scholarly journals Data-driven studies of magnetic two-dimensional materials

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Trevor David Rhone ◽  
Wei Chen ◽  
Shaan Desai ◽  
Steven B. Torrisi ◽  
Daniel T. Larson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dft Calculations ◽  
Density Functional ◽  
Magnetic Coupling ◽  
Magnetic Ordering ◽  
Magnetic Behavior ◽  
Data Driven ◽  
Two Dimensional ◽  
Computationally Efficient ◽  
Data Driven Approach

Abstract We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form $$\hbox {A}_2\hbox {B}_2\hbox {X}_6$$ A 2 B 2 X 6 , based on the known material $$\hbox {Cr}_2\hbox {Ge}_2\hbox {Te}_6$$ Cr 2 Ge 2 Te 6 , using density functional theory (DFT) calculations and machine learning methods to determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability. We show that machine learning tools, combined with DFT calculations, can provide a computationally efficient means to predict properties of such two-dimensional (2D) magnetic materials. Our data analytics approach provides insights into the microscopic origins of magnetic ordering in these systems. For instance, we find that the X site strongly affects the magnetic coupling between neighboring A sites, which drives the magnetic ordering. Our approach opens new ways for rapid discovery of chemically stable vdW materials that exhibit magnetic behavior.

scholarly journals Tailoring the magnetic ordering of the Cr4O5/Fe(001) surface via a controlled adsorption of C60 organic molecules.

2020 ◽  
Author(s):  
Federico Orlando ◽  
Guido Fratesi ◽  
Giovanni Onida ◽  
Simona Achilli
Keyword(s):  
Density Functional ◽  
Organic Molecules ◽  
Molecular Layer ◽  
Magnetic Coupling ◽  
Magnetic Ordering ◽  
Magnetic Interaction ◽  
High Symmetry ◽  
Two Dimensional ◽  
Adsorption Sites ◽  
Controlled Adsorption

We analyse the spinterface formed by a C60 molecular layer on a Fe(001) surface covered by a two-dimensional Cr4O5 layer. We consider different geometries, by combining the high symmetry adsorption sites of the surface with three possible orientations of the molecules in a fully relaxed Density Functional Theory calculation.We show that the local hybridization between the electronic states of the Cr4O5 layer and those of the organic molecules is able to modify the magnetic coupling of the Cr atoms. Both the intra-layer and the inter-layer magnetic interaction is indeed driven by O atoms of the two-dimensional oxide. We demonstrate that the C60 adsorption on the energetically most stable site turns the ferromagnetic intra-layer coupling into an antiferromagnetic one, and that antiferromagnetic to ferromagnetic switching and spin patterning of the substrate are made possible by adsorption on other sites.

Low-dimensional HfS2 as SO2 adsorbent and gas sensor: Effect of water and sulfur vacancies

2021 ◽  
Author(s):  
Amina Bouheddadj ◽  
Tarik Ouahrani ◽  
Gbèdodé Wilfried KANNHOUNON ◽  
Boufatah Reda ◽  
Sumeya Bedrane ◽  
...  
Keyword(s):  
Climate Change ◽  
Density Functional Theory ◽  
Dft Calculations ◽  
Gas Sensor ◽  
First Principles ◽  
Density Functional ◽  
Two Dimensional ◽  
Functional Theory ◽  
Sensor Effect ◽  
Low Dimensional

First-principles based on density functional theory (DFT) calculations were performed to investigate the interaction of two-dimensional (2D) HfS2 with SO2, a harmful gas with implications for climate change. In particular,...

A note on no-free-lunch theorem

2021 ◽  
Author(s):  
Lidong Wu
Keyword(s):  
Machine Learning ◽  
Theoretical Result ◽  
Data Driven ◽  
Np Hard ◽  
Free Lunch ◽  
No Free Lunch Theorem ◽  
Data Driven Approach ◽  
Hard Problems ◽  
No Free Lunch ◽  
Np Hard Problems

The No-Free-Lunch theorem is an interesting and important theoretical result in machine learning. Based on philosophy of No-Free-Lunch theorem, we discuss extensively on the limitation of a data-driven approach in solving NP-hard problems.

scholarly journals Machine Learning Prediction of Surface Segregation Energies on Low Index Bimetallic Surfaces

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2182
Author(s):  
Damilola Ologunagba ◽  
Shyam Kattel
Keyword(s):  
Machine Learning ◽  
Dft Calculations ◽  
Bimetallic Catalysts ◽  
Density Functional ◽  
Surface Segregation ◽  
Surface Composition ◽  
Bulk Composition ◽  
Statistical Machine Learning ◽  
Bimetallic Surfaces ◽  
Computing Platforms

Surface chemical composition of bimetallic catalysts can differ from the bulk composition because of the segregation of the alloy components. Thus, it is very useful to know how the different components are arranged on the surface of catalysts to gain a fundamental understanding of the catalysis occurring on bimetallic surfaces. First-principles density functional theory (DFT) calculations can provide deeper insight into the surface segregation behavior and help understand the surface composition on bimetallic surfaces. However, the DFT calculations are computationally demanding and require large computing platforms. In this regard, statistical/machine learning methods provide a quick and alternative approach to study materials properties. Here, we trained previously reported surface segregation energies on low index surfaces of bimetallic catalysts using various linear and non-linear statistical methods to find a correlation between surface segregation energies and elemental properties. The results revealed that the surface segregation energies on low index bimetallic surfaces can be predicted using fundamental elemental properties.

A Data-Driven Approach using Machine Learning to Enable Real-Time Flight Path Planning

2020 ◽  
Author(s):  
Jung-Hyun Kim ◽  
Simon I. Briceno ◽  
Cedric Y. Justin ◽  
Dimitri Mavris
Keyword(s):  
Machine Learning ◽  
Path Planning ◽  
Real Time ◽  
Flight Path ◽  
Data Driven ◽  
Data Driven Approach

scholarly journals Machine Learning Representation for Atomic Forces and Energies

2020 ◽  
Vol 36 (2) ◽  
Author(s):  
Pham Tien Lam ◽  
Nguyen Van Duy ◽  
Nguyen Tien Cuong
Keyword(s):  
Machine Learning ◽  
Dft Calculations ◽  
Linear Combination ◽  
Density Functional ◽  
Crystalline Silicon ◽  
Basis Functions ◽  
Atomic Energy ◽  
Model Parameters ◽  
Force Model ◽  
L2 Norm

We present machine learning models for fast estimating atomic forces. In our method, the total energy of a system is approximated as the summation of atomic energy which is the interaction energy with its surrounding chemical environment within a certain cutoff radius. Atomic energy is decomposed into two-body terms which are expressed as a linear combination of basis functions. For the force exerted on an atom, we employ a linear combination of a set of basis functions for representing pairwise force. We use least-square linear regression regularized by the l2-norm, known as Ridge regression, to estimate model parameters. We demonstrate that our model can accurately reproduce atomic forces and energies from density-functional-theory (DFT) calculations for crystalline and amorphous silicon. The machine learning force model is then applied to calculate the phonon dispersion of crystalline silicon. The result shows reasonable agreement with DFT calculations.

scholarly journals Reconfiguring primase DNA-recognition sequences by using a data-driven approach

2020 ◽  
Author(s):  
Adam Soffer ◽  
Morya Ifrach ◽  
Stefan Ilic ◽  
Ariel Afek ◽  
Dan Vilenchik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Dna Sequences ◽  
Metabolic Pathways ◽  
Dna Recognition ◽  
Model System ◽  
Data Driven ◽  
Specific Interactions ◽  
Data Driven Approach ◽  
Dna Protein Interactions

AbstractDNA–protein interactions are essential in all aspects of every living cell. Understanding of how features embedded in the DNA sequence affect specific interactions with proteins is challenging but important, since it may contribute to finding the means to regulate metabolic pathways involving DNA–protein interactions. Using a massive experimental benchmark dataset of binding scores for DNA sequences and a machine learning workflow, we describe the binding to DNA of T7 primase, as a model system for specific DNA–protein interactions. Effective binding of T7 primase to its specific DNA recognition se-quences triggers the formation of RNA primers that serve as Okazaki fragment start sites during DNA replication.

scholarly journals Convergence Acceleration in Machine Learning Potentials for Atomistic Simulations

2022 ◽  
Author(s):  
Dylan Bayerl ◽  
Christopher Michael Andolina ◽  
Shyam Dwaraknath ◽  
Wissam A Saidi
Keyword(s):  
Machine Learning ◽  
Density Functional Theory ◽  
Dft Calculations ◽  
Density Functional ◽  
Convergence Acceleration ◽  
Atomistic Simulations ◽  
Functional Theory ◽  
Materials Modeling

Machine learning potentials (MLPs) for atomistic simulations have an enormous prospective impact on materials modeling, offering orders of magnitude speedup over density functional theory (DFT) calculations without appreciably sacrificing accuracy...

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