相关论文: A study of adatom ripening on an Al (111) surface …

Atom-by-atom assembly of functional materials and devices is perceived as one of the ultimate targets of nanoscience and nanotechnology. While traditionally implemented via scanning probe microscopy techniques, recently it has been shown…

Conventional dynamic atomic force microscopy (AFM) can be extended to bimodal and multimodal AFM in which the cantilever is simultaneously excited at two ore more resonance frequencies. Such excitation schemes result in one additional…

Using density-functional theory we investigate several properties of Al(111), Al(100), Al(110), and stepped Al(111) surfaces. We report results of formation energies of surfaces, steps, adatoms, and vacancies. For the adsorption and…

Recent advances in (scanning) transmission electron microscopy have enabled routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for…

Manipulation of metal nanoparticles using atomic force microscope is a promising new technique for probing tribological properties at the nanoscale. In spite of some advancements in experimental investigations, there is no unambiguous…

We investigated the complete thermodynamic cycle of aluminium nanoparticles through classical molecular dynamics simulations, spanning a wide size range from 200 atoms to 11000 atoms. The aluminium-aluminium interactions are modelled using…

Quantum mechanics based ab-initio molecular dynamics (MD) simulation schemes offer an accurate and direct means to monitor the time-evolution of materials. Nevertheless, the expensive and repetitive energy and force computations required in…

Reinforcement learning is concerned with identifying reward-maximizing behaviour policies in environments that are initially unknown. State-of-the-art reinforcement learning approaches, such as deep Q-networks, are model-free and learn to…

First principles based exploration of chemical space deepens our understanding of chemistry, and might help with the design of new materials or experiments. Due to the computational cost of quantum chemistry methods and the immens number of…

Atomic-scale phase-field modeling formulates the probability densities of atomic vibrations as Gaussian distributions and derives a free energy functional using variational Gaussian theory and interatomic potentials. This framework permits…

Reactive chemistry of molecular hydrogen at surfaces, notably dissociative sticking and hydrogen evolution, plays a crucial role in energy storage and fuel cells. Theoretical studies can help to decipher underlying mechanisms and reaction…

Gaussian process (GP) emulator has been used as a surrogate model for predicting force field and molecular potential, to overcome the computational bottleneck of molecular dynamics simulation. Integrating both atomic force and energy in…

A new combination of first principle molecular dynamics (MD) simulations with a rate equation model presented in the preceding paper (paper I) is applied to analyze in detail the scattering of argon atoms from a platinum (111) surface. The…

Melting behaviors of aluminum (111) perfect/nonperfect surfaces, characterized by structure ordering parameter, have been investigated by classical molecular dynamics simulation with embedded atom method potential. Al (111) perfect surface…

Plasmonic nanoparticles generate strongly localized and enhanced light field through localized surface plasmon resonance, thereby playing a central role in plasmonics and nanophotonics. Because the optical properties of plasmonic…

Atomic layer deposition (ALD) is a promising technique to functionalize particle surfaces for energy applications including energy storage, catalysis, and decarbonization. In this work, we present a set of models of ALD particle coating to…

Machine-learned interatomic potentials are revolutionising atomistic materials simulations by providing accurate and scalable predictions within the scope covered by the training data. However, generation of an accurate and robust training…

Machine learning which is a sub-domain of an Artificial Intelligence which is finding various applications in manufacturing and material science sectors. In the present study, Deep Generative Modeling which a type of unsupervised machine…

We propose a new method to investigate interactions involved in atomic force microscopy (AFM). It is a dynamical method relying on the growth of oscillations via parametric resonance. With this method the second and third derivatives of the…

Using atomic force microscopy, we have studied the surface structures of high quality molecular beam epitaxy grown (Ga,Mn)As compound. Several samples with different thickness and Mn concentration, as well as a few (Ga,Mn)(As,P) samples…