Computational Nano-Materials Design (CNMD) laboratory aims to improvematerials properties by developing a fundamental understanding of the integrated physical and chemical processes of materials by using a multidisciplinary computational researches.Research involves the usage of various computational methods that range from classical molecular dynamics, model Hamiltonians, DFT, to many-body first principles approaches. Research projects are performed based on the machine-learning-based multi-scale simulation framework that can be widely applicable to different materials problems and allows predictive engineering for novel materials design.
Understanding changes in materials behavior under various conditionsis of utmost importance inoptimizing materials properties for diverse applications. Although various techniques have been developed to characterize materials behavior at nano and atomic-scale, achieving precise control of materials properties has remained a challenge for material scientists and engineers. Recent developments in theory and modeling have opened up the possibility of gaining a fundamental understanding of the integrated physical and chemical processes of materials behavior, which could in turn be used to drive the development of unique fabrication techniques and design strategies for advanced engineering. First-principles calculations have shown tremendous promise for describing material behaviors at atomistic level, particularly when combining various computational techniques such as molecular dynamics and Monte Carlo approaches. Recently, these approaches have begun to offer truly predictive power in optimizing materials properties for diverse applications. Rather than relying on a single technique, my group will be to leverage an array of capabilities for computationally guided optimization of materials properties towards the design of novel nano-materials, and ultimately towards novel multifunctional materials with tailored properties.
Research projects ofthe group arecarriedout by the machine-learning-basedmaterial design and optimization. This can be done by building relationships between structure and materials propertiesand identifying important featuresof materials properties for specific applications. The built relationshipsare used as the input database for materials screening and optimization based on machine-learning techniques. In addition, the group actively performs extensive collaborations with diverse theoretical and experimental groups in different fields in order to push forward the frontiers of materials research.