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贝叶斯优化与高斯过程,辅助深度学习,用于材料设计
1Institute for Materials Research, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan.
The journal of physical chemistry letters
|May 19, 2025
概括
深度内核学习 (DKL) 通过将神经网络与高斯过程 (GPs) 结合起来,增强材料发现的贝叶斯优化 (BO). 在探索材料属性方面,DKL表现出比标准GP更高的效率,为更快的材料探索铺平了道路.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 机器学习 (ML) 对于加速材料发现至关重要.
- 使用高斯过程 (GPs) 的贝叶斯优化 (BO) 是材料探索的一个常见方法.
- 基于GP的BO效率受到手动特征工程需求的限制.
研究的目的:
- 调查深度内核学习 (DKL) 与BO结合用于材料发现的有效性.
- 为了比较基于DKL的BO与基于标准GP的BO的性能.
主要方法:
- 深度内核学习 (DKL) 的应用,将神经网络与GPs集成到贝叶斯优化 (BO).
- 在氧化物数据集 (922条目) 上对带间隙,介电常数和电子有效质量进行DKL模型效率的评估.
- 对混合有机-无机矿合金带间隙 (610条) 的DKL性能评估和4560合金的库里温度预测.
主要成果:
- 在氧化物和矿数据集上,基于DKL的BO表现出与基于GP的标准BO相比具有可比或更高的效率.
- 标准GP的表现优于DKL,当可以直接使用与里温度有很强的相关性描述符时.
- 德克尔的转移学习能力被证明可以进一步提高其效率.
结论:
- 与深度内核学习增强的贝叶斯优化与标准高斯过程相比,为探索各种材料空间提供了更有效的方法.
- DKL解决了传统的基于GP的BO的特征工程限制.
- 基于DKL的BO具有显著的前景,可以加速新材料的发现.
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