Learning-based approach to plasticity in athermal sheared amorphous packings: Improving softness

Abstract

The plasticity of amorphous solids undergoing shear is characterized by quasi-localized rearrangements of particles. While many models of plasticity exist, the precise relationship between plastic dynamics and the structure of a particle's local environment remains an open question. Previously, machine learning was used to identify a structural predictor of rearrangements, called "softness." Although softness has been shown to predict which particles will rearrange with high accuracy, the method can be difficult to implement in experiments where data is limited and the combinations of descriptors it identifies are often difficult to interpret physically. Here we address both of these weaknesses, presenting two major improvements to the standard softness method. First, we present a natural representation of each particle's observed mobility, allowing for the use of statistical models which are both simpler and provide greater accuracy in limited data sets. Second, we employ persistent homology as a systematic means of identifying simple, topologically-informed, structural quantities that are easy to interpret and measure experimentally. We test our methods on two-dimensional athermal packings of soft spheres under quasi-static shear. We find that the same structural information which predicts small variations in the response is also predictive of where plastic events will localize. We also find that excellent accuracy is achieved in athermal sheared packings using simply a particle's species and the number of nearest neighbor contacts.

arXiv comment21 pages (double column), 12 figures

References in corpus (15)

• Scikit-learn: Machine Learning in Python
• Dynamics of Viscoplastic Deformation in Amorphous Solids
• Jamming at Zero Temperature and Zero Applied Stress: the Epitome of Disorder
• A roadmap for the computation of persistent homology
• Amorphous Systems in Athermal, Quasistatic Shear
• Identifying structural flow defects in disordered solids using machine learning methods
• Vibrational modes identify soft spots in a sheared disordered packing
• Hierarchical structures of amorphous solids characterized by persistent homology
• Elastic consequences of a single plastic event : a step towards the microscopic modeling of the flow of yield stress fluids
• Machine learning determination of atomic dynamics at grain boundaries
• Elastic heterogeneity, vibrational states, and thermal conductivity across an amorphisation transition
• Quantifying force networks in particulate systems
• Contact Changes near Jamming
• Anisotropic Structural Predictor in Glassy Materials
• Two classes of events in sheared particulate matter