Probing the Limit of Heat Transfer in Inorganic Crystals with Deep Learning (2503.11568v2)

Abstract: Heat transfer is a fundamental property of matter. Research spanning decades has attempted to discover materials with exceptional thermal conductivity, yet the upper limit remains unknown. Using deep learning accelerated crystal structure prediction and first-principles calculation, we systematically explore the thermal conductivity landscape of inorganic crystals. We brute-force over half a million ordered crystalline structures, encompassing an extensive coverage of local energy minima in binary compounds with up to four atoms per primitive cell. We confirm diamond sets the upper bound of thermal conductivity within our search space, very likely also among all stable crystalline solids at ambient conditions. We also identify over 20 novel crystals surpassing silicon in thermal conductivity, validated by density functional theory. These include a semiconductor TaN with ultrahigh thermal conductivity (~900 $\mathrm{W\cdot m^{-1}\cdot K^{-1}}$), and metallic compounds such as MnV that exhibit high lattice and electronic thermal conductivity simultaneously, a distinctive feature not observed before. These results as well as the deep learning-driven screening method, redefine the landscape of thermal transport and establish a large open-access database for future materials discovery.

A Systematic Approach to Heat Transfer Limits in Inorganic Crystals using Deep Learning

The paper "Probing the Limit of Heat Transfer in Inorganic Crystals with Deep Learning" addresses a critical inquiry in materials science: the upper limit of heat transfer in matter, specifically inorganic crystals, through the lens of advanced computational methods. Historically, diamond has been considered as the material with the highest thermal conductivity at ambient conditions, a hypothesis that this paper explores comprehensively by leveraging deep learning models to accelerate atomistic simulations and large-scale first-principles calculations.

Core Findings

The research undertakes a systematic exploration of over 642,603 crystalline structures, concentrating on those formed by binary compounds with up to four atoms per unit cell. Within this vast search space, more than 236,000 stable crystals were subjected to thermal conductivity assessments. The findings confirm that diamond remains unmatched in terms of thermal conductivity within the investigated materials, thereby reinforcing the notion that diamond may represent the physical pinnacle of heat conduction when conditions are ambient.

However, the research identifies more than 20 novel crystalline structures that surpass silicon's thermal conductivity at room temperature. These discoveries include binary metallic compounds like MnV, which uniquely exhibit significant lattice and electronic thermal conductivity owing to the bcc structure’s phonon dispersion characteristics. Such a combination in metallic systems provides an intriguing perspective on phonon transport efficiency enhanced by reduced scattering opportunities at specific phonon dispersion points.

Implications and Future Directions

The implications of identifying novel high thermal conductivity materials are multifaceted. Practically, these materials could revolutionize thermal management solutions across a range of industries requiring efficient heat dissipation, such as electronics cooling, photonic device efficiency, and energy conversion systems. The identification of compounds with both high phonon-mediated and electron-mediated thermal conductivity proposes a path forward for developing materials that can integrate seamlessly within semiconductor interfaces, potentially minimizing thermal boundary resistance and optimizing device performance.

Theoretically, the research enriches the landscape of material science by providing insights into the inherent limitations of heat conduction in crystalline materials. This work opens avenues for further exploration into ternary compounds and more complex structural configurations beyond the current limits defined by the binary search space.

Speculative elements of future materials discovery may center around refining machine learning models to predict even more accurately across larger and more varied heterostructures, considering advanced scattering mechanisms. Applying generative models in AI might facilitate further discoveries in lower-conductivity domains and enhance the material design process by illuminating new correlations within the data collected through this extensive paper.

Conclusion

The paper contributes a comprehensive database of predicted thermal conductivities for thousands of crystalline materials, underpinning the development of next-generation materials with bespoke thermal properties. While the challenge to surpass diamond’s thermal limit persists, the findings importantly shape future research directions and highlight the instrumental role of AI-driven simulations in expanding the bounds of contemporary materials science. The commitment to rigorous validation and DFT analysis ensures that the speculated boundaries align with fundamental principles and empirical data, fostering a robust dialogue within the scientific community about the future of heat transfer in solids.