Supporting Material Synthesis via Research Data Management, Data Mining from Literature and Machine Learning—Metal-Organic Frameworks as Use-Case

In this conference contribution we will present how Metal-Organic Framework (MOF) synthesis data can be managed, sored, and automatically extracted from literature. In addition, we will show how this synthesis data can be used to support researchers from the field of MOF synthesis via synthesis prediction tools. [1]<br/>The contribution will cover the four aspects (I) electronic lab notebooks (ELN), (II) repositories and archives for material synthesis, (III) automatic data mining using natural language processing (NLP), and (IV) machine learning (ML) to identify patterns from the extracted synthesis data.<br/>In the aspects (I) and (II) we will focus on research data management tools developed / under development by the German National Research Data Infrastructure initiative (NFDI) with a focus on NFDI<i>4</i>Chem and FAIRmat. In this context we will introduce the ELN Chemotion and the Repository and Archive NOMAD and demonstrate how these tools can be implemented in MOF synthesis.<br/><br/>[1] (a) Jalali, M.; Tsotsalas, M.; Wöll, C. MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis. Nanomaterials 2022, 12, 704.; (b) MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning. Luo, Y., Bag, S., Zaremba, O., Cierpka, A., Andreo, J., Wuttke, S., Friederich, P. and Tsotsalas, M., <i>Angew. Chem. Int. Ed..</i> <b>2022 </b>e202200242.<br/>[2] The Repository Chemotion: Infrastructure for Sustainable Research in Chemistry P. Tremouilhac, C.-L. Lin, P.-C. Huang, Y.-C. Huang, A. Nguyen, N. Jung, F. Bach, R. Ulrich, B. Neumair, A. Streit, S. Bräse, <i>Angew. Chem. Int. Ed.</i> <b>2020</b>, <i>59</i>, 22771.<br/>[3] (a) The NOMAD laboratory: from data sharing to artificial intelligence C. Draxl and M. Scheffler <i>J. Phys. Mater.</i> <b>2019</b>, 2, 036001.: (b) Scheffler, M., Aeschlimann, M., Albrecht, M. et al. FAIR data enabling new horizons for materials research. Nature <b>2022</b>, 604, 635–642.