“Phonon" and electron transport in glass-crystal dual materials

Disorder has been a long-standing driver across all sectors of materials engineering, ranging from energy and transportation, to electronics and communications, and to biomedicine and environment. Disorder engineering has focused primarily on tuning its spatial configuration and distribution to establish desirable structure-property relations. However, while band theory and phenomenological random-walk models are available in the crystalline and amorphous limits respectively, the picture for energy and charge transport in hybrid ordered-disordered materials is still incomplete. This is especially true when structural disorder possesses long-range correlation and dynamic nature. These subjects are the concern of this work. The goal is to numerically understand how different types of disorder can modify phonon and electron transport properties. I first establish how uncorrelated structural disorder affects vibrational energy transport in low-dimensional disordered materials. The recently synthesized amorphous graphene and glassy diamond nanothreads are studied. Modal localization analysis, molecular dynamics simulations, and a generalized analytical model together demonstrate that the thermal properties of these materials exhibit both similarities and differences from disordered 3D materials. Similar to 3D, the low-dimensional systems exhibit both propagating and diffusive vibrational modes. Different from 3D, however, diffusonic contribution to thermal transport in these low-dimensional systems is shown to be negligible, which results from the intrinsically different nature of random walks in lower dimensions. Despite the lack of diffusons, the suppression of thermal conductivity due to disorder in low-dimensional systems is shown to be mild. The mild suppression originates from the presence of low-frequency vibrational modes that maintain well-defined polarizations and help preserve the thermal conductivity in the presence of disorder. This study brings the domains of low-dimensional materials and disordered materials together, and establishes appropriate theoretical approaches to characterize the vibrational energy transport at the atomic scale when disorder is present. The second part deals with correlated but static disorder. The model system will be a hybrid ordered/disordered nanocomposite that consists of a crystalline silicon membrane decorated by regularly patterned disordered regions. Combining molecular dynamics and the Boltzmann theory, I predict a thermoelectric figure of merit ZT ≈ 0.5 at room temperature. To facilitate the Boltzmann theory, I have derived an analytical model for electron scattering with cylindrical defective regions based on partial-wave analysis. Furthermore, I find glass-crystal duality for the vibrational transport in these hybrid systems. Lattice dynamics reveals substantial hybridization between the localized and delocalized modes, which induces avoided crossings and harmonic broadening in the dispersion. Allen/Feldman theory shows that the hybridization and avoided crossings are the dominant mechanisms of the reduction. Anharmonic scattering is also enhanced in the patterned nanocomposites, further contributing to the reduction. These findings indicate that “patterned disorder” can be a viable strategy to tailor vibrational transport, and ion beam irradiation could be a promising fabrication strategy. In the third part, I focus on the disorder that is both correlated and dynamic. Two lead iodide perovskites, XPbI3 (X=Cs, methylammonium), are the representative materials. In these perovskites, sublattice symmetrybreaking and dynamically correlated disorder affect substantially their vibrational and thermal properties. In contrast to the conventional phononic theory, analysis of spectral energy density reveals that thermal carriers exhibit more propagonic and diffusonic characteristics. Strong anharmoncity in these perovskites, two orders higher than silicon, is observed both on the inorganic framework and surprisingly for the interactions between PbI6 framework modes and localized A-site modes. Based on first-principles calculations, I ascribe the former to long-range interactions arising from resonant bonding, while the latter to A-site rattling in CsPbI3, and polar rotor scattering instead in MAPbI3. I also observe “waterfall-like” dispersions, which I show to be an emergent phenomenon due to dynamical averaging of different dispersions that belong to energetically equivalent disordered phases. This work would be of interest to the design and functionalization of a broad family of hybrid materials, including metal-organic frameworks, molecular crystals and hierarchically organized materials

Multimodal machine learning for materials science: composition-structure bimodal learning for experimentally measured properties

The widespread application of multimodal machine learning models like GPT-4 has revolutionized various research fields including computer vision and natural language processing. However, its implementation in materials informatics remains underexplored, despite the presence of materials data across diverse modalities, such as composition and structure. The effectiveness of machine learning models trained on large calculated datasets depends on the accuracy of calculations, while experimental datasets often have limited data availability and incomplete information. This paper introduces a novel approach to multimodal machine learning in materials science via composition-structure bimodal learning. The proposed COmposition-Structure Bimodal Network (COSNet) is designed to enhance learning and predictions of experimentally measured materials properties that have incomplete structure information. Bimodal learning significantly reduces prediction errors across distinct materials properties including Li conductivity in solid electrolyte, band gap, refractive index, dielectric constant, energy, and magnetic moment, surpassing composition-only learning methods. Furthermore, we identified that data augmentation based on modal availability plays a pivotal role in the success of bimodal learning

Charting lattice thermal conductivity for inorganic crystals and discovering rare earth chalcogenides for thermoelectrics

Thermoelectric power generation represents a promising approach to utilize waste heat. The most effective thermoelectric materials exhibit low thermal conductivity κ. However, less than 5% out of about 105 synthesized inorganic materials are documented with their κ values, while for the remaining 95% κ values are missing and challenging to predict. In this work, by combining graph neural networks and random forest approaches, we predict the thermal conductivity of all known inorganic materials in the Inorganic Crystal Structure Database, and chart the structural chemistry of κ into extended van-Arkel triangles. Together with the newly developed κ map and our theoretical tool, we identify rare-earth chalcogenides as promising candidates, of which we measured ZT exceeding 1.0. We note that the κ chart can be further explored, and our computational and analytical tools are applicable generally for materials informatics

Predicting charge density distribution of materials using a local-environment-based graph convolutional network

Electron charge density distribution of materials is one of the key quantities in computational materials science as theoretically it determines the ground state energy and practically it is used in many materials analyses. However, the scaling of density functional theory calculations with number of atoms limits the usage of charge-density-based calculations and analyses. Here we introduce a machine learning scheme with local-environment-based graphs and graph convolutional neural networks to predict charge density on grid-points from crystal structure. We show the accuracy of this scheme through a comparison of predicted charge densities as well as properties derived from the charge density, and the scaling is O(N). More importantly, the transferability is shown to be high with respect to different compositions and structures, which results from the explicit encoding of geometry

High-Pressure-Sintering-Induced Microstructural Engineering for an Ultimate Phonon Scattering of Thermoelectric Half-Heusler Compounds

Thermal management is of vital importance in various modern technologies such as portable electronics, photovoltaics, and thermoelectric devices. Impeding phonon transport remains one of the most challenging tasks for improving the thermoelectric performance of certain materials such as half-Heusler compounds. Herein, a significant reduction of lattice thermal conductivity (κL) is achieved by applying a pressure of ≈1 GPa to sinter a broad range of half-Heusler compounds. Contrasting with the common sintering pressure of less than 100 MPa, the gigapascal-level pressure enables densification at a lower temperature, thus greatly modifying the structural characteristics for an intensified phonon scattering. A maximum κL reduction of ≈83% is realized for HfCoSb from 14 to 2.5 W m−1 K−1 at 300 K with more than 95% relative density. The realized low κL originates from a remarkable grain-size refinement to below 100 nm together with the abundant in-grain defects, as determined by microscopy investigations. This work uncovers the phonon transport properties of half-Heusler compounds under unconventional microstructures, thus showing the potential of high-pressure compaction in advancing the performance of thermoelectric materials

Asynchronous Photoexcited Electronic and Structural Relaxation in Lead-Free Perovskites

Vacancy-ordered lead-free perovskites with more-stable crystalline structures have been intensively explored as the alternatives for resolving the toxic and long-term stability issues of lead halide perovskites (LHPs). The dispersive energy bands produced by the closely packed halide octahedral sublattice in these perovskites are meanwhile anticipated to facility the mobility of charge carriers. However, these perovskites suffer from unexpectedly poor charge carrier transport. To tackle this issue, we have employed the ultrafast, elemental-specific X-ray transient absorption (XTA) spectroscopy to directly probe the photoexcited electronic and structural dynamics of a prototypical vacancy-ordered lead-free perovskite (Cs3Bi2Br9). We have discovered that the photogenerated holes quickly self-trapped at Br centers, simultaneously distorting the local lattice structure, likely forming small polarons in the configuration of Vk center (Br2– dimer). More significantly, we have found a surprisingly long-lived, structural distorted state with a lifetime of ∼59 μs, which is ∼3 orders of magnitude slower than that of the charge carrier recombination. Such long-lived structural distortion may produce a transient “background” under continuous light illumination, influencing the charge carrier transport along the lattice framework