FINALES (06/2022) – Electrolyte Optimization for Minimum Density and Maximum Viscosity
Creators
- 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
* Contact person
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
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References
Journal reference (Paper describing the study and the Materials Acceleration Platform (MAP) that generated this data.) M. Vogler, J. Busk, H. Hajiyani, P. B. Jørgensen, N. Safaei, I. E. Castelli, F. F. Ramirez, J. M. Carlsson, G. Pizzi, S. Clark, F. Hanke, A. Bhowmik, H. S. Stein, Matter 6, 2647–2665 (2023), doi: 10.1016/j.matt.2023.07.016
Software (The source code of FINALES implementation used to generate the dataset.) https://github.com/BIG-MAP/finale/tree/publication, doi: 10.5281/zenodo.8009625