skip to main content


Search for: All records

Creators/Authors contains: "Hu, Ming"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract Although first principles based anharmonic lattice dynamics is one of the most common methods to obtain phonon properties, such method is impractical for high-throughput search of target thermal materials. We develop an elemental spatial density neural network force field as a bottom-up approach to accurately predict atomic forces of ~80,000 cubic crystals spanning 63 elements. The primary advantage of our indirect machine learning model is the accessibility of phonon transport physics at the same level as first principles, allowing simultaneous prediction of comprehensive phonon properties from a single model. Training on 3182 first principles data and screening 77,091 unexplored structures, we identify 13,461 dynamically stable cubic structures with ultralow lattice thermal conductivity below 1 Wm −1 K −1 , among which 36 structures are validated by first principles calculations. We propose mean square displacement and bonding-antibonding as two low-cost descriptors to ease the demand of expensive first principles calculations for fast screening ultralow thermal conductivity. Our model also quantitatively reveals the correlation between off-diagonal coherence and diagonal populations and identifies the distinct crossover from particle-like to wave-like heat conduction. Our algorithm is promising for accelerating discovery of novel phononic crystals for emerging applications, such as thermoelectrics, superconductivity, and topological phonons for quantum information technology. 
    more » « less
    Free, publicly-accessible full text available December 1, 2024
  2. Abstract Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-material basis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottleneck with the developed Elemental Spatial Density Neural Network Force Field, namely Elemental-SDNNFF. The effectiveness and precision of our Elemental-SDNNFF approach are demonstrated on 11,866 full, half, and quaternary Heusler structures spanning 55 elements in the periodic table by prediction of complete phonon properties. Self-improvement schemes including active learning and data augmentation techniques provide an abundant 9.4 million atomic data for training. Deep insight into predicted ultralow lattice thermal conductivity (<1 Wm −1  K −1 ) of 774 Heusler structures is gained by p–d orbital hybridization analysis. Additionally, a class of two-band charge-2 Weyl points, referred to as “double Weyl points”, are found in 68% and 87% of 1662 half and 1550 quaternary Heuslers, respectively. 
    more » « less
    Free, publicly-accessible full text available December 1, 2024
  3. Free, publicly-accessible full text available June 1, 2024
  4. High-throughput screening and material informatics have shown a great power in the discovery of novel materials, including batteries, high entropy alloys, and photocatalysts. However, the lattice thermal conductivity ( κ ) oriented high-throughput screening of advanced thermal materials is still limited to the intensive use of first principles calculations, which is inapplicable to fast, robust, and large-scale material screening due to the unbearable computational cost demanding. In this study, 15 machine learning algorithms are utilized for fast and accurate κ prediction from basic physical and chemical properties of materials. The well-trained models successfully capture the inherent correlation between these fundamental material properties and κ for different types of materials. Moreover, deep learning combined with a semi-supervised technique shows the capability of accurately predicting diverse κ values spanning 4 orders of magnitude, especially the power of extrapolative prediction on 3716 new materials. The developed models provide a powerful tool for large-scale advanced thermal functional materials screening with targeted thermal transport properties. 
    more » « less
    Free, publicly-accessible full text available March 14, 2024
  5. 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 in experiments. 
    more » « less
  6. Induced pluripotent stem cells (iPSCs) have enormous potential in producing human tissues endlessly. We previously reported that type V collagen (COL5), a pancreatic extracellular matrix protein, promotes islet development and maturation from iPSCs. In this study, we identified a bioactive peptide domain of COL5, WWASKS, through bioinformatic analysis of decellularized pancreatic ECM (dpECM)-derived collagens. RNA-sequencing suggests that WWASKS induces the formation of pancreatic endocrine progenitors while suppressing the development of other types of organs. The expressions of hypoxic genes were significantly downregulated in the endocrine progenitors formed under peptide stimulation. Furthermore, we unveiled an enhancement of iPSC-derived islets’ (i-islets) glucose sensitivity under peptide stimulation. These i-islets secrete insulin in a glucose responsive manner. They were comprised of α, β, δ, and γ cells and were assembled into a tissue architecture similar to that of human islets. Mechanistically, the peptide is able to activate the canonical Wnt signaling pathway, permitting the translocation of β-catenin from the cytoplasm to the nucleus for pancreatic progenitor development. Collectively, for the first time, we demonstrated that an ECM-derived peptide dictates iPSC fate toward the generation of endocrine progenitors and subsequent islet organoids. 
    more » « less
  7. Full Heusler compounds have long been discovered as exceptional n-type thermoelectric materials. However, no p-type compounds could match the high n-type figure of merit ( ZT ). In this work, based on first-principles transport theory, we predict the unprecedentedly high p-type ZT = 2.2 at 300 K and 5.3 at 800 K in full Heusler CsK 2 Bi and CsK 2 Sb, respectively. By incorporating the higher-order phonon scattering, we find that the high ZT value primarily stems from the ultralow lattice thermal conductivity ( κ L ) of less than 0.2 W mK −1 at room temperature, decreased by 40% compared to the calculation only considering three-phonon scattering. Such ultralow κ L is rooted in the enhanced phonon anharmonicity and scattering channels stemming from the coexistence of antibonding-induced anharmonic rattling of Cs atoms and low-lying optical branches. Moreover, the flat and heavy nature of valence band edges leads to a high Seebeck coefficient and moderate power factor at optimal hole concentration, while the dispersive and light conduction band edges yield much larger electrical conductivity and electronic thermal conductivity ( κ e ), and the predominant role of κ e suppresses the n-type ZT . This study offers a deeper insight into the thermal and electronic transport properties in full Heusler compounds with strong phonon anharmonicity and excellent thermoelectric performance. 
    more » « less
  8. Abstract

    Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and simulations are labor-intensive or costly with success heavily depending on experts’ heuristic knowledge. Here, we propose a deep learning based Physics Guided Crystal Generative Model (PGCGM) for efficient crystal material design with high structural diversity and symmetry. Our model increases the generation validity by more than 700% compared to FTCP, one of the latest structure generators and by more than 45% compared to our previous CubicGAN model. Density Functional Theory (DFT) calculations are used to validate the generated structures with 1869 materials out of 2000 are successfully optimized and deposited into the Carolina Materials Databasewww.carolinamatdb.org, of which 39.6% have negative formation energy and 5.3% have energy-above-hull less than 0.25 eV/atom, indicating their thermodynamic stability and potential synthesizability.

     
    more » « less
  9. Free, publicly-accessible full text available March 8, 2024