arXiv subjects

Jian Lv

Publications and source records attributed to Jian Lv.

28 records · Page 2Linked to original sources

arXiv2021

JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics

Materials informatics has emerged as a promisingly new paradigm for accelerating materials discovery and design. It exploits the intelligent power of machine learning methods in massive materials data from experiments or simulations to seek for new materials, functionality, principles, etc. Developing specialized facility to generate, collect, manage, learn and mine large-scale materials data is crucial to materials informatics. We herein developed an artificial-intelligence-aided data-driven infrastructure named Jilin Artificial-intelligence aided Materials-design Integrated Package (JAMIP), which is an open-source Python framework to meet the research requirements of computational materials informatics. It is integrated by materials production factory, high-throughput first-principles calculations engine, automatic tasks submission and monitoring progress, data extraction, management and storage system, and artificial intelligence machine learning based data mining functions. We have integrated specific features such as inorganic crystal structure prototype database to facilitate high-throughput calculations and essential modules associated with machine learning studies of functional materials. We demonstrated how our developed code is useful in exploring materials informatics of optoelectronic semiconductors by taking halide perovskites as typical case. By obeying the principles of automation, extensibility, reliability and intelligence, the JAMIP code is a promisingly powerful tool contributing to the fast-growing field of computational materials informatics.

cond-mat.mtrl-sci↗

arXiv2020

Nonlocal Kinetic Energy Density Functionals for Isolated Systems via Local Density Approximation Kernels

Despite a large number of nonlocal kinetic energy density functionals (KEDFs) available for large-scale calculations, most of those nonlocal KEDFs designed for the extended systems cannot be directly applied to isolated systems. In this manuscript, we proposed a generalized scheme to construct nonlocal KEDFs via the local density approximation kernels and construct a family of KEDFs for simulations of isolated systems within orbital-free density functional theory. The performance of KEDFs has been demonstrated by several clusters encompassing Mg, Si and GaAs. The results show that our constructed KEDFs can achieve high numerical accuracy and stability for random clusters, therefore, making orbital-free density functional theory accessible for practical simulations of isolated systems.

cond-mat.mtrl-sci↗

arXiv2019

Metallic liquid H3O in a thin-shell zone inside Uranus and Neptune

The Solar System harbors deep unresolved mysteries despite centuries-long study. A highly intriguing case concerns anomalous non-dipolar and non-axisymmetric magnetic fields of Uranus and Neptune that have long eluded explanation by the prevailing theory. A thin-shell dynamo conjecture captures observed phenomena but leaves unexplained fundamental material basis and underlying mechanism. Here, we report the discovery of trihydrogen oxide (H3O) in metallic liquid state stabilized at extreme pressure and temperature conditions inside these icy planets. Calculated stability pressure field compared to known pressure-radius relation for Uranus and Neptune places metallic liquid H3O in a thin-shell zone near planetary cores. These findings from accurate quantum mechanical calculations rationalize the empirically conjectured thin-shell dynamo model and establish key physical benchmarks that are essential to elucidating the enigmatic magnetic-field anomaly of Uranus and Neptune, resolving a major mystery in planetary science.

physics.comp-ph↗

arXiv2019

Computational Design of Mixed-valence Tin Sulfides as Solar Absorbers

Binary tin sulfides are appealing because of their simple stoichiometry and semiconducting properties and are potentially cost-effective optoelectronic materials. The multivalency of Sn allows yet more intermediate compositions, Sn$_x$S$_y$, whose structures and properties are of interest.Sn$_2$S$_3$ is already under consideration as a mixed-valence semiconductor. Other intermediate compositions have remained elusive. Here we report a comprehensive study of phase stability of the Sn$_x$S$_y$ series compounds, utilizing swarm-intelligence crystal structure search method combined with first-principles energetic calculations. We find that the stability of mixed-valence Sn$_x$S$_y$ compounds with respect to decomposition into pure-valence SnS and SnS$_2$ is in general weaker than the Sn$_x$O$_y$ counterparts, likely due to differences in chemical bonding. Besides identifying the experimentally discovered stable phases of Sn$_2$S$_3$, our calculations indicate that the Sn$_3$S$_4$ phase is another mixed-valence composition which shows marginal stability with respect to decomposition into SnS and SnS$_2$. Other studied compositions may be metastable under ambient conditions, with slightly positive formation enthalpies. We find two structures of Sn$_3$S$_4$ having comparably low energies, both of which feature one-dimensional chain-like fragments obtained by breaking up the edge-connected octahedral layers of SnS$_2$. Both structures indicate lattice phonon stability and one shows quasi-direct band gap with a calculated value of 1.43 eV, ideal for solar absorbers. A further analysis of the composition-structure-property relationship supports the notion that lowdimensional Sn-S motifs and van der Waals interaction may lead to diverse structure types and chemical compositions, having functional properties that are yet to be identified in the Sn$_x$S$_y$ series with mixed valency.

cond-mat.mtrl-sci↗

arXiv2018

Accelerating CALYPSO Structure Prediction by Data-driven Learning of Potential Energy Surface

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.

cond-mat.mtrl-sci↗

arXiv2017

Construction of crystal structure prototype database: methods and applications

Crystal structure prototype data have become a useful source of information for materials discovery in the fields of crystallography, chemistry, physics, and materials science. This work reports the development of a robust and efficient method for assessing the similarity of structures on the basis of their interatomic distances. Using this method, we proposed a simple and unambiguous definition of crystal structure prototype based on hierarchical clustering theory, and constructed the Crystal Structure Prototype Database (CSPD) by filtering the known crystallographic structures in a database. With similar method, a program Structure Prototype Analysis Package (SPAP) was developed to remove similar structures in CALYPSO prediction results and extract predicted low energy structures for a separate theoretical structure database. A series of statistics describing the distribution of crystal structure prototypes in the CSPD was compiled to provide an important insight for structure prediction and high-throughput calculations. Illustrative examples of the application of the proposed database are given, including the generation of initial structures for structure prediction and determination of the prototype structure in databases. These examples demonstrate the CSPD to be a generally applicable and useful tool for materials discovery.

cond-mat.mtrl-sci↗

arXiv2016

Functionality-directed Screening of Pb-free Hybrid Organic-inorganic Perovskites with Desired Intrinsic Photovoltaic Functionalities

The material class of hybrid organic-inorganic perovskites has risen rapidly from a virtually unknown material in photovoltaic applications a short 7 years ago into a ~20% efficient thin-film solar cell material. As promising as this class of materials is, however, there are limitations associated with its poor long-term stability, non-optimal band gap, presence of environmentally-toxic Pb element, etc. We herein apply a functionality-directed theoretical materials selection approach as a filter for initial screening of the compounds that satisfy the desired intrinsic photovoltaic functionalities and might overcome the above limitations. First-principles calculations are employed to systemically study thermodynamic stability and photovoltaic-related properties of hundred of candidate hybrid perovskites. We have identified in this materials selection process fourteen Ge and Sn-based materials with potential superior bulk-material-intrinsic photovoltaic performance. A distinct class of compounds containing NH3COH$^+$ with the organic molecule derived states intriguingly emerging at band-edges is found. Comparison of various candidate materials offers insights on how composition variation and microscopic structural changes affect key photovoltaic relevant properties in this family of materials.

cond-mat.mtrl-sci↗

arXiv2012

Diamondoid Structure of Polymeric Nitrogen at High Pressures

High-pressure polymeric structures of nitrogen have attracted great attention owing to their potential application as high-energy-density materials. We report the density functional structural prediction of the unexpected stabilization of a diamondoid (or N10-cage) structure of polymeric nitrogen at high pressures. The structure adopts a highly symmetric body-centered cubic form with lattice sites occupied by N10 tetracyclic cages, each of which consists of 10 atoms and is covalently bonded with its six next-nearest N10 cages. The prediction of this diamondoid structure rules out the earlier proposed helical tunnel phase and demonstrates the high-order nature of polymeric nitrogen at extreme high pressures. Diamondoid nitrogen is a wide-gap insulator and energetically more favorable than the experimental cubic gauche and previously predicted layered Pba2 phases above 263 GPa, a pressure which is accessible to high pressure experiment.

cond-mat.mtrl-sci↗

arXiv2012

CALYPSO: a method for crystal structure prediction

We have developed a software package CALYPSO (Crystal structure AnaLYsis by Particle Swarm Optimization) to predict the energetically stable/metastable crystal structures of materials at given chemical compositions and external conditions (e.g., pressure). The CALYPSO method is based on several major techniques (e.g. particle-swarm optimization algorithm, symmetry constraints on structural generation, bond characterization matrix on elimination of similar structures, partial random structures per generation on enhancing structural diversity, and penalty function, etc) for global structural minimization from scratch. All of these techniques have been demonstrated to be critical to the prediction of global stable structure. We have implemented these techniques into the CALYPSO code. Testing of the code on many known and unknown systems shows high efficiency and high successful rate of this CALYPSO method [Wang et al., Phys. Rev. B 82 (2010) 094116][1]. In this paper, we focus on descriptions of the implementation of CALYPSO code and why it works.

cond-mat.mtrl-sci↗

arXiv2010

Crystal Structure Prediction via Particle Swarm Optimization

We have developed a powerful method for crystal structure prediction from "scratch" through particle swarm optimization (PSO) algorithm within the evolutionary scheme. PSO technique is dramatically different with the genetic algorithm and has apparently avoided the use of evolution operators (e.g., crossover and mutation). The approach is based on a highly efficient global minimization of free energy surfaces merging total-energy calculations via PSO technique and requires only chemical compositions for a given compound to predict stable or metastable structures at given external conditions (e.g., pressure). A particularly devised geometrical structure factor method which allows the elimination of similar structures during structure evolution was implemented to enhance the structure search efficiency. The application of designed variable unit cell size technique has greatly reduced the computational cost. Moreover, the symmetry constraint imposed in the structure generation enables the realization of diverse structures, leads to significantly reduced search space and optimization variables, and thus fastens the global structural convergence. The PSO algorithm has been successfully applied to the prediction of many known systems (e.g., elemental, binary and ternary compounds) with various chemical bonding environments (e.g., metallic, ionic, and covalent bonding). The remarkable success rate demonstrates the reliability of this methodology and illustrates the great promise of PSO as a major technique on crystal structure determination.

cond-mat.mtrl-sci↗