Chemical Reactive Simulation with Neural Network Potentials (NNPs)
Neural network potentials (NNPs) provide a powerful tool for simulating intricate chemical reactions and processes involved in the design of electrification materials. Applications include the study of ionic transport in solid-state electrolytes, interfacial chemical reactions in battery systems, phase transitions in cathode materials, and structural stability under operating conditions. NNPs combine high computational efficiency with exceptional accuracy, enabling the detailed analysis of material properties and behaviors critical for advancing energy storage technologies.
Materials Informatics for Data-Driven Design
Materials informatics integrates molecular modeling, automated data collection, and machine learning to enhance the reliability and accuracy of material property predictions. Automation in molecular modeling generates consistent and high-quality datasets, which serve as a foundation for training machine learning and deep learning models. These models refine predictions of key material properties such as conductivity, mechanical strength, and chemical durability. By combining data-driven insights with molecular simulations, materials informatics is enabling the design of innovative materials tailored to specific applications while improving the overall trustworthiness of computational predictions.