Monika Vogler^1,2, Jonas Busk³, Hamidreza Hajiyani⁴, Peter Bjørn Jørgensen³, Nehzat Safaei⁴, Ivano E. Castelli³, Francisco Fernando Ramirez^5,6, Johan M. Carlsson⁴, Giovanni Pizzi^5,6,7, Simon Clark⁸, Felix Hanke^9*, Arghya Bhowmik^3*, Helge S. Stein^1,2,10*

1 Helmholtz Institute Ulm, 89073 Ulm, Germany

2 Institute for Physical Chemistry, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany

3 Department of Energy Conversion and Storage, Technical University of Denmark (DTU), 2800 Kgs. Lyngby, Denmark

4 Dassault Systèmes, 51063 Cologne, Germany

5 Theory and Simulation of Materials (THEOS), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

6 National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

7 Laboratory for Materials Simulations (LMS), Paul Scherrer Institut (PSI), 5232 Villingen, Switzerland

8 SINTEF Industry, Battery and Hydrogen Technologies, 7034 Trondheim, Norway

9 Dassault Systèmes, 334 Science Park, Cambridge CB4 0WN, UK

10 Present address: Department of Chemistry, School of Natural Sciences, Technical University of Munich, Lichtenbergstraße 4, 85748 Garching bei München, Germany

* Corresponding authors emails: felix.hanke@3ds.com, arbh@dtu.dk, helge.stein@tum.de

DOI10.24435/materialscloud:ph-jb [version v1]

Publication date: Sep 26, 2024

How to cite this record

Monika Vogler, Jonas Busk, Hamidreza Hajiyani, Peter Bjørn Jørgensen, Nehzat Safaei, Ivano E. Castelli, Francisco Fernando Ramirez, Johan M. Carlsson, Giovanni Pizzi, Simon Clark, Felix Hanke, Arghya Bhowmik, Helge S. Stein, FINALES (06/2022) – Electrolyte Optimization for Minimum Density and Maximum Viscosity, Materials Cloud Archive 2024.142 (2024), https://doi.org/10.24435/materialscloud:ph-jb

Description

This study presents the initial implementation of the Fast INtention-Agnostic LEarning Server (FINALES) in a demonstration of a distributed Materials Acceleration Platform (MAP) including experimental and computational methods and a machine learning (ML)-based optimizer. In this demonstration, the optimizer was configured to minimize the density of the electrolyte solutions while maximizing the viscosity by exploiting experimental and computational results. The tenants (the units connected to FINALES in the MAP) are shortly described in the following: - Autonomous Synthesis and Analysis of Battery electrolytes (ASAB) setup: an experimental tenant providing density and viscosity data using a densimeter of the type DMA 4100M and a viscometer of type Lovis 2000 both by Anton Paar Germany - Molecular dynamics tenant: a computational tenant capable of providing radial distribution functions, diffusion coefficients, ionic conductivity, transference numbers, heat capacity and density data to the MAP - Optimizer: the tenant guiding the optimization by processing the available data and generating requests for electrolyte formulations to be tested subsequently This dataset accompanies the publication: Vogler, M., Busk, J., Hajiyani, H., Jørgensen, P. B., Safaei, N., Castelli, I. E., Ramirez, F. F., Carlsson, J., Pizzi, G., Clark, S., Hanke, F., Bhowmik, A. & Stein, H. S. Brokering between tenants for an international materials acceleration platform. Matter 6, 2647–2665 (2023). DOI: https://doi.org/10.1016/j.matt.2023.07.016

Materials Cloud sections using this data

Files

File name	Size	Description
Readme.txt MD5md5:5ff279298c111f0504ec716864d563b6	4.9 KiB	This file describes the content of this dataset.
session_production.db MD5md5:f71e8e180c5f254f48a31a95b53766a1	92.0 KiB	This SQLite database contains the data generated and posted to FINALES during the study.
session_production_data_extracted.csv MD5md5:c524bc882b116afe442ca79a06964a95	54.3 KiB	This CSV file contains the data stored also in the .db file for facilitated accessibility.
Data_molecular_dynamics.zip MD5md5:44ba090aaef460afc0aeb141fd2baf6d	47.5 KiB	This ZIP file contains additional data generated by the molecular dynamics tenant for RDF and ionic conductivity in CSV format. A Readme file with further details is included in the ZIP file.
Raw_data_ASAB.zip MD5md5:d4f27c8dd7658ef319e5f8023848aa83	139.3 KiB	This ZIP file contains raw and preprocessed data generated by the ASAB tenant for density and viscosity in CSV and JSON format. A Readme file with further details is included in the ZIP file.

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

BIG-MAP MARVEL/P3 Materials Acceleration Platform (MAP) battery electrolyte density viscosity conductivity Radial Distribution Function (RDF) POLiS