Research

Harnessing and implementing artificial intelligence (AI) and neural network models for complex physicochemical processes are essential for achieving more realistic and efficient simulations. These models, while preserving important physical principles, enable precise predictions of material behavior over extended spatial and temporal scales—something traditional methods often struggle to achieve. By utilizing AI, we can accelerate the discovery and optimization of new materials and processes, thereby addressing complex challenges in chemistry, materials science, and engineering. Ultimately, this approach enhances our ability to conduct scalable and accurate simulations, bringing theoretical insights closer to real-world applications.

Developing and applying multiscale simulations, from quantum mechanics to coarse-grained molecular dynamics and continuum scale modeling, is essential for addressing energy and sustainability challenges. These simulations enable us to understand and optimize complex processes across different scales, from atomic-level reactions to large-scale system behavior, which is critical for designing efficient energy conversion and storage systems. By integrating and connecting insights from various scales with enhanced sampling methods, we can develop more sustainable materials and technologies, such as advanced biological and catalytic processes, that minimize environmental impact. Additionally, this approach helps in predicting the performance and lifetime of energy-related materials and systems under realistic operating conditions, guiding the development of future sustainable solutions.

Studying biomolecular modeling is crucial because it allows us to understand the intricate mechanisms of enzymatic reactions at a molecular level, which are essential for life processes. Through multiscale computational simulations, we can investigate and visualize how enzymes interact with substrates, predict reaction outcomes, and identify key factors influencing enzyme efficiency and specificity. This understanding aids in the design of novel drugs and therapeutic interventions by targeting specific enzymes linked to diseases. Additionally, it provides insights into enzyme evolution and aids in the development of industrial catalysts for more sustainable chemical processes.