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  1. 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 discoverymore »in experiments.« less
    Free, publicly-accessible full text available December 1, 2023
  2. Free, publicly-accessible full text available September 28, 2023
  3. The success of graphene created a new era in materials science, especially for two-dimensional (2D) materials. 2D single-crystal carbon nitride (C 3 N) is the first and only crystalline, hole-free, single-layer carbon nitride and its controlled large-scale synthesis has recently attracted tremendous interest in thermal transport. Here, we performed a comparative study of thermal transport between monolayer C 3 N and the parent graphene, and focused on the effect of temperature and strain on the thermal conductivity ( κ ) of C 3 N, by solving the phonon Boltzmann transport equation (BTE) based on first-principles calculations. The κ of C 3 N shows an anomalous temperature dependence, and the κ of C 3 N at high temperatures is larger than the expected value following the common trend of κ ∼ 1/ T . Moreover, the κ of C 3 N is found to be increased by applying a bilateral tensile strain, despite its similar planar honeycomb structure to graphene. The underlying mechanism is revealed by providing direct evidence for the interaction between lone-pair N-s electrons and bonding electrons from C atoms in C 3 N based on the analysis of orbital-projected electronic structures and electron localization function (ELF). Our researchmore »not only conduct a comprehensive study on the thermal transport in graphene-like C 3 N, but also reveal the physical origin of its anomalous properties, which would have significant implications on the future studies of nanoscale thermal transport.« less
    Free, publicly-accessible full text available August 25, 2023
  4. High-throughput computational screening of materials with targeted thermal conductivity ( κ ) plays an important role in promoting the advancement of material design and enormous applications. The Slack model has been widely applied for the fast evaluation of κ with minimal time and resources, showing the potential capability of high-throughput screening of κ . However, after examining the Slack model on a large set of 353 materials, a huge discrepancy is found between the predicted κ and the correspondingly measured κ in experiments for some materials in addition to the generally overestimated κ by the Slack model. Thus, it is necessary to optimize the Slack model for efficiently and accurately evaluating κ . In this study, based on the high-throughput comparison of the κ predicted by the Slack model using elastic properties and those measured in experiments, an optimized Slack model is proposed. As a result, the κ predicted by the optimized Slack model agrees reasonably with the κ measured in experiments, which is much better than the previous prediction. The optimized Slack model proposed in this study can be used for further high-throughput computational evaluation of κ , which would be helpful for finding materials of ultrahigh or ultralowmore »κ with broad applications.« less
    Free, publicly-accessible full text available August 31, 2023
  5. Free, publicly-accessible full text available July 21, 2023
  6. Free, publicly-accessible full text available October 1, 2022
  7. Two-dimensional (2D) materials have emerged as promising functional materials with many applications such as semiconductors and photovoltaics because of their unique optoelectronic properties. Although several thousand 2D materials have been screened in existing materials databases, discovering new 2D materials remains challenging. Herein, we propose a deep learning generative model for composition generation combined with a random forest-based 2D materials classifier to discover new hypothetical 2D materials. Furthermore, a template-based element substitution structure prediction approach is developed to predict the crystal structures of a subset of the newly predicted hypothetical formulas, which allows us to confirm their structure stability using DFT calculations. So far, we have discovered 267 489 new potential 2D materials compositions, where 1485 probability scores are more then 0.95. Among them, we have predicted 101 crystal structures and confirmed 92 2D/layered materials by DFT formation energy calculation. Our results show that generative machine learning models provide an effective way to explore the vast chemical design space for new 2D materials discovery.