اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدUnsupervised learning-based structural analysis: Search for a characteristic low-dimensional space by local structures in atomistic simulations
67
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Owing to the advances in computational techniques and the increase in computational power, atomistic simulations of materials can simulate large systems with higher accuracy. Complex phenomena can be observed in such state-of-the-art atomistic simulations. However, it has become increasingly difficult to understand what is actually happening and mechanisms, for example, in molecular dynamics (MD) simulations. We propose an unsupervised machine learning method to analyze the local structure around a target atom. The proposed method, which uses the two-step locality preserving projections (TS-LPP), can find a low-dimensional space wherein the distributions of datapoints for each atom or groups of atoms can be properly captured. We demonstrate that the method is effective for analyzing the MD simulations of crystalline, liquid, and amorphous states and the melt-quench process from the perspective of local structures. The proposed method is demonstrated on a silicon single-component system, a silicon-germanium binary system, and a copper single-component system.
قيم البحث
اقرأ أيضاً
We present a method for performing atomistic spin dynamic simulations. A comprehensive summary of all pertinent details for performing the simulations such as equations of motions, models for including temperature, methods of extracting data and nume
rical schemes for performing the simulations is given. The method can be applied in a first principles mode, where all interatomic exchange is calculated self-consistently, or it can be applied with frozen parameters estimated from experiments or calculated for a fixed spin-configuration. Areas of potential applications to different magnetic questions are also discussed. The method is finally applied to one situation where the macrospin model breaks down; magnetic switching in ultra strong fields.
As impermeable to gas molecules and at the same time transparent to high-energy ions, graphene has been suggested as a window material for separating a high-vacuum ion beam system from targets kept at ambient conditions. However, accumulation of irra
diation-induced damage in the graphene membrane may give rise to its mechanical failure. Using atomistic simulations, we demonstrate that irradiated graphene even with a high vacancy concentration does not show signs of such instability, indicating a considerable robustness of graphene windows. We further show that upper and lower estimates for the irradiation damage in graphene can be set using a simple model.
This study presents a meshless-based local reanalysis (MLR) method. The purpose of this study is to extend reanalysis methods to the Kriging interpolation meshless method due to its high efficiency. In this study, two reanalysis methods: combined app
roximations CA) and indirect factorization updating (IFU) methods are utilized. Considering the computational cost of meshless methods, the reanalysis method improves the efficiency of the full meshless method significantly. Compared with finite element method (FEM)-based reanalysis methods, the main superiority of meshless-based reanalysis method is to break the limitation of mesh connection. The meshless-based reanalysis is much easier to obtain the stiffness matrix even for solving the mesh distortion problems. However, compared with the FEM-based reanalysis method, the critical challenge is to use much more nodes in the influence domain due to high order interpolation. Therefore, a local reanalysis method which only needs to calculate the local stiffness matrix in the influence domain is suggested to improve the efficiency further. Several typical numerical examples are tested and the performance of the suggested method is verified.
By combining classical molecular dynamics simulations and density functional theory total energy calculations, we study the possibility of doping graphene with B/N atoms using low-energy ion irradiation. Our simulations show that the optimum irradiat
ion energy is 50 eV with substitution probabilities of 55% for N and 40% for B. We further estimate probabilities for different defect configurations to appear under B/N ion irradiation. We analyze the processes responsible for defect production and report an effective swift chemical sputtering mechanism for N irradiation at low energies (~125 eV) which leads to production of single vacancies. Our results show that ion irradiation is a promising method for creating hybrid C-B/N structures for future applications in the realm of nanoelectronics.
We devise automated workflows for the calculation of Helmholtz and Gibbs free energies and their temperature and pressure dependence and provide the corresponding computational tools. We employ non-equilibrium thermodynamics for evaluating the free e
nergy of solid and liquid phases at a given temperature and reversible scaling for computing free energies over a wide range of temperatures, including the direct integration of $P$-$T$ coexistence lines. By changing the chemistry and the interatomic potential, alchemical and upscaling free energy calculations are possible. Several examples illustrate the accuracy and efficiency of our implementation.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
