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Updated: Sep 11, 2025
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通过使用Optuna和TensorFlow优化人工神经网络,提高了对矿密度和热膨胀的预测
Eli I Assaf1, Xueyan Liu1, Sandra Erkens1,2
1Delft University of Technology, Delft, the Netherlands.
MethodsX
|August 14, 2025
概括
人工神经网络 (ANN) 现在比随机森林回归器 (RFR) 更准确地预测青的特性. 这种自动化框架增强了概括性,并降低了材料科学研究的计算成本.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 机器学习 机器学习
背景情况:
- 之前的研究使用随机森林回归器 (RFRs) 来从分子动力学 (MD) 模拟中估计的物理性质.
- 由于它们的决策树结构,RFR在插值和外推方面存在局限性,限制了超出训练数据的预测能力.
研究的目的:
- 开发一个使用人工神经网络 (ANN) 进行预测密度和热膨胀系数的自动化框架.
- 与以前基于RFR的方法相比,提高模型的概括性,连续性和预测准确性.
- 为了使可靠的属性预测组合和条件没有明确覆盖模拟.
主要方法:
- 采用人工神经网络 (ANN) 来增强属性的预测建模.
- 开发了一个完全自动化的框架,用于构建机器学习模型 (MLM).
- 利用Optuna进行自动化超参数优化,以最大限度地提高模型效率和从MD模拟中提取信息.
主要成果:
- 在预测密度方面,ANN模型取得了很高的准确性,R平方值超过0.99.
- 平均平方误差 (MSE) 在测试数据上低于0.1%,最大绝对误差低于5%.
- 开发的模型展示了改进的插值和外推能力,用于预测未见的合物的特性.
结论:
- 从RFRs过渡到ANNs显著提高了模型概括,插值和预测准确性.
- 与Optuna一起的自动化框架优化了超参数调整,最大限度地提高了MD模拟数据的实用性.
- 这种方法扩大了预测建模的适用性,允许进行属性预测,而不需要额外的计算密集型MD模拟.
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