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The Journal of Chemical Physics|February 10, 2020
Exploring the landscape of Buckingham potentials for silica by machine learning: Soft vs hard interatomic forcefieldsHan Liu, Yipeng Li, Zipeng Fu, et al.
The Journal of Chemical Physics|April 9, 2021
Interatomic potential parameterization using particle swarm optimization: Case study of glassy silicaRasmus Christensen, Søren S Sørensen, Han Liu, et al.
The Journal of Physical Chemistry. B|November 24, 2020
Bulk Metallic Glasses' Response to Oscillatory Stress Is Governed by the Topography of the Energy LandscapeLongwen Tang, Gang Ma, Han Liu, et al.
Materials Horizons|November 25, 2021
The energy landscape governs ductility in disordered materialsLongwen Tang, Han Liu, Gang Ma, et al.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)|January 25, 2024
De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine LearningHan Liu, Liantang Li, Zhenhua Wei, et al.
ACS Nano|November 1, 2021
Predicting Fracture Propensity in Amorphous Alumina from Its Static Structure Using Machine LearningTao Du, Han Liu, Longwen Tang, et al.
Journal of Colloid and Interface Science|February 16, 2019
Long-term creep deformations in colloidal calcium-silicate-hydrate gels by accelerated aging simulationsHan Liu, Shiqi Dong, Longwen Tang, et al.
The Journal of Physical Chemistry. B|January 17, 2017
Correlating the Network Topology of Oxide Glasses with their Chemical DurabilityNerea Mascaraque, Mathieu Bauchy, Morten M Smedskjaer
The Journal of Chemical Physics|July 10, 2020
Precipitation of calcium-alumino-silicate-hydrate gels: The role of the internal stressCheng Zhao, Wei Zhou, Qi Zhou, et al.
Langmuir : the ACS Journal of Surfaces and Colloids|April 26, 2016
Topological Control on Silicates' Dissolution KineticsIsabella Pignatelli, Aditya Kumar, Mathieu Bauchy, et al.
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