skip to main content

This content will become publicly available on December 1, 2023

Title: Large scale dataset of real space electronic charge density of cubic inorganic materials from density functional theory (DFT) calculations
Abstract Driven by the big data science, material informatics has attracted enormous research interests recently along with many recognized achievements. To acquire knowledge of materials by previous experience, both feature descriptors and databases are essential for training machine learning (ML) models with high accuracy. In this regard, the electronic charge density ρ ( r ), which in principle determines the properties of materials at their ground state, can be considered as one of the most appropriate descriptors. However, the systematic electronic charge density ρ ( r ) database of inorganic materials is still in its infancy due to the difficulties in collecting raw data in experiment and the expensive first-principles based computational cost in theory. Herein, a real space electronic charge density ρ ( r ) database of 17,418 cubic inorganic materials is constructed by performing high-throughput density functional theory calculations. The displayed ρ ( r ) patterns show good agreements with those reported in previous studies, which validates our computations. Further statistical analysis reveals that it possesses abundant and diverse data, which could accelerate ρ ( r ) related machine learning studies. Moreover, the electronic charge density database will also assists chemical bonding identifications and promotes new crystal discovery more » in experiments. « less
Authors:
; ; ; ;
Award ID(s):
1655740
Publication Date:
NSF-PAR ID:
10326205
Journal Name:
Scientific Data
Volume:
9
Issue:
1
ISSN:
2052-4463
Sponsoring Org:
National Science Foundation
More Like this
  1. Discovering new materials with desired properties has been a dominant and crucial topic of interest in the field of materials science in the past few decades. In this work, novel carbon allotropes and ternary B–C–N structures were generated using the state-of-the-art RG 2 code. All structures were fully optimized using density functional theory with first-principles calculations. Several hundred carbon allotropes and ternary B–C–N structures were identified to be superhard materials. The thermodynamic stability of some randomly selected superhard materials was confirmed by evaluating the full phonon dispersions in the Brillouin zone. The new carbon allotropes and ternary B–C–N structures possessmore »a wide range of mechanical properties generally and Vickers hardness specifically. Through 2D Pearson's correlation map, we first reproduced the well-accepted explanation and relationship of the Vickers hardness of the generated structures with other mechanical properties such as shear modulus, bulk modulus, Pugh's ratio, universal anisotropy, and Poisson's ratio. We then propose two fundamentally new descriptors from the electronic level, namely local potential and electron localization function averaged over a unit cell, both of which exhibit a strong correlation with Vickers hardness. More importantly, these descriptors are easy to access from first-principles calculations (at least two orders of magnitude faster than the traditional calculation of elastic constants), and thus can serve as a fast and accurate approach for screening superhard materials. We also combined these new descriptors with known composition and structural descriptors in the machine learning training process. The new descriptors significantly enhance the performance of the trained machine learning model in predicting the Vickers hardness of unknown materials, which provides strong evidence for local potential and electron localization function to be considered in future high-throughput computation. This work unravels more fundamental but previously unexplored knowledge about superhard materials and the newly proposed electronic level descriptors are expected to accelerate the discovery of new superhard materials.« less
  2. UV absorption is widely used for characterizing proteins structures. The mapping of UV spectra to atomic structure of proteins relies on expensive theoretical simulations, circumventing the heavy computational cost which involves repeated quantum-mechanical simulations of excited-state properties of many fluctuating protein geometries, which has been a long-time challenge. Here we show that a neural network machine-learning technique can predict electronic absorption spectra of N -methylacetamide (NMA), which is a widely used model system for the peptide bond. Using ground-state geometric parameters and charge information as descriptors, we employed a neural network to predict transition energies, ground-state, and transition dipole momentsmore »of many molecular-dynamics conformations at different temperatures, in agreement with time-dependent density-functional theory calculations. The neural network simulations are nearly 3,000× faster than comparable quantum calculations. Machine learning should provide a cost-effective tool for simulating optical properties of proteins.« less
  3. The defining characteristic of hole-doped cuprates is d -wave high temperature superconductivity. However, intense theoretical interest is now focused on whether a pair density wave state (PDW) could coexist with cuprate superconductivity [D. F. Agterberg et al., Annu. Rev. Condens. Matter Phys. 11, 231 (2020)]. Here, we use a strong-coupling mean-field theory of cuprates, to model the atomic-scale electronic structure of an eight-unit-cell periodic, d -symmetry form factor, pair density wave (PDW) state coexisting with d -wave superconductivity (DSC). From this PDW + DSC model, the atomically resolved density of Bogoliubov quasiparticle states N r , E is predicted atmore »the terminal BiO surface of Bi 2 Sr 2 CaCu 2 O 8 and compared with high-precision electronic visualization experiments using spectroscopic imaging scanning tunneling microscopy (STM). The PDW + DSC model predictions include the intraunit-cell structure and periodic modulations of N r , E , the modulations of the coherence peak energy Δ p r , and the characteristics of Bogoliubov quasiparticle interference in scattering-wavevector space q - space . Consistency between all these predictions and the corresponding experiments indicates that lightly hole-doped Bi 2 Sr 2 CaCu 2 O 8 does contain a PDW + DSC state. Moreover, in the model the PDW + DSC state becomes unstable to a pure DSC state at a critical hole density p *, with empirically equivalent phenomena occurring in the experiments. All these results are consistent with a picture in which the cuprate translational symmetry-breaking state is a PDW, the observed charge modulations are its consequence, the antinodal pseudogap is that of the PDW state, and the cuprate critical point at p * ≈ 19% occurs due to disappearance of this PDW.« less
  4. All-solid-state batteries (ASSBs) have garnered increasing attention due to the enhanced safety, featuring nonflammable solid electrolytes as well as the potential to achieve high energy density. 1 The advancement of the ASSBs is expected to provide, arguably, the most straightforward path towards practical, high-energy, and rechargeable batteries based on metallic anodes. 1 However, the sluggish ion transmission at the cathode-electrolyte (solid/solid) interface would result in the high resistant at the contact and limit the practical implementation of these all solid-state materials in real world batteries. 2 Several methods were suggested to enhance the kinetic condition of the ion migration betweenmore »the cathode and the solid electrolyte (SE). 3 A composite strategy that mixes active materials and SEs for the cathode is a general way to decrease the ion transmission barrier at the cathode-electrolyte interface. 3 The active material concentration in the cathode is reduced as much as the SE portion increases by which the energy density of the ASSB is restricted. In addition, the mixing approach generally accompanies lattice mismatches between the cathode active materials and the SE, thus providing only limited improvements, which is imputed by random contacts between the cathode active materials and the SE during the mixing process. Implementing high-pressure for the electrode and electrolyte of ASSB in the assembling process has been verified is a but effective way to boost the ion transmission ability between the cathode active materials and the SE by decreasing the grain boundary impedance. Whereas the short-circuit of the battery would be induced by the mechanical deformation of the electrolyte under high pressure. 4 Herein, we demonstrate a novel way to address the ion transmission problem at the cathode-electrolyte interface in ASSBs. Starting from the cathode configuration, the finite element method (FEM) was employed to evaluate the current concentration and the distribution of the space charge layer at the cathode-electrolyte interface. Hierarchical three-dimensional (HTD) structures are found to have a higher Li + transfer number (t Li+ ), fewer free anions, and the weaker space-charge layer at the cathode-electrolyte interface in the resulting FEM simulation. To take advantage of the HTD structure, stereolithography is adopted as a manufacturing technique and single-crystalline Ni-rich (SCN) materials are selected as the active materials. Next, the manufactured HTD cathode is sintered at 600 °C in an N 2 atmosphere for the carbonization of the resin, which induces sufficient electronic conductivity for the cathode. Then, the gel-like Li 1.4 Al 0.4 Ti 1.6 (PO 4 ) 3 (LATP) precursor is synthesized and filled into the voids of the HTD structure cathode sufficiently. And the filled HTD structure cathodes are sintered at 900 °C to achieve the crystallization of the LATP gel. Scanning transmission electron microscopy (STEM) is used to unveil the morphology of the cathode-electrolyte interface between the sintered HTD cathode and the in-situ generated electrolyte (LATP). A transient phase has been found generated at the interface and matched with both lattices of the SCN and the SE, accelerating the transmission of the Li-ions, which is further verified by density functional theory calculations. In addition, Electron Energy Loss Spectroscopy demonstrates the preserved interface between HTD cathode and SEs. Atomic force microscopy is employed to measure the potential image of the cross-sectional interface by the peak force tapping mode. The average potential of modified samples is lower than the sample that mix SCN and SEs simply in the 2D planar structure, which confirms a weakened space charge layer by the enhanced contact capability as well as the ion transmission ability. To see if the demonstrated method is universally applicable, LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NCM811) is selected as the cathode active material and manufactured in the same way as the SCN. The HTD cathode based on NCM811 exhibits higher electrochemical performance compared with the reference sample based on the 2D planar mixing-type cathode. We believe such a demonstrated universal strategy provides a new guideline to engineer the cathode/electrolyte interface by revolutionizing electrode structures that can be applicable to all-solid-state batteries. Figure 1. Schematic of comparing of traditional 2D planar cathode and HTD cathode in ASSB Tikekar, M. D. , et al. , Nature Energy (2016) 1 (9), 16114 Banerjee, A. , et al. , Chem Rev (2020) 120 (14), 6878 Chen, R. , et al. , Chem Rev (2020) 120 (14), 6820 Cheng, X. , et al. , Advanced Energy Materials (2018) 8 (7) Figure 1« less
  5. Hierarchically porous electrodes made of electrochemically active materials and conductive additives may display synergistic effects originating from the interactions between the constituent phases, and this approach has been adopted for optimizing the performances of many electrode materials. Here we report our findings in design, fabrication, and characterization of a hierarchically porous hybrid electrode composed of α-NiS nanorods decorated on reduced graphene oxide (rGO) (denoted as R-NiS/rGO), derived from water-refluxed metal–organic frameworks/rGO (Ni-MOF-74/rGO) templates. Microanalyses reveal that the as-synthesized α-NiS nanorods have abundant (101) and (110) surfaces on the edges, which exhibit a strong affinity for OH − in KOH electrolyte,more »as confirmed by density functional theory-based calculations. The results suggest that the MOF-derived α-NiS nanorods with highly exposed active surfaces are favorable for fast redox reactions in a basic electrolyte. Besides, the presence of rGO in the hybrid electrode greatly enhances the electronic conductivity, providing efficient current collection for fast energy storage. Indeed, when tested in a supercapacitor with a three-electrode configuration in 2 M KOH electrolyte, the R-NiS/rGO hybrid electrode exhibits a capacity of 744 C g −1 at 1 A g −1 and 600 C g −1 at 50 A g −1 , indicating remarkable rate performance, while maintaining more than 89% of the initial capacity after 20 000 cycles. Moreover, when coupled with a nitrogen-doped graphene aerogel (C/NG-A) negative electrode, the hybrid supercapacitor (R-NiS/rGO/electrolyte/C/NG-A) achieved an ultra-high energy density of 93 W h kg −1 at a power density of 962 W kg −1 , while still retaining an energy density of 54 W h kg −1 at an elevated working power of 46 034 W kg −1 .« less