Leveraging Remote Work to Accelerate Material Informatics by Implementing Machine Learning Web Applications and Introducing Statistical Analysis Tools for Materials Scientists in a Chemical Corporation

Using past experimental data is advantageous in materialMaterials design; however, constructing machine learningMachine learning models based on this data remains challenging for materialsMaterials scientists unfamiliar with machine learningMachine learning. While data scientists can build appropriate machine learningMachine learning models with their expertise in statistics, machine learningMachine learning, and computer science, they may need more domain knowledge, particularly tacit knowledge in materialsMaterials design. Therefore, collaboration between data scientists and materialsMaterials scientists is necessary, but the differences in expertise between the two groups can hinder the development of materials informaticsMaterials informatics. In Resonac Corporation, we have leveraged the remote work opportunities caused by COVID-19 to accelerate the usage of materials informaticsMaterials informatics. We accomplished this by deploying the electronic laboratory notebooks and statistical analysis tool and implementing web applicationsWeb application with a user-friendly Graphical User Interface for materialsMaterials scientists. As a result, remote work has allowed materialsMaterials scientists to focus on data organization, statistical data analysisData analysis, and the usage of web applicationsWeb application.