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1. Advances in the Design and Discovery of Organic Semiconductors Aided by Machine Learning

   https://doi.org/10.1146/annurev-matsci-080423-011746  

   Ogbaje, Moses; Bhat, Vinayak; Risko, Chad (July 2025, Annual Review of Materials Research)

   Organic semiconductors (OSCs) offer the capacity for distinctive and finely tuned electronic, optical, thermal, and mechanical properties, making them of interest across a range of energy generation and storage, sensor, lighting, display, and electronics applications. The pathway from molecular building block design to material, however, is complicated by complex synthesis– processing–structure–property–function relationships that are inherent to OSCs. The adoption of artificial intelligence (AI) tools, including the subset of AI referred to as machine learning (ML), into the materials design and discovery pipeline offers significant potential to overcome the multifaceted roadblocks along this pathway. Here, we review recent advances in the application of AI/ML for OSCs, with a focus on the development and use of ML. We present a brief primer on ML models and then highlight efforts wherein ML is used to predict molecular and material properties and discover new molecular building blocks and OSCs. 

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2. Phonon stability boundary and deep elastic strain engineering of lattice thermal conductivity

   https://doi.org/10.1073/pnas.2313840121  

   Shi, Zhe; Tsymbalov, Evgenii; Shi, Wencong; Barr, Ariel; Li, Qingjie; Li, Jiangxu; Chen, Xing-Qiu; Dao, Ming; Suresh, Subra; Li, Ju (February 2024, Proceedings of the National Academy of Sciences)

   Recent studies have reported the experimental discovery that nanoscale specimens of even a natural material, such as diamond, can be deformed elastically to as much as 10% tensile elastic strain at room temperature without the onset of permanent damage or fracture. Computational work combining ab initio calculations and machine learning (ML) algorithms has further demonstrated that the bandgap of diamond can be altered significantly purely by reversible elastic straining. These findings open up unprecedented possibilities for designing materials and devices with extreme physical properties and performance characteristics for a variety of technological applications. However, a general scientific framework to guide the design of engineering materials through such elastic strain engineering (ESE) has not yet been developed. By combining first-principles calculations with ML, we present here a general approach to map out the entire phonon stability boundary in six-dimensional strain space, which can guide the ESE of a material without phase transitions. We focus on ESE of vibrational properties, including harmonic phonon dispersions, nonlinear phonon scattering, and thermal conductivity. While the framework presented here can be applied to any material, we show as an example demonstration that the room-temperature lattice thermal conductivity of diamond can be increased by more than 100% or reduced by more than 95% purely by ESE, without triggering phonon instabilities. Such a framework opens the door for tailoring of thermal-barrier, thermoelectric, and electro-optical properties of materials and devices through the purposeful design of homogeneous or inhomogeneous strains. 

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3. Fundamentals and advances in thermal transport in thermoelectric materials

   https://doi.org/10.1557/s43577-025-00951-6  

   Esfarjani, Keivan; Shiomi, Junichiro (August 2025, MRS Bulletin)

   Abstract This article attempts to summarize our understanding of heat flow in different solid materials and its relationship to atomistic structure of materials. This knowledge can be used to understand and design materials for electricity generation or cooling through the thermoelectric effect. We start with the fundamentals of heat transport in solids: mechanisms of phonon scattering in crystals, the role of interfaces and coherence, and the relationship between chemical bonding and heat transport will be elucidated. Theories used to model thermal conductivity of solids will be exposed next. They include the Green–Kubo formulation, Boltzmann transport equation and its recent quantum extensions, and Allen–Feldman theory of heat diffusion in noncrystalline solids and its recent extensions. In terms of phenomenology, we will distinguish between the kinetic regime based on independent single carriers and the collective or hydrodynamic one which occurs when normal or momentum-conserving processes dominate. Next, we will focus on advanced measurement and characterization techniques, and the knowledge extracted from them. Nanoscale thermal conductivity methods, such as the pump-probe thermoreflectance methods (TDTR/FDTR), have become fairly common allowing researchers to measure thermal conductivity of thin-film thermoelectrics. We will review recent advances of the method: the Gibbs excess approach, which measures thermal resistance across a grain boundary of polycrystals through mapping TDTR/FDTR measurements, and the transient Raman method, where pump-probe Raman spectroscopy realizes in-plane thermal conductivity measurements of two-dimensional materials even on a substrate. We will also review the progress in mode-resolved phonon property measurements, such as inelastic x-ray scattering for thin-film samples, which allows direct observation of the modulation of phonon band and lifetime by nanostructures, and thermal diffuse scattering for quick characterization of phonon dispersion relations. Finally, because the main focus of this issue is thermoelectrics, we will review different classes of materials and strategies to lower their thermal conductivities. Graphical abstract 

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4. Physics-informed deep learning for solving phonon Boltzmann transport equation with large temperature non-equilibrium

   https://doi.org/10.1038/s41524-022-00712-y  

   Li, Ruiyang; Wang, Jian-Xun; Lee, Eungkyu; Luo, Tengfei (December 2022, npj Computational Materials)

   Abstract Phonon Boltzmann transport equation (BTE) is a key tool for modeling multiscale phonon transport, which is critical to the thermal management of miniaturized integrated circuits, but assumptions about the system temperatures (i.e., small temperature gradients) are usually made to ensure that it is computationally tractable. To include the effects of large temperature non-equilibrium, we demonstrate a data-free deep learning scheme, physics-informed neural network (PINN), for solving stationary, mode-resolved phonon BTE with arbitrary temperature gradients. This scheme uses the temperature-dependent phonon relaxation times and learns the solutions in parameterized spaces with both length scale and temperature gradient treated as input variables. Numerical experiments suggest that the proposed PINN can accurately predict phonon transport (from 1D to 3D) under arbitrary temperature gradients. Moreover, the proposed scheme shows great promise in simulating device-level phonon heat conduction efficiently and can be potentially used for thermal design. 

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5. First-principles prediction of electronic transport in fabricated semiconductor heterostructures via physics-aware machine learning

   https://doi.org/10.1038/s41524-021-00562-0  

   Pimachev, Artem K.; Neogi, Sanghamitra (June 2021, npj Computational Materials)

   Abstract First-principles techniques for electronic transport property prediction have seen rapid progress in recent years. However, it remains a challenge to predict properties of heterostructures incorporating fabrication-dependent variability. Machine-learning (ML) approaches are increasingly being used to accelerate design and discovery of new materials with targeted properties, and extend the applicability of first-principles techniques to larger systems. However, few studies exploited ML techniques to characterize relationships between local atomic structures and global electronic transport coefficients. In this work, we propose an electronic-transport-informatics (ETI) framework that trains on ab initio models of small systems and predicts thermopower of fabricated silicon/germanium heterostructures, matching measured data. We demonstrate application of ML approaches to extract important physics that determines electronic transport in semiconductor heterostructures, and bridge the gap between ab initio accessible models and fabricated systems. We anticipate that ETI framework would have broad applicability to diverse materials classes. 

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