TL;DR
An explainable machine-learning framework was developed for dielectric constant prediction using 52,168 crystalline materials extracted from the Joint Automated Repository for Various Integrated Simulations (JARVIS-DFT) database, demonstrating the complementary roles of electronic structure and elemental chemistry.
Accurate prediction of dielectric constants is essential for the design and optimization of semiconductor devices, energy harvesting technologies, and sensing applications. In this presentation, I will introduce an artificial intelligence (AI) model developed to predict the dielectric constants of a wide range of materials, including traditional semiconductors, ferroelectrics, and advanced materials. Using machine learning algorithms, I have created a model that predicts dielectric constants based on material composition, crystal structure, and other key properties. The model is trained on an extensive dataset of dielectric constants obtained from high-throughput calculations and experimental data. One of the key features of our model is its ability to predict dielectric constants reliably across a broad range of frequencies, regardless of material anisotropy. This makes it applicable to a wide variety of materials with different dielectric behaviors, including those with complex frequency-dependent responses or directional variations in their dielectric properties. I will present the model’s performance, showing rigorous cross-validation and comparisons to both computational results and experimental measurements. The AI model demonstrates high accuracy, even for materials that are less studied or for novel materials like graphene and nanowires. This work highlights the potential of AI-driven models to accelerate the discovery and design of new dielectric materials, reducing the need for time-consuming experimental trials. The model is particularly valuable for applications in high-temperature environments, energy-efficient devices, and advanced sensing technologies, where dielectric properties are crucial. By enabling fast and reliable predictions, this AI model offers a powerful tool for materials discovery and device optimization in modern electronics.
Machine learning has become an effective tool for accelerating materials discovery by predicting material properties with far lower computational cost than exhaustive first-principles calculations. However, its application to dopant engineering in two-dimensional (2D) oxide semiconductors remains relatively unexplored. In this work, we combine supervised machine learning with density functional theory-derived data from the Materials Project database to predict the electronic, thermodynamic, transport, and optical properties of undoped and doped 2D tin monoxide (SnO). Several supervised regression algorithms are evaluated for predicting the band gap, formation energy, carrier mobility, electrical conductivity, and optical absorption coefficient. Among the models considered, gradient boosting regression provides the highest predictive accuracy and consistently reproduces the complex structure–property relationships of both undoped and doped systems. Feature-importance analysis reveals that electronic band descriptors, particularly band width, followed by band asymmetry and electronic anisotropy, are the primary factors governing band-gap prediction, while structural descriptors provide complementary contributions. The predictions indicate that monolayer SnO has a wider band gap, slightly higher formation energy, and higher carrier mobility than bulk SnO due to quantum confinement. Mg and Zn substitution allow systematic tuning of the material properties over a moderate concentration range. The band gap changes only slightly with doping, while Mg incorporation is thermodynamically more favorable than Zn, whose stability decreases with increasing concentration. Dopant-induced impurity scattering reduces carrier mobility and electrical conductivity, whereas optical absorption is enhanced in both Mg- and Zn-doped systems. These results demonstrate that machine learning can efficiently identify promising dopant configurations while providing reliable predictions of dopant-dependent material properties. The proposed framework offers a practical approach for the computational design of doped 2D oxide semiconductors for future optoelectronic and sensing applications.
A unified multiscale system that entails the implementation of density functional theory (DFT), machine learning (ML), and device-level simulation to hasten the search and development of high-performance perovskite solar cell materials is presented.
A design principle is proposed for advanced metal nitride HEDMs: prioritizing high nitrogen-to-metal ratios, light metal elements, and structures wherein nitrogen atoms are spatially separated by the metal matrix, which minimizes N-N bonds and favors dominant M-N bonding.
The Northeast Materials Database is leveraged to develop machine learning models that predict magnetic materials with targeted Curie temperatures from composition-derived descriptors rooted in molecular-level elemental properties, supplemented by a small set of coarse crystal-system and structure-family indicators.
Predicting electrical conductivity in perovskite and double-perovskite materials remains challenging as this property depends on various electronic, chemical, and structural factors. In this work, we evaluate classical machine-learning models trained on different descriptor sets including DFT band gap, non-orbital compositional descriptors, orbital-related descriptors, SOAP structural fingerprints, and a reduced mixed descriptor set to predict DFT-derived transport conductivity. The band gap provides a strong baseline but is insufficient to fully predict the target. The best overall performance is obtained using non-orbital compositional descriptors with Random Forest regression, while orbital-related descriptors achieve nearly comparable accuracy, confirming the importance of valence-electron characteristics. A compact mixed descriptor set preserves nearly the full predictive power of the larger descriptor spaces, showing that accurate prediction can be achieved using a small number of physically motivated variables. In contrast, SISSO showed much lower accuracy, suggesting that sparse symbolic expressions are insufficient to capture the nonlinear relationships underlying our target values. These results demonstrate that physically informed classical machine-learning models can provide an effective surrogate framework for reproducing DFT/BoltzTraP-derived conductivity trends in perovskite and double-perovskite materials.