A5100431334- Machine Learning in Materials Science
- Microstructure and mechanical properties
- Titanium Alloys Microstructure and Properties
- Magnesium Alloys: Properties and Applications
- Nuclear Materials and Properties
- X-ray Diffraction in Crystallography
- Advanced Materials Characterization Techniques
- Metal and Thin Film Mechanics
- Advanced materials and composites
- Ga2O3 and related materials
- Advanced Polymer Synthesis and Characterization
- Hydrogen Storage and Materials
- Intermetallics and Advanced Alloy Properties
- ZnO doping and properties
- Thin-Film Transistor Technologies
- High Temperature Alloys and Creep
- Advancements in Battery Materials
- Aluminum Alloys Composites Properties
- Silicon Nanostructures and Photoluminescence
- Surface and Thin Film Phenomena
- Boron and Carbon Nanomaterials Research
- Glass properties and applications
- Computational Drug Discovery Methods
- Optical Imaging and Spectroscopy Techniques
- Electronic and Structural Properties of Oxides
City University of Hong Kong
2021-2024
Ningbo University
2023
Beijing University of Technology
2023
Institute of Applied Physics and Computational Mathematics
2020
Massachusetts Institute of Technology
2018
Abstract Large scale atomistic simulations provide direct access to important materials phenomena not easily accessible experiments or quantum mechanics-based calculation approaches. Accurate and efficient interatomic potentials are the key enabler, but their development remains a challenge for complex and/or phenomena. Machine learning potentials, such as Deep Potential (DP) approach, robust means produce general purpose potentials. Here, we methodology specialising machine high fidelity of...
To predict and understand the properties of polymer networks, it is necessary to quantify network defects. Of various possible defects, loops are perhaps most pervasive yet difficult directly measure. Network disassembly spectrometry (NDS) has previously enabled counting simplest loops-primary loops-but higher-order loops, e.g., secondary have remained elusive. Here, we report that introduction a nondegradable tracer within NDS framework enables simultaneous measurement primary in end-linked...
BCC transition metals (TMs) exhibit complex temperature and strain-rate dependent plastic deformation behavior controlled by individual crystal lattice defects. Classical empirical semiempirical interatomic potentials have limited capability in modeling defect properties such as the screw dislocation core structures Peierls barriers structure. Machine learning (ML) potentials, trained on DFT-based datasets, shown some successes reproducing properties. However, group VB TMs, most widely used...
Abstract Body-centred-cubic (BCC) transition metals (TMs) tend to be brittle at low temperatures, posing significant challenges in their processing and major concerns for damage tolerance critical load-carrying applications. The brittleness is largely dictated by the screw dislocation core structure; nature control of which has remained a puzzle nearly century. Here, we introduce universal model physics-based material index χ that guides manipulation structure all pure BCC alloys. We show...
BCC transition metals (TMs) exhibit complex temperature and strain-rate dependent plastic deformation behaviour controlled by individual crystal lattice defects. Classical empirical semi-empirical interatomic potentials have limited capability in modelling defect properties such as the screw dislocation core structures Peierls barriers structure. Machine learning (ML) potentials, trained on DFT-based datasets, shown some successes reproducing properties. However, group VB TMs, most...
Ti exhibits complex plastic deformation controlled by active dislocation and twinning systems. Understandings on cores twin interfaces are currently not complete or quantitative, despite extensive experimental simulation studies. Here, we determine all the core interface properties in both HCP BCC using a Deep Potential (DP) DFT. We structures, critical resolved shear stresses mobilities of <a>, <c+a>, <c> dislocations <111>/2 Ti. The <a> slip consists slow migration pyramidal-I planes fast...
Ti exhibits complex plastic deformation controlled by active dislocation and twinning systems. Understandings on cores twin interfaces are currently not complete or quantitative, despite extensive experimental simulation studies. Here, we determine all the core interface properties in both HCP BCC using a Deep Potential (DP) DFT. We structures, critical resolved shear stresses mobilities of hai, hc + ai, hci dislocations h111i/2 Ti. The hai slip consists slow migration pyramidal-I planes...