新型环保绝缘气体分子设计数据集

鉴于绝缘气体所需满足的多维度性能之间存在相互制约或依赖关系，依靠试错试验迄今仍未能发现性能全面优于SF6甚至相当的绝缘气体，筛选或创制新型SF6替代气体是电气、化学、物理、材料等交叉学科领域富有挑战性的研究课题。本数据集针对环保绝缘气体的构效关系、虚拟筛选、分子设计展开研究，基于分子微观参数模拟和共价键参数提取，记录了多维度构效关系模型的性能，获得了满足多约束条件的分子。Given the mutually restrictive or interdependent relationships among the multi-dimensional performance requirements of insulating gases, trial-and-error experiments have so far failed to identify insulating gases that outperform or even match SF6 in all aspects. Screening or developing novel SF6 replacement gases is a challenging research topic in interdisciplinary fields including electrical engineering, chemistry, physics, materials science and other relevant disciplines. This dataset focuses on research related to the structure-activity relationship, virtual screening and molecular design of environmentally friendly insulating gases. Based on molecular micro-parameter simulation and covalent bond parameter extraction, it records the performance of multi-dimensional structure-activity relationship models and has obtained molecules that meet multiple constraint conditions.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于环保绝缘气体的分子设计研究，旨在解决因多维度性能制约而难以找到SF6替代品的问题。通过分子模拟和构效关系建模，它记录了模型性能并筛选出满足多约束条件的候选分子。