Two-Dimensional Hexagonal Sheet of TiO<sub>2</sub>

Eivari, Hossein Asnaashari; Ghasemi, S. Alireza; Tahmasbi, Hossein; Rostami, Samare; Faraji, Somayeh; Rasoulkhani, Robabe; Goedecker, Stefan; Amsler, Maximilian

doi:10.1021/acs.chemmater.7b02031

Cited by 76 publications

(61 citation statements)

References 114 publications

(195 reference statements)

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“…They can solve challenging structural problems, as has been demonstrated for the atomic-scale deposition and growth of amorphous carbon films 13 , for proton-transfer mechanisms 14 or dislocations in materials 15,16 , involving thousands of atoms in the simulation. More recently, it was shown that ML potentials can be suitable tools for global structure searches targeting crystalline phases [17][18][19][20] , clusters [21][22][23][24] , and nanostructures 25 .…”

Section: Introductionmentioning

confidence: 99%

De novo exploration and self-guided learning of potential-energy surfaces

2019

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Interatomic potential models based on machine learning (ML) are rapidly developing as tools for materials simulations. However, because of their flexibility, they require large fitting databases that are normally created with substantial manual selection and tuning of reference configurations. Here, we show that ML potentials can be built in a largely automated fashion, exploring and fitting potential-energy surfaces from the beginning (de novo) within one and the same protocol. The key enabling step is the use of a configuration-averaged kernel metric that allows one to select the few most relevant structures at each step. The resulting potentials are accurate and robust for the wide range of configurations that occur during structure searching, despite only requiring a relatively small number of single-point DFT calculations on small unit cells. We apply the method to materials with diverse chemical nature and coordination environments, marking a milestone toward the more routine application of ML potentials in physics, chemistry, and materials science.

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Section: Introductionmentioning

confidence: 99%

De novo exploration and self-guided learning of potential-energy surfaces

2019

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“…More recently, Goedecker and co-workers proposed an environment-dependent charge equilibration scheme for ionic systems, initially introduced for NaCl clusters in the gas phase, [170] constructing a simple force field just from electrostatic terms. [172] The latter structure appears to be interesting due to its electronic band gap, which is much larger than that of known (3D) TiO 2 polymorphs-which might make the material a candidate for photocatalytic hydrogen production, given that a way can be found to synthesize it (e.g., through epitaxial growth on a suitable substrate). [172] The latter structure appears to be interesting due to its electronic band gap, which is much larger than that of known (3D) TiO 2 polymorphs-which might make the material a candidate for photocatalytic hydrogen production, given that a way can be found to synthesize it (e.g., through epitaxial growth on a suitable substrate).…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

“…Emerging applications of this method deal with surface reconstructions of fluorite-structured materials [173] and the structural prediction of a stable TiO 2 nanosheet (Figure 6c), found in global exploration as accelerated by the ML potential. [172] However, a framework that would "learn" all long-range terms and hierarchically combine this with a short-range fit, thereby making it generally applicable to all kinds of solids (in particular weakly ionic ones), is lacking so far. [172] However, a framework that would "learn" all long-range terms and hierarchically combine this with a short-range fit, thereby making it generally applicable to all kinds of solids (in particular weakly ionic ones), is lacking so far.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

“…For example, a thorough treatment of electrostatics in extended Adv. [172] Structural model drawn with data from ref. 2019, 31, 1902765 Figure 6.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

“…Mater. [172]. Hierarchical ML models for interatomic potentials as one perspective for possible developments in the coming years.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

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Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

2019

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In pursuit of this goal, atomic-scale computer simulations have long been a central approach, and two major families of methods are routinely used today. On the one hand, there are quantum-mechanical simulations, in which we solve Schrödinger's equation for the electronic structure of molecular and periodic systems, most widely based on density-functional theory (DFT). [6][7][8] These methods provide (largely) reliable results for structural models of materials that normally contain a few tens or hundreds of atoms. State-of-the-art DFT methods can be applied to many material classes, and they are increasingly used for high-throughput screening and "in silico" (computer-based) design of materials: new compositions and previously unknown structures have been identified in DFT searches and subsequently experimentally realized. [5,[9][10][11] On the other hand, interatomic potential models ("force fields"), parameterizing interactions between atoms with (relatively) simple functional forms, are widely used in materials science to describe matter in molecular dynamics (MD) simulations. These simulations grant access to larger time and length scales, reaching system sizes of up to hundreds of thousands of atoms. [12] In parameterizing these potentials, a certain physical form of the atomic interactions is assumed, often in terms of bond distances, angles, and so on, and physical properties such as equilibrium lattice parameters or elastic constants enter the fitting of the potential. For this reason, such potentials are often called "empirical." They are several orders of magnitude faster than DFT, but necessarily less accurate and less easily transferable.In this Progress Report, we highlight recent developments in "machine-learned" interatomic potentials, which represent a rapidly growing field that promises to do away with the aforementioned trade-offs. Over the last year, there has been a surge of interest in machine learning (ML) methodology: part of it is due to the dramatic growth of ML throughout the scientific disciplines, and part of it is due to tangible success stories of ML-based interatomic potentials that are now beginning to emerge. We will argue that this is an exciting development with very practical implications, currently on the verge of moving from a somewhat specialized new technology to everyday applicability, poised to enhance and complement the communities' existing strengths in computational materials modeling. We will show selected applications of ML potentials to problems in materials science, discuss the current limitations (and possible pitfalls), and outline what we expect to be interesting directions for the development of the field in the coming years.Atomic-scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic-structure methods such as density-functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materi...

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Rational Design of Stimuli‐Responsive Inorganic 2D Materials via Molecular Engineering: Toward Molecule‐Programmable Nanoelectronics

Muñoz

2023

Advanced Materials

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The ability of electronic devices to act as switches makes digital information processing possible. Succeeding graphene, emerging Inorganic 2D Materials (i2DMs) have been identified as alternative 2D Materials to harbor a variety of active molecular components to move the current silicon‐based semiconductor technology forward to a post‐Moore era based on molecule‐based information processing components. In this regard, i2DMs benefits not only for their prominent physiochemical properties (e.g., the existence of band gap, contrary to graphene), but also their high surface‐to‐volume ratio rich in reactive sites (functionalization capability). Nonetheless, since this field is still in an early stage, having knowledge of both i) the different strategies for molecularly functionalizing the current library of i2DMs, and ii) the different types of active molecular components is a sine qua non condition for a rational design of stimuli‐responsive i2DMs capable of performing logical binary operations at the molecular level. Consequently, this Review provides a comprehensive tutorial for covalently anchoring ad hoc molecular components —as active units triggered by different external inputs— onto pivotal i2DMs to assess their role in the expanding field of Molecule‐Programmable Nanoelectronics for electrically monitoring bistable molecular switches. Limitations, challenges, and future perspectives of this emerging field which crosses materials chemistry with computation are critically discussed.This article is protected by copyright. All rights reserved

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Two-Dimensional Hexagonal Sheet of TiO₂

Cited by 76 publications

References 114 publications

De novo exploration and self-guided learning of potential-energy surfaces

De novo exploration and self-guided learning of potential-energy surfaces

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Rational Design of Stimuli‐Responsive Inorganic 2D Materials via Molecular Engineering: Toward Molecule‐Programmable Nanoelectronics

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Two-Dimensional Hexagonal Sheet of TiO2

Cited by 76 publications

References 114 publications

De novo exploration and self-guided learning of potential-energy surfaces

De novo exploration and self-guided learning of potential-energy surfaces

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Rational Design of Stimuli‐Responsive Inorganic 2D Materials via Molecular Engineering: Toward Molecule‐Programmable Nanoelectronics

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Two-Dimensional Hexagonal Sheet of TiO₂