Tong Xie

Artificial intelligence (AI)–assisted workflows have transformed materials discovery, enabling rapid exploration of chemical spaces of functional materials. Endowed with extraordinary optoelectronic properties, two-dimensional (2D) hybrid perovskites represent an exciting frontier, but current efforts to design 2D perovskites rely heavily on trial-and-error and expert intuition approaches, leaving most of the chemical space unexplored and compromising the design of hybrid materials with desired properties. Here, we introduce an inverse design workflow for Dion-Jacobson perovskites that is built on an invertible fingerprint representation for millions of conjugated diammonium organic spacers. By incorporating high-throughput density functional theory (DFT) calculations, interpretable machine learning, and synthesis feasibility screening, we identified promising organic spacer candidates with deterministic energy level alignment between the organic and the inorganic motifs in the 2D hybrid perovskites. These results highlight the power of integrating invertible, physically meaningful molecular representations into AI-assisted design, streamlining the property-targeted design of hybrid materials.

An asymmetric graph neural network framework (SolarPCE-Net) is introduced for organic photovoltaics, achieving improved power conversion efficiency prediction and offering chemically interpretable design insights.

Materials discovery and design aim to find compositions and structures with desirable properties over highly complex and diverse physical spaces. Traditional solutions, such as high-throughput simulations or machine learning, often rely on complex descriptors, which hinder generalizability and transferability across different material systems. Moreover, these descriptors may inadequately represent macro-scale material properties, which are influenced by structural imperfections and compositional variations in real-world samples, thus limiting their practical applicability. To address these challenges, we propose DARWIN 1.5, the largest 1 open-source large language model tailored for materials science. By utilizing natural language as input, DARWIN eliminates the need for task-specific descriptors and facilitates the integration of human knowledge representation with computational models, enabling a more flexible and unified approach to material property prediction and discovery. Our approach integrates over 6M materials science papers and 21 experimental datasets with information of 49,256 materials, allowing for efficient cross-task knowledge transfer and improved generalization. Through systematic exploration, we show how domain-specific know-how can be effectively integrated into language models while harnessing the inherent syn-ergies between tasks to enhance predictive performance across diverse material science applications. The enhanced model achieves up to 59.1% improvement in prediction accuracy over the base LLaMA-7B model architecture and outper-forms state-of-the-art machine learning approaches across eight materials design tasks. These results highlight the potential of LLMs as a foundation for developing versatile and scalable models in materials science.

The rise of urbanization coupled with pollution has highlighted the importance of outdoor self-cleaning coatings. These revolutionary coatings contribute to the longevity of various surfaces and reduce maintenance costs for a wide range of applications. Despite ongoing research to develop efficient and durable self-cleaning coatings, adopting systematic research methodologies could accelerate these advancements. In this work, we use Natural Language Processing (NLP) strategies to generate open- and traceable-sourced datasets about self-cleaning coating materials from 39,011 multi-disciplinary papers. The data are from function-based and property-based corpora for self-cleaning purposes. These datasets are presented in four different formats for diverse uses or combined uses: material frequency statistics, material dictionary, measurement value datasets for self-cleaning-related properties and optical properties, and sentiment statistics of material stability and durability. This provides a literature-based data resource for the development of self-cleaning coatings and also offers potential pathways for material discovery and prediction by machine learning.

The scientific literature contains valuable information that can be used for future applications, but manual analysis presents challenges due to its size and disciplinary boundaries. The prevailing solution involves natural language processing (NLP) techniques such as information retrieval. Nonetheless, existing automated systems primarily provide either statistically based shallow information or deep information without traceability, thereby falling short of delivering high-quality and reliable insights. To address this, we propose an innovative approach of leveraging sentiment information embedded within the literature to track the opinions toward materials. In this study, we integrated material knowledge into text representation and constructed opinion data sets to hierarchically train deep learning models, named as Scientific Sentiment Network (SSNet). SSNet can effectively extract knowledge from the energy material literature and accurately categorize expert opinions into challenges and opportunities (94% and 92% accuracy, respectively). By incorporating sentiment features determined by SSNet, we can predict the ranking of emerging thermoelectric materials with a 70% correlation to experimental outcomes. Furthermore, our model achieves a commendable 68% accuracy in predicting suitable nanomaterials for atomic layer deposition (ALD) over time. These promising results offer a practical framework to extract and synthesize knowledge from the scientific literature, thereby accelerating research in the field of nanomaterials.

Based on the increasing number of smartphones used by the elderly in China and the difficulties encountered by the elderly when they seek medical treatment, a new appointment registration method based on Android and a medical convenience assistant designed by navigation are adopted. The App has four functional modules: login registration, medical route planning, medical consultation, and registration order management. The function of the client and the compatibility test of different mobile terminals proved that the App could realize intelligent registration, simulated payment, route planning, and traffic planning as one of the intelligent appointment registration medical services.