Review of progress in calculation and simulation of high-temperature oxidation

Progress in Materials Science, Journal Year: 2024, Volume and Issue: 147, P. 101348 - 101348

Published: July 31, 2024

Language: Английский

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Introduction to Predicting Properties of Organic Materials

Challenges and advances in computational chemistry and physics, Journal Year: 2025, Volume and Issue: unknown, P. 27 - 63

Published: Jan. 1, 2025

Language: Английский

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Multiscale computational modeling techniques in study and design of 2D materials: recent advances, challenges, and opportunities

2D Materials, Journal Year: 2024, Volume and Issue: 11(4), P. 042004 - 042004

Published: Sept. 9, 2024

Abstract This article provides an overview of recent advances, challenges, and opportunities in multiscale computational modeling techniques for study design two-dimensional (2D) materials. We discuss the role understanding structures properties 2D materials, followed by a review various length-scale models aiding their synthesis. present integration including density functional theory, molecular dynamics, phase-field modeling, continuum-based mechanics, machine learning. The focuses on advancements, future prospects tailored emerging Key challenges include accurately capturing intricate behaviors across scales environments. Conversely, lie enhancing predictive capabilities to accelerate materials discovery applications spanning from electronics, photonics, energy storage, catalysis, nanomechanical devices. Through this comprehensive review, our aim is provide roadmap research simulation

Language: Английский

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

Polymers, Journal Year: 2024, Volume and Issue: 16(23), P. 3368 - 3368

Published: Nov. 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

Language: Английский

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A computational approach for prediction of viscosity of chemical compounds based on molecular structures

Results in Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 102039 - 102039

Published: Jan. 1, 2025

Language: Английский

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ConvFeatNet Ensemble: Integrating Microstructure and Pre-defined Features for Enhanced Prediction of Porous Material Properties

Materials Science and Engineering A, Journal Year: 2025, Volume and Issue: unknown, P. 148173 - 148173

Published: March 1, 2025

Language: Английский

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Physics-informed machine learning for Na-Ion conductivity and activation energy

Journal of Non-Crystalline Solids, Journal Year: 2025, Volume and Issue: 657, P. 123497 - 123497

Published: March 11, 2025

Language: Английский

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Leveraging high-throughput molecular simulations and machine learning for the design of chemical mixtures

npj Computational Materials, Journal Year: 2025, Volume and Issue: 11(1)

Published: March 17, 2025

Language: Английский

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Knowledge-driven eutectic electrolyte design for Zn-ion batteries

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 161712 - 161712

Published: March 1, 2025

Language: Английский

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Role of artificial intelligence in the design and discovery of next-generation battery electrolytes

Chemical Physics Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 1, 2025

Adverse climate change, global warming, and energy security have emerged as challenges, demanding advancements in high-performance battery technologies to drive sustainability. In this scenario, developing electrolytes has gained significant momentum among various innovations, given their critical role determining safety performance. However, the conventional trial-and-error approach electrolyte discovery is costly, complex, time-consuming, often inefficient. Recent artificial intelligence (AI) over past decade catalyzed innovations across diverse fields, ranging from nanotechnology space explorations, are now emerging a powerful tool for materials discovery. Numerous studies demonstrated effectiveness of AI screening characterizing next-generation electrolytes. This review offers comprehensive outlook on transformative designing novel Examination key parameters that influence electrochemical performance batteries conducted. The challenges opportunities using design with tailored properties explored. Furthermore, futuristic vision integrating science-driven AI-based approaches existing experimental theoretical methods accelerate presented. By offering such understanding, aims provide researchers, industries, policymakers insights into how can be leveraged electrolytes, paving way toward progress technology.

Language: Английский

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