Authors: Simon Krarup Steensen | Monika Vogler | Helge S. Stein | Ivano E. Castelli | Tejs Vegge
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Finales - Developing Infrastructure for Materials Acceleration Platforms to Expedite Data-Driven Battery Research and Design Incorporating the materials optimised at the component level may not always lead to the best device outcome, as their combined behavior is frequently complex and non-linearly linked to their individual properties. Consequently, enhancing an entire device requires innovative automation strategies that extend beyond traditional laboratory methods, emphasising seamless integration and communication among diverse research methods and techniques. FINALES (fast intention-agnostic learning server) aims to broker information between researchers and impose a data infrastructure easing the deployment of ML-guided optimisation of material properties. By registering researchers' capabilities, whether this be an experimental setup or computational model, FINALES infers a robust platform for posting requests and subsequent results. Acceleration of optimised material properties can be reached by having an overarching ML train on available data in the server and automatically post new requests to efficiently map out the chemical space. The asynchronous server is designed to be hardware-independent allowing for both autonomous methods as well as human-in-the-loop experiments. This independence is possible since all communication is handled through HTTP protocols orchestrating requests and results in the server. An early proof of concept of FINALES is reported in [1]. Here it was deemed a new server implementation was needed, to among other things, utilise rigid communication schemas, traceability of events in the server and design an append-only database schema. The communication schemas standardise the communication and promote interoperability of methods registered to the MAP. The newly developed framework has recently been used to carry out optimisation of electrolyte-formulation distributed across Europe. Here the aim was to optimise conductivity and end-of-lifetime (EOL). The deployed MAP consisted of autonomous electrolyte-mixing and subsequent conductivity measurements, conductivity simulations, autonomous coin cell assembly, battery cycling, early lifetime prediction ML model, etc. The active learning optimiser requests mixing ratios of the electrolyte consisting of EC, EMC, and LiPF 6 and uses the subsequent result for finding the global maxima. The conductivity optimisation was shown to replicate a known trend in literature for the mixing ratios underlining the applicability of the approach. The EOL optimisation investigations were cut rather short due to time constraints. During the demonstration, the produced data was regularly autonomously uploaded to an Archive (BIG-MAP archive [2]) adhering to the FAIR principles. All results were subsequently embedded into a knowledge graph enriched with ontology concepts using the newly developed BattINFO [3] ontology for battery research. By adding well-described ontological concepts to the data, unambiguous concepts are directly embedded into the results. Which adheres to the standardisation for sharing of data described for the BIG-MAP project [4]. Beyond the unambiguity, this has the major advantage of enabling the use of SPARQL queries directly on the data. If adding knowledge graphs to data becomes the norm, SPARQL queries could be run on archive level graphs. A query could e.g. look for all results with electrolyte containing LiPF6 ratio above 0.2, which would be much easier than looking up individual results and manually discarding them. FINALES has been shown to be deployable for conductivity and EOL optimisation distributed across Europe. The main importance of the framework, however, lies in the problem agnostic design allowing many different future optimisation implementations. References [1] Monika Vogler et al. “Brokering between tenants for an international materials acceleration platform”. In: Matter 6.9 (2023), pp. 2647–2665. issn: 2590-2385. doi:https://doi.org/10.1016/ j. matt. 2023.07.016. url: https://www.sciencedirect.com /science/article/pii/S2590238523003739. [2] Fran¸cois Liot et al. BIG-MAP Archive. Feb. 2023. url: https://archive.big-map.eu/. [3] Simon Clark et al. BIG-MAP/BattINFO: v0.6.0. Version v0.6.0. Aug. 2023. doi:10.5281/zenodo.8260800. url: https://doi.org/10.5281/zenodo.8260800. [4] Prof. Ivano E. Castelli et al. “Data Management Plans: the Importance of Data Management in the BIG-MAP Project”. In: Batteries Supercaps 4(12 Dec. 2021), pp. 1803–1812. doi: https://doi.org/10.1002/batt.202100117. url: https://chemistry- europe.onlinelibrary. wiley.com/doi/full/10.1002/batt.202100117. Battery (electricity) ; Acceleration ; Computer science ; Systems engineering ; Data science ; Engineering ; Physics Machine Learning in Materials Science ; Semiconductor materials and devices ; Electron and X-Ray Spectroscopy Techniques