Attainable Volumetric Targets for Adsorption-Based Hydrogen Storage in Porous Crystals: Molecular Simulation and Machine Learning

2018 ◽  
Vol 123 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Grace Anderson ◽  
Benjamin Schweitzer ◽  
Ryther Anderson ◽  
Diego A. Gómez-Gualdrón
Keyword(s):  
Machine Learning ◽  
Hydrogen Storage ◽  
Molecular Simulation

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

Role of Pore Chemistry and Topology in the CO2Capture Capabilities of MOFs: From Molecular Simulation to Machine Learning

2018 ◽  
Vol 30 (18) ◽  
pp. 6325-6337 ◽  
Author(s):  
Ryther Anderson ◽  
Jacob Rodgers ◽  
Edwin Argueta ◽  
Achay Biong ◽  
Diego A. Gómez-Gualdrón
Keyword(s):  
Machine Learning ◽  
Molecular Simulation ◽  
And Topology

scholarly journals Reducing molecular simulation time for AFM images based on super-resolution methods

2021 ◽  
Vol 12 ◽  
pp. 775-785
Author(s):  
Zhipeng Dou ◽  
Jianqiang Qian ◽  
Yingzi Li ◽  
Rui Lin ◽  
Jianhai Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Simulation ◽  
Super Resolution ◽  
Training Data ◽  
Reconstruction Algorithms ◽  
Force Microscopy ◽  
Nanoscale Imaging ◽  
Speed Up ◽  
Energy Maps ◽  
Simulation Time

Atomic force microscopy (AFM) has been an important tool for nanoscale imaging and characterization with atomic and subatomic resolution. Theoretical investigations are getting highly important for the interpretation of AFM images. Researchers have used molecular simulation to examine the AFM imaging mechanism. With a recent flurry of researches applying machine learning to AFM, AFM images obtained from molecular simulation have also been used as training data. However, the simulation is incredibly time consuming. In this paper, we apply super-resolution methods, including compressed sensing and deep learning methods, to reconstruct simulated images and to reduce simulation time. Several molecular simulation energy maps under different conditions are presented to demonstrate the performance of reconstruction algorithms. Through the analysis of reconstructed results, we find that both presented algorithms could complete the reconstruction with good quality and greatly reduce simulation time. Moreover, the super-resolution methods can be used to speed up the generation of training data and vary simulation resolution for AFM machine learning.

scholarly journals A GPU-Accelerated Machine Learning Framework for Molecular Simulation: HOOMD-blue with TensorFlow

2019 ◽  
Author(s):  
Rainier Barrett ◽  
Maghesree Chakraborty ◽  
Dilnoza Amirkulova ◽  
Heta Gandhi ◽  
Andrew White
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Model Building ◽  
Dynamics Simulation ◽  
Model Specification ◽  
Force Matching ◽  
Lennard Jones ◽  
Chemistry Research ◽  
The Rich

<div> <div> <div> <p>As interest grows in applying machine learning force-fields and methods to molecular simulation, there is a need for state-of-the-art inference methods to use trained models within efficient molecular simulation engines. We have designed and implemented software that enables integration of a scalable GPU-accelerated molecular mechanics engine, HOOMD-blue, with the machine learning (ML) TensorFlow package. TensorFlow is a GPU-accelerated, scalable, graph-based tensor computation model building package that has been the implementation of many recent innovations in deep learning and other ML tasks. TensorFlow models are constructed in Python and can be visualized or debugged using the rich set of tools implemented in the TensorFlow package. In this article, we present four major examples of tasks this software can accomplish which would normally require multiple different tools: (1) we train a neural network to reproduce a force field of a Lennard-Jones simulation; (2) we perform online force matching of methanol; (3) we compute the maximum entropy bias of a Lennard-Jones collective variable; (4) we calculate the scattering profile of an ongoing TIP4P water molecular dynamics simulation. This work should accelerate both the design of new neural network based models in computational chemistry research and reproducible model specification by leveraging a widely-used ML package.</p></div></div></div>

Hydrogen Storage in Mesoporous Coordination Frameworks: Experiment and Molecular Simulation

2009 ◽  
Vol 113 (34) ◽  
pp. 15106-15109 ◽  
Author(s):  
Zhonghua Xiang ◽  
Jianhui Lan ◽  
Dapeng Cao ◽  
Xiaohong Shao ◽  
Wenchuan Wang ◽  
...  
Keyword(s):  
Hydrogen Storage ◽  
Molecular Simulation ◽  
Coordination Frameworks
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