Constitutive modeling of amorphous thermoplastics from low to high strain rates: Formulation and critical comparison employing an optimization-based parameter identification

Alves, A. Francisca Carvalho; Ferreira, Bernardo P.; Pires, F.M. Andrade

doi:10.1016/j.ijsolstr.2023.112258

Cited by 5 publications

(1 citation statement)

References 36 publications

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“…We also know that the cohesive energy, which is also related to the Debye-Waller factor, is used to compute the bulk modulus which governs all other mechanical properties in models like the group interaction model (GIM) 49 and other constitutive laws. 50 This implies that there is a correlation between molecular mobility and mechanical properties. The outperformance of the model using d=B-factor supports the notion that the segmental molecular mobility in proteins is strongly related to macroscale mechanical properties.…”

Section: Choice Of Enrichment Descriptor Of Amino Acidsmentioning

confidence: 99%

Sequence-based data-constraint deep learning framework to predict spider dragline mechanical properties

Keten,

Pandey,

Chen

2023

Preprint

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We establish a deep-learning framework for describing the mechanical behavior of spider dragline silks to clarify the missing link between the sequence and mechanics of this exceptionally strong and tough biomaterial. The method utilizes sequence and mechanical property data of dragline spider silk as well as enriching descriptors such as residue-level mobility (B-factor) predictions. Our sequence representation captures the relative position, repetitiveness, as well as descriptors of amino acids that serve to physically enrich the model. We obtain high Pearson correlation coefficients (0.76-0.88) for strength, toughness, and other properties, which show that our B-factor based representation outperforms pure sequence-based models or models that use other descriptors. We prove the utility of our framework by identifying influential motifs, and also by demonstrating how the B-factor serves to pinpoint potential mutations that improve strength and toughness, thereby establishing a validated, predictive, and interpretable sequence model for designing sustainable biomaterials with sequence-defined properties.

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Section: Choice Of Enrichment Descriptor Of Amino Acidsmentioning

confidence: 99%

Sequence-based data-constraint deep learning framework to predict spider dragline mechanical properties

Keten,

Pandey,

Chen

2023

Preprint

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Modeling the viscoelastic-viscoplastic behavior of glassy polymer membrane with consideration of non-uniform sub-chains in entangled networks

Zhou,

Qiu,

Peng

et al. 2023

International Journal of Solids and Structures

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Sequence-based data-constrained deep learning framework to predict spider dragline mechanical properties

Pandey,

Chen,

Keten

2024

Commun Mater

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Spider dragline silk is known for its exceptional strength and toughness; hence understanding the link between its primary sequence and mechanics is crucial. Here, we establish a deep-learning framework to clarify this link in dragline silk. The method utilizes sequence and mechanical property data of dragline spider silk as well as enriching descriptors such as residue-level mobility (B-factor) predictions. Our sequence representation captures the relative position, repetitiveness, as well as descriptors of amino acids that serve to physically enrich the model. We obtain high Pearson correlation coefficients (0.76–0.88) for strength, toughness, and other properties, which show that our B-factor based representation outperforms pure sequence-based models or models that use other descriptors. We prove the utility of our framework by identifying influential motifs and demonstrating how the B-factor serves to pinpoint potential mutations that improve strength and toughness, thereby establishing a validated, predictive, and interpretable sequence model for designing tailored biomaterials.

show abstract

Constitutive modeling of amorphous thermoplastics from low to high strain rates: Formulation and critical comparison employing an optimization-based parameter identification

Cited by 5 publications

References 36 publications

Sequence-based data-constraint deep learning framework to predict spider dragline mechanical properties

Sequence-based data-constraint deep learning framework to predict spider dragline mechanical properties

Modeling the viscoelastic-viscoplastic behavior of glassy polymer membrane with consideration of non-uniform sub-chains in entangled networks

Sequence-based data-constrained deep learning framework to predict spider dragline mechanical properties

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