主动学习优化实验设计-洞察钙钛矿氧化物

IF 1.1 4区 化学 Q3 CHEMISTRY, MULTIDISCIPLINARY
M. P. Lourenço, A. Tchagang, K. Shankar, V. Thangadurai, D. Salahub
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引用次数: 1

摘要

为特定应用寻找具有改进性能的最佳材料具有挑战性,因为材料科学和化学中的数据采集既耗时又昂贵。因此,处理小数据集在化学中是一个现实,无论数据是通过合成还是计算实验获得的。在这项工作中,我们提出了一种新的基于主动学习(AL)的人工智能方法,以尽可能少的数据指导新的实验,以优化实验设计。AL方法应用于ABO3钙钛矿,并开发了基于原子性质的描述符。采用了几种回归算法:人工神经网络、高斯过程和支持向量回归。所开发的AL方法应用于两种重要材料的实验设计:非化学计量钙钛矿(Ba(1-x)AxTi(1-y)ByO3),这是由于取代了不同浓度和元素的离子位点(A = Ca, Sr, Cd;B = Zr, Sn, Hf),以储能密度最大化为目标;化学计量ABO3钙钛矿,其中不同的元素改变在A和B位,以最小化的形成能量。实验设计人工智能在化学与设计机器学习代理(MLChem4D)软件中实现;它具有应用于无机和有机合成(例如:寻找最佳浓度、催化剂、反应物、温度和pH值以提高产量)和材料科学(例如:搜索元素周期表以寻找适当的元素及其浓度以改善材料性能)的潜力。后者标志着MLChem4D在钙钛矿设计中的首次应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Active Learning for Optimum Experimental Design – Insight into Perovskite Oxides
Finding the optimum material with improved properties for a given application is challenging because data acquisition in materials science and chemistry is time consuming and expensive. Therefore, dealing with small datasets is a reality in chemistry, whether the data is obtained from synthesis or computational experiments. In this work, we propose a new artificial intelligence method based on active learning (AL) to guide new experiments with as little data as possible, for optimum experimental design. The AL method is applied to ABO3 perovskites where a descriptor based on atomic properties was developed. Several regressor algorithms were employed: artificial neural network, Gaussian process and support vector regressor. The developed AL method was applied in the experimental design of two important materials: non-stoichiometric perovskites (Ba(1-x)AxTi(1-y)ByO3) due to substituting ionic sites with different concentrations and elements (A = Ca, Sr, Cd; B = Zr, Sn, Hf), aiming at the maximization of the energy storage density; stoichiometric ABO3 perovskites where different elements are changed in the A and B sites for the minimization of the formation energy. AL for experimental design is implemented in the machine learning agent for chemistry and design (MLChem4D) software; which has the potential to be applied in inorganic and organic synthesis (e.g.: search for the optimum concentrations, catalysts, reactants, temperatures and pH to improve the yield) and materials science (e.g.: search the periodic table for the proper elements and their concentrations to improve the materials properties). The latter marks the first MLChem4D application for the design of perovskites.
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来源期刊
Canadian Journal of Chemistry
Canadian Journal of Chemistry 化学-化学综合
CiteScore
1.90
自引率
9.10%
发文量
99
审稿时长
1 months
期刊介绍: Published since 1929, the Canadian Journal of Chemistry reports current research findings in all branches of chemistry. It includes the traditional areas of analytical, inorganic, organic, and physical-theoretical chemistry and newer interdisciplinary areas such as materials science, spectroscopy, chemical physics, and biological, medicinal and environmental chemistry. Articles describing original research are welcomed.
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