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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 difficultiesmore »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 in experiments.« less
    Free, publicly-accessible full text available December 1, 2023
  2. Abstract Despite the machine learning (ML) methods have been largely used recently, the predicted materials properties usually cannot exceed the range of original training data. We deployed a boundless objective-free exploration approach to combine traditional ML and density functional theory (DFT) in searching extreme material properties. This combination not only improves the efficiency for screening large-scale materials with minimal DFT inquiry, but also yields properties beyond original training range. We use Stein novelty to recommend outliers and then verify using DFT. Validated data are then added into the training dataset for next round iteration. We test the loop of training-recommendation-validationmore »in mechanical property space. By screening 85,707 crystal structures, we identify 21 ultrahigh hardness structures and 11 negative Poisson’s ratio structures. The algorithm is very promising for future materials discovery that can push materials properties to the limit with minimal DFT calculations on only ~1% of the structures in the screening pool.« less
    Free, publicly-accessible full text available December 1, 2023
  3. Abstract Optical manipulation of coherent phonon frequency in two-dimensional (2D) materials could advance the development of ultrafast phononics in atomic-thin platforms. However, conventional approaches for such control are limited to doping, strain, structural or thermal engineering. Here, we report the experimental observation of strong laser-polarization control of coherent phonon frequency through time-resolved pump-probe spectroscopic study of van der Waals (vdW) materials Fe 3 GeTe 2 . When the polarization of the pumping laser with tilted incidence is swept between in-plane and out-of-plane orientations, the frequencies of excited phonons can be monotonically tuned by as large as 3% (~100 GHz). Our first-principlesmore »calculations suggest the strong planar and vertical inter-atomic interaction asymmetry in layered materials accounts for the observed polarization-dependent phonon frequencies, as in-plane/out-of-plane polarization modifies the restoring force of the lattice vibration differently. Our work provides insightful understanding of the coherent phonon dynamics in layered vdW materials and opens up new avenues to optically manipulating coherent phonons.« less
    Free, publicly-accessible full text available December 1, 2023
  4. Free, publicly-accessible full text available July 21, 2023
  5. 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
    Free, publicly-accessible full text available December 14, 2022
  6. Free, publicly-accessible full text available November 1, 2022
  7. Tissue microenvironments are rich in signaling molecules. However, factors in the tissue matrix that can serve as tissue-specific cues for engineering pancreatic tissues have not been thoroughly identified. In this study, we performed a comprehensive proteomic analysis of porcine decellularized pancreatic extracellular matrix (dpECM). By profiling dpECM collected from subjects of different ages and genders, we showed that the detergent-free decellularization method developed in this study permits the preservation of approximately 62.4% more proteins than a detergent-based method. In addition, we demonstrated that dpECM prepared from young pigs contained approximately 68.5% more extracellular matrix proteins than those prepared from adultmore »pigs. Furthermore, we categorized dpECM proteins by biological process, molecular function, and cellular component through gene ontology analysis. Our study results also suggested that the protein composition of dpECM is significantly different between male and female animals while a KEGG enrichment pathway analysis revealed that dpECM protein profiling varies significantly depending on age. This study provides the proteome of pancreatic decellularized ECM in different animal ages and genders, which will help identify the bioactive molecules that are pivotal in creating tissue-specific cues for engineering tissues in vitro.« less
    Free, publicly-accessible full text available November 1, 2022
  8. Abstract The lattice thermal conductivity ( κ L ) of the monolayers of partial group-VA elements and binary compounds are systemically investigated by the first-principles calculations and phonon Boltzmann transport equation (PBTE), including aW-antimonene, α -arsenene, black phosphorus, α -SbAs, α -SbP and α -AsP. The κ L values decrease with the increasing of atomic mass for these materials with similar geometry and valence structures. It is ascribed to phonon branches softening, low phonon group velocity, and large Grüneisen parameters. Due to the neutralization of phonon group velocity and phonon lifetime, κ L of binary compounds is between their correspondingmore »elements. As the atomic radius and mass increase, the bond strength and the phonon group velocity decreases. Furthermore, the dimensionless parameter γ 2 / A , which comes from the Slack equation and only has the dependence of Grüneisen parameter, grows up with the atomic mass rising, which indicates that a larger anharmonicity is present in the heavier V-V monolayers. For SbAs and SbP compounds, the thermal conductivity anisotropy mainly results from the anisotropy of elastic coefficients along armchair and zigzag directions. Our results highlight the impact of atomic arrangement on the thermal conductivity of group VA binary compounds. This work paves a way to modulate the thermal conductivity of 2D VA elements by incorporation atoms with suitable mass and may guide to improve thermoelectrical performance via the alloying method.« less