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...
Systematic atomistic studies of surface reactivity for amorphous materials have not been possible in the past because of the complexity of these materials and the lack of the computer power necessary to draw representative statistics. With the emergence and popularization of machine learning (ML) approaches in materials science, systematic (and accurate) studies of the surface chemistry of disordered materials are now coming within reach. In this paper, we show how the reactivity of amorphous carbon (a-C) surfaces can be systematically quantified and understood by a combination of ML interatomic potentials, ML clustering techniques, and density functional theory calculations. This methodology allows us to process large amounts of atomic data to classify carbon atomic motifs on the basis of their geometry and quantify their reactivity toward hydrogen- and oxygen-containing functionalities. For instance, we identify subdivisions of sp and sp2 motifs with markedly different reactivities. We therefore draw a comprehensive, both qualitative and quantitative, picture of the surface chemistry of a-C and its reactivity toward −H, −O, −OH, and −COOH. While this paper focuses on a-C surfaces, the presented methodology opens up a new systematic and general way to study the surface chemistry of amorphous and disordered materials.
We study the deposition of tetrahedral amorphous carbon (ta-C) films from molecular dynamics simulations based on a machine-learned interatomic potential trained from density-functional theory data. For the first time, the high sp^{3} fractions in excess of 85% observed experimentally are reproduced by means of computational simulation, and the deposition energy dependence of the film's characteristics is also accurately described. High confidence in the potential and direct access to the atomic interactions allow us to infer the microscopic growth mechanism in this material. While the widespread view is that ta-C grows by "subplantation," we show that the so-called "peening" model is actually the dominant mechanism responsible for the high sp^{3} content. We show that pressure waves lead to bond rearrangement away from the impact site of the incident ion, and high sp^{3} fractions arise from a delicate balance of transitions between three- and fourfold coordinated carbon atoms. These results open the door for a microscopic understanding of carbon nanostructure formation with an unprecedented level of predictive power.
Carbonaceous materials, especially tetrahedral amorphous carbon (ta-C), can form complex functionalized surface structures and are thus promising candidates for applications in biomedical devices and electrochemistry. Functional groups at ta-C surfaces have been widely studied by spectroscopic techniques; however, interpretation of the experimental data is extremely difficult, especially in the case of X-ray photoelectron spectroscopy (XPS) and X-ray absorption spectroscopy (XAS). The assignments of experimental XPS and XAS signals are normally based on references obtained from molecular or crystalline samples, which are simplified approximations for the far more complex amorphous structures. Here, we use extensive density functional theory (DFT) simulations to predict XAS and XPS signatures for carbon-based materials in more realistic environments, building on large data sets of structural models generated by a machine-learning (ML) interatomic potential. The results indicate clear signatures: individual fingerprint XAS spectra and distinctive XPS binding energy distributions, both in terms of center and broadness of the signal, for chemically different groups. The results point out what kind of structural information can and cannot be extracted with X-ray spectroscopy. This study will enable a deeper physicochemical understanding of experimental data and ultimately theory-based identification and quantification of functional groups in carbonaceous materials.
We present an atomistic description of the electronic and optical properties of In0.25Ga0.75N/GaN quantum wells. Our analysis accounts for fluctuations of well width, local alloy composition, strain and built-in field fluctuations as well as Coulomb effects. We find a strong hole and much weaker electron wave function localization in InGaN random alloy quantum wells. The presented calculations show that while the electron states are mainly localized by well-width fluctuations, the holes states are already localized by random alloy fluctuations. These localization effects affect significantly the quantum well optical properties, leading to strong inhomogeneous broadening of the lowest interband transition energy. Our results are compared with experimental literature data.
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