Natural language processing (NLP) techniques have revolutionized the extraction of material knowledge from scientific papers and patents. By leveraging advanced machine learning models like BERT and GPT, researchers can systematically analyze vast volumes of text to identify key material properties, trends, and relationships. These methods enable automated entity recognition, trend analysis, and knowledge graph construction, significantly accelerating the discovery of novel semiconductor materials and their applications.

Transformer-based models such as BERT and GPT are particularly effective for processing scientific literature due to their ability to understand context and semantic relationships. BERT, or Bidirectional Encoder Representations from Transformers, excels in tasks requiring deep comprehension of technical language, such as named entity recognition (NER) for material properties. For instance, BERT can identify mentions of carrier mobility, thermal conductivity, or bandgap values within research papers, even when expressed in varied linguistic forms. GPT, or Generative Pre-trained Transformer, is adept at generating summaries or predicting trends based on existing literature, making it useful for synthesizing large-scale material datasets.

Entity recognition for material properties involves training models to detect and classify specific terms and their associated values. For example, a model might extract phrases like "electron mobility of 1500 cm²/Vs" or "thermal conductivity of 130 W/mK" from a paper. These extracted entities can then be standardized and stored in structured databases for further analysis. Tools like MatScholar, developed by the Materials Project, employ such techniques to build comprehensive repositories of material properties. By parsing thousands of papers, these tools create searchable datasets that link materials to their experimentally reported characteristics.

Trend analysis across decades is another critical application of NLP in material science. By processing historical publications, models can identify shifts in research focus, such as the growing interest in wide-bandgap semiconductors like GaN and SiC over the past two decades. Temporal analysis can reveal correlations between technological advancements and material innovations, such as the rise of perovskite solar cells following breakthroughs in solution-processing techniques. Such insights help researchers anticipate future directions and allocate resources efficiently.

Knowledge graph construction integrates extracted entities and relationships into a unified framework, enabling intuitive exploration of material science data. A knowledge graph might link a semiconductor material to its synthesis methods, properties, and device applications, creating a web of interconnected information. For example, graphene could be connected to its high electron mobility, mechanical strength, and applications in flexible electronics. These graphs facilitate hypothesis generation by revealing indirect relationships, such as shared synthesis challenges between dissimilar materials.

Several challenges exist in applying NLP to material science texts. Technical jargon, inconsistent terminology, and implicit knowledge pose difficulties for standard language models. For instance, a paper might refer to "carrier concentration" as "doping level" or "charge density" without explicit definitions. Domain-specific fine-tuning of models is often necessary to improve accuracy. Additionally, patents present unique challenges due to their legalistic language and deliberate obfuscation of key details.

Tools like MatScholar address these challenges by incorporating domain-specific training data and advanced preprocessing techniques. They employ pipelines that combine NER, relation extraction, and entity linking to build coherent databases from unstructured text. For example, a pipeline might first identify mentions of "ZnO" in a paper, then extract its bandgap value, and finally link this information to relevant synthesis conditions. Such automation drastically reduces the manual effort required for literature reviews.

Beyond property extraction, NLP models can predict material performance by analyzing textual patterns. For instance, a model trained on thermoelectric materials might learn that certain crystal structures are frequently associated with high ZT values, even if the exact mechanism is not explicitly stated. This capability enables data-driven material design, where models suggest promising candidates for further experimental validation.

The integration of NLP with other AI techniques further enhances material discovery. For example, combining text mining with density functional theory (DFT) calculations allows researchers to validate hypothetical materials proposed in literature. Similarly, NLP can guide high-throughput experimentation by prioritizing synthesis methods described in recent patents. These synergies create a closed-loop system where computational predictions, experimental validation, and literature analysis continuously inform each other.

Ethical considerations also arise when using NLP for material science. Automated extraction may inadvertently propagate biases present in historical literature, such as the underrepresentation of certain material classes. Additionally, reliance on published data risks excluding negative results, which are less likely to be reported. Transparent model training and diverse dataset curation are essential to mitigate these issues.

Future advancements in NLP will likely focus on multimodal approaches, combining text with figures, tables, and chemical formulas for richer material representations. Improved few-shot learning techniques could enable models to generalize better across subfields with limited training data. Furthermore, real-time literature monitoring systems may emerge, alerting researchers to breakthroughs as they are published.

In summary, NLP techniques like BERT and GPT provide powerful tools for extracting and analyzing material knowledge from scientific texts. By automating entity recognition, trend analysis, and knowledge graph construction, these methods accelerate the discovery and optimization of semiconductor materials. Tools such as MatScholar demonstrate the potential of AI-driven literature mining to transform material science research. As models continue to improve, their integration with experimental and computational approaches will further advance the field, enabling faster innovation and more efficient resource allocation.