First-principles calculations of 2x2 reconstructions of GaN(0001) surfaces involving N, Al, Ga, In, and as atoms

The ab initio studies presented here employed a pseudopotential-plane-wave method in order to obtain the minimum-energy configurations of various 22 GaN0001 surfaces involving N, Al, Ga, In, and As atoms. Comparison of the various possible reconstructions allows predictions to be made regarding the most energetically favorable configurations. Such comparisons depend on the value of the effective chemical potential of each atomic species, which can be related directly to experimental growth conditions. The most stable structure as a function of chemical potentials is determined. Based on these results we have characterized the effect of N in the adlayer surface and the stability dependence with number of substitutions as a function of the model employed and the possible surfactant character of some of the added atoms. Surface phase diagrams as a function of the chemical potential have been calculated to show the phase transition between the different reconstructions

Machine-Learning Interatomic Potentials Enable First-Principles Multiscale Modeling of Lattice Thermal Conductivity in Graphene/Borophene Heterostructures

One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical information. First-principles approaches such as the density functional theory (DFT) provide the best possible accuracy on electronic properties but they are limited to systems up to a few hundreds, or at most thousands of atoms. On the other hand, classical molecular dynamics (CMD) simulations and finite element method (FEM) are extensively employed to study larger and more realistic systems, but conversely depend on empirical information. Here, we show that machine-learning interatomic potentials (MLIPs) trained over short ab-initio molecular dynamics trajectories enable first-principles multiscale modeling, in which DFT simulations can be hierarchically bridged to efficiently simulate macroscopic structures. As a case study, we analyze the lattice thermal conductivity of coplanar graphene/borophene heterostructures, recently synthesized experimentally (Sci. Adv. 2019; 5: eaax6444), for which no viable classical modeling alternative is presently available. Our MLIP-based approach can efficiently predict the lattice thermal conductivity of graphene and borophene pristine phases, the thermal conductance of complex graphene/borophene interfaces and subsequently enable the study of effective thermal transport along the heterostructures at continuum level. This work highlights that MLIPs can be effectively and conveniently employed to enable first-principles multiscale modeling via hierarchical employment of DFT/CMD/FEM simulations, thus expanding the capability for computational design of novel nanostructures

First-Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine-Learning Interatomic Potentials

Density functional theory calculations are robust tools to explore the mechanical properties of pristine structures at their ground state but become exceedingly expensive for large systems at finite temperatures. Classical molecular dynamics (CMD) simulations offer the possibility to study larger systems at elevated temperatures, but they require accurate interatomic potentials. Herein the authors propose the concept of first-principles multiscale modeling of mechanical properties, where ab initio level of accuracy is hierarchically bridged to explore the mechanical/failure response of macroscopic systems. It is demonstrated that machine-learning interatomic potentials (MLIPs) fitted to ab initio datasets play a pivotal role in achieving this goal. To practically illustrate this novel possibility, the mechanical/failure response of graphene/borophene coplanar heterostructures is examined. It is shown that MLIPs conveniently outperform popular CMD models for graphene and borophene and they can evaluate the mechanical properties of pristine and heterostructure phases at room temperature. Based on the information provided by the MLIP-based CMD, continuum models of heterostructures using the finite element method can be constructed. The study highlights that MLIPs were the missing block for conducting first-principles multiscale modeling, and their employment empowers a straightforward route to bridge ab initio level accuracy and flexibility to explore the mechanical/failure response of nanostructures at continuum scale

Quality Assessment of MORL Algorithms: A Utility-Based Approach

Sequential decision-making problems with multiple objectives occur often in practice. In such settings, the utility of a policy depends on how the user values different trade-offs between the objectives. Such valuations can be expressed by a so-called scalarisation function. However, the exact scalarisation function can be unknown when the agents should learn or plan. Therefore, instead of a single solution, the agents aim to produce a solution set that contains an optimal solution for all possible scalarisations. Because it is often not possible to produce an exact solution set, many algorithms have been proposed that produce approximate solution sets instead. We argue that when comparing these algorithms we should do so on the basis of user utility, and on a wide range of problems. In practice however, comparison of the quality of these algorithms have typically been done with only a few limited benchmarks and metrics that do not directly express the utility for the user. In this paper, we propose two metrics that express either the expected utility, or the maximal utility loss with respect to the optimal solution set. Furthermore, we propose a generalised benchmark in order to compare algorithms more reliably

Electronic, Optical, Mechanical and Li-Ion Storage Properties of Novel Benzotrithiophene-Based Graphdiyne Monolayers Explored by First Principles and Machine Learning

Recently, benzotrithiophene graphdiyne (BTT-GDY), a novel two-dimensional (2D) carbon-based material, was grown via a bottom-up synthesis strategy. Using the BTT-GDY lattice and by replacing the S atoms with N, NH and O, we designed three novel GDY lattices, which we named BTHP-, BTP- and BTF-GDY, respectively. Next, we explored structural, electronic, mechanical, optical, photocatalytic and Li-ion storage properties, as well as carrier mobilities, of novel GDY monolayers. Phonon dispersion relations, mechanical and failure behavior were explored using the machine learning interatomic potentials (MLIPs). The obtained HSE06 results reveal that BTX-GDYs (X = P, F, T) are direct gap semiconductors with band gaps in the range of 2.49–2.65 eV, whereas the BTHP-GDY shows a narrow indirect band gap of 0.06 eV. With appropriate band offsets, good carrier mobilities and a strong capability for the absorption of visible and ultraviolet range of light, BTF- and BTT-GDYs were predicted to be promising candidates for overall photocatalytic water splitting. The BTHP-GDY nanosheet, noticeably, was found to yield an ultrahigh Li-ion storage capacity of over 2400 mAh/g. The obtained findings provide a comprehensive vision of the critical physical properties of the novel BTT-based GDY nanosheets and highlight their potential for applications in nanoelectronics and energy storage and conversion systems

Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

It is well-known that the calculation of thermal conductivity using classical molecular dynamics (MD) simulations strongly depends on the choice of the appropriate interatomic potentials. As proven for the case of graphene, while most of the available interatomic potentials estimate the structural and elastic constants with high accuracy, when employed to predict the lattice thermal conductivity they however lead to a variation of predictions by one order of magnitude. Here we present our results on using machine-learning interatomic potentials (MLIPs) passively fitted to computationally inexpensive ab-initio molecular dynamics trajectories without any tuning or optimizing of hyperparameters. These first-attempt potentials could reproduce the phononic properties of different two-dimensional (2D) materials obtained using density functional theory (DFT) simulations. To illustrate the efficiency of the trained MLIPs, we consider polyaniline CN nanosheets. CN monolayer was selected because the classical MD and different first-principles results contradict each other, resulting in a scientific dilemma. It is shown that the predicted thermal conductivity of 418 ± 20 W mK for CN monolayer by the non-equilibrium MD simulations on the basis of a first-attempt MLIP evidences an improved accuracy when compared with the commonly employed MD models. Moreover, MLIP-based prediction can be considered as a solution to the debated reports in the literature. This study highlights that passively fitted MLIPs can be effectively employed as versatile and efficient tools to obtain accurate estimations of thermal conductivities of complex materials using classical MD simulations. In response to remarkable growth of 2D materials family, the devised modeling methodology could play a fundamental role to predict the thermal conductivity

Exploring Phononic Properties of Two-Dimensional Materials using Machine Learning Interatomic Potentials

Phononic properties are commonly studied by calculating force constants using the density functional theory (DFT) simulations. Although DFT simulations offer accurate estimations of phonon dispersion relations or thermal properties, but for low-symmetry and nanoporous structures the computational cost quickly becomes very demanding. Moreover, the computational setups may yield nonphysical imaginary frequencies in the phonon dispersion curves, impeding the assessment of phononic properties and the dynamical stability of the considered system. Here, we compute phonon dispersion relations and examine the dynamical stability of a large ensemble of novel materials and compositions. We propose a fast and convenient alternative to DFT simulations which derived from machine-learning interatomic potentials passively trained over computationally efficient ab-initio molecular dynamics trajectories. Our results for diverse two-dimensional (2D) nanomaterials confirm that the proposed computational strategy can reproduce fundamental thermal properties in close agreement with those obtained via the DFT approach. The presented method offers a stable, efficient, and convenient solution for the examination of dynamical stability and exploring the phononic properties of low-symmetry and porous 2D materials