William Yi Wang

Bio: William Yi Wang is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Integrated computational materials engineering & Stacking. The author has an hindex of 3, co-authored 4 publications receiving 62 citations.

A brief review of data-driven ICME for intelligently discovering advanced structural metal materials: Insight into atomic and electronic building blocks

Abstract: This article presents a brief review of our case studies of data-driven Integrated Computational Materials Engineering (ICME) for intelligently discovering advanced structural metal materials, including light-weight materials (Ti, Mg, and Al alloys), refractory high-entropy alloys, and superalloys. The basic bonding in terms of topology and electronic structures is recommended to be considered as the building blocks/units constructing the microstructures of advanced materials. It is highlighted that the bonding charge density could not only provide an atomic and electronic insight into the physical nature of chemical bond of materials but also reveal the fundamental strengthening/embrittlement mechanisms and the local phase transformations of planar defects, paving a path in accelerating the development of advanced metal materials via interfacial engineering. Perspectives on the knowledge-based modeling/simulations, machine-learning knowledge base, platform, and next-generation workforce for sustainable ecosystem of ICME are highlighted, thus to call for more duty on the developments of advanced structural metal materials and enhancement of research productivity and collaboration.

Local Lattice Distortion Mediated Formation of Stacking Faults in Mg Alloys

Abstract: Long periodic stacking ordered phases (LPSOs), consisting of various configurations of stacking faults, play an important role in developing ultrastrong Mg alloys with moderate ductility. However, their formation mechanisms are far from clear as no apparent defects are introduced during their formation as it is commonly believed that stacking faults are induced by defects. Here, we present the atomic and electronic basis for lattice-distortion-mediated formation of stacking faults, i.e., localized face-centred-cubic (FCC) structures, within a Mg-Zn-Y alloy with a hexagonal close-packed (HCP) structure. The atomic motion trajectories from ab-initio molecular dynamic simulations show that the Mg atoms occupying the nearest neighbour positions of Zn and Y solute atoms undergo a local HCP-to-FCC transition. It is revealed that a local lattice distortion caused by the solute atoms enables the Mg atoms to move and rearrange into a local FCC configuration, which is validated by high resolution scanning transmission microscopy and in-situ synchrotron X-ray diffraction. Our simulations provide profound insight into the formation mechanism of stacking faults in HCP Mg and their physical nature of phase transformations; this is not only critically important because conventional defects, such as dislocations and vacancies, are important to deformation are rare for Mg and its alloys, but also because they serve as a potential new approach to the design of advanced Mg alloys when defects are actually phase transformations.

A simple analytic theory for the statistics of avalanches in sheared granular materials

Abstract: An analytic model now provides quantitative predictions for the effect of void fractions within a granular medium on avalanche statistics. It will help us understand the dynamics of earthquakes as well as plasticity. Slowly sheared granular materials at high packing fractions deform via slip avalanches with a broad range of sizes. Conventional continuum descriptions1 are not expected to apply to such highly inhomogeneous, intermittent deformations. Here, we show that it is possible to analytically compute the dynamics using a simple model that is inherently discrete. This model predicts quantities such as the avalanche size distribution, power spectra and temporal avalanche profiles as functions of the grain number fraction v and the frictional weakening ɛ. A dynamical phase diagram emerges with quasi-static avalanches at high number fractions, and more regular, fluid-like flow at lower number fractions. The predictions agree with experiments and simulations for different granular materials, motivate future experiments and provide a fresh approach to data analysis. The simplicity of the model reveals quantitative connections to plasticity and earthquake statistics.

Perspectives of using machine learning in laser powder bed fusion for metal additive manufacturing

Abstract: The adoption of laser powder bed fusion (L-PBF) for metals by the industry has been limited despite the significant progress made in the development of the process chain. One of the key obstacles i...

Structure-property relationships from universal signatures of plasticity in disordered solids

Abstract: Behavioral universality across size scales Glassy materials are characterized by a lack of long-range order, whether at the atomic level or at much larger length scales. But to what extent is their commonality in the behavior retained at these different scales? Cubuk et al. used experiments and simulations to show universality across seven orders of magnitude in length. Particle rearrangements in such systems are mediated by defects that are on the order of a few particle diameters. These rearrangements correlate with the material's softness and yielding behavior. Science, this issue p. 1033 A range of particle-based and glassy systems show universal features of the onset of plasticity and a universal yield strain. When deformed beyond their elastic limits, crystalline solids flow plastically via particle rearrangements localized around structural defects. Disordered solids also flow, but without obvious structural defects. We link structure to plasticity in disordered solids via a microscopic structural quantity, “softness,” designed by machine learning to be maximally predictive of rearrangements. Experimental results and computations enabled us to measure the spatial correlations and strain response of softness, as well as two measures of plasticity: the size of rearrangements and the yield strain. All four quantities maintained remarkable commonality in their values for disordered packings of objects ranging from atoms to grains, spanning seven orders of magnitude in diameter and 13 orders of magnitude in elastic modulus. These commonalities link the spatial correlations and strain response of softness to rearrangement size and yield strain, respectively.