Shengfeng Yang
Materials Science · Purdue University West Lafayette
Publications
44
Citations
752
Est. group size
~1
Recurring co-author estimate
Active years
19
Publishing since 2008
Shengfeng Yang's work uses atomistic and computational simulations to study how tiny defects—like grain boundaries, vacancies, and dopants—affect the strength and behavior of metals and other materials at the nanoscale. Recent projects apply machine learning methods, including generative models, to predict how cracks and microstructures evolve in materials such as aluminum nitride and nanocrystalline metals. The publication record also includes some unrelated topics, such as marketing analytics, water treatment materials, and seismic response of tunnels, suggesting involvement in varied collaborative projects.
Publication output has been irregular over the last decade, with a peak in 2018, a gap in 2022, and a recent uptick from 2023 to 2026, averaging under 3 papers per year over the last 5 years.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Comparative Study on Model Applicability for Longitudinal Seismic Response of Shield Tunnels Under Design Earthquake Loading
Buildings · 2026
- Diffusion-based Generative Machine Learning Model for Predicting Crack Propagation in Aluminum Nitride at the Atomic Scale
arXiv (Cornell University) · 2026
- A machine learning framework for missing and imbalanced data in marketing analytics
Journal of Marketing Analytics · 2025
- Atomistic Modeling of Interfacial Cracking in Copper-To-Copper Direct Bonding
2025
- Software anomaly detection technology based on deep learning
Procedia Computer Science · 2025
- Modeling of Microstructural Evolution within TSVs Using Atomistic Simulations
2025
- Rapid prediction of grain boundary network evolution in nanomaterials utilizing a generative machine learning approach
Extreme Mechanics Letters · 2024
- Rapid Prediction of Grain Boundary Network Evolution in Nanomaterials Utilizing a Generative Machine Learning Approach
SSRN Electronic Journal · 2024
- First-Principles Study of Beryllium Thermodynamics and Clustering Mechanism in Molybdenum: Effects of Vacancies and Self-Interstitial Atoms
SSRN Electronic Journal · 2024
- Polysaccharides from marine biological resources and their anticancer activity on breast cancer
RSC Medicinal Chemistry · 2023
- Mn–Ce oxide-modified activated carbon composites as efficient adsorbents for removing As( <scp>iii</scp> ) from water: adsorption performance and mechanisms
New Journal of Chemistry · 2023
- Phase-field-lattice Boltzmann method for dendritic growth with melt flow and thermosolutal convection–diffusion
Computer Methods in Applied Mechanics and Engineering · 2021
- First-principles study of vacancy interaction with grain boundaries of tungsten under tensile strains
Computational Materials Science · 2021
- Effects of magnesium dopants on grain boundary migration in aluminum-magnesium alloys
Computational Materials Science · 2020
- The application of numerical simulation coupled flow and geomechanics in the study of geology-engineering integration
IOP Conference Series Earth and Environmental Science · 2020
- arXiv (Cornell University)×3
- Computational Materials Science×2
- JOM×2
- Author eBooks×2
- IOP Conference Series Earth and Environmental Science×2
- Stephen R. Niezgoda
Materials Science · The Ohio State University
- Bo Yang
Materials Science · Purdue University West Lafayette
- Gregory Sparks
Materials Science · The Ohio State University
- Anter El–Azab
Materials Science · Purdue University West Lafayette
- Chaitali S. Patil
Materials Science · The Ohio State University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile