Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAccelerating CALYPSO Structure Prediction by Data-driven Learning of Potential Energy Surface
297
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Ab initio structure prediction methods have been nowadays widely used as powerful tools for structure searches and material discovery. However, they are generally restricted to small systems owing to the heavy computational cost of underlying density functional theory (DFT) calculations. In this work, by combining state-of-art machine learning (ML) potential with our in-house developed CALYPSO structure prediction method, we developed two acceleration schemes for structure prediction toward large systems, in which ML potential is pre-constructed to fully replace DFT calculations or trained in an on-the-fly manner from scratch during the structure searches. The developed schemes have been applied to medium- and large-sized boron clusters, which are challenging cases for both construction of ML potentials and extensive structure searches. Experimental structures of B36 and B40 clusters can be readily reproduced, and the putative global minimum structure for B84 cluster is proposed, where substantially less computational cost by several orders of magnitude is evident if compared with full DFT-based structure searches. Our results demonstrate a viable route for structure prediction toward large systems via the combination of state-of-art structure prediction methods and ML techniques.
rate research
Read More
We present a new version of the Ogre open source Python package with the capability to perform structure prediction of epitaxial inorganic interfaces by lattice and surface matching. In the lattice matching step a scan over combinations of substrate and film Miller indices is performed to identify the domain-matched interfaces with the lowest mismatch. Subsequently, surface matching is conducted by Bayesian optimization to find the optimal interfacial distance and in-plane registry between the substrate and film. For the objective function, a geometric score function is proposed, based on the overlap and empty space between atomic spheres at the interface. The score function reproduces the results of density functional theory (DFT) at a fraction of the computational cost. The optimized interfaces are pre-ranked using a score function based on the similarity of the atomic environment at the interface to the bulk environment. Final ranking of the top candidate structures is performed with DFT. Ogre streamlines DFT calculations of interface energies and electronic properties by automating the construction of interface models. The application of Ogre is demonstrated for two interfaces of interest for quantum computing and spintronics, Al/InAs and Fe/InSb.
The adsorption and dissociation of O$_{2}$ molecules at the Be(0001) surface is studied by using density-functional theory within the generalized gradient approximation and a supercell approach. The physi- and chemisorbed molecular precursor states are identified to be along the parallel and vertical channels, respectively. It is shown that the HH-Z (see the text for definition) channel is the most stable channel for the molecular chemisorption of O$_{2}$. The electronic and magnetic properties of this most stable chemisorbed molecular state are studied, which shows that the electrons transfer forth and back between the spin-resolved antibonding $pi^{ast}$ molecular orbitals and the surface Be $sp$ states. A distinct covalent weight in the molecule-metal bond is also shown. The dissociation of O$_{2}$ is determined by calculating the adiabatic potential energy surfaces, wherein the T-Y channel is found to be the most stable and favorable for the dissociative adsorption of O$_{2}$. Remarkably, we predict that unlike the other simple $sp$ metal surfaces such as Al(111) and Mg(0001), the textit{adiabatic} dissociation process of O$_{2}$ at Be(0001) is an activated type with a sizeable energy barrier.
We show that the work function of exfoliated single layer graphene can be modified by irradiation with swift (E_{kin}=92 MeV) heavy ions under glancing angles of incidence. Upon ion impact individual surface tracks are created in graphene on SiC. Due to the very localized energy deposition characteristic for ions in this energy range, the surface area which is structurally altered is limited to ~ 0.01 mum^2 per track. Kelvin probe force microscopy reveals that those surface tracks consist of electronically modified material and that a few tracks suffice to shift the surface potential of the whole single layer flake by ~ 400 meV. Thus, the irradiation turns the initially n-doped graphene into p-doped graphene with a hole density of 8.5 x 10^{12} holes/cm^2. This doping effect persists even after heating the irradiated samples to 500{deg}C. Therefore, this charge transfer is not due to adsorbates but must instead be attributed to implanted atoms. The method presented here opens up a new way to efficiently manipulate the charge carrier concentration of graphene.
The energy dispersive X-ray fluorescence (EDXRF) is used to determine the chemical composition of celadon body and glaze in Longquan kiln (at Dayao County) and Jingdezhen kiln. Forty typical shards in four cultural eras were selected to investigate the raw materials and firing technology. Random forests, a relatively new statistical technique, has been adopted to identify chemical elements that are strongest explanatory variables to classify samples into defferent cultural eras and kilns. The results indicated that the contents of Na2O, Fe2O3, TiO2, SiO2 and CaO vary in celadon bodies from Longquan and Jingdezhen, which implies that local clay was used to manufacture celadon bodies in Jingdezhen kiln. By comparing the chemical composition in glaze, we find that the chemical elements and firing technology of Jingdezhen kiln are very similar to those in Longquan kiln, especially for Ming dynasty. This study reveals the inheritance between Jingdezhen kiln and Longquan kiln, and explains the differences between those two kilns.
Crystal structure prediction is one of the major unsolved problems in materials science. Traditionally, this problem is formulated as a global optimization problem for which global search algorithms are combined with first principle free energy calculations to predict the ground-state crystal structure given only a material composition or a chemical system. These ab initio algorithms usually cannot exploit a large amount of implicit physicochemical rules or geometric constraints (deep knowledge) of atom configurations embodied in a large number of known crystal structures. Inspired by the deep learning enabled breakthrough in protein structure prediction, herein we propose AlphaCrystal, a crystal structure prediction algorithm that combines a deep residual neural network model that learns deep knowledge to guide predicting the atomic contact map of a target crystal material followed by reconstructing its 3D crystal structure using genetic algorithms. Based on the experiments of a selected set of benchmark crystal materials, we show that our AlphaCrystal algorithm can predict structures close to the ground truth structures. It can also speed up the crystal structure prediction process by predicting and exploiting the predicted contact map so that it has the potential to handle relatively large systems. We believe that our deep learning based ab initio crystal structure prediction method that learns from existing material structures can be used to scale up current crystal structure prediction practice. To our knowledge, AlphaCrystal is the first neural network based algorithm for crystal structure contact map prediction and the first method for directly reconstructing crystal structures from materials composition, which can be further optimized by DFT calculations.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
