An Overview of Utilizing Artificial Intelligence in theSimulation Process and Predicting Epoxy Resin Properties

سال انتشار: 1402
محل انتشار: هفتمین کنفرانس بین المللی توسعه فناوری در مهندسی شیمی
کد COI اختصاصی: CHEMISB07_004
زبان مقاله: انگلیسی
تعداد مشاهده: 150

نویسندگان

Centre for Future Materials, University of Southern Queensland, Springfield ۴۳۴۴, Australia

School of Agriculture and Environmental Science, University of Southern Queensland, Springfield ۴۳۴۴,Australia

چکیده

Epoxy resin is extensively employed in a wide range of applications due to its exceptional properties. Theperformance of epoxy systems exhibits substantial variations based on the specific combination of the baseresin and curing agent. The characteristics of the resin system undergo significant variations because of thediverse combinations of the base resin, curing agent, and toughening agent. Thus, predicting the properties ofepoxy resin composites is a challenge for researchers since the simulation process can be affected by variousvariables. Molecular dynamics (MD) simulation has always been used as an effective tool for designing andpredicting the properties of epoxy resin systems. However, molecular dynamic approaches can be timeconsumingand, in some cases, costly. Recently, the integration of machine learning (ML) with moleculardynamics (MD) simulations has attracted the attention of researchers as an effective tool for designing epoxyresin composites. Herein, the attempts to predict the properties of epoxy systems have been reviewed in thispaper to fully investigate the advantages of the integration of molecular dynamics (MD) and machinelearning approaches. Moreover, the prospects and research gaps of this new method have been expressed forfuture research opportunities.

کلیدواژه ها

Machine Learning, Molecular Dynamics, Epoxy Resins

مقالات مرتبط جدید

اطلاعات بیشتر در مورد COI

COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.

کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.