Unexpected coexistence of diaphragmatic and abdominal wall endometriosis in a case of bilateral ovarian endometriomas: a case report.

Pb-free piezoelectric material, (K, Na)NbO composition is an attractive candidate with a low theoretical density of 4.5 g/cm and the ability to form complete solid solutions with alkali, alkaline, and rare-earth metals, as well as transition metals. The issue lies with inherently low electromechanical properties ( ~ 160 pC/N) in base composition, even after meticulous optimization of the K/Na ratio. To address the issue, the co-doped strategy has been profoundly used, where the base KNaNbO (KNN) composition is modified with more than two dopants, each contributing to a particular set of properties. Apart from individual elements, in recent years, the doping methodology has evolved in form of additional ABO systems like BiAZrO (A: Na, K, Ag, and Li etc.) and BiBO (B: Al, Fe, and Sc) which consolidates both the ferroelectric-ferroelectric phase boundary (R-O and O-T) at room temperature resulting in significant improvement of electrical properties along with diffused transition resulting in temperature stability. By developing microstructural orientation (texturing), the magnitudes can be further enhanced by exploiting the crystal anisotropy that exhibit the most favorable properties. Literature reports, textured R-O-T multiphase KNN compositions proved to be a benchmark against conventional MPB-type systems (such as the PZT system) in terms of figure of merit (FoM) required for tonpilz transducer. Broadly, the FoM of piezoelectric materials can be tailored for specific applications. High-power devices, such as transducers, transformers, and ultrasonic motors, require a high mechanical quality factor () with stable electromechanical coupling () and piezoelectric coefficient () to operate reliably at resonance. However, resonant operation introduces challenges including self-heating, domain-wall-induced nonlinearity, and thermal runaway, potentially leading to depolarization. These effects are mitigated through acceptor doping, which produces “hard” compositions suitable for high powered operation. In contrast, low-power or off-resonance applications, such as precision sensors, actuators, and energy harvesting, demand high sensitivity, achieved via enhanced and voltage coefficients () in donor-doped “soft” compositions. In both regimes, thermal stability is critical to maintain linearity and prevent depolarization. While these application-specific performance requirements are well understood, achieving them simultaneously through compositional engineering of complex piezoelectric systems remains a significant materials design challenge. The optimization of multi-element compositions is typically governed by tedious, iterative experimentation, with limited systematic frameworks available to disentangle the individual and synergistic roles of multiple dopants. This issue can be addressed using complex pattern recognition techniques from the existing literature, employing a data-driven approach. Additionally, generative modeling approaches, especially large language models (LLMs), has advanced pattern recognition capabilities and offer a promising route to uncover the correlation between complex dopants and their corresponding properties. LLMs, despite originally being developed for text generation, have recently emerged as powerful tools for pattern recognition, knowledge retrieval, and hypothesis generation in scientific and engineering contexts. We hypothesized that LLMs can help guide human experts through high-dimensional design spaces by identifying correlations, inferring dopant trends, and proposing composition-phase relationships. Although these models are effective at extracting statistical patterns across large textual corpora, their ability to perform causal reasoning remains unproven. To mitigate this problem, a human-in-the-loop framework is adopted where the role of the LLM is explicitly constrained to the generation of chemically diverse candidate compositions within expert-defined design spaces. This division of labor is intentionally structured to utilize the complementary strengths of both LLM (facilitating efficient exploration of a high-dimensional compositional landscape) and human intervention (ensuring adherence to physically meaningful principles and guarding against heuristic biases). Utilizing the pattern recognition capabilities of LLMs along with expert refinement, this work accelerates the discovery of new Pb-free compositions for high-performance piezoelectric materials.