Ke Xu
Materials Science · Purdue University West Lafayette
Publications
188
Citations
4,103
Est. group size
—
Recurring co-author estimate
Active years
41
Publishing since 1986
Ke Xu's work centers on computational materials science, particularly developing and applying machine-learning interatomic potentials (models trained to predict how atoms interact, allowing large and fast atomistic simulations) to study heat transport, mechanical behavior, and structural transitions in materials such as graphene, diamond, silicon nanowires, and two-dimensional materials. A notable focus is the 'neuroevolution potential' (NEP) method and the GPUMD simulation software, which are used to model thermal conductivity, phonon transport, and thermoelectric performance. Note that the publication list also includes several unrelated biomedical and food-science papers, suggesting either a name-sharing issue in the data or a very broad, multidisciplinary output; prospective students should verify authorship carefully.
Publication output has grown from a handful of papers in 2017 to over 20 per year by 2022 and 2025, indicating an overall increasing and fairly high publication activity in recent years, though with some year-to-year fluctuation.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- qNEP: A Highly Efficient Neuroevolution Potential with Dynamic Charges for Large-Scale Atomistic Simulations
Journal of Chemical Theory and Computation · 2026
- Recent Advances and Challenges of Textile-Based Triboelectric Nanogenerators for Smart Healthcare and Sports Applications
Nanomaterials · 2026
- Machine Learning-Based Multiscale Geomechanical Modeling
Energy & Fuels · 2026
- Synergistic Mechanisms and Product Regulation in the Co-Pyrolysis of Biomass and Food Packaging Waste: A Study Based on Reaction Kinetics and GHG Calculation
Foods · 2026
- WTAP-regulated m6A modification contributes to cuproptosis in cardiomyocytes and diabetic cardiomyopathy
Free Radical Biology and Medicine · 2026
- Accelerated phonon transport calculations for nanostructures: Combining neuroevolution potentials and compressed sensing
Journal of Applied Physics · 2026
- Effect of Various Mulberry Leaf Powders on the Quality of Artificial Diet for Domestic Silkworm, Bombyx mori
Insects · 2026
- GPUMD 4.0: A high‐performance molecular dynamics package for versatile materials simulations with machine‐learned potentials
Materials Genome Engineering Advances · 2025
- Advances in modeling complex materials: The rise of neuroevolution potentials
Chemical Physics Reviews · 2025
- Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materials
The Journal of Chemical Physics · 2025
- Effect of ultrasound synergistic pH shift modification treatment on Hericium erinaceus protein structure and its application in 3D printing
International Journal of Biological Macromolecules · 2025
- NEP-MB-pol: a unified machine-learned framework for fast and accurate prediction of water’s thermodynamic and transport properties
npj Computational Materials · 2025
- PYSED: A tool for extracting kinetic-energy-weighted phonon dispersion and lifetime from molecular dynamics simulations
Journal of Applied Physics · 2025
- Probing the ideal limit of interfacial thermal conductance in two-dimensional van der Waals heterostructures
npj Computational Materials · 2025
- Stress‐Driven Grain Boundary Structural Transition in Diamond by Machine Learning Potential
Small · 2025
- arXiv (Cornell University)×15
- The Journal of Chemical Physics×4
- Nanoscale×4
- Research Square×4
- Journal of Applied Physics×3
- Prabudhya Roy Chowdhury
Materials Science · Purdue University West Lafayette
- Nan Jiang
Materials Science · Purdue University West Lafayette
- Aalok U. Gaitonde
Materials Science · Purdue University West Lafayette
- Thomas E. Beechem
Materials Science · Purdue University West Lafayette
- Timothy S. Fisher
Materials Science · Purdue University West Lafayette
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.
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