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.

Unveiling the layer-dependent electronic properties in transition-metal dichalcogenides heterostructures assisted by machine learning

Nanoscale ◽  
2022 ◽  
Author(s):  
Tao Wang ◽  
Xiaoxing Tan ◽  
Yadong Wei ◽  
Hao Jin
Keyword(s):  
Machine Learning ◽  
Transition Metal ◽  
Electronic Properties ◽  
Van Der Waals ◽  
Transition Metal Dichalcogenides ◽  
Two Dimensional ◽  
Layer Number ◽  
Metal Dichalcogenides ◽  
Computational Resources

The electronic properties of layered two-dimensional (2D) transition-metal dichalcogenides (TMDs) van der Waals (vdW) heterostructures are strongly dependent on their layer number (N). However, it requires extremely large computational resources...

Bonding and structure of intermetallics: a new bond order potential

1991 ◽  
Vol 334 (1635) ◽  
pp. 439-449 ◽  
Keyword(s):  
Transition Metal ◽  
Bond Order ◽  
Cubic Metals ◽  
Embedded Atom ◽  
Chemical Ordering ◽  
Bond Order Potential ◽  
The Moment ◽  
Three Body ◽  
Body Potential ◽  
Metal Aluminides

Intermetallics such as the transition metal aluminides present theorists with a challenge since bonding is not well described by currently available pair or embedded atom potentials. We show that a new angularly dependent, many-body potential for the bond order has all the necessary ingredients for an adequate description. In particular, by linearizing the moment-recursion coefficient relations, a cluster expansion is derived which is applicable to any lattice and chemical ordering and which allows a derivation of the earlier ring ansatz. It can account for both the negative Cauchy pressure of cubic metals and the oscillatory behaviour across the transition metal aluminide series of the three-body cluster interaction Φ 3 .

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.

Intercalation of First Row Transition Metals inside Covalent-Organic Frameworks (COF): a Strategy to Fine Tune the Electronic Properties of Porous Crystalline Materials

2018 ◽  
Author(s):  
Srimanta Pakhira ◽  
Jose Mendoza-Cortes
Keyword(s):  
Transition Metal ◽  
Transition Metals ◽  
Electronic Properties ◽  
High Surface ◽  
Electronic Devices ◽  
High Surface Area ◽  
Main Role ◽  
Covalent Organic Frameworks ◽  
Porous Network ◽  
Crystalline Materials

<div>Covalent organic frameworks (COFs) have emerged as an important class of nano-porous crystalline materials with many potential applications. They are intriguing platforms for the design of porous skeletons with special functionality at the molecular level. However, despite their extraordinary properties, it is difficult to control their electronic properties, thus hindering the potential implementation in electronic devices. A new form of nanoporous material, COFs intercalated with first row transition metal is proposed to address this fundamental drawback - the lack of electronic tunability. Using first-principles calculations, we have designed 31 new COF materials <i>in-silico</i> by intercalating all of the first row transition metals (TMs) with boroxine-linked and triazine-linked COFs: COF-TM-x (where TM=Sc-Zn and x=3-5). This is a significant addition considering that only 187 experimentally COFs structures has been reported and characterized so far. We have investigated their structure and electronic properties. Specifically, we predict that COF's band gap and density of states (DOSs) can be controlled by intercalating first row transition metal atoms (TM: Sc - Zn) and fine tuned by the concentration of TMs. We also found that the $d$-subshell electron density of the TMs plays the main role in determining the electronic properties of the COFs. Thus intercalated-COFs provide a new strategy to control the electronic properties of materials within a porous network. This work opens up new avenues for the design of TM-intercalated materials with promising future applications in nanoporous electronic devices, where a high surface area coupled with fine-tuned electronic properties are desired.</div>

Electronic properties of quasi two-dimensional transition metals chalcogenides with low-dimensional magnetism

Doklady BGUIR ◽  
2020 ◽  
Vol 18 (7) ◽  
pp. 87-95
Author(s):  
M. S. Baranava ◽  
P. A. Praskurava
Keyword(s):  
Transition Metal ◽  
Transition Metals ◽  
Exchange Interaction ◽  
Electronic Properties ◽  
Metal Atom ◽  
Transition Metal Atom ◽  
Two Dimensional ◽  
Transition Metal Atoms ◽  
Ground Magnetic ◽  
Low Dimensional

The search for fundamental physical laws which lead to stable high-temperature ferromagnetism is an urgent task. In addition to the already synthesized two-dimensional materials, there remains a wide list of possible structures, the stability of which is predicted theoretically. The article suggests the results of studying the electronic properties of MAX3 (M = Cr, Fe, A = Ge, Si, X = S, Se, Te) transition metals based compounds with nanostructured magnetism. The research was carried out using quantum mechanical simulation in specialized VASP software and calculations within the Heisenberg model. The ground magnetic states of twodimensional MAX3 and the corresponding energy band structures are determined. We found that among the systems under study, CrGeTe3 is a semiconductor nanosized ferromagnet. In addition, one is a semiconductor with a bandgap of 0.35 eV. Other materials are antiferromagnetic. The magnetic moment in MAX3 is localized on the transition metal atoms: in particular, the main one on the d-orbital of the transition metal atom (and only a small part on the p-orbital of the chalcogen). For CrGeTe3, the exchange interaction integral is calculated. The mechanisms of the formation of magnetic order was established. According to the obtained exchange interaction integrals, a strong ferromagnetic order is formed in the semiconductor plane. The distribution of the projection density of electronic states indicates hybridization between the d-orbital of the transition metal atom and the p-orbital of the chalcogen. The study revealed that the exchange interaction by the mechanism of superexchange is more probabilistic.

Lateral-size control of exfoliated transition-metal–oxide 2D materials by machine learning on small data

Nanoscale ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 3853-3859
Author(s):  
Ryosuke Mizuguchi ◽  
Yasuhiko Igarashi ◽  
Hiroaki Imai ◽  
Yuya Oaki
Keyword(s):  
Machine Learning ◽  
Transition Metal ◽  
Metal Oxide ◽  
2D Materials ◽  
Size Control ◽  
Transition Metal Oxide ◽  
Small Data ◽  
Lateral Size

Lateral sizes of the exfoliated transition-metal–oxide nanosheets were predicted and controlled by the assistance of machine learning. 

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