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Thermal transport in amorphous lithium‐sulfur (a‐Li_xS) is systematically investigated using molecular dynamics and the contributions from different types of heat carriers are quantitatively evaluated. In general, the thermal conductivity (TC) ofa‐Li_xS changes largely by varying the concentration (x) of Li ions ina‐Li_xS. Interestingly, the TC ofa‐Li_xS shows three distinct regimes of dependence on Li concentration. For low Li concentration (x = 0.4–1.2), the TC grows slowly, followed by a rapid increase in TC for medium Li concentration (x = 1.2–1.6), where the growth rate is three times that of the first regime, and finally, the TC is independent of Li concentration (x = 1.6–2.0). The TC enhancement in the first and second regimes is mainly attributed to propagating and non‐propagating vibrational modes ina‐Li_xS, respectively. In contrast, the stable thermal transport regime is governed by the competition between propagating and non‐propagating phonons. These investigations provide quantitative TC data of various polysulfides for shuttling analysis, and a fundamental understanding of the thermal transport mechanism of complexa‐Li_xS structures, which is beneficial for the rational design of thermal management of Li‐S batteries.

 

Nanosized perovskite ferroelectrics are widely employed in several electromechanical, photonics, and thermoelectric applications. Scaling of ferroelectric materials entails a severe reduction in the lattice (phonon) thermal conductivity, particularly at sub‐100 nm length scales. Such thermal conductivity reduction can be accurately predicted using the information of phonon mean free path (MFP) distribution. The current understanding of phonon MFP distribution in perovskite ferroelectrics is still inconclusive despite the critical thermal management implications. Here, high‐quality single‐crystalline barium titanate (BTO) thin films, a representative perovskite ferroelectric material, are grown at several thicknesses. Using experimental thermal conductivity measurements and first‐principles based modeling (including four‐phonon scattering), the phonon MFP distribution is determined in BTO. The simulation results agree with the measured thickness‐dependent thermal conductivity. The results show that the phonons with sub‐100 nm MFP dominate the thermal transport in BTO, and phonons with MFP exceeding 10 nm contribute ≈35% to the total thermal conductivity, in significant contrast to previously published experimental results. The experimentally validated phonon MFP distribution is consistent with the theoretical predictions of other complex crystals with strong anharmonicity. This work paves the way for thermal management in nanostructured and ferroelectric‐domain‐engineered systems for oxide perovskite‐based functional materials.

 

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-validation 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.

 

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. 

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. 

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. 

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. 

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.