能源一直都是全世界關注的議題，如何能夠更有效率並降低污染的轉換能源一直是綠能科學家們所努力的方向。其中又以幾乎零汙染的聚光型太陽能發電在大型電廠中最受矚目，這種方式為收集太陽光熱能進行發電，但並非全天候皆有日照，意味著夜間無法持續發電，因此工程上便期望在有日照時將多餘的熱能儲存，藉此在無日照時也能夠利用儲存的熱能來發電。此外藉由儲存熱能來保存能量之方式亦可用於各式廢熱回收，將浪費之能量回收再利用，或是藉由將熔融鹽儲熱儲能之體積縮小，取代目前容易自放電之電池來達到攜帶與運送能量之目標。
儲存熱能的介質可用便宜大量的熔融鹽 (molten salt)，此通常為無機鹽類，常溫下為固態的離子晶體，在高溫下會熔化形成液態的離子熔融鹽。儲熱熔融鹽通常為調配熱性質而採用多種無機鹽類的混合物，本研究選擇三種目前較為常用的熔融鹽 (硝酸鋰LiNO3、硝酸鈉NaNO3、硝酸鉀KNO3)，期望能夠調配出最佳的比例，使混合的熔融鹽能夠擁有最佳的儲熱表現。
本論文利用古典分子動力學及第一原理分子動力學模擬方式進行計算，預測混合熔融鹽在不同溫度不同比例下的性質。模擬方面先建立符合實際原子狀態的定分子數定壓定溫系綜　(NPT ensemble)，之後對純物質做熱傳與質傳性質模擬並與實驗值進行比對，確定模擬方式的準確性後，進而開始調整各純物質的相對比例，進行計算混合物的熱傳導率、比熱、黏滯係數、離子傳導率等等與儲熱相關的重要性質，期望能夠以分子動力學計算方式快速找出最佳熱質傳性質的混合比例，以取代昂貴及重複之嘗試錯誤實驗，數據提供日後建立儲熱材料之大數據資料庫。

Nowadays, energy conversion has become a crucial issue and green energy scientists are devoted to enhancing energy transfer efficient without pollution. The concentrated solar power system is a remarkable method that generates electric power by storing solar energy in daytime and releasing it at night so that electric power can be produced all days long. Additionally, we can recover the waste heat from industrial appliances and store the thermal energy for power regeneration in some other time at some other places. This is better than direct electric energy storage which has serious leakage problems.
For thermal storage materials, we used inorganic salts which remain ionic solids at room temperature and melted to molten salts at high temperature. That material typically was mixed by enormous inorganic salts. Therefore, we mixed three most common nitrates (LiNO3, NaNO3 and KNO3) to find out the specific proportion of mixture which had best storage performance.
The methodology of this research combined the computational quantum mechanics with molecular dynamics to predict the properties of molten salt mixtures with different proportion at various temperatures. For simulations, we build the realistic molecular model in the NPT ensemble. At first, the heat/mass transfer properties of the pure substance will be calculated to compare with experimental result, in order to verify the correctness of this method. Then, mixed three pure substance to mixtures and tuned the mixtures to calculate the important properties of thermal storage, like thermal conductivity, heat capacity, viscosity and melting point. It is expected to find out the proportion of mixtures which have the best thermal storage performance and can be used to replace expensive experiments. Furthermore, the simulation results can be used to build up the Big Data databases for future thermal storage research.

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