(Invited) High-Throughput Synthesis, Evaluation, Analysis, and Data Visualization Technology for Exploring Multi-Components Functional Oxides Powder

Abstract

In the sessions of Materials Genome Initiative (MGI) and Materials Informatics (MI) at recent international and domestic conferences, majority of research presentations are on material prediction using a combination of computational chemistry and machine learning. On the other hand, the contents incorporating high-throughput synthesis and evaluation experiments are only occasionally seen. It is also true that the number of samples that can be handled per hour is small compared to the former data science.However, if a series of experimental processes linked to synthesis, evaluation, and analysis is made user-friendly and high-throughput, it is possible to efficiently generate multi-condition and multi-component data in a unified experimental environment such as starting materials. As a result, even when creating a data set by extracting text from a paper, the data shortage can be supplemented with experimental data.Unlike thin films and polymers, powder synthesis is a heterogeneous reaction that requires the use of a wet process to perform high-throughput experiments. We have hitherto developed powder synthesis apparatus equipped with a micropump for solution dispensing, and high-throughput exploration system for powder and thin-film library based on electrostatic spray deposition consisted by combining multiple syringe pumps and a high-voltage power supply. By using these system, we have been exploring multi-component cathode materials for lithium ion battery and thermoelectric materials. In addition, recently, high-throughput experiments in a high-pressure environment of 200 MPa and 500ºC are possible.If the time required for physical property evaluation and data analysis is the same as before, even if only the synthesis based on experiments becomes high throughput, the attractiveness of MI will be weakened. Therefore, it is necessary to continue to develop jigs and software for efficient evaluation and analysis of huge libraries obtained in high-throughput experiments.In this presentation, we will introduce the exploration for A-site and B-site substitutes of perovskite-type CaMnO3, which is expected as an n-type thermoelectric material. In the preliminary search, it was confirmed that Ca1-x Bi x Mn1-y Ni y O3 contributes to the improvement of thermoelectric power by substituting elements at each site. As the next step, the amount of Bi substitution was determined to be within 10% from the combination of high-throughput synthesis and high-throughput Seebeck coefficient measurement. Synchrotron XRD and XAFS measurements were used to investigate the correlation between physical properties and crystal structure. We have succeeded in collecting data without filling the capillaries and making pellets by developing special tools. Synchrotron XRD and XAFS measurements were used to investigate the correlation between physical properties and crystal structure. We have succeeded in collecting data without filling the capillaries and making pellets by developing special tools. We also developed software that automates Rietveld analysis, although we need basic knowledge to set up an initial structural model. As a result, we have made it easier to generate data sets for visualizing crystallographic data and physical property data at once. In the obtained Bi-substituted CaMnO3 powder, when Bi was 8% or less, the conductivity and power factor increased due to the increase in carrier concentration. At Bi8% and higher, PF reaches its limit value, which involves a large change in the MnO6 octahedral plane bond distances Mn-O1 (1) and Mn-O1 (2), that is, a significant increase in octahedral distortion. High-throughput experiments including the collection of crystallographic information through the development of these technologies are expected to be an effective tool for future machine learning.