.png){kind=icon}
返回
Can exact conditions improve machine-learned density functionals?
.png){kind=icon}
PRE
AI
DOI：10.1063/1.5025668.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Historical methods of functional development in density functional theory have often been guided by analytic conditions that constrain the exact functional one is trying to approximate. Recently, machine-learned functionals have been created by interpolating the results from a small number of exactly solved systems to unsolved systems that are similar in nature. For a simple one-dimensional system, using an exact condition, we find improvements in the learning curves of a machine learning approximation to the non-interacting kinetic energy functional. We also find that the significance of the improvement depends on the nature of the interpolation manifold of the machine-learned functional. Published by AIP Publishing.
Keyword:
QUANTUM
APPROXIMATION
CHEMISTRY
MODEL
DFT
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.1
论文数:
7.2W
被引数:
23.2W
机构
引用论文
Job tenure and quality of work life of people with psychiatric disabilities working in social enterprises在社会企业工作的精神病患者的工作任期和工作生活质量
PREAI
分享收藏One-dimensional mimicking of electronic structure: The case for exponentials.png)
PHYSICAL REVIEW B
OAAI
PHYSICAL REVIEW BIF3.7
OAAI分享收藏分享收藏分享收藏分享收藏Semi-local machine-learned kinetic energy density functional with third-order gradients of electron density具有电子密度三阶梯度的半局部机器学习动能密度泛函
PREAI
分享收藏Robust Optimization of Power Consumption for Public Buildings Considering Forecasting Uncertainty of Environmental Factors考虑环境因素预测不确定性的公共建筑用电鲁棒优化EnergiesOAAI
EnergiesIF0
OAAI分享收藏
查看更多内容