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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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深度学习算法应用于计算化学.

Abimael Guzman-Pando1, Graciela Ramirez-Alonso2, Carlos Arzate-Quintana1

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概括
此摘要是机器生成的。

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关键词:
人工智能的人工智能深度学习是一种深度学习.图形表示图形表示.分子设计分子设计

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科学领域:

  • 计算化学是一种计算化学.
  • 分子科学 分子科学
  • 化学领域的人工智能

背景情况:

  • 深度学习 (DL) 在分子科学中越来越多地使用,显示出高性能和通用性.
  • 现有的DL模型有局限性,它们的比较优势/劣势对于新手来说并不清楚.
  • 对DL算法的清晰理解对于推进计算化学至关重要.

研究的目的:

  • 审查应用到计算化学中的分子挑战的深度学习算法.
  • 提供传统和几何深度学习模型的全面分类.
  • 分析这些算法的关键特性,开放问题和应用.

主要方法:

  • 将DL算法分为传统和几何方法的分类.
  • 分析输入描述符,数据集,代码可用性和任务解决方案.
  • 对分子算法设计的研究应用,趋势和未来方向的审查.

主要成果:

  • 详细分析传统和几何DL算法,包括它们的优缺点.
  • 关于数据集,库,计算成本 (GPU,时间) 和优化方案的信息.
  • 确定适合特定任务的算法和共同的数据/输入实践.

结论:

  • 该综述提供了计算化学中DL的结构概述,有助于算法选择.
  • 它作为数据集,输入数据和算法技术的参考.
  • 对好处和开放问题的洞察力支持新型计算化学系统的开发.