Multifunctional structural design of graphene thermoelectrics by Bayesian optimization

Biomimetic Mechanical Robust Cement-Resin Composites with Machine Learning-Assisted Gradient Hierarchical Structures

Biological materials relying on hierarchically ordered architectures inspire the emergence of advanced composites with mutually exclusive mechanical properties, but the efficient topology optimization and large-scale manufacturing remain challenging. Herein, this work proposes a scalable bottom-up approach to fabricate a novel nacre-like cement-resin composite with gradient brick-and-mortar (BM) structure, and demonstrates a machine learning-assisted method to optimize the gradient structure. The fabricated gradient composite exhibits an extraordinary combination of high flexural strength, toughness, and impact resistance. Particularly, the toughness and impact resistance of such composite attractively surpass the cement counterparts by factors of approximately 700 and 600 times, and even outperform natural rocks, fiber-reinforced cement-based materials and even some alloys. The strengthening and toughening mechanisms are clarified as the regional-matrix densifying and crack-tip shielding effects caused by the gradient BM structure. The developed gradient composite not only endows a promising structural material for protective applications in harsh scenarios, but also paves a new way for biomimetic metamaterials designing.

Modulating Thermal Conductivity via Targeted Phonon Excitation

Thermal conductivity is a critical material property in numerous applications, such as those related to thermoelectric devices and heat dissipation. Effectively modulating thermal conductivity has become a great concern in the field of heat conduction. Here, a quantum modulation strategy is proposed to modulate the thermal conductivity/heat flux by exciting targeted phonons. It shows that the thermal conductivity of graphene can be tailored in the range of 1559 W m-1 K-1 (decreased to 49%) to 4093 W m-1 K-1 (increased to 128%), compared with the intrinsic value of 3189 W m-1 K-1. The effects are also observed for graphene nanoribbons and bulk silicon. The results are obtained through both density functional theory calculations and molecular dynamics simulations. This novel modulation strategy may pave the way for quantum heat conduction.

Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization

Recent advances in machine learning (ML) have impacted research communities based on statistical perspectives and uncovered invisibles from conventional standpoints. Though the field is still in the early stage, this progress has driven the thermal science and engineering communities to apply such cutting-edge toolsets for analyzing complex data, unraveling abstruse patterns, and discovering non-intuitive principles. In this work, we present a holistic overview of the applications and future opportunities of ML methods on crucial topics in thermal energy research, from bottom-up materials discovery to top-down system design across atomistic levels to multi-scales. In particular, we focus on a spectrum of impressive ML endeavors investigating the state-of-the-art thermal transport modeling (density functional theory, molecular dynamics, and Boltzmann transport equation), different families of materials (semiconductors, polymers, alloys, and composites), assorted aspects of thermal properties (conductivity, emissivity, stability, and thermoelectricity), and engineering prediction and optimization (devices and systems). We discuss the promises and challenges of current ML approaches and provide perspectives for future directions and new algorithms that could make further impacts on thermal energy research.

Where Nanosensors Meet Machine Learning: Prospects and Challenges in Detecting Disease X

Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive portable devices that can swiftly screen disease biomarkers on site, reducing the reliance on laboratory-based analyses. However, conventional data analytics limit the progress of nanosensor research. In this Perspective, we highlight the integral role of machine learning (ML) algorithms in advancing nanosensing strategies toward Disease X detection. We first summarize recent progress in utilizing ML algorithms for the smart design and fabrication of custom nanosensor platforms as well as realizing rapid on-site prediction of infection statuses. Subsequently, we discuss promising prospects in further harnessing the potential of ML algorithms in other aspects of nanosensor development and biomarker detection.

High-Throughput Screening of Rattling-Induced Ultralow Lattice Thermal Conductivity in Semiconductors

Thermoelectric (TE) materials with rattling model show ultralow lattice thermal conductivity for high-efficient energy conversion between heat and electricity. In this work, by analysis of the key spirit of the rattling model, we propose an efficient empirical descriptor to realize the high-throughput screening of ultralow thermal conductivity in a series of semiconductors. This descriptor extracts the structural information of rattling atoms whose bond lengths with all the nearest neighboring atoms are larger than the sum of corresponding covalent radiuses. We obtain 1171 candidates from the Materials Project (MP) Database that contains more than 100 000 materials. Combining the empirical equation of high-throughput computation with a machine learning algorithm, we compute the approximate lattice thermal conductivities (κ_L) and find the κ_L values of 532 materials are less than 2.0 W m^-1 K^-1 at 300 K, which can be regarded as the criteria of ultralow κ_L in general. In particular, we demonstrate that halide double perovskites structures show ultralow κ_L, which provides valuable references for promising low κ_L materials in future experiments. In order to further verify our computational results, we calculate accurate κ_L for Rb₂SnBr₆ and CsCu₃O₂ as candidates with the low lattice thermal conductivity by solving the phonon Boltzmann transport equation. In particular, we demonstrate that Rb₂SnBr₆ has the lowest κ_L value of 0.1 W m^-1 K^-1 at 300 K of all known thermal conductivity materials with the rattling model so far.

Designing a multilayer film via machine learning of scientific literature

Scientists who design chemical substances often use materials informatics (MI), a data-driven approach with either computer simulation or artificial intelligence (AI). MI is a valuable technique, but applying it to layered structures is difficult. Most of the proposed computer-aided material search techniques use atomic or molecular simulations, which are limited to small areas. Some AI approaches have planned layered structures, but they require a physical theory or abundant experimental results. There is no universal design tool for multilayer films in MI. Here, we show a multilayer film can be designed through machine learning (ML) of experimental procedures extracted from chemical-coating articles. We converted material names according to International Union of Pure and Applied Chemistry rules and stored them in databases for each fabrication step without any physicochemical theory. Compared with experimental results which depend on authors, experimental protocol is superiority at almost unified and less data loss. Connecting scientific knowledge through ML enables us to predict untrained film structures. This suggests that AI imitates research activity, which is normally inspired by other scientific achievements and can thus be used as a general design technique.

Machine Learning-Driven Biomaterials Evolution

Biomaterials is an exciting and dynamic field, which uses a collection of diverse materials to achieve desired biological responses. While there is constant evolution and innovation in materials with time, biomaterials research has been hampered by the relatively long development period required. In recent years, driven by the need to accelerate materials development, the applications of machine learning in materials science has progressed in leaps and bounds. The combination of machine learning with high-throughput theoretical predictions and high-throughput experiments (HTE) has shifted the traditional Edisonian (trial and error) paradigm to a data-driven paradigm. In this review, each type of biomaterial and their key properties and use cases are systematically discussed, followed by how machine learning can be applied in the development and design process. The discussions are classified according to various types of materials used including polymers, metals, ceramics, and nanomaterials, and implants using additive manufacturing. Last, the current gaps and potential of machine learning to further aid biomaterials discovery and application are also discussed.

Biomaterials by design: Harnessing data for future development

Biomaterials is an interdisciplinary field of research to achieve desired biological responses from new materials, regardless of material type. There have been many exciting innovations in this discipline, but commercialization suffers from a lengthy discovery to product pipeline, with many failures along the way. Success can be greatly accelerated by harnessing machine learning techniques to comb through large amounts of data. There are many potential benefits of moving from an unstructured empirical approach to a development strategy that is entrenched in data. Here, we discuss the recent work on the use of machine learning in the discovery and design of biomaterials, including new polymeric, metallic, ceramics, and nanomaterials, and how machine learning can interface with emerging use cases of 3D printing. We discuss the steps for closer integration of machine learning to make this exciting possibility a reality.

Bayesian optimization for goal-oriented multi-objective inverse material design

Bayesian optimization (BO) can accelerate material design requiring time-consuming experiments. However, although most material designs require tuning of multiple properties, the efficiency of multi-objective (MO) BO in time-consuming experimental material design remains unclear, due to the complexity of handling multiple objectives. This study introduces MO BO method that efficiently achieves predefined goals and shows that by focusing on achieving the goals, BO can efficiently accelerate realistic MO design problems with small efforts. Benchmarks showed that the proposed BO method dramatically reduced the number of experiments needed to achieve goals relative to a baseline method. Virtual MO inverse design experiments with realistic material design problems were also performed, during which the proposed method could achieve goals within only around ten experiments in average and showed over 1000-fold acceleration relative to the random sampling for the most difficult case. The introduction of goal-oriented BO will precede real-world application of BO.

Anomalous hybridization complementation effect on phonon transport in heterogeneous nanowire cross junction

Controlling phonon transport via its wave nature in nanostructures can achieve unique properties for various applications. In this paper, thermal conductivity of heterogeneous nano cross junction (hetero-NCJ) is studied through molecular dynamics simulation. It is found that decreasing or increasing the atomic mass of four side wires (SWs) severed as resonators, thermal conductivity of hetero-NCJ is enhanced, which is larger than that of homogeneous NCJ (homo-NCJ). Interestingly, by setting two SWs with larger atomic mass and other two SWs with smaller atomic mass, thermal conductivity of hetero-NCJ is abnormally decreased, which is even smaller than that of homo-NCJ. After further non-equilibrium Green's function calculations, it is demonstrated that origin of increase is attributed to the hybridization broken induced by unidirectional shift of resonant modes. However, the decrease in thermal conductivity originates from hybridization complementation induced by bidirectional shift of resonant modes, which synergistically blocks phonon transport. This work provides a mechanism for further strengthening resonant hybridization effect and manipulating thermal transport.

Thermal Transport and Thermoelectric Effect in Composites of Alumina and Graphene-Augmented Alumina Nanofibers

The remarkable tunability of 2D carbon structures combined with their non-toxicity renders them interesting candidates for thermoelectric applications. Despite some limitations related to their high thermal conductivity and low Seebeck coefficients, several other unique properties of the graphene-like structures could out-weight these weaknesses in some applications. In this study, hybrid structures of alumina ceramics and graphene encapsulated alumina nanofibers are processed by spark plasma sintering to exploit advantages of thermoelectric properties of graphene and high stiffness of alumina. The paper focuses on thermal and electronic transport properties of the systems with varying content of nanofillers (1-25 wt.%) and demonstrates an increase of the Seebeck coefficient and a reduction of the thermal conductivity with an increase in filler content. As a result, the highest thermoelectric figure of merit is achieved in a sample with 25 wt.% of the fillers corresponding to ~3 wt.% of graphene content. The graphene encapsulated nanofibrous fillers, thus, show promising potential for thermoelectric material designs by tuning their properties via carrier density modification and Fermi engineering through doping.

Prediction of Bi2Te3-Sb2Te3 Interfacial Conductance and Superlattice Thermal Conductivity Using Molecular Dynamics Simulations

Bismuth telluride (Bi₂Te₃) and its alloys with antimony telluride (Sb₂Te₃) have long been considered to be the best room-temperature bulk thermoelectric (TE) materials. In recent decades, proof-of-concept demonstrations on Bi₂Te₃-Sb₂Te₃ nanostructures have shown high TE performance due to reduction in lattice thermal conductivities. Particularly, ultra-low thermal conductivities have been observed in Bi₂Te₃-Sb₂Te₃ 1D superlattices, leading to thermoelectric figures of merit (ZT) as high as 2.4. In contrast, very few computational studies have been performed to provide insight into the phonon transport across these nanostructures. In this work, we use non-equilibrium molecular dynamics simulations with previously developed force fields to simulate thermal transport across Bi₂Te₃-Sb₂Te₃ interfaces and superlattices. We first calculate the thermal conductance associated with a Bi₂Te₃-Sb₂Te₃ interface across a temperature range of 200-400 K. The values are also compared with thermal conductances calculated by a modified Landauer transport formalism using phonon transmission coefficients obtained from the diffuse mismatch model. Our results show that inelastic scattering processes contribute to an increase in interfacial thermal conductance at higher temperatures. Finally, we calculate the thermal conductivities of Bi₂Te₃-Sb₂Te₃ superlattices with varying period lengths from 2 to 18 nm. A minimum thermal conductivity of 0.27 W/mK is observed at a period length of 4 nm, which is attributed to the competition between incoherent and coherent phonon transport regimes. In comparison with previous experimental measurements in the literature, our results show good agreement with respect to the range of thermal conductivity values and the period length corresponding to the minimum superlattice thermal conductivity.

Identifying Optimal Strain in Bismuth Telluride Thermoelectric Film by Combinatorial Gradient Thermal Annealing and Machine Learning

The thermoelectric properties of bismuth telluride thin film (BTTF) was tuned by inducing internal strain through a combination of combinatorial gradient thermal annealing (COGTAN) and machine learning. BTTFs were synthesized via magnetron sputter coating and then treated by COGTAN. The crystal structure and thermoelectric properties, namely Seebeck coefficient and thermal conductivity, of the treated samples were analyzed via micropoint X-ray diffraction and scanning thermal probe microimaging, respectively. The obtained combinatorial data reveals the correlation between internal strain and the thermoelectric properties. The Seebeck coefficient of BTTF exhibits largest sensitivity, where the value ranges from 7.9 to -108 μV/K. To further explore the possibility to enhance Seebeck coefficient, the combinatorial data were subjected to machine learning. The trained model predicts that optimal strains of 3-4% and 1-2% along the a- and c-axis, respectively, significantly improve Seebeck coefficient. The technique demonstrated herein can be used to predict and enhance the performance of thermoelectric materials by inducing internal strain.

Towards a simplified description of thermoelectric materials: accuracy of approximate density functional theory for phonon dispersions

We calculate the phonon-dispersion relations of several two-dimensional materials and diamond using the density-functional based tight-binding approach (DFTB). Our goal is to verify if this numerically efficient method provides sufficiently accurate phonon frequencies and group velocities to compute reliable thermoelectric properties. To this end, the results are compared to available DFT results and experimental data. To quantify the accuracy for a given band, a descriptor is introduced that summarizes contributions to the lattice conductivity that are available already in the harmonic approximation. We find that the DFTB predictions depend strongly on the employed repulsive pair-potentials, which are an important prerequisite of this method. For carbon-based materials, accurate pair-potentials are identified and lead to errors of the descriptor that are of the same order as differences between different local and semi-local DFT approaches.

Enhanced Thermoelectric Performance of As-Grown Suspended Graphene Nanoribbons

Conventionally, graphene is a poor thermoelectric material with a low figure of merit (ZT) of 10^-4-10^-3. Although nanostructuring was proposed to improve the thermoelectric performance of graphene, little experimental progress has been accomplished. Here, we carefully fabricated as-grown suspended graphene nanoribbons with quarter-micron length and ∼40 nm width. The ratio of electrical to thermal conductivity was enhanced by 1-2 orders of magnitude, and the Seebeck coefficient was several times larger than bulk graphene, which yielded record-high ZT values up to ∼0.1. Moreover, we observed a record-high electronic contribution of ∼20% to the total thermal conductivity in the nanoribbon. Concurrent phonon Boltzmann transport simulations reveal that the reduction of lattice thermal conductivity is mainly attributed to quasi-ballistic phonon transport. The record-high ratio of electrical to thermal conductivity was enabled by the disparate electron and phonon mean free paths as well as the clean samples, and the enhanced Seebeck coefficient was attributed to the band gap opening. Our work not only demonstrates that electron and phonon transport can be fundamentally tuned and decoupled in graphene but also indicates that graphene with appropriate nanostructures can be very promising thermoelectric materials.

Disorder limits the coherent phonon transport in two-dimensional phononic crystal structures

Recently, increasing efforts are being made to control thermal transport via coherent phonons in periodic phononic structures; however, the direct observation of coherent phonon transport is experimentally very difficult at ambient temperature, and the importance of coherent phonons to the total thermal conductivity has not been critically assessed to date. In this study, using the non-equilibrium molecular dynamics simulations, we studied coherent phonon transport in a C3N phononic crystal (CNPnC) structure at room temperature by changing the porosity. When the holes were randomly distributed to construct the disordered C3N (D-C3N) structure, the localization of the coherent phonons was revealed by the phonon transmission coefficient, phonon wave packet simulation, phonon participation ratio and spatial energy density, which led to a significant reduction in the thermal conductivity. Finally, the effects of the length, temperature and strain on the thermal conductivity of CNPnC and D-C3N have also been discussed. Our study provides a solid understanding of the coherent phonon transport behavior, which will be beneficial for phononic-related control based on coherent phonons.

Materials Discovery and Properties Prediction in Thermal Transport via Materials Informatics: A Mini Review

There has been increasing demand for materials with functional thermal properties, but traditional experiments and simulations are high-cost and time-consuming. The emerging discipline, materials informatics, is an effective approach that can accelerate materials development by combining material science and big data techniques. Recently, materials informatics has been successfully applied to designing thermal materials, such as thermal interface materials for heat-dissipation, thermoelectric materials for power generation, and so forth. This Mini Review summarizes the research progress associated with studies regarding the prediction and discovery of materials with desirable thermal transport properties by using materials informatics. On the basis of the review of past research, perspectives are discussed and future directions for studying functional thermal materials by materials informatics are given.

Ultranarrow-Band Wavelength-Selective Thermal Emission with Aperiodic Multilayered Metamaterials Designed by Bayesian Optimization

We computationally designed an ultranarrow-band wavelength-selective thermal radiator via a materials informatics method alternating between Bayesian optimization and thermal electromagnetic field calculation. For a given target infrared wavelength, the optimal structure was efficiently identified from over 8 billion candidates of multilayers consisting of multiple components (Si, Ge, and SiO₂). The resulting optimized structure is an aperiodic multilayered metamaterial exhibiting high and sharp emissivity with a Q-factor of 273. The designed metamaterials were then fabricated, and reasonable experimental realization of the optimal performance was achieved with a Q-factor of 188, which is significantly higher than those of structures empirically designed and fabricated in the past. This is the first demonstration of the experimental realization of metamaterials designed by Bayesian optimization. The results facilitate the machine-learning-based design of metamaterials and advance our understanding of the narrow-band thermal emission mechanism of aperiodic multilayered metamaterials.

Tuning phonon transport spectrum for better thermoelectric materials

The figure of merit of thermoelectric materials can be increased by suppressing the lattice thermal conductivity without degrading electrical properties. Phonons are the carriers for lattice thermal conduction, and their transport can be impeded by nanostructuring, owing to the recent progress in nanotechnology. The key question for further improvement of thermoelectric materials is how to realize ultimate structure with minimum lattice thermal conductivity. From spectral viewpoint, this means to impede transport of phonons in the entire spectral domain with noticeable contribution to lattice thermal conductivity that ranges in general from subterahertz to tens of terahertz in frequency. To this end, it is essential to know how the phonon transport varies with the length scale, morphology, and composition of nanostructures, and how effects of different nanostructures can be mutually adopted in view of the spectral domain. Here we review recent advances in analyzing such spectral impedance of phonon transport on the basis of various effects including alloy scattering, boundary scattering, and particle resonance.