Investigating the use of Deep Learning, in Materials Research for Predicting Material Properties, Identifying new Materials, and Optimizing Material Selection for Mechanical Components

Q4 Engineering
弹道学报 Pub Date : 2024-01-10 DOI:10.52783/dxjb.v36.124
Et al. Mohan Raparthi
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引用次数: 0

Abstract

The rapid advancements in deep learning techniques have spurred a paradigm shift in materials research, revolutionizing the way we predict material properties, identify novel materials, and optimize material selection for mechanical components. This paper explores the integration of deep learning methodologies into materials science, presenting a comprehensive investigation into their efficacy and potential applications. The paper explores the development of deep learning models for predicting material properties.[1] Leveraging vast datasets containing information on diverse materials and their corresponding properties, we delve into the application of neural networks to establish robust predictive models. By extracting complex relationships within the data, deep learning facilitates the accurate estimation of material characteristics, enabling researchers and engineers to streamline the materials discovery process. In addition to property prediction, the study explores the role of deep learning in the identification of new materials with superior or tailored attributes. By training models on extensive databases encompassing known materials and their functionalities, we investigate the ability of deep learning algorithms to suggest novel materials with specific desired properties. This capability holds immense promise for accelerating the discovery of innovative materials, especially in fields where tailored material performance is critical. Furthermore, the paper examines the utilization of deep learning in optimizing material selection for mechanical components. By considering a holistic approach that factors in mechanical, thermal, and other relevant properties, we explore how neural networks can assist in selecting the most suitable materials for specific applications. This not only enhances the efficiency of the design process but also contributes to the development of more durable, efficient, and sustainable mechanical components. Through a systematic exploration of the integration of deep learning in materials research, this paper provides valuable insights into the transformative potential of these techniques. The findings contribute to the ongoing discourse on the intersection of artificial intelligence and materials science, paving the way for accelerated advancements in materials discovery, design, and application.
研究深度学习在材料研究中的应用,以预测材料特性、识别新材料并优化机械部件的材料选择
深度学习技术的飞速发展推动了材料研究范式的转变,彻底改变了我们预测材料特性、识别新型材料和优化机械部件材料选择的方式。本文探讨了将深度学习方法融入材料科学的问题,对其功效和潜在应用进行了全面研究。本文探讨了用于预测材料特性的深度学习模型的开发。[1] 利用包含各种材料及其相应特性信息的庞大数据集,我们深入研究了神经网络的应用,以建立稳健的预测模型。通过提取数据中的复杂关系,深度学习有助于准确估计材料特性,使研究人员和工程师能够简化材料发现过程。除特性预测外,该研究还探讨了深度学习在识别具有卓越或定制属性的新材料中的作用。通过在包含已知材料及其功能的广泛数据库中训练模型,我们研究了深度学习算法建议具有特定所需属性的新型材料的能力。这种能力为加速发现创新材料带来了巨大希望,尤其是在定制材料性能至关重要的领域。此外,本文还探讨了如何利用深度学习优化机械部件的材料选择。通过考虑机械、热和其他相关性能因素的整体方法,我们探索了神经网络如何帮助为特定应用选择最合适的材料。这不仅能提高设计过程的效率,还有助于开发更耐用、更高效、更可持续的机械部件。通过系统地探索深度学习在材料研究中的整合,本文对这些技术的变革潜力提供了宝贵的见解。这些研究成果为当前有关人工智能与材料科学交叉的讨论做出了贡献,为加快材料发现、设计和应用的进步铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
弹道学报
弹道学报 Engineering-Mechanical Engineering
CiteScore
0.90
自引率
0.00%
发文量
2632
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