|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Physics-informed Data-driven Surrogate Modeling for Advancing Experiments and the Study of Novel Materials
||Anup Pandey, Reeju Pokharel
|On-Site Speaker (Planned)
With the advancements in experimental facilities like high-energy X-ray sources, there is an enormous increase in the experimental data, which has imposed challenges in their timely analysis and interpretation. In recent years, more practical applications of state-of-the-art machine learning (ML)algorithms are emerging as a powerful tool in accelerating the computation time, providing real-time feedback during the experiments, and accelerating the data collection and reconstruction process. I will discuss the use of physics-informed data-driven surrogate modeling in providing real-time feedback and accelerate the reconstruction process during the experiments, such as high energy X-ray diffraction microscopy and electron backscattering diffraction. On the other hand, the molecular dynamics based atomistic study of materials such as multicomponent alloys is limited to fewer candidates due to the lack of accurate potentials. I will discuss using ML force field trained on data obtained from quantum mechanics calculations in atomistic modeling of a wide range of alloys.