Statistical models are able to predict ionic liquid viscosity across a wide range of chemical functionalities and experimental conditions

Wesley Beckner a, Coco M. Mao a and Jim Pfaendtner *ab
aDepartment of Chemical Engineering, University of Washington, Seattle, Washington 98195, USA. E-mail:
bSenior Scientist, Pacific Northwest National Laboratory, Richland, Washington, USA

Received 6th September 2017 , Accepted 12th January 2018

First published on 12th January 2018


Herein we present a method of developing predictive models of viscosity for ionic liquids (ILs) using publicly available data in the ILThermo database and the open-source software toolkits PyChem, RDKit, and SciKit-Learn. The process consists of downloading ∼700 datapoints from ILThermo, generating ∼1200 physiochemical features with PyChem and RDKit, selecting 11 features with the least absolute shrinkage selection operator (LASSO) method, and using the selected features to train a multi-layer perceptron regressor—a class of feedforward artificial neural network (ANN). The interpretability of the LASSO model allows a physical interpretation of the model development framework while the flexibility and non-linearity of the hidden layer of the ANN optimizes performance. The method is tested on a range of temperatures, pressures, and viscosities to evaluate its efficacy in a general-purpose setting. The model was trained on 578 datapoints including a temperature range of 273.15–373.15 K, pressure range of 60–160 kPa, viscosity range of 0.0035–0.993 Pa s, and ILs of imidazolium, phosphonium, pyridinium, and pyrrolidinium classes to give 33 different salts altogether. The model had a validation set mean squared error of 4.7 × 10−4 ± 2.4 × 10−5 Pa s or relative absolute average deviation of 7.1 ± 1.3%.

Design, System, Application

This work presents a method of developing viscosity models for ionic liquids (ILs). The process utilizes the python modules PyChem and SciKit-Learn and data from the ILThermo database provided by the National Institute of Standards and Technology (NIST). Beginning with a 1271 feature space, the optimization strategy includes parameterizing a least absolute shrinkage selection operator (LASSO) method, using bootstrap and LASSO to select and create confidence intervals for the top features, and training a neural network (NN). The final output is a NN with 11 features that is accurate for categorically different ILs across a broad range of temperature, pressure, and viscosity. We show that the method works with reasonable accuracy even when the NN has been trained on categorically ILs. This is especially true if, within the training data, at least either the cationic or anionic moiety has been encountered. Because the feature space does not include interaction parameters between the ions, the NN does not need to be retrained to evaluate new cation/anion pairs, a design constraint that has been typical of recent viscosity models. Future applications could include using the NN as a fitness test in evolutionary algorithms to search for ILs with desirable properties.


Recent years have seen a huge rise in the successful application of machine or statistical learning type approaches to the discovery and design of new materials. Efforts such as Materials Genome Initiative (MGI) have led to the creation of public data repositories like the Harvard Clean Energy Project,1 Materials Project,2 Open Quantum Materials Database (OQMD),3 and Automatic FLOW for Materials Discovery (AFLOW).4 In the area of solid crystalline materials, research pipelines based on high throughput calculations have enabled rapid population of massive databases. However, many important materials for a wide range of applications are liquids, including emerging solvent classes such as ionic liquids (ILs) or deep eutectic solvents.5 In contrast to crystalline materials, liquids present a host of challenges that prevent direct mimic of the successful MGI type approaches. For example, calculation of relevant properties with molecular simulations requires statistical sampling (e.g., molecular dynamics (MD) or Monte Carlo) compared with the energy minimization and structural calculations used in solid systems. Intrinsic properties of liquids show much stronger dependence on thermodynamic state variables. Finally, in a potential advantage compared to crystalline materials, public datasets of experimental measurements such as those available from NIST Webbook offer huge opportunities for training statistical learning models.

Many of the current applications of linear and non-linear statistical learning methods in physical sciences are inspired by studies using quantitative structure–property relationships, QSPR; or quantitative structure–activity relationships, QSAR, which were widely used starting in the 1960s and beyond.6,7 The thousands of QSPR/QSAR models that have been developed in the previous half century have incited both praise and criticism of their reliability and limitations.8,9 In response to this and in the interest of promoting high-quality models, the Organization for Economic Co-operation and Development (OECD) developed five guiding principles; that a QSAR/QSPR model should have: 1) a defined endpoint, 2) an unambiguous algorithm, 3) a defined domain of applicability, 4) appropriate measures of goodness of fit, robustness and predictive-power, and 5) a mechanistic interpretation when possible.10 In the interest of objectives 4) and 5) some have stressed creating a small and focused set of descriptors for model development.11 Others have stressed resourcing the highest levels of domain area knowledge and statistical knowledge, via collaborations between experimental and computational scientists when necessary, to aid with these objectives.8 However, the continued exponential growth in available data, computing power, and open source software, further complicates the challenge by affecting the weight one would appropriate to any one of these principles. For instance, the modern ability to access extremely large experimental datasets and chemical search spaces introduces quite a different problem than that in the pioneering work of Hansch, Leo, and others,7 where relatively small datasets confined them to a limited feature space.

Taking into account these guidelines and the realities of dealing with ever-growing data sets,12 this paper describes our application of statistical machine learning models to the prediction of the viscosity of ionic liquid (IL) solvents. ILs have great potential for application in nanomaterials synthesis, bioremediation, and bio-catalysis/enzyme stabilization.5 They have also been identified as a potential supporting electrolyte material for redox flow batteries (RFBs).13–15 Especially in the case of RFBs, the solvent viscosity is critical to understand and control as it directly relates to the device's efficiency (via the total energy density). Within RFBs, there are two primary methods of increasing the energy density, and therefore efficiency, of a flow battery: 1) increasing the solubility of the active material or 2) reducing the viscosity of the electrolyte.16–18 Both of these methods are a consequence of having to actively pump the electrolyte across a membrane to facilitate charge transfer—one would desire that, that volume of fluid have either a high chemical potential energy or a low viscosity. Unfortunately these two characteristics, energy density and viscosity, have tended to be inversely correlated. It is for this reason that an accurate algorithm to determine viscosity based on the molecular constituents of an IL is extremely valuable. With these challenges in mind, we set out to use the public data available in the NIST ILThermo19 database for training and testing of different predictive models.

Apart from RFBs, in many applications the viscosity of the IL plays a huge economical factor; essentially whenever active transport of the IL is needed. Because of this, many predictive models of IL viscosity have been attempted. They have, however, been largely unsuccessful due to either not reproducing experimental values across categorically different ILs or requiring the use of IL-specific experimental data in predictions.20–30 Briefly, Matsuda et al. employed group contribution (GC) type descriptors with some accuracy. Their model, however, did not perform very well on a test dataset, reporting an R-squared value of 0.6226.30 Another GC approach was introduced by Gardas and Coutinho, where they fit the GC-type inputs to the Vogel–Tammann–Fulcher (VTF) equation. This is considered to be one of the most accurate, temperature dependent viscosity models to date but is limited to a narrowly defined set of ILs.25,28 Zhao et al., used GC-VTF methods to parameterize a UNIFAC-VISCO model. While they reported a low error rate for the regression on their training data, their model is meant to predict binary mixtures of ILs and so is not very useful in terms of exploring a structural search space.31 As a last look at the GC-type models, Padusyzński & Domańska did an extensive data scraping of the literature to produce a feed forward artificial neural network (FF-ANN) using GC-type inputs and Fatehi et al. applied an FF-ANN to GC-type inputs supplemented with electronegativity descriptors.25,32 Their models did very well across many IL types. They did not however, examine how their models might perform given an IL type from outside their training data, something we determine for our model in this work.

Other attempts at IL viscosity models have been made without the use of GC-type inputs, notably, hole theory models by Bandrés et al. and volumetric VTF models by Slattery et al., but have required the use of experimental data in some form or another.27,29 In this work, we introduce a method to accurately predict viscosity for categorically different ILs and broad ranges of temperature (T), pressure (P), and viscosity. Additionally, we explore the sensitivity of the approach to underlying molecular structure and include these results in the ESI.

The remainder of this manuscript is organized as follows. The next section combines methodological details with the model development. Following this we apply linear and nonlinear statistical learning methods to understand key structural predictors of viscosity and provide robust statistical analysis on a large data set of experimentally measured IL viscosities. Finally, we discuss the applicability of the model across different IL types as well as the underlying features that explain the variance in the viscosity across our training data.

Methods and model development

Data collection and structure dependence

Many prior attempts to model IL viscosity21,22,31,33–35 required narrow definition of cation or anion classes. Therefore, we filtered our starting dataset to emphasize variance in structure in an attempt to understand the limits of a single statistical model. We began with 1405 experimental data points from the ILThermo database and screened for a T range of 273.15–373.15 K, P range of 60–160 kPa, and viscosity range of 0.0035–0.993 Pa s. The original dataset (including experimental references) before screening is available in the ESI as viscosity_data.csv. Classes of structure included imidazolium, phosphonium, pyridinium, and pyrrolidinium based salts. After this initial screening, the final dataset contained 723 data points consisting of 33 unique salts; 22 anions and 16 cations. We then created a subset of 28 unique imidazolium salts containing 403 data points including a temperature range of 273.15–373.15 K, pressure range of 100–160 kPa, viscosity range of 0.004229–0.982 Pa s. We then applied the following protocol to this subset to evaluate how our general model might perform on salt-types not included in its training data, see Fig. 1.
image file: c7me00094d-f1.tif
Fig. 1 723 data points with temperature, pressure, and viscosity ranges of 273.15–373.15 K, 60–160 kPa, and 0.0035–0.993 Pa s, respectively. Inset: 453 data points with temperature, pressure, and viscosity ranges of 273.15–373.15 K, 100–160 kPa, and 0.004229–0.982 Pa s, respectively. The imidazolium base structure is illustrated in the whitespace.

Feature generation

The open source python packages PyChem36,37 and RDKit38 were used to generate 633 physiochemical descriptors for each cation and anion in the starting dataset (1266 per IL). T and P, 1/T, T2, and ln(T) were included in the feature set to give a final data frame of dimensions 723 by 1271. All features were centered about zero and scaled to unit variance. After removing columns with zero variance the final data frame consisted of 723 data points with 771 descriptors. This data frame is included in the ESI as viscosity_processed.csv.

LASSO: model parameterization

The least absolute shrinkage and selection operator (LASSO)39 algorithm from the SciKit-learn40 machine learning toolkit was used to shrink the feature space. The primary hyper-parameter of the LASSO model (λ) was optimized in three separate schemes: 5-fold cross-validation (CV), shuffle-split, and bootstrap confidence test algorithms (see Fig. 2). Explained briefly, these algorithms break the data into 80/20 training/testing sets for 300 iterations. In the bootstrap scheme, the final training set is sampled from the training fraction with replacement, offering the possibility of the same data point being sampled multiple times. The shuffle-split schematic is identical to bootstrap apart from that the data is sampled without replacement; the original dataset is randomly shuffled and split between train and test. 5-fold CV was performed with slight modification. Keeping in line with the theme of the other methods, a random 80/20 split was made of the original dataset. 5-fold CV was then implemented in the standard way on the 80% fraction until the next iteration, in which another random 80/20 split was made. Each of these algorithms was implemented 300 times; trained and the mean squared error (MSE) evaluated on either 1) the testing portion of the dataset (bootstrap and shuffle-split) or 2) the aggregate from the five folds (5-fold CV).
image file: c7me00094d-f2.tif
Fig. 2 Shuffle-split, cross validation, and bootstrap algorithms were used to systematically search for the optimum λ value, the tuning parameter that determines the shrinkage penalty for LASSO. The red crosshairs in the bootstrap panel show that the most conservative (highest) value of λ, 0.021, is still within a standard deviation of the test MSE of a λ value as high as 0.054, i.e. a good selection for λ is highly contingent on the population of training data.

LASSO: feature selection

The three confidence tests provided a starting point for feature shrinkage, however, the statistical variance in the test MSE indicated these optimum λ values were highly dependent on the randomly selected training data. Noting the bootstrap scheme in the bottom panel of Fig. 2, the average test MSE for a λ value of 0.021 was still within a standard deviation of the same test MSE for a λ value as high as 0.054 (or log[thin space (1/6-em)]λ −2.9 in Fig. 2)—meaning, these two λ values share the same test MSE 68% of the time. While not certain, it is reasonable to posit that a λ value of 0.021 may be leaking noise into the model based on training data selections with incidental patterns in their feature vectors not related in any physical way to viscosity. To test for this, we selected 0.021 as the value for λ and trained the LASSO models on bootstrapped datasets for 1000 iterations to obtain confidence intervals for individual feature coefficients, see Fig. 3. A final, bootstrapped model was taken as the mean value of every non-zero return of the coefficients. This model was then used to predict viscosities for a validation set. Features were sorted by the absolute value of their mean and progressively removed from the model to determine at what point the test MSE no longer improved, see the insets of Fig. 3. Test MSE no longer improved after the top 11 most influential features were included in the models—regardless of whether all four categories of salt or only imidazolium-type salts constituted the training data. These top 11 features had p-values close to 0. After converging on the top 11 features, the LASSO models were trained at λ values ranging from 0 to 1 on their respective feature vectors. The expected approach to zero of the coefficients for each model are shown in Fig. 4.
image file: c7me00094d-f3.tif
Fig. 3 Confidence intervals for the most influential features in the respective LASSO models. Insets show that the mean squared error does not improve past the 11 (red, vertical bar) most influential features. Models were trained 1000 times on bootstrapped datasets. X-axis displays the absolute values of the coefficients (values below the red, horizontal line are negative). Y-axis is sorted in ascending order (top to bottom) by the mean value of the coefficient. Green line indicates the median value, red box indicates the mean value, blue box indicates the 2nd and 3rd quantiles, and small, red bars indicate the range. The p-values for all coefficients are very close to zero, with the highest being 1 × 10−66.

image file: c7me00094d-f4.tif
Fig. 4 Coefficient values versus log[thin space (1/6-em)]λ for the most influential features in the imidazolium and general model, respectively.

Neural network

The respective selected feature sets were used to train neural networks. The random search algorithm, RandomizedSearchCV was used to parameterize the multilayer perceptron (MLP) Regressor algorithm (a type of FF-ANN), both from SciKit-Learn. In the random search algorithm, ten settings were tested among the following distributions for the specified parameter. For activation: identity, logistic, tanh, and relu; for solver: limited memory Broyden–Fletcher–Goldfarb–Shanno algorithm (lbfgs), stochastic gradient descent (sgd), and adam; for learning rate: constant, invscaling, and adaptive; and a uniform distribution from zero to one was sampled for the regularization parameter, α. The listed parameter values were sampled without replacement while the uniform distribution for α was sampled with replacement. The final, selected settings for the specified parameter were the following. For activation: tanh, specifying a hyperbolic tan function for the activation function of the hidden layer; for solver: lbfgs, specifying a quasi-Newton method of solving for the weight optimization (a fast and accurate solver for smaller datasets, note that this stochastic solver recalculates its learning rate, α, at every step and nullifies any user-specified starting α); and max_iter: 1e8, the maximum number of iterations.

The remaining parameters were left at their default values: batch_size: auto; early_stopping: false; hidden_layer_sizes: 100; random_state: none; validation_fraction: 0.1; warm_start: false. A full description of these parameters are available in the SciKit-Learn documentation.40

Final evaluation

For both the LASSO and the final ANN, bootstrapping was performed to estimate the variance in the predictions of a validation set. In the typical case, bootstrapping is a method of “internal validation” where subsets of the data are sampled with replacement and the resulting model is evaluated on the data excluded from the sample. The process is repeated to obtain error estimates for the entire dataset.41,42 We adapted this typical use case with “external validation” i.e. a portion of our dataset was reserved for validation, was never included in the model training, and the resulting models were used to obtain error estimates for this validation set. The process was as follows. For the ANN model, an 80–10–10% split of the entire dataset was used for train-test-validation. Bootstrapping was performed on the 80–10% sets and validated on the same 10% validation set for 300 iterations (i.e. the validation set data never entered the training data). The same procedure was performed on the imidazolium salt-types only—this time using the other salt-types as the validation set—to investigate how the general model might perform when exposed to salt-types not included in its training data. The procedure was identical for the LASSO model with the exception that a 90–10% split was made between training and validation since a test set is not required to determine the LASSO coefficients.

Results and discussion

LASSO: feature selection

There have been prior approaches to develop models that predict viscosities for ILs. Few of them, however, have been successful across categorically different ILs.20–24 We investigated the dependency of our approach on structure by applying our procedure on only imidazolium-type salts, investigating the difference in selected features from the general model, and evaluating the ANN performance on the remaining three categories of salts. These selections of the data had a similar distribution of viscosity, T, and P to isolate the dependency of the model on core IL structure, see Fig. 1.

The LASSO was used to shrink the physiochemical feature space. The selected features were similar between the imidazolium and general model, see Fig. 3. Both models consistently under-predicted viscosity values whose true values were above 0.2 Pa s and over-predicted the values of those below. The general LASSO model on average had a validation set error of 0.0108 ± 0.0008 Pa s while the imidazolium LASSO model had a higher validation set error of 0.025 ± 0.003 Pa s when evaluated on non-imidazolium salts and—as expected—a lower validation set error when evaluated on imidazolium salts, 0.0079 ± 0.0006 Pa s.

Description of the selected features

In the following we briefly explain the features that were selected by the imidazolium or general model, see also Table 1.
Table 1 Brief summary of selected descriptors by LASSO
Descriptor Type Description
bcute5 Autocorrelation Burden autocorrelation of Sanderson electronegativity with topological interval 5
bcute2 Autocorrelation Burden autocorrelation of Sanderson electronegativity with topological interval 2
MATSp5 Autocorrelation Moran autocorrelation of polarizability with topological interval 5
MATSe5 Autocorrelation Moran autocorrelation of Sanderson electronegativity with topological interval 5
GATSp1 Autocorrelation Geary autocorrelation of polarizability with topological interval 1
PEOEVSA12 MOE Sum of atomic van der Waals surface area contributions to partial charges within 0.25–0.3
PEOEVSA6 MOE Sum of atomic van der Waals surface area contributions to partial charges within −0.05–0
EstateVSA3 MOE Sum of atomic van der Waals surface area contributions to electropological states within 0.717–1.165
SIC0 Basak Complementary information content with 0th order neighborhood of vertices in a hydrogen-filled graph
IC4 Basak Structural information content with 4th order neighborhood of vertices in a hydrogen-filled graph
Smax14 Electropological Maximum electrotopological state of sp hybridized carbon
Chi4c Connectivity Fourth order cluster index
Hato Topological Topological index of molecular branching

The features are described extensively in the (ESI) and additional information can be found in the corresponding references.

Spatial autocorrelation descriptors. Autocorrelation is a general statistical measure of, broadly defined, how a property of pairwise variables spaced at temporal or spatial intervals are more (positive autocorrelation) or less (negative autocorrelation) similar than they would be for a set of stochastic observations. Several autocorrelation calculations have been introduced in the past century. In ecological processes these have been appropriated primarily due to the importance of stochastic independence to apply the assumptions of classical statistics.43 Perhaps more fundamentally, time correlation functions have lent themselves to the exact mathematical expression for transport coefficients such as those found in the Green–Kubo relations. Many spatial autocorrelation functions are included in RDKit.

In the following acronyms the small letters signify the type of weighting used in the autocorrelation: atomic masses (m), van der Waals volumes (v), Sanderson electronegativities (e), atomic electronegativities (ae), and polarizabilities (p). The number represents the topological distance between atoms, i.e., the lag associated with the atomic property evaluated at those atomic points. Our models selected two Moran autocorrelations:44 MATSe5-cation and MATSp5-anion; two Burden autocorrelations:45 BCUTae5-cation and BCUTae2-cation; and one Geary autocorrelation:46 GATSp1-anion.

Inspecting the coefficients in Fig. 3, a negative Moran autocorrelation of the Sanderson electronegativities and polaraizabilities of the 5th topographical interval coincides with a decrease in viscosity in both models. In the imidazolium model, a positive Geary autocorrelation of polarizability on the 1st topographical interval coincides with an increase in viscosity. Burden autocorrelations of atomic electronegativies of the 2nd (imidazolium) and 5th (all salts) topographical interval coincides with an increase in viscosity in both models. Also of note, even with the large variance (the greatest in all the selected features by the model) in the Burden coefficients, the associated p-value is extremely low (1 × 10−66), indicating a high probability that this is a descriptive feature for viscosity.

Electrotopological state (E-state) descriptors. The E-state formalism was introduced as a way to economically navigate molecular structure space. In this formalism three intrinsic states of a molecular substructure within a molecule are quantified: its elemental content, its valance state (electronic organization), and its topological state in regard to its atomic neighbors.47–50 The idea for this approach is that the information density per descriptor can be far greater than an atomic substructure count, where relational/environmental information is lost. However, the “leanness” of the descriptor comes at a cost: ambiguity is introduced when multiple fragments of the same substructure are contained in the molecule. This has been the subject of some studies where either averages, max/min, or sums are returned for atomic fragments or the molecule as a whole.48,49 One E-state descriptor was selected by the general model: Smax14-anion, which is the maximum E-state of any carbon with a triple bonded neighbor. A high maximum E-state for this molecular substructure decreases the viscosity of the IL.
Molecular operating environment (MOE)-type descriptors. The MOE-type descriptors use connectivity information and van der Waals radii to calculate the atomic van der Waals surface area (VSA) contribution of an atom-type to a given property.11 Our models selected three MOE-type descriptors. Gasteiger51 partial charges (a rapid, iterative approach to calculation of partial charges using only topological data): PEOE-VSA12-cation (both models, increases viscosity), PEOE-VSA6-anion (imidazolium model, increases viscosity); and E-state indices: E-state-VSA3-cation (both models, decreases viscosity).
Basak descriptors. Basak descriptors contain weighted structural and chemical information content for describing physicochemical properties.52–54 Our models selected two of these types of descriptors. SIC0-anion, complementary information content with 0th order neighborhood of vertices in a hydrogen filled topological graph (general model, decreases viscosity). IC4-cation, structural information content with 4th order neighborhood of vertices in a hydrogen-filled topological graph (imidazolium model, increases viscosity).
Connectivity descriptors. The connectivity descriptors are distinguished by path, cluster, and chain calculations of bond orders (fragments of one bond, two bonds, etc.).45 They are similar to the Basak and E-state families of descriptors in that a count is made of a specified fragment type. With the connectivity descriptors however, the final score for a given fragment is not influenced by the occurrence of other fragments (as is the case with structural information i.e. entropic calculation in Basak) and all valance/electronic state of atoms/fragments are lost (which are encapsulated in the E-state formalism). Our models selected one of these types of descriptors. Chi4c, a simple fourth order cluster index (general model, increases viscosity).
Topological descriptors. Hato-anion, a harmonic topological index, is a metric of molecular branching proposed by Narumi.55 One advantage of this descriptor is that the connectivity state of every atom is used in the calculation of the index, leading to a highly unique index for a given molecule. In the Hato calculation, a lower value indicates a higher degree of molecular branching (e.g. neopentane will have a lower index than pentane). Both models selected this descriptor and it is the most influential molecular-structure based feature leading to a decrease in viscosity (i.e. highly branched anions lead to a more viscous salt).
Constitutional descriptors. Nhyd-anion is a count of hydrogen atoms contained in the molecule. Both models selected this feature as the most influential structural component leading to an increase in viscosity.

Comparison between models

Of the five cation features selected by the imidazolium model, only MATSe5, E-state-VSA3, and PEOE-VSA12 were included in the general model. It is worth nothing, however, that the similar variance and mean value of the Burden features (BCUTEae5 and BCUTae2) for the cation imply a covariant relationship between these two variations of autocorrelating atomic electronegativity. A similar number of the anion-specific features are shared by both models: nhyd, MATSp5, and Hato. Indeed, the anionic features nhyd and Hato are extremely influential in both models (the absolute value of their coefficients are large), second only to log(T). For the cation-specific features: the imidazolium model included BCUTae2, and IC4 while the general model included BCUTae5. For the anion-specific features: the imidazolium model included GATSp1 and PEOE-VSA6 while the general model included Chi4c, SIC0, and Smax14. Interestingly, despite the structural difference between the two models being that of the cationic moiety, the largest difference in the feature vectors pertains to the anion (four shared features with a fifth that is likely to be covariant for the cation compared to three shared features for the anion). At first this would appear unlikely, even more so when considering the cation/anion differences between the models—22 anions and 16 cations for the general model and 21 anions and 10 cations for the imidazolium model. That is to say, even though the imidazolium model is only missing a single anion compared to the general model, it appears to select quite a different set of anion-specific features. However, considering the large coefficient values for the Hato and nhyd descriptors, the overall effect of the anionic moiety on viscosity is very similar for both models, as these two features have an overwhelming influence compared to the other anionic features that are present.

In addition to performing 1000 bootstrap iterations of training LASSO at the optimum λ value, we also evaluated the coefficient values of those top selected features at λ values ranging from 0.01 to 1 to track their approach to 0. One might expect the feature coefficients to fall to zero in the order of their absolute value ranking at λ 0.021. However, since two features working in tandem might better approximate the descriptive quality of one, this may very well not be the case.

There are clear parallels in both models. The features approach zero in three separate clusters. log(T) formed the first cluster in solitude, holding its coefficient value ahead of the other features at higher values of λ. The models begin to differ in the next two clusters. The inverse-ranked approach to zero of the second cluster is as follows; beginning with the imidazolium model: BCUTe2-cation, Hato-anion, MATSe5-cation, Estate-VSA3-cation, GATSp1-anion; for the general model: Smax14-anion, BCUTe5-cation, MATSe5-cation, and Hato-anion. The inverse-ranked approach to zero of the third cluster is as follows; beginning with the imidazolium model: nhyd-anion, PEOE-VSA6-anion, PEOE-VSA12-cation, MATSp5-anion, and IC4-cation; for the general model: SIC0-anion, Chi4c-anion, Estate-VSA3-cation, nhyd-anion, MATSp5-anion, and PEOE-VSA12-cation.

There are a few interesting observations worth noting. First, although the Burden descriptors had the highest variance, p-values, and very low coefficients at λ 0.021, they both were included in the second cluster. This indicates that although descriptive, the response of this variable to selections of the underlying training data varies greatly. Second, the Moran autocorrelations MATSe5 and MATSp5 appear covariant: they both approach zero but as MATSp5 becomes zero, MATSe5 begins to increase and doesn't hit zero until the second cluster. Lastly, a qualitative comparison can be made between log(T) and the molecular coefficients. log(T) approaches zero on a very smooth curve, regardless of the behavior of the other features. In comparison, the physiochemical features in the model approach zero tortuously, acting in response to one another to some degree in all cases.

Neural network

The general ANN model was highly accurate on its validation set, with validation set MSE of 4.7 × 10−4 ± 2.4 × 10−5 Pa s, see Fig. 5. Recently, Zhao, et al.23 published a UNIFAC-VISO model of IL viscosity for the same class of structures but a narrower range of temperature (293.15–363.15 K) and a single pressure (0.1 mPa). Their approach resulted in a relative absolute average deviations (RAAD) of 3.92% on a test set with the caveat that their test set contained structurally identical ILs as that of the training data, only differing by the mole fractions of the binary IL mixtures. Converting our validation MSE to RAAD,56 we have a comparative performance in our final general ANN model of 7.1 ± 1.3 %. Table S1 in the ESI breaks this RAAD down by structure and T. Perhaps more relevant, Padusyzński & Domańska reported a testing set RAAD (reported as AARD in their publication) of 14.7% for their GC FF-ANN model for nine classes of pure ILs with a broad range of temperature and pressure (253–573 K and 0.1–350 mPa, respectively). A comparison of these results and others are presented in Table 2.
image file: c7me00094d-f5.tif
Fig. 5 Viscosity prediction versus experimental value for the models. The bootstrap was performed on 90% of the available data. The remaining 10% of the data is shown in the figure, along with the prediction and error estimates from the bootstrap models. These error estimates are produced from the variance in the aggregate predictions of all the bootstrapped models. Some of the predictions have a relatively high variance compared to others. There are two possible explanations for this. For one, higher viscosities will inherently have a larger variance simply due to scaling (the same percent variance will appear larger for higher raw values of viscosity, note the two phosphonium predictions in the top right corner, bottom panel). Second, some of the anion-types occur in salt pairs less often than others. Subsequently, whether or not that anion appeared in the subsampled training set will influence the prediction on the validation datum. The three imidazolium-type salts with the highest variance contained either tetrafluoroborate or dimethylphosphate as their anions. The inset numbers indicate the value and standard deviation of the error for both models. The ANN models had, on average, mean squared errors of 4.7 × 10−4 Pa s for all data points in the validation set and a standard deviation of 2.4 × 10−5 Pa s for viscosity values ranging from 0.006 to 0.99 Pa s—an error that translates into a relative absolute average deviation (RAAD) of 7.1 ± 1.3%. Top: LASSO model; bottom: ANN model.
Table 2 Summary of exemplary IL viscosity models
Structurally predictive Pure or mixed IL Model Parameters Data points Test set error Disadvantage Reference
a 16 parameters per IL pair (32 parameters for binary mixtures). b At least 200 data points collected per IL, eight ILs were included in final regression. c Three parameters for VTF model, two of which were determined from 24 GC parameters. d The test datasets were provided by Padusyzński & Domańska, not the original authors. e The authors reported an R2 of 0.6226 on a test data set.
Yes Pure Physiochemical FF-ANN 11 723 7.1 % Higher test set error than comparable FF-ANN Our model
Yes Pure Physiochemical FF-ANN 13 736 1.3 % Not tested on categorically different ILs from training data Fatehi et al., 2017 (ref. 32)
Yes Pure GC FF-ANN 242 13[thin space (1/6-em)]470 14.7 % Not tested on categorically different ILs from training data Padusyzński & Domańska, 2014 (ref. 25)
No Pure/mixed UNIFAC-VISCO GC VTF 16/32a 52 3.92 % Requires pure IL experimental viscosity data Zhao et al., 2016 (ref. 23)
No Pure/mixed QSPR N/A 5046 N/A QSPRs proposed without model Yu et al., 2012 (ref. 26)
No Pure Hole theory 7 8b N/A Requires experimental surface tension data Bandrés et al., 2011 (ref. 27)
Yes Pure GC VTF 3/24c 482 13–21%d Applicable to limited set of ILs Gardas & Coutinho, 2009 (ref. 28)
Semi Pure Volumetric VTF 3 23 9% Some coefficients are anion-specific, others require QM calculations Slattery et al., 2007 (ref. 29)
Yes Pure GC 8 300 N/Ae Poor prediction for test dataset Matsuda et al., 2007 (ref. 30)

As discussed throughout this document, we are interested in how the general model would perform when predicting viscosity for salt-types not included in its training data (i.e. imidazolium, phosphonium, pyridinium, and pyrrolidinium). As a proxy for this, we applied the same protocol to the imidazolium salt-types only and—after observing the changes in selected descriptors in the previous section—evaluated the model on the other three salt-types. This model had a validation set MSE of 0.006 ± 0.001 Pa s when evaluated on imidazolium ILs and a validation set MSE of 0.08 ± 0.01 Pa s when evaluated on the non-imidazolium ILs: phosphonium, pyridinium, and pyrrolidnium. This leads us to emphasize caution when applying the general model to salts that are very structurally different than those used in the training data. The comparison of prediction vs. experimental viscosity is presented in the ESI, Fig. S1. To our knowledge, other statistical models in the literature have not performed a similar such evaluation. We stress, however, that considering salt-types not in the training data is paramount in the construction of a predictive model for the purposes of designing as-of-yet undiscovered ILs.


We have demonstrated a method of generating accurate models for viscosity using publicly available data from ILThermo and open source software PyChem, RDKit and SciKit-Learn. We present a model that is highly predictive of viscosity across categorically different IL's: imidazolium, phosphonium, pyridinium, and pyrrolidinium based salts. We also evaluated the methodology by which we produced those models; applying the same steps but to a structural separate subset of our data—the imidazolium salts—and tested the model on salt-types it had not seen in its training set, the phosphonium, pyridinium, and pyrrolidinium salts. We found that with structurally different training data, the imidazolium model was able to encapsulate viscosity trends for the other salt-types.

The methodology of using LASSO to pre-select features to then use in a neural network allowed us to benefit from the high interpretability of the LASSO method but also the high flexibility of the neural net. That is, we could evaluate the physical/chemical significance of the features that were selected while also arriving at a highly accurate model with the final neural network. It also allowed more rapid parameterization of the neural net and to avoid overfitting to our training data; i.e. keeping the feature size to training data ratio as low as possible.

In future work, the full value of the models should be actualized by combining them with structural search algorithms to high-throughput screen for low viscosity ILs. One of the most promising search algorithms recently introduced have been genetic algorithms, which allow for flexible fitness tests and a tree-like search structure. The fitness tests can prioritize certain model features, such as those ranked highest by the LASSO coefficient versus log[thin space (1/6-em)]λ evaluations, searching a semi-infinite structural space.

Conflicts of interest

There are no conflicts to declare.


This project was funded under the Data Intensive Research Enabling Clean Technology (DIRECT) NSF National Research Traineeship (DGE-1633216).

Notes and references

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Electronic supplementary information (ESI) available. See DOI: 10.1039/c7me00094d

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