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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. 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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4. 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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5. Generative AI for discovering porous oxide materials for next-generation energy storage

   https://doi.org/10.1016/j.xcrp.2025.102665  

   Datta, Joy; Nadimpally, Amruth; Koratkar, Nikhil; Datta, Dibakar (June 2025, Cell Reports Physical Science)

   The development of next-generation energy storage systems relies on discovering new materials that support multivalent-ion transport. Transition metal oxides (TMOs) are promising due to their structural versatility, high ionic conductivity, and ability to accommodate multiple charge carriers. However, their vast compositional and structural diversity makes traditional exploration inefficient. This work presents a generative AI framework combining a crystal diffusion variational autoencoder (CDVAE) and a fine-tuned large language model (LLM) to discover porous oxide materials. Thousands of candidate structures are generated and screened for structural validity, thermodynamic stability, and electronic properties using a graph-based machine learning model and density functional theory (DFT) calculations. CDVAE identifies a broader variety of structures, including five novel TMO-based candidates, while LLM excels in generating highly stable structures near equilibrium. This approach demonstrates the power of generative AI in accelerating the discovery of advanced battery materials for multivalent-ion storage. 

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