Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review

Polymers, Год журнала: 2024, Номер 16(23), С. 3368 - 3368

Опубликована: Ноя. 29, 2024

The integration of machine learning (ML) into material manufacturing has driven advancements in optimizing biopolymer production processes. ML techniques, applied across various stages production, enable the analysis complex data generated throughout identifying patterns and insights not easily observed through traditional methods. As sustainable alternatives to petrochemical-based plastics, biopolymers present unique challenges due their reliance on variable bio-based feedstocks processing conditions. This review systematically summarizes current applications techniques aiming provide a comprehensive reference for future research while highlighting potential enhance efficiency, reduce costs, improve product quality. also shows role algorithms, including supervised, unsupervised, deep

Язык: Английский

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Automated atomistic simulations of dissociated dislocations with ab initio accuracy

Physical review. B./Physical review. B, Год журнала: 2024, Номер 109(9)

Опубликована: Март 27, 2024

In a previous work [M. Hodapp and A. Shapeev, Mach. Learn.: Sci. Technol. 1, 045005 (2020)], we proposed an algorithm that fully automatically trains machine-learning interatomic potentials (MLIPs) during large-scale simulations, successfully applied it to simulate screw dislocation motion in body-centered-cubic tungsten. The identifies local subregions of the simulation region where potential extrapolates, then constructs periodic configurations 100--200 atoms out these nonperiodic can be efficiently computed with plane-wave density functional theory (DFT) codes. this work, extend dissociated dislocations arbitrary character angles apply partial face-centered-cubic aluminum. Given excellent agreement available DFT reference results, argue our has become universal way simulating possibly other materials, such as hexagonal-closed-packed magnesium, their alloys. Moreover, used construct reliable training sets for MLIPs simulations curved dislocations.

Язык: Английский

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Surrogate Models for Vibrational Entropy Based on a Spatial Decomposition

Multiscale Modeling and Simulation, Год журнала: 2025, Номер 23(1), С. 514 - 544

Опубликована: Фев. 17, 2025

Язык: Английский

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A posteriori error estimate and adaptivity for QM/MM models of crystalline defects

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2024, Номер 428, С. 117097 - 117097

Опубликована: Июнь 3, 2024

Язык: Английский

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