CarbNN: A Novel Active Transfer Learning Neural Network To Build De Novo Metal Organic Frameworks (MOFs) for Carbon Capture

• URL: http://arxiv.org/abs/2311.16158v1
• Date: Thu, 9 Nov 2023 01:21:19 GMT
• Title: CarbNN: A Novel Active Transfer Learning Neural Network To Build De Novo Metal Organic Frameworks (MOFs) for Carbon Capture
• Authors: Neel Redkar
• Abstract summary: Current materials used in CO2 capture are lacking either in efficiency, sustainability, or cost. Electrocatalysis of CO2 is a new approach where CO2 can be reduced and the components used industrially as fuel. The goal therefore is computationally design a MOF that can adsorb CO2 and catalyze carbon monoxide & oxygen with low cost.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Over the past decade, climate change has become an increasing problem with one of the major contributing factors being carbon dioxide (CO2) emissions; almost 51% of total US carbon emissions are from factories. Current materials used in CO2 capture are lacking either in efficiency, sustainability, or cost. Electrocatalysis of CO2 is a new approach where CO2 can be reduced and the components used industrially as fuel, saving transportation costs, creating financial incentives. Metal Organic Frameworks (MOFs) are crystals made of organo-metals that adsorb, filter, and electrocatalyze CO2. The current available MOFs for capture & electrocatalysis are expensive to manufacture and inefficient at capture. The goal therefore is to computationally design a MOF that can adsorb CO2 and catalyze carbon monoxide & oxygen with low cost. A novel active transfer learning neural network was developed, utilizing transfer learning due to limited available data on 15 MOFs. Using the Cambridge Structural Database with 10,000 MOFs, the model used incremental mutations to fit a trained fitness hyper-heuristic function. Eventually, a Selenium MOF (C18MgO25Se11Sn20Zn5) was converged on. Through analysis of predictions & literature, the converged MOF was shown to be more effective & more synthetically accessible than existing MOFs, showing the model had an understanding of effective electrocatalytic structures in the material space. This novel network can be implemented for other gas separations and catalysis applications that have limited training accessible datasets.

Related papers

• Carbon Market Simulation with Adaptive Mechanism Design [55.25103894620696]
  A carbon market is a market-based tool that incentivizes economic agents to align individual profits with the global utility. We propose an adaptive mechanism design framework, simulating the market using hierarchical, model-free multi-agent reinforcement learning (MARL) Numerical results show MARL enables government agents to balance productivity, equality, and carbon emissions.
  arXiv  Detail & Related papers  (2024-06-12T05:08:51Z)
• Quantum Simulations for Carbon Capture on Metal-Organic Frameworks [0.0]
  DAC is a technical solution that does not rely on natural processes to capture CO2 from the atmosphere. We aim to leverage the potential of quantum computing to improve the filters used in DAC.
  arXiv  Detail & Related papers  (2023-11-21T07:58:02Z)
• A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture [3.7693836475281297]
  GHP-MOFassemble is a generative artificial intelligence framework for the rational and accelerated design of MOFs with high CO2 capacity and synthesizable linkers. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity. We present the top six AI-generated MOFs with CO2 capacities greater than 2 $m mol/g$, higher than 96.9% of structures in the hypothetical MOF dataset.
  arXiv  Detail & Related papers  (2023-06-14T18:32:26Z)
• Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning [77.62876532784759]
  Machine learning (ML) requires using energy to carry out computations during the model training process. The generation of this energy comes with an environmental cost in terms of greenhouse gas emissions, depending on quantity used and the energy source. We present a survey of the carbon emissions of 95 ML models across time and different tasks in natural language processing and computer vision.
  arXiv  Detail & Related papers  (2023-02-16T18:35:00Z)
• PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design [102.9593507372373]
  Catalyst materials play a crucial role in the electrochemical reactions involved in industrial processes. Machine learning holds the potential to efficiently model materials properties from large amounts of data. We propose task-specific innovations applicable to most architectures, enhancing both computational efficiency and accuracy.
  arXiv  Detail & Related papers  (2022-11-22T05:24:30Z)
• Real-time high-resolution CO$_2$ geological storage prediction using nested Fourier neural operators [58.728312684306545]
  Carbon capture and storage (CCS) plays an essential role in global decarbonization. Scaling up CCS deployment requires accurate and high-resolution modeling of the storage reservoir pressure buildup and the gaseous plume migration. We introduce Nested Fourier Neural Operator (FNO), a machine-learning framework for high-resolution dynamic 3D CO2 storage modeling at a basin scale.
  arXiv  Detail & Related papers  (2022-10-31T04:04:03Z)
• Building Open Knowledge Graph for Metal-Organic Frameworks (MOF-KG): Challenges and Case Studies [63.61566811532431]
  Metal-Organic Frameworks (MOFs) have great potential to revolutionize applications such as gas storage, molecular separations, chemical sensing, crystalline and drug delivery. The Cambridge Structural Database (CSD) reports 10,636 synthesized MOF crystals which in addition contains ca. 114,373 MOF-like structures. In this demo paper, we describe our effort on leveraging knowledge graph methods to facilitate MOF prediction, discovery, and synthesis.
  arXiv  Detail & Related papers  (2022-07-10T16:41:11Z)
• Predicting the Efficiency of CO$_2$ Sequestering by Metal Organic Frameworks Through Machine Learning Analysis of Structural and Electronic Properties [0.0]
  This project aims to create an algorithm that predicts the uptake of CO$$ adsorbing Metal-Organic Frameworks (MOFs) by using Machine Learning. This algorithm will save resources such as time and equipment as scientists will be able to disregard hypothetical MOFs with low efficiencies.
  arXiv  Detail & Related papers  (2021-10-12T05:55:47Z)
• Carbon Emissions and Large Neural Network Training [19.233899715628073]
  We calculate the energy use and carbon footprint of several recent large models-T5, Meena, GShard, Switch Transformer, and GPT-3. We highlight the following opportunities to improve energy efficiency and CO2 equivalent emissions (CO2e) To help reduce the carbon footprint of ML, we believe energy usage and CO2e should be a key metric in evaluating models.
  arXiv  Detail & Related papers  (2021-04-21T04:44:25Z)
• Towards explainable message passing networks for predicting carbon dioxide adsorption in metal-organic frameworks [2.1445455835823624]
  Metal-organic framework (MOFs) are nanoporous materials that could be used to capture carbon dioxide from the exhaust gas of fossil fuel power plants. In this work, we design and train a message passing neural network (MPNN) to predict simulated CO$$ in MOFs.
  arXiv  Detail & Related papers  (2020-12-02T12:54:26Z)
• Optimizing carbon tax for decentralized electricity markets using an agent-based model [69.3939291118954]
  Averting the effects of anthropogenic climate change requires a transition from fossil fuels to low-carbon technology. Carbon taxes have been shown to be an efficient way to aid in this transition. We use the NSGA-II genetic algorithm to minimize average electricity price and relative carbon intensity of the electricity mix.
  arXiv  Detail & Related papers  (2020-05-28T06:54:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.