Neural network representation and optimization of thermoelectric states of multiple interacting quantum dots

2020 ◽  
Vol 22 (28) ◽  
pp. 16165-16173
Author(s):  
Hangbo Zhou ◽  
Gang Zhang ◽  
Yong-Wei Zhang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Quantum Dots ◽  
Seebeck Coefficient ◽  
Master Equation ◽  
Thermoelectric Properties ◽  
Electrical Conductance ◽  
Thermal Conductance ◽  
Quantum Master Equation ◽  
Network Representation

We perform quantum master equation calculations and machine learning to investigate the thermoelectric properties of multiple interacting quantum dots, including electrical conductance, Seebeck coefficient, thermal conductance and ZT.

scholarly journals Impurity Substitution Enhances Thermoelectric Figure of Merit in Zigzag Graphene Nanoribbons

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Saeideh Ramezani Akbarabadi ◽  
Mojtaba Madadi Asl
Keyword(s):  
Thermoelectric Properties ◽  
Figure Of Merit ◽  
Density Functional ◽  
Graphene Nanoribbons ◽  
Electrical Conductance ◽  
Thermal Conductance ◽  
Simultaneous Increase ◽  
Thermoelectric Figure Of Merit ◽  
Thermoelectric Figure ◽  
Zigzag Graphene Nanoribbons

The thermoelectric properties of zigzag graphene nanoribbons (ZGNRs) are sensitive to chemical modification. In this study, we employed density functional theory (DFT) combined with the nonequilibrium green’s function (NEGF) formalism to investigate the thermoelectric properties of a ZGNR system by impurity substitution of single and double nitrogen (N) atoms into the edge of the nanoribbon. N-doping changes the electronic transmission probability near the Fermi energy and suppresses the phononic transmission. This results in a modified electrical conductance, thermal conductance, and thermopower. Ultimately, simultaneous increase of the thermopower and suppression of the electron and phonon contributions to the thermal conductance leads to the significant enhancement of the figure of merit in the perturbed (i.e., doped) system compared to the unperturbed (i.e., nondoped) system. Increasing the number of dopants not only changes the nature of transport and the sign of thermopower but also further suppresses the electron and phonon contributions to the thermal conductance, resulting in an enhanced thermoelectric figure of merit. Our results may be relevant for the development of ZGNR devices with enhanced thermoelectric efficiency.

Ensemble-machine-learning-based correlation analysis of internal and band characteristics of thermoelectric materials

2020 ◽  
Vol 8 (37) ◽  
pp. 13079-13089
Author(s):  
Lihao Chen ◽  
Ben Xu ◽  
Jia Chen ◽  
Ke Bi ◽  
Changjiao Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electrical Conductivity ◽  
Correlation Analysis ◽  
Seebeck Coefficient ◽  
Thermoelectric Properties ◽  
Thermoelectric Materials ◽  
Ensemble Machine Learning ◽  
Properties Of Materials

Machine learning can significantly help to predict the thermoelectric properties of materials, such as the Seebeck coefficient and electrical conductivity.

Measurements of Thermoelectric Properties of Bismuth Telluride Nanowires

2005 ◽  
Author(s):  
Jianhua Zhou ◽  
Li Shi ◽  
Chuangui Jin ◽  
Xiaoguang Li
Keyword(s):  
Electrical Conductivity ◽  
Seebeck Coefficient ◽  
Thermoelectric Properties ◽  
Bismuth Telluride ◽  
Electrical Resistance ◽  
Room Temperature ◽  
Theoretical Calculations ◽  
Thermal Conductance ◽  
Surface Oxide Layer ◽  
Increasing Temperature

Theoretical calculations have predicted that nanowire materials may have enhanced thermoelectric figure of merit compared to their bulk counterparts due to classical and quantum size effects. We have measured the thermoelectric properties of bismuth telluride nanowires deposited using an electrochemical deposition method in porous anodized alumina templates with the average pore size of about 60 nm. Transmission electron microscopy results of these nanowires showed that the nanowires were single crystalline with a composition of 54% Te and 46% Bi and the thickness of the surface oxide layer was in the range of 5–10 nm. The thermal conductance and Seebeck coefficient of the nanowires were measured using a microfabricated device that consists of two suspended membranes, across which the nanowire sample was placed. The obtained Seebeck coefficient of a bundle consisting of two 100 nm bismuth telluride nanowires increased with increasing temperature from 160 K to 360 K, and the room temperature value was 260 μV/K, which was 60% higher than the bulk value. The thermal conductance of the sample also increased with increasing temperature from 25 K to 360 K. Current design of the microdevice does not allow for four-probe electrical resistance measurement of the nanowire. We have measured the four-probe electrical resistance of a 57 nm diameter and a 43 nm diameter bismuth telluride nanowires from the same template, and found that the room-temperature electrical conductivity of the nanowires was close to the bulk value and showed much weaker temperature dependence than bulk electrical conductivity.

scholarly journals Anchoring Groups Determine Conductance, Thermopower and Thermoelectric Figure of Merit of an Organic Molecular Junction

2021 ◽  
Vol 9 ◽  
Author(s):  
Saeideh Ramezani Akbarabadi ◽  
Mojtaba Madadi Asl
Keyword(s):  
Thermoelectric Properties ◽  
Figure Of Merit ◽  
Electrical Conductance ◽  
Thermal Conductance ◽  
Wide Band ◽  
Transmission Probability ◽  
Molecular Junctions ◽  
Side Group ◽  
Group Position ◽  
Anchoring Groups

Transport properties of molecular junctions are prone to chemical or conformational modifications. Perturbation of the molecule-electrode coupling with anchoring groups or functionalization of the molecule with side groups is a well-characterized method to modulate the thermoelectric properties of molecular junctions. In this study, we used wide-band approximation combined with the non-equilibrium Green’s function (NEGF) formalism to inspect conductance, thermopower and figure of merit of an anthracene molecule coupled to gold (Au) electrodes. To provide a comparative study, three different anchoring groups were used, i.e., thiol, isocyanide and cyanide. The molecule was then perturbed with the amine side group in two positions to explore the interplay between anchoring groups and the side group. We showed that the introduction of side group alters transmission probability near the Fermi energy where transmission peaks are shifted relative to the Fermi level compared to the unperturbed molecule (i.e., without side group), ultimately leading to modified electrical and thermoelectric properties. The greatest value of electrical conductance was achieved when the side-group-perturbed molecule was anchored with isocyanide, whereas the thiol-terminated molecule perturbed with the side group yielded the greatest value of thermal conductance. We found that the Wiedemann-Franz law is violated in the Au-anthracene-Au device. Furthermore, the highest thermopower and figure of merit were attained in the cyanide-terminated perturbed molecule. Our results indicate that charge donating/accepting character of the anchoring group and its interplay with the side group position can modify temperature dependency of conductance, thermopower and figure of merit which is in agreement with experimental findings in organic molecular junctions. Such modifications may potentially contribute to the understanding of emerging conductance-based memory devices designed to mimic the behavior of brain-like synapses.

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>

Thermoelectric properties of B12N12 molecule

2019 ◽  
Vol 16 ◽  
Author(s):  
Mohammad Reza Niazian ◽  
Laleh Farhang Matin ◽  
Mojtaba Yaghobi ◽  
Amir Ali Masoudi
Keyword(s):  
Molecular Electronics ◽  
Thermoelectric Properties ◽  
Electronic Devices ◽  
Thermal Conductance ◽  
Wide Band ◽  
Oscillatory Behavior ◽  
Mapping Technique ◽  
Junction Points ◽  
Inelastic Behaviour ◽  
New Generation

Background: Recently, molecular electronics have attracted the attention of many researchers, both theoretically and applied electronics.Nanostructures have significant thermal properties, which is why they are considered as good options for designing a new generation of integrated electronic devices. Objective: In this paper, the focus is on the thermoelectric properties of the molecular junction points with the electrodes. Also, the influence of the number of atom contacts was investigated on the thermoelectric properties of molecule located between two electrodes metallic.Therefore, the thermoelectric characteristics of the B12 N12 molecule are investigated. Methods: For this purpose, the Green’s function theory as well as mapping technique approach with the wide-band approximation and also the inelastic behaviour is considered for the electron-phonon interactions. Results & Conclusion: Results & Conclusion:It is observed that the largest values of the total part of conductance as well as its elastic (G(e,n)max) depends on the number of atom contacts and are arranged as: G(e,1)max>G(e,4)max>G(e,6)max. Furthermore, the largest values of the electronic thermal conductance, i.e. Kpmax is seen to be in the order of K(p,4)max < K(p,1)max < K(p,6)max that the number of main peaks increases in four-atom contacts at (E<Ef). Furthermore, it is represented that the thermal conductance shows an oscillatory behavior which is significantly affected by the number of atom contacts.

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