enables the direct experimental observation of the Co segregation within the intrinsic stacking faults of Co-Ni alloys. This parameter-free formulation can be generalized to study interfacial properties such as stacking faults, twin boundaries, and local phase transformation in the metallic and intermetallic systems.

报告人简介：

Dr. Dongsheng Wen obtained his Ph.D in Michael S. Titus’group at Purdue University, West Lafayette, United States. He is now an postdoctoral researcher in Matthew Rosseinsky’s group in the Chemistry Department at the University of Liverpool, United Kingdom. His research interests cover a wide range of materials covering high temperature structural alloys, functional intermetallics, and energy materials, within the philosophy of the integrated computational materials engineering framework. His experimental and modeling works study the microstructural/atomic/electronic mechanisms to guide materials design, including modeling strengthening mechanisms, crystalline defects and dislocations, phase transformation, magnetic and electronic properties. His current work employs first-principles modeling, thermodynamic modeling, and active learning for high-throughput computations to facilitate materials discovery in the vast chemical space.