A physics-informed multi-agents model to predict thermo-oxidative/hydrolytic aging of elastomers

Ghaderi, A.; Morovati, Vahid; Yang, Chen; Dargazany, Roozbeh

doi:10.1016/j.ijmecsci.2022.107236

Cited by 16 publications

(3 citation statements)

References 80 publications

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“…The desired metrics are more like composite functions of these directly measurable and well-defined quantities, and obtaining empirically a general form for the relevant equations is often not straightforward and can require significant expert knowledge and experience. Thankfully, a variety of other methods have built on or expanded beyond the original approach to incorporate information on chemical kinetics, [112,115,116] thermochemical/thermophysical information, [115,[117][118][119][120] mass/energy balances, [121][122][123] reactor engineering dynamics, [124,125] computed interatomic potentials, [126] and optics. [127] Many of these expanded approaches enable embedding of chemical information and may no longer be restricted to only finding solutions to and/or fitting PDEs with a previously known form.…”

Section: A Way Forwardmentioning

confidence: 99%

“…Thankfully, a variety of other methods have built on or expanded beyond the original approach to incorporate information on chemical kinetics, [ 112 , 115 , 116 ] thermochemical/thermophysical information, [ 115 , 117 , 118 , 119 , 120 ] mass/energy balances, [ 121 , 122 , 123 ] reactor engineering dynamics, [ 124 , 125 ] computed interatomic potentials, [ 126 ] and optics. [ 127 ] Many of these expanded approaches enable embedding of chemical information and may no longer be restricted to only finding solutions to and/or fitting PDEs with a previously known form.…”

Section: A Way Forwardmentioning

confidence: 99%

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Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design

Loh,

Wang,

Mohan

et al. 2024

Advanced Science

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Although solar fuels photocatalysis offers the promise of converting carbon dioxide directly with sunlight as commercially scalable solutions have remained elusive over the past few decades, despite significant advancements in photocatalysis band‐gap engineering and atomic site activity. The primary challenge lies not in the discovery of new catalyst materials, which are abundant, but in overcoming the bottlenecks related to material‐photoreactor synergy. These factors include achieving photogeneration and charge‐carrier recombination at reactive sites, utilizing high mass transfer efficiency supports, maximizing solar collection, and achieving uniform light distribution within a reactor. Addressing this multi‐dimensional problem necessitates harnessing machine learning techniques to analyze real‐world data from photoreactors and material properties. In this perspective, the challenges are outlined associated with each bottleneck factor, review relevant data analysis studies, and assess the requirements for developing a comprehensive solution that can unlock the full potential of solar fuels photocatalysis technology. Physics‐informed machine learning (or Physics Neural Networks) may be the key to advancing this important area from disparate data towards optimal reactor solutions.

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Section: A Way Forwardmentioning

confidence: 99%

Section: A Way Forwardmentioning

confidence: 99%

Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design

Loh,

Wang,

Mohan

et al. 2024

Advanced Science

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show abstract

“…Doblies et al [142] used an artificial neural network (ANN) and Fourier-transform infrared spectroscopy (FTIR) to predict and quantify the aging time, temperature, and residual strength of an epoxy resin. Ghaderi et al [143] introduced a physics-informed multi-agent constitutive model to describe the effect of single-mechanism chemical aging on the behavior of the cross-linked elastomer. Therefore, the development of lifetime prediction models for polymer materials through artificial intelligence and big data methods will be a promising direction in the subsequent research.…”

Section: The Aging Database and Machine Learningmentioning

confidence: 99%

Investigation of Polymer Aging Mechanisms Using Molecular Simulations: A Review

Zhang

Yang

2023

Polymers

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Aging has a serious impact on the properties of functional polymers. Therefore, it is necessary to study the aging mechanism to prolong the service and storage life of polymer-based devices and materials. Due to the limitations of traditional experimental methods, more and more studies have adopted molecular simulations to analyze the intrinsic mechanisms of aging. In this paper, recent advances in molecular simulations of the aging of polymers and their composites are reviewed. The characteristics and applications of commonly used simulation methods in the study of the aging mechanisms (traditional molecular dynamics simulation, quantum mechanics, and reactive molecular dynamics simulation) are outlined. The current simulation research progress of physical aging, aging under mechanical stress, thermal aging, hydrothermal aging, thermo-oxidative aging, electric aging, aging under high-energy particle impact, and radiation aging is introduced in detail. Finally, the current research status of the aging simulations of polymers and their composites is summarized, and the future development trend has been prospected.

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Constitutive behavior and failure prediction of crosslinked polymers exposed to concurrent fatigue and thermal aging: a reduced-order knowledge-driven machine-learned model

Ghaderi,

Ayoub,

Dargazany

2023

J Mater Sci

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A physics-informed multi-agents model to predict thermo-oxidative/hydrolytic aging of elastomers

Cited by 16 publications

References 80 publications

Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design

Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design

Investigation of Polymer Aging Mechanisms Using Molecular Simulations: A Review

Constitutive behavior and failure prediction of crosslinked polymers exposed to concurrent fatigue and thermal aging: a reduced-order knowledge-driven machine-learned model

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