This Perspective traces the evolution of thermal transport science from its empirical origins through the current AI-driven renaissance, and examines the limitations of conventional density-functional-theory-based phonon workflows, the emergence of universal machine learning interatomic potentials (uMLPs), graph-neural-network screening architectures such as the Crystal Attention Graph Neural Network (CATGNN), and generative inverse-design frameworks.
Abstract
The discovery of materials with extreme lattice thermal conductivity (κL)—spanning from sub-air thermal insulators to metallic conductors that rival diamond—represents one of the most consequential frontiers in contemporary materials physics and engineering. This Perspective traces the evolution of thermal transport science from its empirical origins through the current AI-driven renaissance. We examine the limitations of conventional density-functional-theory-based phonon workflows, the emergence of universal machine learning interatomic potentials (uMLPs), graph-neural-network screening architectures such as the Crystal Attention Graph Neural Network (CATGNN), and generative inverse-design frameworks including the Physics-Guided Crystal Generative Model (PGCGM) and InvDesFlow-AL. Representative discovery and AI opportunities—including a computationally predicted room-temperature κL of 0.071 Wm−1 K−1 in Cs2HgPtCl6 and approximately 1,100 Wm−1 K−1 in metallic θ-TaN—are presented as concrete demonstrations of AI-driven computational power and human physical intuition, respectively. We conclude with a forward-looking discussion spanning five frontier challenges: machine learning frameworks for phonon–electron and phonon–ion interactions (with emphasis on electron–phonon coupling and its role in superconductivity, thermoelectrics, and carrier mobility); interpretable and symbolic AI approaches that extract closed-form physical principles from learned representations; out-of-distribution generalization strategies essential for discovering genuinely unprecedented materials; autonomous self-driving laboratory ecosystems that close the loop between computational prediction and experimental validation; and AI-accelerated design of interfacial thermal transport for high-power electronics and data center thermal management.
A seven-tier physics-informed inverse-design framework integrating chemistry, thermodynamics, transport, electrochemistry, durability, cell compatibility, and manufacturability is proposed, providing a general framework for AI-enabled autonomous materials discovery across energy-storage materials and other functional materials.
Low-temperature flexible organic crystals represent a critical frontier in extreme-environment materials science, exhibiting remarkable macroscopic elastoplasticity and dynamic structural adaptability under deep cryogenic conditions. However, the thermodynamic suppression of thermal activation at these temperatures typically induces severe ductile-to-brittle transitions, rendering the underlying microscopic mechanisms of stress dissipation largely unresolved. While machine learning has emerged as an essential computational bridge to resolve the accuracy-scale dilemma inherent in traditional simulations, its application in deep cryogenic regimes remains fundamentally restricted. This Perspective critically examines the state-of-the-art computational methodologies driving the field, highlighting the necessary transition from empirical black-box algorithms to physics-informed sequential reasoning frameworks. We dissect the persistent algorithmic challenges that hinder current models, specifically the extreme scarcity of cryogenic mechanical data, the prevalence of unpredictable structural property cliffs, and the systemic out-of-distribution generalization failures caused by temperature-biased training data sets. To surmount these barriers, we outline a future research trajectory focused on advanced machine learning force fields, Δ-learning strategies, and multimodal generative artificial intelligence. Ultimately, we advocate for the establishment of a rigorous, closed-loop computation-experiment ecosystem to enable the autonomous inverse design of next-generation resilient organic materials.
Rui Shi, Xuesong Yang, Hongyu Zhang et al.· Journal of Physical Chemistr...· 0 citations
Thermoelectric materials, which enable the direct conversion of waste heat into electricity, offer a sustainable pathway to address energy scarcity and environmental concerns. The efficiency of thermoelectric conversion is characterized by the dimensionless figure of merit, where optimizing electrical transport while suppressing lattice thermal conductivity is crucial. In this study, we investigate the structural, bonding, phonon, and thermoelectric properties of CuGaTe2 using first-principles calculations combined with self-consistent phonon theory and four-phonon scattering analysis. Our findings reveal distinct layered bonding features, consisting of ionic Cu–Te bonds and polar covalent Ga–Te bonds, which create a pronounced bonding hierarchy and induce strong local anharmonicity. Based on Heyd–Scuseria–Ernzerhof hybrid functional band structures and ab initio scattering and transport electronic transport calculations, p-type CuGaTe2 achieves a maximum power factor of 6.65 mWm–1 K–2 and a peak ZT of approximately 1.76 at 900 K, surpassing its n-type counterpart. These results establish CuGaTe2 as a promising p-type thermoelectric material for intermediate- to high-temperature applications and highlight the critical role of four-phonon scattering in accurately predicting thermal transport in strongly anharmonic materials.
Liu-Fu Yuan, Lang Chen, Wenjie Yuan et al.· Journal of Physical Chemistr...· 0 citations
While the prediction of lattice thermal conductivity has traditionally relied on three-phonon interaction frameworks, accurately modeling highly anharmonic materials or extreme temperature regimes increasingly demands the inclusion of higher-order effects. This tutorial presents a comprehensive computational framework for predicting four-phonon scattering processes and the associated phonon thermal conductivity. The physical conditions that necessitate fourth-order interactions are first outlined, followed by a detailed analysis of quartic interatomic force constant (IFC) extraction, four-phonon scattering calculations, and machine-learning acceleration modules. Utilizing Lennard-Jones argon and Stillinger–Weber silicon as model systems, a systematic sensitivity analysis is performed to isolate the numerical artifacts and error propagation inherent to finite-difference routines vs Taylor-series regression-fitting methods for quartic IFC extraction. The role of primary density functional theory parameters, specifically plane wave energy cutoffs, electronic k-point grids, minimization algorithms, and parallelization schemes, on the fidelity of computed atomic forces is established using cubic boron arsenide as a representative test system. Crucially, the convergence behavior of four-phonon-driven thermal conductivity across various numerical parameters is shown to be highly material-dependent. Finally, it is demonstrated that the severe computational bottlenecks traditionally associated with establishing parameter convergence can be effectively mitigated by integrating machine-learning-accelerated approaches, offering a robust paradigm for next-generation, high-fidelity transport simulations.
Ankit Jain, Rajan Kumar, Jyotishraj Thoudam et al.· Journal of Applied Physics· 0 citations
This work pursues a multi-tier development strategy in which machine learning (ML) algorithms are combined with exact physical symmetries and constraints to significantly accelerate computations of electronic structure and atomistic dynamics.
The discovery of advanced alloys capable of withstanding extreme environmental conditions such as high temperatures, intense radiation, corrosive atmospheres, and mechanical stress is critical for applications in aerospace, nuclear energy, deep-sea exploration, and space missions. Traditional experimental and computational approaches to alloy design are often time-consuming and resource-intensive. Recent advances in artificial intelligence (AI) and machine learning (ML) offer powerful tools to accelerate the discovery process by enabling high-throughput screening, property prediction, and design optimization. This paper presents a comprehensive review and methodology for AI-assisted alloy discovery, focusing on the integration of data-driven models with physical principles, high-fidelity simulations, and experimental validation. We highlight successful case studies, discuss the challenges of data scarcity and model interpretability, and propose a framework for closed-loop design that incorporates generative models and active learning. This AI-driven approach represents a paradigm shift toward faster, more cost-effective discovery of next-generation materials for extreme environments.
Venkatesh Iyer, Nandhini Ravi· International Journal of Mod...· 0 citations
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