Carbon Capture Machine Learning Literature

Survey of all published Carbon Capture w/ ML papers, data, code and supplemental materials for the benefit of all humanity.

This repo came to life following the ML in Carbon Capture reading group that I led for Climate Change AI in early 2022. We reviewed some of the papers in our reading group discussions but felt the need to unify and centralize the ML based Carbon Capture literature, make it easily accessible with relevant code and data so that published works can be duplicated, verified and improved for the rest of us.

Table of Contents

• Survey Papers
• Material Screening / Design
• Surrogate Models
• Process Modeling / Monitoring
• Capture by Forests / Farming
• Capture by Ocean
• Capture by Wetland
• OTHERS
• Data
  
• Citation

Survey Papers

1. Toward smart carbon capture with machine learning. Cell Reports Physical Science, 2021. paper

2. Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review. Energy Environ. Sci., 2021. paper

3. The Potential of Machine Learning for Enhancing CO2 Sequestration, Storage, Transportation, and Utilization-based Processes: A Brief Perspective, 2022, paper

4. Accelerated discovery of porous materials for carbon capture by machine learning: A review, 2022, paper

5. Technology development and applications of artificial intelligence for post-combustion carbon dioxide capture: Critical literature review and perspectives, 2021, paper

Material Screening / Design / Generation

1. The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture, 2024, paper, data

2. Graph Neural Network based Screening of Metal Organic Framework for CO2 Capture, 2024, paper

3. Graph Neural Network Generated Metal-Organic Frameworks for Carbon Capture, 2023, paper

4. MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design, 2023, paper

5. Modeling and Optimizing N/O-Enriched Bio-Derived Adsorbents for CO2 Capture: Machine Learning and DFT Calculation Approaches, 2022, paper

6. MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks, 2022, paper, github, demo

7. Computational screening methodology identifies effective solvents for CO2 capture, 2022, paper, github, data

8. Design and prediction of metal organic framework-based mixed matrix membranes for CO2 capture via machine learning, 2022, paper, github, data

9. Deep-Learning-Based End-to-End Predictions of CO2 Capture in Metal–Organic Frameworks, 2022, paper, github, data

10. Inverse Design of Nanoporous Crystalline Reticular Materials with Deep Generative Models, 2021, paper, github, data

11. Graph neural network predictions of metal organic framework CO2 adsorption properties, 2021, paper, github,

12. Robust smart schemes for modeling carbon dioxide uptake in metal − organic frameworks, 2022, paper

13. Machine Learning-Driven Discovery of Metal–Organic Frameworks for Efficient CO2 Capture in Humid Condition, 2021, paper

14. Machine Learning-driven High-Throughput Screening of Alloy-Based Catalysts for Selective CO2 Hydrogenation to Methanol., 2021, paper

15. Realizing the Data-Driven, Computational Discovery of Metal-Organic Framework Catalysts, 2021, paper

16. Diversifying Databases of Metal Organic Frameworks for HighThroughput Computational Screening, 2021, paper

17. Modeling of CO2 adsorption capacity by porous metal organic frameworks using advanced decision tree-based models, 2021, paper

18. Machine Learning-based approach for Tailor-Made design of ionic Liquids: Application to CO2 capture, 2021, paper

19. High-Performing Deep Learning Regression Models for Predicting Low-Pressure CO2 Adsorption Properties of Metal−Organic Frameworks, 2020, paper, github, data

20. Machine Learning Enabled Tailor-Made Design of Application-Specific Metal–Organic Frameworks, 2020, paper

21. Prediction of mof performance in vacuum swing adsorption systems for postcombustion CO2 capture based on integrated molecular simulations, process optimizations, and machine learning models., 2020, paper

22. Designing exceptional gas-separation polymer membranes using machine learning, 2020, paper

23. Insights into CO2/N2 Selectivity in Porous Carbons from Deep Learning, 2020, paper

24. Robust Machine Learning Models for Predicting High CO2 Working Capacity and CO2/H2 Selectivity of Gas Adsorption in Metal Organic Frameworks for Precombustion Carbon Capture, 2019, paper

25. Machine-learning approach to the design of OSDAs for zeolite beta, 2018, paper

26. Rapid and accurate machine learning recognition of high performing metal organic frameworks for CO2 capture, 2014, paper

Surrogate Models

1. Prediction of CO2 Adsorption in Nano-Pores with Graph Neural Networks, 2022, paper

2. Performance-based ranking of porous materials for PSA carbon capture under the uncertainty of experimental data, 2022, paper

3. Deep neural network learning of complex binary sorption equilibria from molecular simulation data., 2019, paper

4. Efficient surrogates construction of chemical processes: Case studies on pressure swing adsorption and gas-to-liquids, 2022, paper

5. Surrogate modelling of VLE: Integrating machine learning with thermodynamic constraints, 2020, paper

6. Experimental data, thermodynamic and neural network modeling of CO2 absorption capacity for 2-amino-2-methyl-1-propanol (AMP)+ Methanol (MeOH)+ H2O system, 2020, paper

7. Computational Material Screening Using Artificial Neural Networks for Adsorption Gas Separation, 2020, paper

8. Experimentally validated machine learning frameworks for accelerated prediction of cyclic steady state and optimization of pressure swing adsorption processes, 2020, paper

9. Ensemble Learning of Partition Functions for the Prediction of Thermodynamic Properties of Adsorption in Metal-Organic and Covalent Organic Frameworks, 2020, paper

10. 110th Anniversary: Surrogate Models Based on Artificial Neural Networks To Simulate and Optimize Pressure Swing Adsorption Cycles for CO2 Capture, 2019, paper

11. Analysis of CO2 equilibrium solubility of seven tertiary amine solvents using thermodynamic and ANN models, 2019, paper

12. Application of decision tree learning in modelling CO2 equilibrium absorption in ionic liquids, 2017, paper

13. Thermodynamics and ANN models for predication of the equilibrium CO2 solubility in aqueous 3-dimethylamino-1-propanol solution, 2017, paper

14. Artificial neural network models for the prediction of CO2 solubility in aqueous amine solutions, 2015, paper

Process Modeling / Monitoring

1. Machine learning for industrial processes: Forecasting amine emissions from a carbon capture plant, 2023, paper, github, data

2. Agent-Based and Stochastic Optimization Incorporated with Machine Learning for Simulation of Postcombustion CO2 Capture Process, 2022, paper

3. Prediction of CO2 capture capability of 0.5 MW MEA demo plant using three different deep learning pipelines, 2022, paper

4. A machine learning approach for modeling and optimization of a CO2 post-combustion capture unit, 2021, paper

5. Deep learning for industrial processes: Forecasting amine emissions from a carbon capture plant, 2021, paper

6. Raman calibration models for chemical species determination in CO2-loaded aqueous MEA solutions using PLS and ANN techniques, 2021, paper

7. Learning the properties of a water-lean amine solvent from carbon capture pilot experiments, 2021, paper

8. Application of long short-term memory neural networks for co2 concentration forecast on amine plants, 2020, paper

9. Surrogate-Assisted Modeling and Robust Optimization of a Micro Gas Turbine Plant with Carbon Capture, 2020, paper

10. Is hydrothermal treatment coupled with carbon capture and storage an energy-producing negative emissions technology?, 2020, paper

11. Machine Learning-Based Multiobjective Optimization of Pressure Swing Adsorption, 2019, paper

12. Cost reduction of CO2 capture processes using reinforcement learning based iterative design: A pilot-scale absorption–stripping system, 2014, paper

Capture by Forests / Farming

1. Quantification of Carbon Sequestration in Urban Forests. ArXiv, 2021. paper

2. Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees., ArXiv, 2020. paper, github, data

3. Machine learning for carbon stock prediction in a tropical forest in Southeastern Brazil., SciELO, Brazil, 2021. paper

4. Predictive Models to Estimate Carbon Stocks in Agroforestry Systems., . paper

5. Estimation of Future Changes in Aboveground Forest Carbon Stock in Romania. A Prediction Based on Forest-Cover Pattern Scenario., paper

6. Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms, 2020, paper

7. A Data Driven Approach to Decision Support in Farming, 2020, paper

Capture by Ocean

1. Improved Quantification of Ocean Carbon Uptake by Using Machine Learning to Merge Global Models and pCO2 Data. paper

2. A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall? paper

3. Estimating spatial and temporal variation in ocean surface pCO2 in the Gulf of Mexico using remote sensing and machine learning techniques, paper

Capture by Wetland

1. ArcticNet: A Deep Learning Solution to Classify Arctic Wetlands, paper

2. Mechanistic Modeling of Marsh Seedling Establishment Provides a Positive Outlook for Coastal Wetland Restoration Under Global Climate Change, paper

3. The Wetland Intrinsic Potential Tool: Identifying Forested Wetlands Through Machine Learning of Lidar Derived Datasets, paper

4. Wetland Change Mapping Using Machine Learning Algorithms, and Their Link with Climate Variation and Economic Growth: A Case Study of Guangling County, China, paper

5. The Google Earth Engine Mangrove Mapping Methodology (GEEMMM), 2020, paper

OTHERS

1. Applied Machine Learning for Prediction of CO2 Adsorption on Biomass Waste-Derived Porous Carbons, 2021, paper

2. Multiparameter Neural Network Modeling of Facilitated Transport Mixed Matrix Membranes for Carbon Dioxide Removal, 2022, paper

3. High-performing deep learning regression models for predicting low-pressure CO2 adsorption properties of metal−organic frameworks, 2020, paper

4. Performance evaluation of the machine learning approaches in modeling of CO2 equilibrium absorption in Piperazine aqueous solution., 2018, paper

5. Application of decision tree learning in modelling CO2 equilibrium absorption in ionic liquids., 2017, paper

DATA

1. OpenDAC by Meta AI, URL

2. Amazon Sustainability Data Initiative, URL

3. NETL's Energy Data eXchange, URL

4. Data-driven design of metal–organic frameworks for wet flue gas CO2 capture, 2019, paper, data, code

5. OSDB: A database of organic structure-directing agents for zeolites, data

Citation