同时提高材料属性预测任务的参数约束下的精度和计算成本
Vishu Gupta1, Youjia Li1, Alec Peltekian2
1Department of Electrical and Computer Engineering, Northwestern University, Evanston, USA.
Journal of cheminformatics
|February 16, 2024
概括
我们介绍了iBRNet,这是一个用于材料属性预测的新型深度神经网络. 这种模型显著减少了培训时间和计算资源,同时与现有的机器学习和深度学习方法相比,提高了准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 在材料中的机器学习
背景情况:
- 机器学习 (ML) 和深度学习 (DL) 是有效的材料属性预测.
- 当前的深度神经网络 (DNN) 模型通常需要大量的计算资源和长时间的训练时间.
- 需要有效的DNN来平衡材料研究的预测准确性和计算可行性.
研究的目的:
- 提出一个新的深度神经网络框架,iBRNet,用于基于回归的材料属性预测.
- 开发一个DNN架构,减少模型参数和训练时间,而不牺牲准确性.
- 评估iBRNet在各种数据集中与传统的ML和现有的DL模型对比的性能.
主要方法:
- 开发了iBRNet,这是一个具有分支跳过连接和多个调度器的深度回归神经网络.
- 使用基于构成的数量向量来表示元素分数,作为模型训练的输入.
- 在多个数据集上比较了iBRNet与最先进的ML和DL架构的性能.
主要成果:
- 在所有数据集大小的材料属性预测中,iBRNet 始终优于传统的 ML 和已知的 DL 模型.
- 在iBRNet的分支结构和多个调度器导致了模型参数的减少.
- 与其他神经网络架构相比,iBRNet表现出更快的模型训练时间和更好的融合.
结论:
- 拟议的iBRNet框架为基于回归的材料属性预测提供了一个高效和准确的解决方案.
- 在DNN中结合多个回调函数,有效地减少训练时间,并在计算约束下最大限度地提高准确性.
- iBRNet为研究人员提供了一种有价值的工具,使先进的材料属性预测能够减少计算需求.
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