scholarly journals Imputation of Missing Gas Permeability Data for Polymer Membranes using Machine Learning

2020 ◽  
Author(s):  
Qi Yuan ◽  
Mariagiulia Longo ◽  
Aaron Thornton ◽  
Neil B. McKeown ◽  
Bibiana Comesana-Gandara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficient ◽  
Missing Values ◽  
Gas Permeability ◽  
Polymer Membranes ◽  
Gas Separations ◽  
Initial Stage ◽  
Sparse Features ◽  
Potential Use ◽  
Insight Into

<p><a>Polymer-based membranes can be used for energy efficient gas separations. Successful exploitation of new materials requires accurate knowledge of the transport properties of all gases of interest. An open source database of such data is of significant benefit to the research community. The Membrane Society of Australasia (https://membrane-australasia.org/) hosts a database for experimentally measured and reported polymer gas permeabilities. However, the database is incomplete, limiting its potential use as a research tool. Here, missing values in the database were filled using machine learning (ML). The ML model was validated against gas permeability measurements that were not recorded in the database. Through imputing the missing data, it is possible to re-analyse historical polymers and look for potential “missed” candidates with promising gas selectivity. In addition, for systems with limited experimental data, ML using sparse features was performed, and we suggest that once the permeability of CO<sub>2</sub> and/or O<sub>2</sub> for a polymer has been measured, most other gas permeabilities and selectivities, including those for CO<sub>2</sub>/CH<sub>4</sub> and CO<sub>2</sub>/N<sub>2</sub>, can be quantitatively estimated. This early insight into the gas permeability of a new system can be used at an initial stage of experimental measurements to rapidly identify polymer membranes worth further investigation.</a></p>

scholarly journals Imputation of Missing Gas Permeability Data for Polymer Membranes using Machine Learning

2020 ◽  
Author(s):  
Qi Yuan ◽  
Mariagiulia Longo ◽  
Aaron Thornton ◽  
Neil B. McKeown ◽  
Bibiana Comesana-Gandara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficient ◽  
Missing Values ◽  
Gas Permeability ◽  
Polymer Membranes ◽  
Gas Separations ◽  
Initial Stage ◽  
Sparse Features ◽  
Potential Use ◽  
Insight Into

<p><a>Polymer-based membranes can be used for energy efficient gas separations. Successful exploitation of new materials requires accurate knowledge of the transport properties of all gases of interest. An open source database of such data is of significant benefit to the research community. The Membrane Society of Australasia (https://membrane-australasia.org/) hosts a database for experimentally measured and reported polymer gas permeabilities. However, the database is incomplete, limiting its potential use as a research tool. Here, missing values in the database were filled using machine learning (ML). The ML model was validated against gas permeability measurements that were not recorded in the database. Through imputing the missing data, it is possible to re-analyse historical polymers and look for potential “missed” candidates with promising gas selectivity. In addition, for systems with limited experimental data, ML using sparse features was performed, and we suggest that once the permeability of CO<sub>2</sub> and/or O<sub>2</sub> for a polymer has been measured, most other gas permeabilities and selectivities, including those for CO<sub>2</sub>/CH<sub>4</sub> and CO<sub>2</sub>/N<sub>2</sub>, can be quantitatively estimated. This early insight into the gas permeability of a new system can be used at an initial stage of experimental measurements to rapidly identify polymer membranes worth further investigation.</a></p>

scholarly journals Imputation of Missing Gas Permeability Data for Polymer Membranes using Machine Learning

2021 ◽  
Author(s):  
Qi Yuan ◽  
Mariagiulia Longo ◽  
Aaron Thornton ◽  
Neil B. McKeown ◽  
Bibiana Comesana-Gandara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficient ◽  
Missing Values ◽  
Gas Permeability ◽  
Polymer Membranes ◽  
Gas Separations ◽  
Initial Stage ◽  
Sparse Features ◽  
Potential Use ◽  
Insight Into

<p><a>Polymer-based membranes can be used for energy efficient gas separations. Successful exploitation of new materials requires accurate knowledge of the transport properties of all gases of interest. An open source database of such data is of significant benefit to the research community. The Membrane Society of Australasia (https://membrane-australasia.org/) hosts a database for experimentally measured and reported polymer gas permeabilities. However, the database is incomplete, limiting its potential use as a research tool. Here, missing values in the database were filled using machine learning (ML). The ML model was validated against gas permeability measurements that were not recorded in the database. Through imputing the missing data, it is possible to re-analyse historical polymers and look for potential “missed” candidates with promising gas selectivity. In addition, for systems with limited experimental data, ML using sparse features was performed, and we suggest that once the permeability of CO<sub>2</sub> and/or O<sub>2</sub> for a polymer has been measured, most other gas permeabilities and selectivities, including those for CO<sub>2</sub>/CH<sub>4</sub> and CO<sub>2</sub>/N<sub>2</sub>, can be quantitatively estimated. This early insight into the gas permeability of a new system can be used at an initial stage of experimental measurements to rapidly identify polymer membranes worth further investigation.</a></p>

scholarly journals Discovery of Innovative Polymers for Next-Generation Gas-Separation Membranes using Interpretable Machine Learning

2021 ◽  
Author(s):  
Jason Yang ◽  
Lei Tao ◽  
Jinlong He ◽  
Jeffrey McCutcheon ◽  
Ying Li
Keyword(s):  
Machine Learning ◽  
Polymer Membranes ◽  
Polymeric Membranes ◽  
Gas Separations ◽  
Separation Performance ◽  
High Fidelity ◽  
Environmental Implications ◽  
Separation Membranes ◽  
Gas Separation Membranes ◽  
Dynamics Simulations

Polymer membranes perform innumerable separations with far-reaching environmental implications. Despite decades of research on membrane technologies, design of new membrane materials remains a largely Edisonian process. To address this shortcoming, we demonstrate a generalizable, accurate machine-learning (ML) implementation for the discovery of innovative polymers with ideal separation performance. Specifically, multitask ML models are trained on available experimental data to link polymer chemistry to gas permeabilities of He, H2, O2, N2, CO2, and CH4. We interpret the ML models and extract chemical heuristics for membrane design, through Shapley Additive exPlanations (SHAP) analysis. We then screen over nine million hypothetical polymers through our models and identify thousands of candidates that lie well above current performance upper bounds. Notably, we discover hundreds of never-before-seen ultrapermeable polymer membranes with O2 and CO2 permeability greater than 104 and 105 Barrer, respectively, orders of magnitude higher than currently available polymeric membranes. These hypothetical polymers are capable of overcoming undesirable trade-off relationship between permeability and selectivity, thus significantly expanding the currently limited library of polymer membranes for highly efficient gas separations. High-fidelity molecular dynamics simulations confirm the ML-predicted gas permeabilities of the promising candidates, which suggests that many can be translated to reality.

scholarly journals Imputation of missing gas permeability data for polymer membranes using machine learning

2021 ◽  
pp. 119207
Author(s):  
Qi Yuan ◽  
Mariagiulia Longo ◽  
Aaron Thornton ◽  
Neil B. McKeown ◽  
Bibiana Comesaña-Gándara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gas Permeability ◽  
Polymer Membranes ◽  
Permeability Data

scholarly journals Designing exceptional gas-separation polymer membranes using machine learning

2020 ◽  
Vol 6 (20) ◽  
pp. eaaz4301 ◽  
Author(s):  
J. Wesley Barnett ◽  
Connor R. Bilchak ◽  
Yiwen Wang ◽  
Brian C. Benicewicz ◽  
Laura A. Murdock ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gas Separation ◽  
Gas Permeability ◽  
Small Body ◽  
Polymer Membranes ◽  
Polymer Membrane ◽  
Experimental Investigations ◽  
Separation Performance ◽  
Empirical Observation ◽  
Membrane Design

The field of polymer membrane design is primarily based on empirical observation, which limits discovery of new materials optimized for separating a given gas pair. Instead of relying on exhaustive experimental investigations, we trained a machine learning (ML) algorithm, using a topological, path-based hash of the polymer repeating unit. We used a limited set of experimental gas permeability data for six different gases in ~700 polymeric constructs that have been measured to date to predict the gas-separation behavior of over 11,000 homopolymers not previously tested for these properties. To test the algorithm’s accuracy, we synthesized two of the most promising polymer membranes predicted by this approach and found that they exceeded the upper bound for CO2/CH4 separation performance. This ML technique, which is trained using a relatively small body of experimental data (and no simulation data), evidently represents an innovative means of exploring the vast phase space available for polymer membrane design.

scholarly journals Discovery of Innovative Polymers for Next-Generation Gas-Separation Membranes using Interpretable Machine Learning

2021 ◽  
Author(s):  
Jason Yang ◽  
Lei Tao ◽  
Jinlong He ◽  
Jeffrey R. McCutcheon ◽  
Ying Li
Keyword(s):  
Machine Learning ◽  
Polymer Membranes ◽  
Upper Bounds ◽  
Gas Separations ◽  
Separation Performance ◽  
High Fidelity ◽  
Environmental Implications ◽  
Separation Membranes ◽  
Gas Separation Membranes ◽  
Dynamics Simulations

Abstract Polymer membranes perform innumerable separations with far-reaching environmental implications. Despite decades of research on membrane technologies, design of new membrane materials remains a largely Edisonian process. To address this shortcoming, we demonstrate a generalizable, accurate machine-learning (ML) implementation for the discovery of innovative polymers with ideal separation performance. Specifically, multitask ML models are trained on available experimental data to link polymer chemistry to gas permeabilities of He, H2, O2, N2, CO2, and CH4. We interpret the ML models and extract chemical heuristics for membrane design, through Shapley Additive exPlanations (SHAP) analysis. We then screen over nine million hypothetical polymers through our models and identify thousands of candidates that lie well above current performance upper bounds. Notably, we discover hundreds of never-before-seen ultrapermeable polymer membranes with O2 and CO2 permeability greater than 104 and 105 Barrer, respectively. These hypothetical polymers are capable of overcoming undesirable trade-off relationship between permeability and selectivity, thus significantly expanding the currently limited library of polymer membranes for highly efficient gas separations. High-fidelity molecular dynamics simulations confirm the ML-predicted gas permeabilities of the promising candidates, which suggests that many can be translated to reality.

Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.

2020 ◽  
Vol 21 ◽  
Author(s):  
Sukanya Panja ◽  
Sarra Rahem ◽  
Cassandra J. Chu ◽  
Antonina Mitrofanova
Keyword(s):  
Machine Learning ◽  
Missing Values ◽  
Therapeutic Response ◽  
Patient Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Human Machine Interaction ◽  
Data Repositories ◽  
Response Modeling

Background: In recent years, the availability of high throughput technologies, establishment of large molecular patient data repositories, and advancement in computing power and storage have allowed elucidation of complex mechanisms implicated in therapeutic response in cancer patients. The breadth and depth of such data, alongside experimental noise and missing values, requires a sophisticated human-machine interaction that would allow effective learning from complex data and accurate forecasting of future outcomes, ideally embedded in the core of machine learning design. Objective: In this review, we will discuss machine learning techniques utilized for modeling of treatment response in cancer, including Random Forests, support vector machines, neural networks, and linear and logistic regression. We will overview their mathematical foundations and discuss their limitations and alternative approaches all in light of their application to therapeutic response modeling in cancer. Conclusion: We hypothesize that the increase in the number of patient profiles and potential temporal monitoring of patient data will define even more complex techniques, such as deep learning and causal analysis, as central players in therapeutic response modeling.

scholarly journals Missing the missing values: The ugly duckling of fairness in machine learning

2021 ◽  
Author(s):  
Martínez‐Plumed Fernando ◽  
Ferri Cèsar ◽  
Nieves David ◽  
Hernández‐Orallo José
Keyword(s):  
Machine Learning ◽  
Missing Values ◽  
Ugly Duckling

scholarly journals Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 930
Author(s):  
Fahimeh Hadavimoghaddam ◽  
Mehdi Ostadhassan ◽  
Ehsan Heidaryan ◽  
Mohammad Ali Sadri ◽  
Inna Chapanova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Viscosity Data ◽  
Oil Viscosity ◽  
Pvt Data ◽  
Proposed Model ◽  
Wide Range ◽  
Engineering Problems ◽  
Functional Forms ◽  
Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

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