Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks

• URL: http://arxiv.org/abs/2106.13327v1
• Date: Thu, 24 Jun 2021 21:35:26 GMT
• Title: Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks
• Authors: Aditya Nandy, Chenru Duan, and Heather J. Kulik
• Abstract summary: MOFs hold promise for engineering challenges ranging from gas separations to stability. To overcome limitation, we extract thousands of published reports of the key aspects of MOF stability necessary for their practical application. We use natural language processing and automated image analysis to obtain over 2,000 solvent-removal measures and 3,000 thermal temperatures.
• Score: 0.9187159782788578
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Although the tailored metal active sites and porous architectures of MOFs hold great promise for engineering challenges ranging from gas separations to catalysis, a lack of understanding of how to improve their stability limits their use in practice. To overcome this limitation, we extract thousands of published reports of the key aspects of MOF stability necessary for their practical application: the ability to withstand high temperatures without degrading and the capacity to be activated by removal of solvent molecules. From nearly 4,000 manuscripts, we use natural language processing and automated image analysis to obtain over 2,000 solvent-removal stability measures and 3,000 thermal degradation temperatures. We analyze the relationships between stability properties and the chemical and geometric structures in this set to identify limits of prior heuristics derived from smaller sets of MOFs. By training predictive machine learning (ML, i.e., Gaussian process and artificial neural network) models to encode the structure-property relationships with graph- and pore-structure-based representations, we are able to make predictions of stability orders of magnitude faster than conventional physics-based modeling or experiment. Interpretation of important features in ML models provides insights that we use to identify strategies to engineer increased stability into typically unstable 3d-containing MOFs that are frequently targeted for catalytic applications. We expect our approach to accelerate the time to discovery of stable, practical MOF materials for a wide range of applications.

Related papers

• Machine Learning for Methane Detection and Quantification from Space -- A survey [49.7996292123687]
  Methane (CH_4) is a potent anthropogenic greenhouse gas, contributing 86 times more to global warming than Carbon Dioxide (CO_2) over 20 years. This work expands existing information on operational methane point source detection sensors in the Short-Wave Infrared (SWIR) bands. It reviews the state-of-the-art for traditional as well as Machine Learning (ML) approaches.
  arXiv  Detail & Related papers  (2024-08-27T15:03:20Z)
• Physics-Enhanced TinyML for Real-Time Detection of Ground Magnetic Anomalies [0.0]
  Space weather phenomena like geomagnetic disturbances (GMDs) pose significant risks to critical technological infrastructure. This paper develops a physics-guided TinyML framework to address the above challenges. It integrates physics-based regularization at the stages of model training and compression, thereby augmenting the reliability of predictions.
  arXiv  Detail & Related papers  (2023-11-19T23:20:16Z)
• Scalable Diffusion for Materials Generation [99.71001883652211]
  We develop a unified crystal representation that can represent any crystal structure (UniMat) UniMat can generate high fidelity crystal structures from larger and more complex chemical systems. We propose additional metrics for evaluating generative models of materials.
  arXiv  Detail & Related papers  (2023-10-18T15:49:39Z)
• From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields [5.013279299982324]
  We propose a transformer architecture called SO3krates that combines sparse equivariant representations with a self-attention mechanism. SO3krates achieves a unique combination of accuracy, stability, and speed that enables insightful analysis of quantum properties of matter on extended time and system size scales.
  arXiv  Detail & Related papers  (2023-09-21T09:22:05Z)
• 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)
• A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models [0.3710026260502075]
  We leverage community knowledge and machine learning models to identify metal-organic frameworks (MOFs) that are thermally stable and stable upon activation. We make a new hypothetical MOF database of over 50,000 structures that samples orders of magnitude more connectivity nets and inorganic building blocks than prior databases. This database shows an order of magnitude enrichment of ultrastable MOF structures that are stable upon activation and more than one standard deviation more thermally stable than the average experimentally characterized MOF.
  arXiv  Detail & Related papers  (2022-10-25T17:38:42Z)
• Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [64.17887333976593]
  Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
  arXiv  Detail & Related papers  (2022-06-21T20:38:13Z)
• Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
  The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
  arXiv  Detail & Related papers  (2021-10-18T14:43:50Z)
• MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks [0.0]
  We use natural language processing to mine literature on metal-organic framework (MOF) stability measures. We train machine learning models to predict stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs.
  arXiv  Detail & Related papers  (2021-09-16T16:37:37Z)
• BIGDML: Towards Exact Machine Learning Force Fields for Materials [55.944221055171276]
  Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 atoms.
  arXiv  Detail & Related papers  (2021-06-08T10:14:57Z)
• Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene [3.708456605408296]
  We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels. This allows for automated active learning of models combining near-quantum accuracy, built-in uncertainty, and constant cost of evaluation.
  arXiv  Detail & Related papers  (2020-08-26T20:27:19Z)

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.