Interpretable Machine Learning-Based Predictions of Methane Uptake Isotherms in Metal–Organic Frameworks

Gurnani, Rishi; Yu, Zhenzi; Kim, Chiho; Sholl, David S.; Ramprasad, Rampi

doi:10.1021/acs.chemmater.0c04729

Cited by 46 publications

(46 citation statements)

References 58 publications

(92 reference statements)

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“…There is an increasing interest in the application of model interpretability tools to problems in materials science. [62][63][64][65][66] The expectation that the ML model should also explain the underlying patterns of materials phenomena in addition to the predictions has been steadily increasing. There are also papers from other disciplines, such as bioinformatics, that share similar goals.…”

Section: Discussionmentioning

confidence: 99%

Phase Classification of Multi-Principal Element Alloys via Interpretable Machine Learning

Lee,

Ayyasamy,

Delsa

et al. 2021

Preprint

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There is intense interest in uncovering design rules that govern the formation of various structural phases as a function of chemical composition in multi-principal element alloys (MPEAs). In this paper, we develop a machine learning (ML) approach built on the foundations of ensemble learning, post hoc model interpretability of black-box models and clustering analysis to establish a quantitative relationship between the chemical composition and experimentally observed phases of MPEAs. The novelty of our work stems from performing instance-level (or local) variable attribution analysis of ML predictions based on the breakdown method, and then identifying similar instances based on k-means clustering analysis of the breakdown results. We also complement the breakdown analysis with Ceteris Paribus profiles that showcase how the model response changes as a function of a single variable, when the values of all other variables are fixed. Results from local model interpretability analysis uncover key insights into variables that govern the formation of each phase. Our developed approach is generic, model-agnostic, and valuable to explain the insights learned by the black-box models. An interactive web application is developed to facilitate model sharing and accelerate the design of novel MPEAs with targeted properties.

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Section: Discussionmentioning

confidence: 99%

Phase Classification of Multi-Principal Element Alloys via Interpretable Machine Learning

Lee,

Ayyasamy,

Delsa

et al. 2021

Preprint

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“…As a result, explainable ML methods have attracted a great deal of attention in order to further advance the reliability and applicability of ML-based approaches. Model explainability is also becoming increasingly important in the materials science domain as ML and artificial intelligence (AI)-driven algorithms are beginning to show success in simplifying various workflows [10][11][12][13][14][15][16][17][18][19][20][21][22] . However, the idea of incorporating explainable ML methods into the current data-driven materials design and discovery workflow is still in its infancy.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the global understanding of a model can be enhanced by exploiting collective SHAP values. For this reason, the SHAP method has been the predominant method used to analyze machine learning results in various materials science publications on alloys, catalysts, photovoltaics, metal-organic frameworks and oxide glasses to name a few 13,[25][26][27][28][29] .…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Explainable Artificial Intelligence Methods in the Phase Classification of Multi-Principal Element Alloys

Lee

Ayyasamy

et al. 2022

Preprint

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We demonstrate the capabilities of two model-agnostic local post hoc model interpretability methods, namely breakDown (BD) and shapley (SHAP), to explain the predictions of a black-box classification learning model that establishes a quantitative relationship between chemical composition and multi-principal element alloys phase formation. We trained an ensemble of support vector machines using a dataset with 1,821 instances, 12 features with low pair-wise correlation, and seven phase labels. Feature contributions to the model prediction are computed by BD and SHAP for each composition. The resulting BD and SHAP transformed data are then used as inputs to identify similar composition groups using k-means clustering. Explanation-of-clusters by features reveal that the results from SHAP agree more closely with the literature. Visualization of compositions within a cluster using Ceteris-Paribus (CP) profile plots show the functional dependencies between the feature values and predicted response. Despite the differences between BD and SHAP in variable attribution, only minor changes were observed in the CP profile plots. Explanation-of-clusters by examples show that the clusters that share a common phase label contain similar compositions, which clarifies the similar-looking CP profile trends. In the limits of a dataset with independent and non-interacting features, SHAP and BD appear to capture similar pattern.

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“…Most previous applications of ML to adsorption in nanoporous materials have used geometrical descriptors, although there have been studies using energy-based or chemical motif descriptors. − Anticipating that purely geometric descriptors are unlikely to be sufficient for accurately predicting Henry’s constants, below we combine energy-based and atomic property weighted radial distribution function (AP-RDF) descriptors to capture key features of the potential energy surface of adsorbed molecules in a diverse range of MOFs.…”

Section: Introductionmentioning

confidence: 99%

Efficient Models for Predicting Temperature-Dependent Henry’s Constants and Adsorption Selectivities for Diverse Collections of Molecules in Metal–Organic Frameworks

Choi

Tang

et al. 2021

J. Phys. Chem. C

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Adsorption-based separations using metal–organic frameworks (MOFs) are a promising alternative to traditional energy-intensive separation process. Machine learning (ML) methods have been applied to predict large collections of adsorption isotherms in MOFs. Previous ML models, however, focus only on predicting single-component adsorption isotherms of a small number of molecules at a single temperature and lack accuracy in the dilute limit. Here we describe a useful strategy for predicting Henry’s constants and heats of adsorption for a diverse set of molecules in large collections of MOFs. To achieve this, a data set containing 21,195 MOF–molecule pairs with 45 adsorbates in 471 MOFs is generated, and a set of 135 descriptors combining energy and chemical information is developed. Robust ML models are developed to predict Henry’s constants and heats of adsorption after removing physically unfavorable adsorption pairs. The adsorption selectivity of near-azeotropic mixtures at two temperatures (300 and 373 K) is predicted with acceptable accuracy by using the predicted Henry’s constants and heats of adsorption. The ability to make temperature-dependent predictions is important for many practical separation applications. Our work sheds light on important challenges and opportunities for developing accurate models predicting adsorption properties for diverse collection of adsorbates and adsorbents.

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Interpretable Machine Learning-Based Predictions of Methane Uptake Isotherms in Metal–Organic Frameworks

Cited by 46 publications

References 58 publications

Phase Classification of Multi-Principal Element Alloys via Interpretable Machine Learning

Phase Classification of Multi-Principal Element Alloys via Interpretable Machine Learning

A Comparison of Explainable Artificial Intelligence Methods in the Phase Classification of Multi-Principal Element Alloys

Efficient Models for Predicting Temperature-Dependent Henry’s Constants and Adsorption Selectivities for Diverse Collections of Molecules in Metal–Organic Frameworks

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