Journal
SCIENCE
Volume 358, Issue 6366, Pages 1033-1037Publisher
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/science.aai8830
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Funding
- NSF through the University of Pennsylvania Materials Research Science and Engineering Center (MRSEC) [DMR-1720530]
- Rheology Optical and Electron Microscopy Shared Experimental Facilities
- NSF [DMR-1107642, DMR-1305199, DMR-160738, DMREF-1628407, CMMI-1724519, INSPIRE/EAR-134428]
- LRSM Research Experience for Undergraduates program
- Simons Foundation [327939]
- NASA [NNX0800G]
- Agence Nationale de la Recherche through the Materials World Network program [ANR0110NS09-01]
- American Chemical Society Petroleum Research Fund [ACS-PRF-53948-ND9]
- Division Of Materials Research
- Direct For Mathematical & Physical Scien [1305199] Funding Source: National Science Foundation
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When deformed beyond their elastic limits, crystalline solids flow plastically via particle rearrangements localized around structural defects. Disordered solids also flow, but without obvious structural defects. We link structure to plasticity in disordered solids via a microscopic structural quantity, softness, designed by machine learning to be maximally predictive of rearrangements. Experimental results and computations enabled us to measure the spatial correlations and strain response of softness, as well as two measures of plasticity: the size of rearrangements and the yield strain. All four quantities maintained remarkable commonality in their values for disordered packings of objects ranging from atoms to grains, spanning seven orders of magnitude in diameter and 13 orders of magnitude in elastic modulus. These commonalities link the spatial correlations and strain response of softness to rearrangement size and yield strain, respectively.
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