2023-02-202024-05-13https://scholars.lib.ntu.edu.tw/handle/123456789/650032Parag Parashar博士在此計畫中將協助團隊在三個研究方向的進行：首先，他將參與機器學習（ML）模型的開發，以分析和預測金屬有機框架（MOF）材料中的儲氣性能。其次，他將透過研究各種幾何和化學特徵，建立與每個ML模型的儲氣容量之間的相關性。第三，他將在遷移學習的幫助下建立一個廣義吸附模型，該模型可用於在各種不同條件下對任意氣體分子進行吸附預測。 Dr. Parag Parashar will assist the team in three research directions in this project: First, he will participate in the development of machine learning (ML) models to analyze and predict gas storage performance in metal-organic frameworks (MOFs). Second, he will establish correlations with the gas storage capacity of each ML model by studying various geometric and chemical features. Third, he will develop a generalized adsorption model with the help of transfer learning, which can be used to make adsorption predictions for arbitrary gas molecules under a variety of different conditions.機器學習;金屬有機框架;儲氣;吸附模型;machine learning; MOF; gas storage; adsorption model