摘要
由一名新闻记者兼机器人与机器学习的新闻编辑每日新闻-调查人员发布了关于马学习的新报告。根据NewsRx记者来自印度钦奈的新闻报道，研究表明：“热电材料在利用余热并将其转化为有价值的电能、应对能源可持续性挑战方面发挥着关键作用。这项研究引入了一种创新的方法来预测基本的热电性能-导热系数(Kappa)、电导率(Sigma)、塞贝克系数(S)和绩效图(Zt)。”我们的新闻编辑从印度理工学院(IIT)马德拉斯的研究中获得了一句话，“采用先进的机器学习(ML)技术，包括随机森林、梯度提升回归、XGB回归和神经网络，我们利用各种热电化合物数据集开发了一个稳健的预测模型。值得注意的是，随机森林表现出出色的预测性能，Kappa、Sigma、S和ZT的R-2值分别为0.91、0.95、0.95和在利用随机森林模型检验Bi2Te1-xSex热释参数的预测能力时,该模型与实验Kappa、Sigma、S和ZT具有非常一致的定量预测能力,且Kappa、Sigma、Szt和Sigma的预测能力均优于实验Kappa、Sigma、Szt的预测能力。用First原理密度泛函理论和Boltzmann输运方程计算了Bi2Te2Se的S和ZT,得到了相应的ML预测的热电性能,虽然Bi2Te2Se的Kappa、Sigma、S和ZT的理论值与室温ML预测的结果一致,但Kappa、Sigma、Szt的温度相关理论值与室温ML预测的结果一致。实验数据训练后,Bi2Te2Se的S和ZT与ml-预测值有较大的偏差,这表明了基于分类的模型在捕捉复杂图案方面的优越性,并利用化学成分作为唯一输入,简化了实验过程,大大促进了高性能热电材料的发展。为探索和优化提供了一条有效的途径，从而使材料科学领域发生了革命。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting originating from Chennai, India, by NewsRx correspondents, research stated, “Thermoelectric materials play a pivotal role in harnessing waste heat and converting it into valuable electrical energy , addressing energy sustainability challenges. This study introduces an innovati ve methodology to predict essential thermoelectric properties-thermal conductivi ty (kappa), electrical conductivity (sigma), Seebeck coefficient (S), and the fi gure of merit (ZT)-solely from the chemical formula of materials.” Our news editors obtained a quote from the research from the Indian Institute of Technology (IIT) Madras, “Employing advanced machine learning (ML) techniques, including random forest, gradient boosting regression, XGBRegressor, and Neural Network, we developed a robust predictive model utilizing a diverse dataset of t hermoelectric compounds. Notably, random forest exhibits outstanding predictive performance, boasting R-2 values of 0.91, 0.95, 0.95, and 0.90 for kappa, sigma, S and ZT, respectively. While testing the prediction competency of thermoelectr ic parameters of Bi2Te1-xSex using a random forest model, the model provides a v ery consistent quantitative prediction with experimental kappa, sigma, S and ZT. Furthermore, the kappa, sigma, S and ZT of Bi2Te2Se were calculated using the f irst principles density functional theory and Boltzmann transport equation to co mpare the corresponding ML-predicted thermoelectric properties. Although the ord er of theoretical values of kappa, sigma, S and ZT of Bi2Te2Se is consistent wit h the room temperature ML prediction, the temperature-dependent theoretical valu e of kappa, sigma, S and ZT of Bi2Te2Se shows a deviation from the ML-prediction values as the model is trained with the experimental data. The findings highlig ht the superiority of classification-based models in capturing complex patterns. By leveraging chemical composition as the exclusive input, our streamlined appr oach eliminates the need for extensive laboratory experiments. This research sig nificantly propels the advancement of high-performance thermoelectric materials, offering an efficient pathway for exploration and optimization, thus revolution izing the field of materials science.”
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