Robotics & Machine Learning Daily News2024,Issue(Nov.12) :73-73.

Researcher from ShanghaiTech University Details Findings in Machine Learning (He at diffusion coefficient study of polymers based on interpretable machine learni ng)

上海科技大学研究员详细介绍机器学习的发现(他在基于可解释机器学习的聚合物扩散系数研究)

Robotics & Machine Learning Daily News2024,Issue(Nov.12) :73-73.

Researcher from ShanghaiTech University Details Findings in Machine Learning (He at diffusion coefficient study of polymers based on interpretable machine learni ng)

上海科技大学研究员详细介绍机器学习的发现(他在基于可解释机器学习的聚合物扩散系数研究)

扫码查看

摘要

由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-人工智能的新数据在一份新的报告中呈现。据新闻报道NewsRx记者从中华人民共和国上海报道,研究称,"摘要"。新闻记者从上海科技大学的研究中获得了一句话:“聚合物”在不同的应用场景下,在模式rn社会的各个领域具有重要的应用价值需要比热差系数。寻找具有靶向热扩散的高分子材料IES至关重要。然而,由于聚合物种类繁多,结构复杂,构建了一个统一的聚合物机器学习建模的结构化数据集具有挑战性。尽管机器学习已经证明它在材料科学中具有很大的应用潜力,但在预测材料的热扩散系数方面却鲜有应用奥林匹克。本文建立了一个用于预测聚合物热扩散系数的数据集通过将聚合物的SMI LES代码转换为八个特征,使用公开可用的数据集实用物理化学分析。使用随机森林算法,用400个这样的数据和随机选取其中200个进行交叉验证,测试集准确度达到0.9.。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Fresh data on artificial intelligence are presented in a new report. According to newsreporting from Shanghai, People ’s Republic of China, by NewsRx journalists, research stated, “Abstract.”The news journalists obtained a quote from the research from ShanghaiTech Univer sity: “Polymershold significant application value across various fields of mode rn society, with different application scenariosrequiring specific thermal diff usivity coefficients. Finding polymer materials with targeted thermal diffusivities is crucial. However, due to the vast variety and complex structures of polym ers, constructing a unifiedstructured dataset for machine learning modeling is challenging. Although machine learning has showngreat potential in materials sc ience, it has rarely been applied to predict the heat diffusion coefficient ofp olymers. This paper constructs a dataset for predicting the thermal diffusion co efficient of polymersusing a publicly available dataset by transforming the SMI LES code of polymers into eight features withpractical physical and chemical me anings. Using the Random Forest algorithm, training with 400 of thesedata and r andomly selecting 200 of them for cross-validation, the accuracy of the test set reached 0.9.”

Key words

ShanghaiTech University/Shanghai/Peopl e’s Republic of China/Asia/Cyborgs/Emerging Technologies/Machine Learning

引用本文复制引用

出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
段落导航相关论文