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

Advancing Thermal Management Technology for Power Semiconductors through Materials and Interface Engineering

Power semiconductors and chips are essential in modern electronics, driving applications from personal devices and data centers to energy technologies, vehicles, and Internet infrastructure. However, efficient heat dissipation remains a critical challenge, directly affecting their performance, reliability, and lifespan. High-power electronics based on wide- and ultrawide-bandgap semiconductors can exhibit power densities exceeding 10 kW/cm2, hundreds of times higher than digital electronics, posing significant thermal management challenges. Addressing this issue requires advanced materials and interface engineering, alongside a comprehensive understanding of materials physics, chemistry, transport dynamics, and various electronic, thermal, and mechanical properties. Despite progress in thermal management solutions, the complex interplay of phonons, electrons, and their interactions with material lattices, defects, boundaries, and interfaces presents persistent challenges. This Account highlights key advancements in thermal management for power semiconductors and chips, with a focus on our group's recent contributions. Our approach addresses several critical issues: (1) developing materials with ultrahigh thermal conductivity for enhanced heat dissipation, (2) reducing thermal boundary resistance between power semiconductors and emerging 2D materials, (3) improving thermal and mechanical contacts between chips and heat sinks, (4) innovating dynamic thermal management solutions, and (5) exploring novel principles of thermal transport and design for future technologies. Our research philosophy integrates multiscale theoretical predictions with experimental validation to achieve a paradigm shift in thermal management. By leveraging first-principles calculations, the recent studies redefined traditional criteria for high-thermal-conductivity materials. Guided by these insights, we developed boron arsenide and boron phosphide, which exhibit record-high thermal conductivities of up to 1300 W/mK. Through phonon band structure engineering, we reduced TBR in GaN/BAs interfaces by over 8-fold compared to GaN/diamond interfaces. The combination of low TBR and high thermal conductivity significantly reduced hotspot temperatures, setting new benchmarks in thermal design for power electronics. We further explored the anisotropic TBR properties of two-dimensional materials and Moiré patterns in twisted graphene, expanding the thermal design landscape. To address challenges at device-heat sink interfaces, we developed self-assembled boron arsenide composites with a thermal conductivity of 21 W/mK and exceptional mechanical compliance (∼100 kPa). These composites provide promising solutions for thermal management in flexible electronics and soft robotics. In dynamic thermal management, we pioneered the concept of solid-state thermal transistors, enabling electrically controlled heat flow with unparalleled tunability, speed, reliability, and compatibility with integrated circuit fabrication. These innovations not only enhance thermal performance but also enable the exploration of novel transport physics, improving our fundamental understanding of thermal energy transport under extreme conditions. Looking forward, we reflect on remaining challenges and identify opportunities for further advancements. These include scaling up the production of high-performance materials, integrating thermal solutions with existing manufacturing processes, and uncovering new physics to inspire next-generation power electronics technologies. By addressing these challenges, we aim to inspire future codesign strategies that enable the development of more efficient, reliable, sustainable, and high-performance electronic systems.

Machine Learning for Thermal Transport and Phonon High-order Anharmonicity in High Thermal Conductivity Materials: A Case Study in Boron Arsenide

Materials with high thermal conductivity are at the forefront of research in advancing thermal management, as benchmarked by the recent discovery of cubic BAs. In this study, we utilized BAs as a prototype material to assess the predictive capabilities of a machine learning approach for thermal transport, particularly in scenarios where high-order phonon anharmonicity plays a crucial role. We developed a training methodology for the moment tensor potential based on ab initio molecular dynamics, which provides accurate predictions of atomic energies, forces, stresses, phonon dispersion relations, elastic modulus, and thermal expansion coefficients. Our approach yields quantitative predictions of thermal conductivity and phonon mean free paths, closely matching first-principles calculations and experimental measurements under varied conditions and size confinements. The predictions of pressure-dependent thermal conductivity, taking into account complex interactions from phonon anharmonicity, isotope scattering, and defect scattering, reveal intrinsic behavior resulting from competing 3-phonon and 4-phonon processes in high-quality BAs, while also showing weak pressure dependence in samples dominated by defects. This study explores the feasibility of using machine learning for simulating high-order phonon scattering and demonstrates its potential as a high-throughput computational approach in advancing thermal management solutions.

Characterization and machine learning analysis of hybrid alumina-copper oxide nanoparticles in therminol 55 for medium temperature heat transfer fluid

Efficient heat dissipation is crucial for various industrial and technological applications, ensuring system reliability and performance. Advanced thermal management systems rely on materials with superior thermal conductivity and stability for effective heat transfer. This study investigates the thermal conductivity, viscosity, and stability of hybrid Al2O3-CuO nanoparticles dispersed in Therminol 55, a medium-temperature heat transfer fluid. The nanofluid formulations were prepared with CuO-Al2O3 mass ratios of 10:90, 20:80, and 30:70 and tested at nanoparticle concentrations ranging from 0.1 wt% to 1.0 wt%. Experimental results indicate that the hybrid nanofluids exhibit enhanced thermal conductivity, with a maximum improvement of 32.82% at 1.0 wt% concentration, compared to the base fluid. However, viscosity increases with nanoparticle loading, requiring careful optimization for practical applications. To further analyze and predict thermal conductivity, a Type-2 Fuzzy Neural Network (T2FNN) was employed, demonstrating a correlation coefficient of 96.892%, ensuring high predictive accuracy. The integration of machine learning enables efficient modeling of complex thermal behavior, reducing experimental costs and facilitating optimization. These findings provide insights into the potential application of hybrid nanofluids in solar thermal systems, heat exchangers, and industrial cooling applications.

Convergence of nanotechnology and artificial intelligence in the fight against liver cancer: a comprehensive review

Liver cancer is one of the most challenging malignancies, often associated with poor prognosis and limited treatment options. Recent advancements in nanotechnology and artificial intelligence (AI) have opened new frontiers in the fight against this disease. Nanotechnology enables precise, targeted drug delivery, enhancing the efficacy of therapeutics while minimizing off-target effects. Simultaneously, AI contributes to improved diagnostic accuracy, predictive modeling, and the development of personalized treatment strategies. This review explores the convergence of nanotechnology and AI in liver cancer treatment, evaluating current progress, identifying existing research gaps, and discussing future directions. We highlight how AI-powered algorithms can optimize nanocarrier design, facilitate real-time monitoring of treatment efficacy, and enhance clinical decision-making. By integrating AI with nanotechnology, clinicians can achieve more accurate patient stratification and treatment personalization, ultimately improving patient outcomes. This convergence holds significant promise for transforming liver cancer therapy into a more precise, individualized, and efficient process. However, data privacy, regulatory hurdles, and the need for large-scale clinical validation remain. Addressing these issues will be essential to fully realizing the potential of these technologies in oncology.

Nanoscale hyperthermia mesostructures for sustainable antimicrobial design

Sustainability is critical in addressing global challenges posed by prolonged pandemics that impact health, economies, and the environment. Here, we introduce a molecular engineering approach for thermoregulated antimicrobial management inspired by firewalking rituals. The study uses in situ spectroscopy and multi-scale modeling to validate a hierarchical design. Efficient light-to-thermal energy conversion is achieved by engineering the molecular band structure. Rapid nanoscale hyperthermia is facilitated through thermal engineering. This approach significantly reduces the half-life of pathogens such as Escherichia coli, influenza A, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to 1.4 min while maintaining a low perceived temperature on human skin. Standard disease infection and epidemic models show this technology's potential to flatten outbreak curves and delay peak infection rates, which is crucial during the early stages of pandemics when developing vaccines and antiviral drugs takes time. The scalable manufacturing and broad antimicrobial applicability hold great promise for controlling emerging infectious diseases and diverse bioprotective applications.

Ab initio investigations on hydrodynamic phonon transport: From diffusion to convection

In classical theory, heat conduction in solids is regarded as a diffusion process driven by a temperature gradient, whereas fluid transport is understood as convection process involving the bulk motion of the liquid or gas. In the framework of ab initio theory, which is directly built upon quantum mechanics without relying on measured parameters or phenomenological models, we observed and investigated the fluid-like convective transport of energy carriers in solid heat conduction. Thermal transport, carried by phonons, is simulated in graphite by solving the Boltzmann transport equation using a Monte Carlo algorithm. To capture convective transport, with phonon distributions deviating significantly from equilibrium Bose-Einstein distribution, we determined phonon interactions using ab initio approaches that go beyond relaxation time approximations. The presence of strong momentum-conserved Normal scatterings in graphite introduces a regime for hydrodynamic phonon transport. Fluid-like features, such as vortex and jet flow, are visualized and compared with classical theories on heat diffusion and fluid convection. Our study on phonon convection enhances fundamental understandings of heat conduction in solids from both atomic scale and quantum aspects, innovating thermal designs for future microelectronic devices and other thermal management applications. This potentially offers solutions for heat dissipation challenges in the post-Moore era.

Non-Perturbative Determination of Isotope-induced Anomalous Vibrational Physics

In general, vibrational physics has been well described by quantum perturbation theory to provide footprint characteristics for common crystals. However, despite weak phonon anharmonicity, the recently discovered cubic crystals have shown anomalous vibrational dynamics with elusive fundamental origin. Here, we developed a non-perturbative ab initio approach, in together with spectroscopy and high-pressure experiments, to successfully determine the exact dynamic evolutions of the vibrational physics for the first time. We found that the local fluctuation and coupling isotopes significantly dictate the vibrational spectra, through the Brillouin zone folding that has been previously ignored in literature. By decomposing vibrational spectra into individual isotope eigenvectors, we observed both positive and negative contributions to Raman intensity from constitutional atoms (10B, 11B, 75As or 31P). Importantly, our non-perturbative theory predicts that a novel vibrational resonance appears at high hydrostatic pressure due to broken translational symmetry, which was indeed verified by experimental measurement under a pressure up to 31.5 GPa. Our study develops fundamental understandings for the anomalous lattice physics under the failure of quantum perturbation theory and provides a new approach in exploring novel transport phenomena for materials of extreme properties.

Ultrafast and Cost-Effective Fabrication of High-Performance Carbon-Based Flexible Thermoelectric Hybrid Films and Their Devices

Due to their cost-effectiveness and industry-scale feasibility, carbon-based composites have been considered to be promising thermoelectric materials for low-grade power generation. However, current fabrications for carbon-based composites are time-consuming, and their thermoelectric properties are still low. Herein, we develop an ultrafast and cost-effective hot-pressing method to fabricate a novel carbon-based hybrid film, which consists of ionic liquid/phenolic resin/carbon fiber/expanded graphite. This method only costs no more than 15 min. We found that the expanded graphite as the major component enables high flexibility and the introduction of phenolic resin and carbon fiber enhances the shear resistance and toughness of the film, while the ion-induced carrier migration contributes to a high power factor of 38.7 μW m^-1 K^-2 at 500 K in the carbon-based hybrid film. After the comparison based on the ratios between the power factor with fabrication time and cost among the current conventional carbon-based thermoelectric composites, our hybrid films show the best cost-effective property. Besides, a flexible thermoelectric device, assembled by the as-designed hybrid films, shows a maximum output power density of 79.3 nW cm^-2 at a temperature difference of 20 K. This work paves a new way to fabricate cost-effective and high-performance carbon-based thermoelectric hybrids with promising application potential.

Anomalous thermal transport under high pressure in boron arsenide

High pressure represents extreme environments and provides opportunities for materials discovery1-8. Thermal transport under high hydrostatic pressure has been investigated for more than 100 years and all measurements of crystals so far have indicated a monotonically increasing lattice thermal conductivity. Here we report in situ thermal transport measurements in the newly discovered semiconductor crystal boron arsenide, and observe an anomalous pressure dependence of the thermal conductivity. We use ultrafast optics, Raman spectroscopy and inelastic X-ray scattering measurements to examine the phonon bandstructure evolution of the optical and acoustic branches, as well as thermal conductivity under varied temperatures and pressures up to 32 gigapascals. Using atomistic theory, we attribute the anomalous high-pressure behaviour to competitive heat conduction channels from interactive high-order anharmonicity physics inherent to the unique phonon bandstructure. Our study verifies ab initio theory calculations and we show that the phonon dynamics-resulting from competing three-phonon and four-phonon scattering processes-are beyond those expected from classical models and seen in common materials. This work uses high-pressure spectroscopy combined with atomistic theory as a powerful approach to probe complex phonon physics and provide fundamental insights for understanding microscopic energy transport in materials of extreme properties.

Recent Advances on Composition-Microstructure-Properties Relationships of Precipitation Hardening Stainless Steel

Precipitation hardening stainless steels have attracted extensive interest due to their distinguished mechanical properties. However, it is necessary to further uncover the internal quantitative relationship from the traditional standpoint based on the statistical perspective. In this review, we summarize the latest research progress on the relationships among the composition, microstructure, and properties of precipitation hardened stainless steels. First, the influence of general chemical composition and its fluctuation on the microstructure and properties of PHSS are elaborated. Then, the microstructure and properties under a typical heat treatment regime are discussed, including the precipitation of B2-NiAl particles, Cu-rich clusters, Ni3Ti precipitates, and other co-existing precipitates in PHSS and the hierarchical microstructural features are presented. Next, the microstructure and properties after the selective laser melting fabricating process which act as an emerging technology compared to conventional manufacturing techniques are also enlightened. Thereafter, the development of multi-scale simulation and machine learning (ML) in material design is illustrated with typical examples and the great concerns in PHSS research are presented, with a focus on the precipitation techniques, effect of composition, and microstructure. Finally, promising directions for future precipitation hardening stainless steel development combined with multi-scale simulation and ML methods are prospected, offering extensive insight into the innovation of novel precipitation hardening stainless steels.