Process optimisation for NASICON-type solid electrolyte synthesis using a combination of experiments and bayesian optimisation

Takeda, Hayami; Fukuda, Hiroko; Nakano, K.; Hashimura, Syogo; Tanibata, Naoto; Nakayama, Masanobu; Ono, Yasuharu; Natori, Takaaki

doi:10.1039/d2ma00731b

Cited by 14 publications

(6 citation statements)

References 44 publications

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“…We also applied two active learning algorithms, Bayesian optimization (BO) and a genetic algorithm (GA), to the same task by using a one-hot representation of each precursor (Supplementary Note 2 ). These algorithms are known to perform well on numerical inputs such as temperature 47 , 48 ; however, their effectiveness with respect to categorical inputs is less well proven. To specifically probe the latter case, we constrained BO and GA to optimize the selection of precursors while sampling all temperatures for each precursor set.…”

Section: Resultsmentioning

confidence: 99%

Autonomous and dynamic precursor selection for solid-state materials synthesis

Szymanski,

Nevatia,

Bartel

et al. 2023

Nat Commun

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Solid-state synthesis plays an important role in the development of new materials and technologies. While in situ characterization and ab-initio computations have advanced our understanding of materials synthesis, experiments targeting new compounds often still require many different precursors and conditions to be tested. Here we introduce an algorithm (ARROWS3) designed to automate the selection of optimal precursors for solid-state materials synthesis. This algorithm actively learns from experimental outcomes to determine which precursors lead to unfavorable reactions that form highly stable intermediates, preventing the target material’s formation. Based on this information, ARROWS3 proposes new experiments using precursors it predicts to avoid such intermediates, thereby retaining a larger thermodynamic driving force to form the target. We validate this approach on three experimental datasets, containing results from over 200 synthesis procedures. In comparison to black-box optimization, ARROWS3 identifies effective precursor sets for each target while requiring substantially fewer experimental iterations. These findings highlight the importance of domain knowledge in optimization algorithms for materials synthesis, which are critical for the development of fully autonomous research platforms.

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Section: Resultsmentioning

confidence: 99%

Autonomous and dynamic precursor selection for solid-state materials synthesis

Szymanski,

Nevatia,

Bartel

et al. 2023

Nat Commun

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“…In recent years, machine learning (ML) has actively integrated into the fields of chemistry and materials science, opening fundamentally new opportunities for designing new compounds/materials or functionalities. Thus, materials informatics methodologies have been successfully applied for the rational screening of compounds with tailored characteristics [99][100][101][102][103], predicting crystal structures [104], designing experiments [105][106][107], using natural language processing for experimental data acquisition and analysis [108,109], analyzing data from physicochemical characterization methods [110,111], and microstructural informatics [112][113][114].…”

Section: Machine Learning Modeling and Data Analysismentioning

confidence: 99%

Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning

Kireeva,

Tsivadze,

Pervov

2023

Batteries

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All-solid-state batteries (ASSBs) are the important attributes of the forthcoming technologies for electrochemical energy storage. A key element of ASSBs is the solid electrolyte materials. Garnets are considered promising candidates for solid electrolytes of ASSBs due to their chemical stability with Li metal anodes, reasonable kinetic characteristics (σLi∼ 10−3–10−4 S · cm−1) and a wide electrochemical window. This study is aimed at the analysis of the experimental data available for garnet thin films, examining the ionic conductivity through the film/substrate lattice mismatch, the elastic properties and the difference in the thermal expansion characteristics of the film and the substrate, the deposition temperature of the film, and the melting point and the dielectric constant of the substrate. Based on the results of this analysis and by introducing the corresponding characteristics involved as the descriptors, the quantitative models for predicting the ionic conductivity values were developed. Some important characteristic features for ion transport in garnet films, which are primarily concerned with the film/substrate misfit, elastic properties, deposition temperature, cation segregation and the space charge effects, are discussed.

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“…Materials informatics was emphasized as an efficient way towards the rational synthesis of new compounds and new properties. It was efficiently applied in the design of the materials with desired characteristics [5,6,7,8,9], in the design of synthesis and for the autonomous laboratories [10,11,12,13], for natural language processing to obtain and analyze experimental data in chemistry and materials science [14,15,16,17,18], for modeling the microstructure [19,20], for the analysis of the output of physicochemical methods of characterization [21,22], for inverse design of materials [23,24] and in many other areas of their application.…”

Section: Introductionmentioning

confidence: 99%

Novelty Detection in the Design of Synthesis of Energy Storage Materials: a Case Study of Garnet-Structured Solid Electrolytes

Kireeva,

Tsivadze

2024

Preprint

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Recent decades have shown arising growth-on-demand of integrating the machine learning into all areas of chemistry and materials science. In this study, we consider one of the aspects of applying these technologies to gain advantage in the search for new knowledge extracted from experimental data obtained in ever-growing number of studies. The novelty detection approaches are aimed to identify the artefacts in these data that may be of importance in many direc- tions. The analysis of "outliers" in details of the synthesis in the research studies of garnet-structured solid electrolytes was chosen as the object of demonstration of one of the practical applications of this methodology. Particular attention was paid to the choice of precursors. The thermodynamic data such as the heat of formation from the pure oxides as well as the results of drop solution calorimetry for simple oxides were involved as the descriptors of the studied systems. The overall performance of novelty/outlier detection of all types of outliers was characterized for the data described varying the complexity of description using ROC-AUC statistics and was assessed to be 0.71 – 0.72 using the Area-Under-Curve statistics. It was found that all “outlier” compounds related to those as the result of using the rare precursors in synthesis were successfully identified. The complementary regression analysis was performed to elucidate the relationship between the data diversity and the complexity of data description.

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Process optimisation for NASICON-type solid electrolyte synthesis using a combination of experiments and bayesian optimisation

Abstract: Na superionic conductor (NASICON)-type LiZr2(PO4)3 (LZP) is an oxide-based solid electrolyte candidate for use in all-solid-state Li-ion batteries. However, as the ionic conductivity is insufficient, doping with aliovalent cations has...

Cited by 14 publications

References 44 publications

Autonomous and dynamic precursor selection for solid-state materials synthesis

Autonomous and dynamic precursor selection for solid-state materials synthesis

Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning

Novelty Detection in the Design of Synthesis of Energy Storage Materials: a Case Study of Garnet-Structured Solid Electrolytes

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