Machine learnt bond order potential to model metal–organic (Co–C) heterostructures

Nanoscale ◽  
2017 ◽  
Vol 9 (46) ◽  
pp. 18229-18239 ◽  
Author(s):  
Badri Narayanan ◽  
Henry Chan ◽  
Alper Kinaci ◽  
Fatih G. Sen ◽  
Stephen K. Gray ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
First Principles ◽  
Bond Order ◽  
Interatomic Potential ◽  
Thermodynamic Surface ◽  
Bond Order Potential ◽  
Metal Organic ◽  
Robust Framework

We develop a bond-order based interatomic potential for cobalt–carbon from first-principles data using machine learning. This model accurately captures structural, thermodynamic, surface and mechanical properties of metal–organic heterostructures within a single robust framework.

scholarly journals First‐Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine‐Learning Interatomic Potentials

2021 ◽  
pp. 2102807
Author(s):  
Bohayra Mortazavi ◽  
Mohammad Silani ◽  
Evgeny V. Podryabinkin ◽  
Timon Rabczuk ◽  
Xiaoying Zhuang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Multiscale Modeling ◽  
First Principles ◽  
Interatomic Potentials

scholarly journals Machine learning a bond order potential model to study thermal transport in WSe2 nanostructures

Nanoscale ◽  
2019 ◽  
Vol 11 (21) ◽  
pp. 10381-10392 ◽  
Author(s):  
Henry Chan ◽  
Kiran Sasikumar ◽  
Srilok Srinivasan ◽  
Mathew Cherukara ◽  
Badri Narayanan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transition Metal ◽  
Electronic Properties ◽  
Thermal Transport ◽  
Bond Order ◽  
Potential Model ◽  
Bond Order Potential

Nanostructures of transition metal di-chalcogenides (TMDCs) exhibit exotic thermal, chemical and electronic properties, enabling diverse applications from thermoelectrics and catalysis to nanoelectronics.

scholarly journals Describing the Diverse Geometries of Gold from Nanoclusters to Bulk—A First-Principles-Based Hybrid Bond-Order Potential

2016 ◽  
Vol 120 (25) ◽  
pp. 13787-13800 ◽  
Author(s):  
Badri Narayanan ◽  
Alper Kinaci ◽  
Fatih G. Sen ◽  
Michael J. Davis ◽  
Stephen K. Gray ◽  
...  
Keyword(s):  
First Principles ◽  
Bond Order ◽  
Bond Order Potential

A generalizable machine learning potential of Ag-Au nanoalloys and its application t surface reconstruction, segregation and diffusion

2021 ◽  
Author(s):  
Yinan Wang ◽  
Linfeng Zhang ◽  
Ben Xu ◽  
Xiaoyang Wang ◽  
Han Wang
Keyword(s):  
Machine Learning ◽  
Surface Properties ◽  
Surface Reconstruction ◽  
First Principles ◽  
Interatomic Potential ◽  
Learning Potential ◽  
Free Surfaces ◽  
Wide Range ◽  
Empirical Force Field ◽  
And Diffusion

Abstract Owing to the excellent catalytic properties of Ag-Au binary nanoalloys, nanostructured Ag-Au, such as Ag-Au nanoparticles and nanopillars, has been under intense investigation. To achieve high accuracy in molecular simulations of Ag-Au nanoalloys, the surface properties must be modeled with first-principles precision. In this work, we constructed a generalizable machine learning interatomic potential for Ag-Au nanoalloys based on deep neural networks trained from a database constructed with first-principles calculations. This potential is highlighted by the accurate prediction of Au (111) surface reconstruction and the segregation of Au toward the Ag-Au nanoalloy surface, where the empirical force field failed in both cases. Moreover, regarding the adsorption and diffusion of adatoms on surfaces, the overall performance of our potential is better than the empirical force fields. We stress that the reported surface properties are blind to the potential modeling in the sense that none of the surface configurations is explicitly included in the training database; therefore, the reported potential is expected to have a strong generalization ability to a wide range of properties and to play a key role in investigating nanostructured Ag-Au evolution, where accurate descriptions of free surfaces are necessary.

Electronic and thermal transport in novel carbon-based bilayer with tetragonal rings: a combined study from first-principles and machine learning approach

2021 ◽  
Author(s):  
Supriya Ghosal ◽  
Suman Chowdhury ◽  
Debnarayan Jana
Keyword(s):  
Machine Learning ◽  
First Principles ◽  
Thermal Transport ◽  
Interatomic Potential ◽  
Learning Approach ◽  
First Principles Calculations ◽  
Potential Approach ◽  
Machine Learning Approach ◽  
Tg Structure ◽  
Carbon Based

In this article, the structural, electronic and thermal transport characteristics of bilayer tetragonal graphene (TG) structure are systematically explored combining both first-principles calculations and machine-learning interatomic potential approach. Optimized ground...

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>

scholarly journals DFT Study of Electronic Structure and Optical Properties of Kaolinite, Muscovite, and Montmorillonite

Crystals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 618
Author(s):  
Layla Shafei ◽  
Puja Adhikari ◽  
Wai-Yim Ching
Keyword(s):  
Mechanical Properties ◽  
Optical Properties ◽  
Electronic Structure ◽  
Hydrogen Bonds ◽  
Clay Minerals ◽  
Clay Mineral ◽  
Bond Order ◽  
Deep Understanding ◽  
Pharmaceutical Applications ◽  
Structure Properties

Clay mineral materials have attracted attention due to their many properties and applications. The applications of clay minerals are closely linked to their structure and composition. In this paper, we studied the electronic structure properties of kaolinite, muscovite, and montmorillonite crystals, which are classified as clay minerals, by using DFT-based ab initio packages VASP and the OLCAO. The aim of this work is to have a deep understanding of clay mineral materials, including electronic structure, bond strength, mechanical properties, and optical properties. It is worth mentioning that understanding these properties may help continually result in new and innovative clay products in several applications, such as in pharmaceutical applications using kaolinite for their potential in cancer treatment, muscovite used as insulators in electrical appliances, and engineering applications that use montmorillonite as a sealant. In addition, our results show that the role played by hydrogen bonds in O-H bonds has an impact on the hydration in these crystals. Based on calculated total bond order density, it is concluded that kaolinite is slightly more cohesive than montmorillonite, which is consistent with the calculated mechanical properties.

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