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

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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自动机器学习管道:大型语言模型辅助自动数据集生成用于训练机器学习的原子间潜力.

Adam Lahouari1, Jutta Rogal1,2, Mark E Tuckerman1,3,4,5,6

  • 1Department of Chemistry, NYU, New York, New York 10003, United States.

Journal of chemical theory and computation
|December 26, 2025
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概括
此摘要是机器生成的。

我们开发了一个自动机器学习管道 (AMLP),以简化机器学习原子间潜能 (MLIP) 的创建和验证. 这条管道以较低的计算成本实现了分子模拟的近量子精度.

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科学领域:

  • 计算化学计算化学
  • 材料科学 材料科学 材料科学
  • 人工智能的人工智能

背景情况:

  • 机器学习原子间潜力 (MLIP) 为分子模拟提供了近量子精度,但其开发具有挑战性.
  • 目前的MLIP开发需要广泛的数据生成,结构预处理和模型培训/验证.

研究的目的:

  • 引入一个自动化机器学习管道 (AMLP),集成整个MLIP工作流.
  • 利用大型语言模型代理来自动化代码选择,输入准备和输出转换.

主要方法:

  • 该AMLP管道使用大型语言模型代理和MACE架构.
  • 基于ASE的分析套件 (AMLP-Analysis) 支持各种分子模拟.
  • 该管道在acridine多态上得到了验证.

主要成果:

  • 微调基础模型实现了平均绝对误差为1.7 meV/原子 (能量) 和7.0 meV/Å (力).
  • 由此产生的MLIP准确地复制了DFT几何形状 (精度低于Å).
  • 在微规范和规范组合分子动力学模拟中,MLIP表现出稳定性.

结论:

  • 在AMLP显著简化和自动化可靠的MLIPs的发展.
  • 开发的MLIP提供了准确和稳定的模拟,扩展了计算方法的功能.