# Fabrication of High-Quality Thin Solid-State Electrolyte Films Assisted by Machine Learning

Yu-Ting Chen, Marc Duquesnoy, Darren H. S. Tan, Jean-Marie Doux, Hedi

Yang, Grayson Deysher, Phillip Ridley, Alejandro A. Franco, Ying Shirley

Meng, Zheng Chen

► To cite this version:

Yu-Ting Chen, Marc Duquesnoy, Darren H. S. Tan, Jean-Marie Doux, Hedi Yang, et al. Fabrication of High-Quality Thin Solid-State Electrolyte Films Assisted by Machine Learning. ACS Energy Letters, 2021, 6 (4), pp.1639-1648. 10.1021/acsenergylett.1c00332. hal-03610986

HAL Id: hal-03610986

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Title

Fabrication of High-Quality Thin Solid-State Electrolyte Films Assisted by Machine Learning

Permalink

https://escholarship.org/uc/item/1d4494vf

Journal

ACS Energy Letters, 6(4)

ISSN

2380-8195

Authors

Chen, Yu-Ting
Duquesnoy, Marc
Tan, Darren HS
et al.

Publication Date

2021

DOI

10.1021/acsenergylett.1c00332

Peer reviewed
# Fabrication of High-Quality Thin Solid-State Electrolyte Films Assisted by Machine Learning

Yu-Ting Chen, Marc Duquesnoy, Darren H. S. Tan, Jean-Marie Doux, Hedi Yang, Grayson Deysher, Phillip Ridley, Alejandro A. Franco,* Ying Shirley Meng,* and Zheng Chen*

**ABSTRACT:** Solid-state electrolytes (SSEs) are promising candidates to circumvent flammability concerns of liquid electrolytes. However, enhancing energy densities by thinning SSE layers and enabling scalable coating processes remain challenging. While previous studies have addressed thin and flexible SSEs, mainly ionic conductivity was considered for performance evaluation, and no systematic research on the effects of manufacturing conditions on the quality of SSE films was performed. Here, both uniformity and ionic conductivity are considered for evaluating the SSE films under the guidance of machine learning (ML). Three algorithms, principal component analysis, *K-means* clustering, and support vector machine, are employed to decipher the interdependencies between manufacturing conditions and film performance. Guided by ML, a 40 µm SSE film with high ionic conductivity and good uniformity is used to construct a LiNi<sub>0.8</sub>Co<sub>0.1</sub>Mn<sub>0.1</sub>O<sub>2</sub> || Li<sub>6</sub>PS<sub>5</sub>Cl || LiIn cell demonstrating 100 cycles. This study presents an efficient ML-assisted approach to optimize scalable production of high-quality SSE films.

<figure><img src="image_2.png" /></figure>

Li-ion batteries (LIBs) are currently the predominant energy storage technology widely used in portable electronic devices, electric vehicles, and grid energy storage.¹ Conventional LIBs employ liquid electrolytes containing flammable organic solvents, making them susceptible to leakage and potential flammability concerns.²⁻⁵ One of the solutions to circumvent this is to replace liquid electrolytes with inorganic solid-state electrolytes (SSEs) in order to produce all-solid-state batteries (ASSBs) with improved safety and wider operating temperature ranges.²,⁶ As a result, significant efforts have been devoted to developing various SSEs, some of which have exhibited ionic conductivities reaching 10⁻² S cm⁻¹ at room temperature, close to those of liquid electrolytes.⁷⁻⁹ However, because of mechanical property limitations and under-developed manufacturing processes for inorganic SSEs, cells with pellet-type SSE layers thicker than 200 µm are still predominantly employed in ASSB research.¹⁰ Unlike cathode and anode materials, electrolytes do not store energy, and thus, using thick SSE layer limits energy density.²,¹¹ To compete with conventional LIBs, it is therefore crucial to develop new fabrication processes that enable the manufacturing of cells using film-type SSE layers with reduced thicknesses, ideally 25–50 µm, to reach high energy density, while still maintaining ideal mechanical properties.¹¹ These

fabrication techniques must also be compatible with existing manufacturing methods, such as conventional doctor-blade or roll-to-roll coating processes.

The most common way to address these aforementioned issues is to include polymer binders in the SSE composite, as they provide an added flexibility to the fabricated SSE films to compensate for mechanical weakness of pellet-type SSEs. Indeed, mechanical failures of SSEs in ASSBs are detrimental to battery performance.² This approach also enables a scalable doctor-blade or roll-to-roll solution process.¹² Film-type SSEs, combining sulfide inorganic SSEs with polymer binders and fabricated with doctor-blade, have been reported in the literature (Table 1) with ionic conductivities ranging between 10⁻⁵ and 10⁻³ S cm⁻¹. This wide conductivity variation can be attributed in part to the usage of different polymer binders and SSEs. However, even when similar polymer binders and
<table><caption>Table 1. Summary of Ionic Conductivity and Thickness of Recently Reported SSE Films Processed with Different Manufacturing Conditions<sup>a</sup></caption><thead><tr><th>electrolyte</th><th>binder</th><th>solvent</th><th>binder content</th><th>liquid: solid ratio</th><th>ionic conductivity (S cm<sup>-1</sup>)</th><th>thickness (μm)</th><th>ref</th></tr></thead><tbody><tr><td>Li<sub>6</sub>PS<sub>5</sub>Cl</td><td>acrylate type</td><td>xylene: isobutyl isobutyrate (50:50)</td><td>1 wt %</td><td></td><td>1.3 × 10<sup>-3</sup></td><td>30</td><td>13</td></tr><tr><td>Li<sub>3</sub>PS<sub>4</sub></td><td>SBS</td><td>anisole</td><td>3 wt %</td><td></td><td>2 × 10<sup>-4</sup></td><td>60</td><td>14</td></tr><tr><td>Li<sub>7</sub>P<sub>3</sub>S<sub>11</sub></td><td>SEBS</td><td>xylene</td><td>2.5 wt %</td><td>1</td><td>7 × 10<sup>-4</sup></td><td>55</td><td>15</td></tr><tr><td>Li<sub>3</sub>PS<sub>4</sub></td><td>PEO</td><td>acetonitrile</td><td>5 wt %</td><td>4.6</td><td>8.4 × 10<sup>-6</sup></td><td>10</td><td>16</td></tr><tr><td>Li<sub>6</sub>PS<sub>5</sub>Cl</td><td>PEO</td><td>acetonitrile</td><td>5 wt %</td><td></td><td>2.0 × 10<sup>-4</sup></td><td>65</td><td>17</td></tr><tr><td>Li<sub>3</sub>PS<sub>4</sub></td><td>NBR</td><td>xylene</td><td>3 wt %</td><td></td><td>4.2 × 10<sup>-4</sup></td><td></td><td>18</td></tr><tr><td>Li<sub>3</sub>PS<sub>4</sub></td><td>NBR</td><td>THF</td><td>5.5 wt %</td><td></td><td>1.0 × 10<sup>-4</sup></td><td>70</td><td>19</td></tr><tr><td>Li<sub>6</sub>PS<sub>5</sub>Cl</td><td>NBR</td><td>xylene</td><td></td><td></td><td></td><td>30</td><td>20</td></tr><tr><td>Li<sub>6</sub>PS<sub>5</sub>Cl</td><td>poly(tert-butyl acrylate)-b-poly(1,4-butadiene)</td><td>isobutyl isobutyrate</td><td>2.5 wt %</td><td></td><td>1.7 × 10<sup>-3</sup></td><td>150</td><td>21</td></tr><tr><td>Li<sub>6</sub>PS<sub>5</sub>Cl</td><td>PBMA</td><td>xylene: isobutyl isobutyrate (0:100-100:0)</td><td>3%–10%</td><td>0.6–1.4</td><td>1.5 × 10<sup>-4</sup>–8.6 × 10<sup>-4</sup></td><td>40</td><td>this work</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>Figure 1. Schematic presenting the methodology developed in this work. First, ball-milling is used to reduce the particle size of the LPSCl electrolyte. Then, 110 slurries consisting of different polymer contents, liquid-to-solid ratios, and cosolvent ratios are fabricated to obtain the data set. This data set is fed into the machine learning workflow to categorize samples with different properties, as shown in the fourth step.</figcaption></figure>

electrolytes were used, large variations in ionic conductivity can still be observed, indicating a high sensitivity to processing parameters (e.g., solvents, binder ratio, or liquid-to-solid ratio of the slurries) over the resulting ionic conductivity. Moreover, most reports limited evaluation of SSE films to mainly ionic conductivity, often ignoring the thickness uniformity of the produced film. Uniform and pinhole-free SSE layers are essential to electronically separate cathodes from anodes and to guide a uniform current and stress distribution, thus preventing short-circuit of ASSBs, which is one of the most

important safety requirements for batteries. They are also crucial for maintaining consistent quality in mass production. Thus, a systematic study of the relationship between manufacturing conditions and the performance of SSE films is required.

Unfortunately, analyzing the correlations between multiple manufacturing parameters and performance metrics is challenging and requires intensive trial and error with conventional experimental methods.²² Moreover, there may be nonlinear trends resulting from the synergistic effects of

<footnote><sup>a</sup>SBS, polystyrene-*block*-*polybutadiene-*block-*polystyrene*; SEBS, polystyrene-*block*-*poly(ethylene-*ran*-butylene)-*block*-*polystyrene*; PEO, poly-(ethylene oxide); NBR, nitrile butadiene rubber; PBMA, poly(butadiene methacrylate); THF, tetrahydrofuran.</footnote>
different manufacturing parameters, making the multivariable
puzzle even more complicated. Mesoscale physical simulations
of the actual manufacturing process can offer deep insights on
the mechanism of how manufacturing parameters impact the
properties of SSE films,²³⁻²⁶ but they are considered
computationally expensive for high-throughput use with
current hardware capabilities.²⁷ Machine learning (ML), with
its proficiency in analyzing complex data sets with a large
number of variables,²²,²⁸⁻³² provides a new pathway to limit
time-consuming trial and error processes. Moreover, ML, in
combination with statistical methods, allows fast interpretation
of data. In fact, ML has already been utilized in the battery
field, either in estimating the state of charge or cycle life of cells
under operation,³¹,³³ as well as to assist in the manufacturing
process of LIB electrodes.²²,²⁷ However, the application of ML
to manufacturing flexible SSE films remains unexplored,
presenting an opportunity to apply such capabilities to improve
performance and overall quality of SSE films used in ASSBs.

This work seeks to predict the quality of SSE films by
analyzing the multivariable interdependencies between per-
formance and manufacturing parameters. The methodology
used in this work is schematically presented in Figure 1. Before
casting, Li₆PS₅Cl (LPSCI) powder, a commonly used inorganic
Li⁺ conductor, is wet-milled to homogenize its particle size and
keeps it approximately 1 order of magnitude smaller than the
target thickness of SSE films, preventing it from negatively
affecting their uniformity. Of the multiple input parameters
that need to be considered for performance evaluation,
polymer content (P%), liquid-to-solid ratio (L:S), and
cosolvent ratio (X:B) are chosen as the manufacturing
parameters because they have a significant influence over the
rheological properties of SSE slurries. After casting the SSE
films, several output parameters [density (ρ), ohmic resistance
(Ω), normalized conductivity (σᵢⁿ), deviation of thickness
(DoT; the standard deviation of thickness measured over 9
different points on the SSE films), and relative thickness
deviation (RD; the relative standard deviation of thickness,
DoT/thickness)] are obtained. The obtained experimental
data set, consisting of 110 slurry compositions, is used to
perform a principal component analysis (PCA) to determine
the most representative observables for performance metrics
and manufacturing conditions.³⁴ Following PCA, the manu-
facturing conditions are classified according to a *K*-means
clustering in order to define the quality of the SSE film in terms
of σᵢⁿ, DoT, and RD. Afterward, hyperplanes are assigned
using support vector machine (SVM) for a graphical
visualization of specific effects.³⁵,³⁶ This common thread
aims to both present groups of manufacturing conditions
with similar performances and find the effect of each
manufacturing parameter on SSE films. Finally, an SSE film,
with manufacturing conditions guided by the ML study, is
applied to a NCM811 || LPSCI || LiIn cell to demonstrate the
viability of this approach.

As broad particle size distributions of the SSE will adversely
affect the uniformity of the SSE films, sizes need to be reduced
to around 1 order of magnitude smaller than the target
thickness of the films. This ensures consistency and allows for
better film uniformity. As shown in Figure S1a, the pristine
LPSCI powder exhibits a nonuniform particle size distribution
with particles larger than 20 µm. Hence, reducing the particle
size of the pristine powder is necessary. After dry ball-milling,
some of the particles remain larger than 15 µm, suggesting that
the process is not effective enough (Figure S1b). To overcome

this, inert solvents are employed in wet ball-milling to prevent
the aggregation of the particles and thus allow for reduced
particle size. No particles larger than 10 µm are observed after
wet ball-milling, as seen in Figure S1c–f. When the milling
speed is increased to 400 and 500 rpm, the primary particle
size can be further reduced, but simultaneously the ionic
conductivity of the electrolyte powder decreases significantly.
To avoid severe ionic conductivity loss, the milling speed is
limited to 300 rpm, with an optimized milling time of 2 h.
Under these milling conditions, the particle size is also further
reduced while still preserving the ionic conductivity of the
powder. As a result, all the electrolyte powders used for this
study are processed using these wet ball-milling conditions. To
evaluate the impact of ball-milling on the electrolyte’s crystal
structure, X-ray diffraction (XRD) patterns of the LPSCI
powder are collected before and after various ball-milling
procedures (Figure S2a). The positions of all Bragg peaks
remain unchanged after ball-milling, indicating that the original
LPSCI crystal structure is preserved. However, the samples can
be distinguished by different extents of peak broadening
(Figure S2b), indicating reduction in crystallinity after ball-
milling. The peak broadening with increased milling speed is in
good agreement with the lower conductivity measured by
electrochemical impedance spectroscopy (EIS) presented in
Figure S2c.

To reaffirm the importance of controlling SSE particle size,
SSE films are fabricated using pristine and ball-milled LPSCI
powder, and their focused-ion-beam (FIB) cross-section
images are presented in Figure S3. When pristine LPSCI
powder is employed, the film exhibits particles larger than 20
µm and is susceptible to crack formation during calendering.
This can increase the risk of short circuit when the SSE films
are used in ASSBs. In contrast, a more homogeneous film is
obtained when employing ball-milled LPSCI, and no cracks are
observed in the cross-sectional image. Thus, the ball-milled
electrolyte powder is used for the collection of the data set
necessary for the ML study.

To perform the doctor-blade process for LPSCI, solvents
with good chemical compatibility with sulfide-based SSEs must
be selected; namely, nonpolar solvents with high dielectric
constant are required.¹⁵ Moreover, suitable vapor pressures (at
room temperature: xylene, 10 mmHg; isobutyl isobutyrate, 5
mmHg) and boiling points (xylene, 138 °C; isobutyl
isobutyrate, 147 °C) are also essential for obtaining an
optimum drying rate, which significantly influences the quality
of SSE films. Thus, *p*-xylene and isobutyl isobutyrate are
employed in conjunction for the LPSCI slurry preparation. To
evaluate their chemical compatibility with the SSE, LPSCI
powder is immersed into each of these solvents and no
significant change is observed in both XRD patterns and ionic
conductivities after drying (Figure S4), indicating that LPSCI
does not degrade in either of these solvents.

The full experimental data set is presented in Table S1. After
this training data set is collected, ML algorithms are used to
analyze the effect of the polymer content (P%), the liquid-to-
solid ratio (L:S; volume of solvent/weight of LPSCI, in µL/
mg), and the cosolvent ratio (X:B; xylene to isobutyl
isobutyrate ratio, represented by the volumetric percentage
of xylene) on the quality of the SSE films. After casting, ρ, Ω,
σᵢⁿ, DoT, and RD are measured. Because eight different
variables are experimentally generated, a PCA³⁴ is conducted
to project manufacturing conditions in a reduced dimensional
subspace for reflecting (i) interdependencies between variables
<figure><img src="image_4.png" /><figcaption>Figure 2. Data analysis results coming from the implementation of the PCA and the *K*-means algorithm which show the linear dependence between initial variables and the grouping of the samples in terms of performance respectively. (a) Projection of the initial variables (symbolized by black arrows) onto the 2D plan formed by the first two principal components resulting from the PCA implementation, with the purpose to visualize and analyze the correlation between each pair of variables. For the PCA implementation, P% is not taken into account because of a low number of different modalities. It is considered as a qualitative variable and did not appear in the initial PCA features for better results. However, P% is used for the rest of the analysis. (b) All samples are grouped into 3 clusters with the *K*-means algorithm, here represented within the two first principal components. Those clusters are explicitly defined as *classes* in the rest of the study. Box charts for the comparison of distribution of (c) normalized conductivity and (d) relative thickness deviation for all 3 classes. Abbreviations: L:S, liquid-to-solid ratio; X:B, cosolvent ratio; ρ, density; Ω, ohmic resistance; σᵢ², normalized conductivity; DoT, deviation of thickness; RD, relative thickness deviation.</figcaption></figure>

and (ii) the spread of manufacturing conditions along new principal components. Indeed, such data compression is based on a search for directions of maximum variance to build a new subspace with fewer dimensions, in which the initial raw data are projected. The resulting principal components are a linear combination of initial variables that are orthogonal to each other. This PCA implementation aims at selecting a subset of principal components that can reflect the variance of the experimental data set to define the relationship between manufacturing conditions and film quality.

The correlations between the initial variables can be visualized by projecting them onto two-dimensional subspace, and the result is presented in Figure 2a. As expected, the PCA shows that σᵢ² is inversely correlated with resistance (eq 1 in the Supporting Information). However, it appears that while DoT and RD are strongly correlated together, they are not linearly correlated with σᵢ². As a result, σᵢ² (corresponding to the ionic conductivity of the film) and RD (representing the film uniformity) are employed to evaluate performances of the SSE films. The independence between σᵢ² and RD highlights the shortcomings of evaluating SSE films solely based on ionic conductivities alone, as commonly done in the literature.¹³⁻²¹

Based on the PCA analysis, it is conceivable that highly conductive films might be perceived as being of high quality, while they exhibit large RDs that result in poor cell performance during electrochemical testing.

To visualize the interdependencies among manufacturing conditions and performance, thickness, DoT, and σᵢ² are plotted as a function of L:S and X:B for different P%. The results are presented in Movies S1, S2, and S3, respectively. As P% increases, the overall thickness also increases, while σᵢ² decreases. This may be a result of higher slurry viscosities that limit the flow along the surface of the substrate. Higher P% also increases the tortuosity factor of Li⁺ electromigration, resulting in poorer bulk σᵢ² compared to bare LPSCl.¹² The thickness increases drastically when high X:B, low L:S, and P% larger than 5% are used. With these manufacturing conditions, slurries become too viscous, causing the LPSCl powder to aggregate. While trends in ionic conductivity are relatively intuitive, the plots for DoT exhibit a complex nonlinear behavior. The region with low deviation shifts dramatically as the P% changes. To obtain a better overview, DoT values are averaged over the four P% and plotted in Figure 3. The region with low deviation is found to be distributed in a counter-
<figure><img src="image_5.png" /><figcaption>Figure 3. DoT vs L:S and X:B contour plot averaging through P% of 3%, 5%, 7%, and 10%.</figcaption></figure>

intuitive way. This implies that it is challenging to deconvolute the interdependencies among manufacturing conditions and performance using simple statistical methods. Hence, clustering analysis is employed in the next step.

Before explicitly looking at the effect of manufacturing conditions, it is useful to create groups with similar characteristics in terms of performance, namely, $\sigma_i^N$ and RD from the PCA results. The *K-means* algorithm is directly applied on principal components, and the results are presented in Figure 2b.35 The purpose of the algorithm is to obtain clusters of manufacturing conditions represented by centroids in the low dimensional subspace with the same characteristics in terms of performances, without any prior information (patterns of manufacturing values) on the obtained labels. In order to separate the data, *K-means* algorithm finds patterns where manufacturing conditions inside clusters are similar. The algorithm finds in total 3 different clusters. For the rest of the study, a cluster of manufacturing conditions is called a *class* in order to characterize properly a type of SSE films. The apparent interception of the boundaries from class 1 and class 2 is due to the projection onto a 2D space, whereas the clustering algorithm is applied to more than two principal components. These classes are well-separated from each other by other dimensions before projecting on the 2D space. The statistics of $\sigma_i^N$ and RD in all classes are presented in Figure 2c,d, where a nonparametric *Kruskal-Wallis* test is applied to statistically compare the distributions of $\sigma_i^N$ and RD between classes.37 This test is relevant to compare more than two samples, assume that residuals of the tests are not following a normal distribution, and conclude on possible significant differences between medians of distributions. In that sense, it will validate the *K-means* clustering in order to discriminate SSE films based on their properties. For the test itself, the *p*-value, generally written as “*p*”, represents the probability to reject the hypothesis of the differences in distributions. As a consequence, the test presents significant differences (*p* ≤ 0.05) of distribution among the classes in this study.37 It appears that both class 1 and class 2 exhibit a high ionic conductivity $\sigma_i^N$ compared to class 3. Class 1 can be further separated from class 2 as it exhibits the lowest RD (i.e., higher film uniformity). To conclude, class 1 exhibits good $\sigma_i^N$ and low RD; class 2 maintains the good $\sigma_i^N$ from class 1 while exhibiting an increased RD; class 3 shows the worst performance in terms of both $\sigma_i^N$ and RD. As a consequence, the classification of the overall performance of SSE films can be

defined by common characteristics of observables within each class.

It is also worth looking at the manufacturing condition's relationships with different classes. As the polymer is insulative compared to LPSI, the ionic conductivity decreases as the P% increases. As expected, the average P% in class 3 is higher than that of class 1 and 2. Nevertheless, no statistical effect is found for P% on RD. When looking at L:S, one may conclude that employing higher L:S facilitates higher $\sigma_i^N$ according to the PCA result. Indeed, the classes with higher conductivity contain higher ranges of L:S. However, to minimize the RD, the ratio must be kept in an intermediate range, namely, 0.92 ± 0.23, which is the average value within class 1. Class 3 appears to have the highest X:B, implying a possible adverse effect of X:B over performance. However, an Anova calculation (with a logarithmic correction on the dependent variable)38 indicates that X:B and L:S have synergistic effects on RD. Although qualitative conclusions can be drawn after conducting the *K-means* algorithm, the link between manufacturing conditions and performance remains obscure because of the synergistic effect among different variables. Thus, numeric relationships between manufacturing conditions and performance need to be deciphered before the ML model can gain the ability to predict the results.

While the manufacturing conditions-performance relationship appears clearer after *K-means* clustering, it needs to be further deconvoluted through SVM.36 Such a powerful and widely used classification learning technique optimizes the separation of classes from each other by constructing a linear hyperplane in the low-dimensional subspace defined above, to maximize the margin between different classes (Figure S5). Therefore, the margin is defined as the distance between the separating hyperplane (also called decision boundary) and training raw data. The larger the margin, the better the classification prediction. Consequently, the separation of classes and the decision boundaries are interesting as a visualization tool of the results. Indeed, it is possible to fix one manufacturing parameter and display the classification results as well as the decision boundary in a 2D plot formed by ranges of the two other parameters. In that sense, it is possible to analyze the evolution of the boundary when manufacturing conditions change, which reflects their impact on the performance.22

This process allows the prediction of film performance associated with a specific set of manufacturing conditions. To train the SVM model, 80% of the total number of raw data are randomly selected as the training data set, and the remaining data are used as the testing data set to validate the model. To overcome the unbalanced class distribution, a random oversampling procedure method is applied on the training dataset to fix the class imbalance (Figure S6) by adjusting class distribution over the minority classes (i.e., class 2 and 3).39 Such a random oversampling approach allows the SVM algorithm to learn data distribution in equal proportions of samples per class, whereas unbalanced numbers of samples would cause SVM to yield inaccurate results by prioritizing the class with the largest number of samples. In other words, the metric used to validate the model considers the weighted training of the SVM algorithm by providing a better value when predicting manufacturing conditions as a minor class.

The best linear hyperplane is assigned by SVM to divide the low dimensional subspace spanned by the 1st and 2nd principal components into three regions. The results based on $\sigma_i^N$ and
<figure><img src="image_6.png" /><figcaption>Figure 4. SVM classification in terms of the ionic conductivity ($\sigma_i^N$) and the relative thickness deviation (RD) of SSE films as a function of L:S and X:B ratios. The manufacturing conditions with P% of 3%, 5%, 7%, and 10% are obtained through experiment, while 4%, 6%, 8%, and 9% are interpolated using the ML model.</figcaption></figure>

RD are presented in Figure 4. When accounting only for $\sigma_i^N$ as the performance metric, most of the manufacturing conditions are considered to result in good film quality. However, when considering the RD as well, a large portion of manufacturing conditions, with mid-to-high L:S are no longer qualified so. Therefore, the three regions, from low L:S to high L:S, can be related to class 3, class 1, and class 2, respectively. Besides the data points obtained through experiment, SVM is also able to predict using new manufacturing conditions, interpolating the results with P% of 4%, 6%, 8%, and 9%. When projected on the L:S-X:B plane, the hyperplane appears to be sloped and will shift as P% changes, showing the counterintuitive trend caused by the high interdependency of each manufacturing parameter. When comparing the trends found in Figure 4 with the interpretations of K-means clustering and the statistical tests, they match in terms of the effects of manufacturing parameters, indicating that the ML classifier is suitable for analyzing interdependencies between manufacturing parameters and quality of SSE films. The model is validated with the testing data set through the F1-score metric$^{40}$ that reaches a value of 94%. Such a metric is relevant for multilabel classification learning because it helps to balance the metric across the sensitivity and the specificity of the model. The F1-score is an

average definition of precision and recall, and an example of a binary classification task is shown below:

$$F1 = 2 \times \frac{\text{precision} \times \text{recall}}{\text{precision} + \text{recall}} \qquad (1)$$

$$\text{precision} = \frac{\text{RP}}{\text{RP} + \text{FP}} \qquad (2)$$

$$\text{recall} = \frac{\text{RP}}{\text{RP} + \text{FN}} \qquad (3)$$

where RP, FP, and FN are the true predicted values, the mis-predicted false values, and the mis-predicted true values, respectively. The true/false prediction is defined as a manufacturing condition correctly/wrongly classified by the algorithm over the positive/negative value of the output. In the case of the multilabel classification developed in this study, the F1-score is averaged over the values between each pairs of labels for the output of the classification task. Because of the definition of three classes of film quality, it is possible to distinguish good and bad SSE films with respect to each performance variable.

To probe the connection between performance and morphologies of the SSE films, one sample is selected from
<figure><img src="image_7.png" /><figcaption>Figure 5. Images of representative SSE films from each class. (a–c) Digital images, (d–f) SEM top view, and (g–i) FIB cross-sectional views of the SSE films from (a, d, and g) class 1, (b, e, and h) class 2, and (c, f, and i) class 3.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Figure 6. SEM cross-sectional image, first-cycle voltage profiles capacity retention and Coulombic efficiency of NCM811 || LPSCI || LiIn cells with a cathode loading of 4.75 mg cm⁻². (a) SEM cross-sectional image indicating that an intimate contact is formed between the electrolyte and the cathode composite. The measured thickness of the SSE film is 40 µm. (b) First cycle voltage profiles of NCM811 || LPSCI || LiIn cells prepared using SSE films from class 1 and class 2. Because of lower uniformity, the cell using the class 2 film shorts during the first cycle. (c) Capacity retention and Coulombic efficiency of the class 1 cell.</figcaption></figure>

each class and their morphology observed using digital and scanning electron microscopy (SEM) images, as presented in Figure 5. From the digital images (Figure 5a–c), class 1 appears to be the most uniform and exhibits a clear-cut boundary with its substrate. Compared to class 1, class 2 exhibits inferior uniformity with a fading boundary to the substrate, likely because of the higher L:S in its manufacturing conditions. Severe aggregations can be observed in class 3 where the solvents fail to properly disperse the LPSCI powder because of insufficient L:S. When looking at the SEM top-down view images (Figure 5d–f), both class 1 and class 2 show uniform surface morphology, with class 1 appearing to be slightly superior. On the other hand, aggregated particles and cracks can be observed on the surface of the class 3 sample. FIB cross sections (Figure 5g–i) are collected to probe the interior morphology of the films. The particle distributions of classes 1 and 3 are similar, with class 3 being more porous. Class 2 exhibits smaller overall particle size, especially on the top part of the image, likely because of phase separation between the smaller and larger particles resulting from excessive L:S. To verify this assumption, another cross section is taken from the bottom side (Figure S7), and it appears that the average particle size is comparable to that of classes 1 and

3. Nevertheless, the overall porosity of class 2 is lower than that of class 1, probably because of the close packing of LPSCI particles facilitated by higher L:S, explaining the slightly higher $\sigma_i^N$ in class 2 than class 1. Even though class 2 exhibits a dense cross-sectional morphology, its tendency to phase separate is not desirable for maintaining consistent quality in mass production. To prove its superior quality over the other two classes, a uniform free-standing SSE film from class 1 is fabricated and tested (Figure S8).

Before the application of SSE films to batteries, it is important to evaluate their electronic conductivity. An SSE with sufficient electronic insulation, preferably below the order of $10^{-9}$ S cm⁻¹, will prevent charge leakage or even short circuiting of the cells.⁴,⁴² DC polarization and EIS results of the LPSCI powder and film are presented in Figure S9. While the pristine LPSCI powder exhibits an ionic conductivity of $2.1 \times 10^{-3}$ S cm⁻¹ and an electronic conductivity of $5.1 \times 10^{-9}$ S cm⁻¹, the optimized LPSCI film has an ionic conductivity of $8.6 \times 10^{-4}$ S cm⁻¹ and an electronic conductivity of $1.1 \times 10^{-9}$ S cm⁻¹. For both samples, the electronic conductivities are six orders smaller than their ionic conductivity. It is worth noting that although the LPSCI film exhibits a lower ionic conductivity than its powder form, the ability to be fabricated
with reduced thicknesses leads to a lower total impedance, as can be seen in Figure S9b. The fabrication of SSE films therefore not only is beneficial to energy density of the battery but also reduces ohmic losses during cycling.

SSE films from class 1 and 2 are used to prepare NCM811 || LPSCI || LiIn cells. An FIB cross-sectional image showing the SSE and the cathode layers after calendering is presented in Figure 6a. The whole cross section is observed to be uniform and dense. The cathode composite–SSE layer contact is also observed to be compliant, with no delamination, voids, or cracks observed. Their voltage profiles are presented in Figure 6b. When the class 1 film is employed, the cell cycles as expected, while a short circuit is observed during the first charge with the class 2 film. Nonuniform thickness of SSE films can cause uneven stress distribution during calendering and impact negatively on the integrity of the cell structure, leading to short circuit during the first charge. This illustrates the importance of considering both the uniformity and ionic conductivity of the SSE films for performance evaluation. Figure 6c shows the capacity retention and Coulombic efficiency (CE) of the NCM811 || LPSCI || LiIn cell made with the class 1 film. A first cycle CE of 74.1% along with a first discharge capacity of 149 mAh g⁻¹ is obtained. The initial capacity loss in the first 10 cycles can be attributed to kinetic effects of contact losses and initial impedance growth at the cathode, which is commonly observed in the interfaces between sulfide SSEs and layered-oxide cathodes.43,44 Variation in CE can be observed, and these may be attributed to the ionic conductivity change of SSE resulting from the slight fluctuation of room temperature. A capacity of 94 mAh g⁻¹ is retained after 100 cycles. Another NCM811 || LPSCI || graphite cell with a cathode areal loading of 10 mg cm⁻² was fabricated, and its voltage profiles and EIS are presented in Figure S10. The cell delivered a first discharge capacity of 1.61 mAh cm⁻² and a first CE of 76.2%. The cell maintained stable charge–discharge cycling during the following cycles. This result further demonstrates the possibility of utilizing the ML approach in practical applications. Nevertheless, similar to the NCM811 || LPSCI || LiIn cell, the NCM811 || LPSCI || graphite cell also exhibits some contact losses at the cathode side, and its EIS slightly increases from 142 to 178 Ω after the first cycle. Future work will be focused on optimizing the cycling pressure of film-type ASSBs to mitigate the capacity loss caused by the volume change of layered-oxide cathode materials43–45 and to increase the energy density by enabling Li-metal anodes.46–49

In summary, this study demonstrates how ML algorithms can be used to predict the performance of the sulfide-based SSE films by deconvoluting the interdependencies between the manufacturing parameters and performance metrics. After collection of the experimental data set, three algorithms (PCA, K-means algorithm, and SVM) in combination with statistical tests are employed to analyze the data. PCA determines the most significant observables for performance evaluation and allows to represent the manufacturing conditions in a low dimensional subspace. A clustering method, the K-means algorithm, is then applied in this subspace to properly define classes of films quality based on the similarity of performance within groups. These 3 classes are defined as class 1 (high $\sigma_i^N$ and low RD), class 2 (high $\sigma_i^N$ and high RD), and class 3 (low $\sigma_i^N$ and high RD). Finally, the SVM model reveals the trends of the effect of the manufacturing parameters on the quality of SSE films according to classes, and supports the results found in the K-means clustering and statistical analysis. Finally,

guided by ML, a NCM811 || LPSCI || LiIn cell, utilizing an SSE film with a thickness of 40 µm from class 1, is shown to be able to cycle successfully for 100 cycles. Our results highlight the necessity to account for both uniformity and ionic conductivity when fabricating SSE films and demonstrate how ML can be a powerful tool to guide experiments toward the optimal fabrication parameters. The methodology provided in this study may benefit the development of future scalable manufacturing process for flexible and uniform SSE films for ASSB applications.

## ■ ASSOCIATED CONTENT

### ■ Supporting Information

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsenergylett.1c00332.

Experimental and machine algorithm details; SEM images, a digital image, XRD patterns, EIS data, and DC polarization of LPSCI powder or SSE films; DoT vs L:S and X:B plot; number of samples in each class (PDF)

Table S1: Full experimental data set (XLSX)

Movie S1: Visualization of the thickness as a function of L:S and X:B for different P% (MPG)

Movie S2: Visualization of the DoT as a function of L:S and X:B for different P% (MPG)

Movie S3: Visualization of $\sigma_i^N$ as a function of L:S and X:B for different P% (MPG)

## ■ AUTHOR INFORMATION

### Corresponding Authors

Alejandro A. Franco - Laboratoire de Réactivité et Chimie des Solides (LRCS), Université de Picardie Jules Verne, UMR CNRS 7314, 80039 Amiens, France; Réseau sur le Stockage Electrochimique de l'Energie (RS2E), FR CNRS 3459, 80039 Amiens, France; Alistore-ERI European Research Institute, FR CNRS 3104, 80039 Amiens, France; Institut Universitaire de France, 75005 Paris, France;
Email: alejandro.franco@u-picardie.fr

Ying Shirley Meng - Program of Materials Science and Engineering, Department of NanoEngineering, and Sustainable Power & Energy Center (SPEC), University of California San Diego, California 92093, United States;
● orcid.org/0000-0001-8936-8845; Email: shmeng@ucsd.edu

Zheng Chen - Program of Materials Science and Engineering, Department of NanoEngineering, Sustainable Power & Energy Center (SPEC), and Program of Chemical Engineering, University of California San Diego, California 92093, United States; ● orcid.org/0000-0002-9186-4298;
Email: zhengchen@eng.ucsd.edu

### Authors

Yu-Ting Chen - Program of Materials Science and Engineering, University of California San Diego, California 92093, United States; ● orcid.org/0000-0001-9525-8407

Marc Duquesnoy - Laboratoire de Réactivité et Chimie des Solides (LRCS), Université de Picardie Jules Verne, UMR CNRS 7314, 80039 Amiens, France; Réseau sur le Stockage Electrochimique de l'Energie (RS2E), FR CNRS 3459, 80039 Amiens, France; Alistore-ERI European Research Institute, FR CNRS 3104, 80039 Amiens, France
Darren H. S. Tan – Department of NanoEngineering,
University of California San Diego, California 92093, United
States

Jean-Marie Doux – Department of NanoEngineering,
University of California San Diego, California 92093, United
States

Hedi Yang – Department of NanoEngineering, University of California San Diego, California 92093, United States

Grayson Deysher – Program of Materials Science and
Engineering, University of California San Diego, California
92093, United States; ● orcid.org/0000-0003-0482-8991

Phillip Ridley – Department of NanoEngineering, University of California San Diego, California 92093, United States

Complete contact information is available at:
https://pubs.acs.org/10.1021/acsenergylett.1c00332

Author Contributions

Y.-T.C. and M.D. contributed equally to this work.

Notes

The authors declare no competing financial interest.

■ ACKNOWLEDGMENTS

This work was partially supported by LG Chem through the Battery Innovation Contest (BIC) program as well as the Energy & Biosciences Institute through the EBI-Shell program. Z.C. acknowledges funding from the start-up fund support from the Jacob School of Engineering at University of California San Diego. Y.S.M. acknowledges funding support from Zable Endowed Chair Fund. Part of this work was performed at the San Diego Nanotechnology Infrastructure (SDNI) of the UCSD, a member of the National Nanotechnology Coordinated Infrastructure, supported by the National Science Foundation (Grant ECCS-1542148). M.D. and A.A.F. acknowledge the European Union's Horizon 2020 research and innovation programme for the funding support through the European Research Council (Grant Agreement 772873, "ARTISTIC" project). M.D. and A.A.F. acknowledge the ALISTORE European Research Institute for funding support. A.A.F. acknowledges the Institut Universitaire de France for the support.

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