本研究針對185種鋰電池電解液添加劑分子進行計算篩選，以第一原理分別計算出氣態與溶液態添加劑分子之分子軌域能量值、氧化與還原電位，並針對各項能量值與電位值進行相依性分析。再以類神經網路模型進一步建立分子結構因子—如分子內所含原子種類、個數與鍵結數量等等—與分子軌域能量值、氧化還原電位之間的映射關係。
本研究第一部分為將計算的分子軌域能量值和氧化還原電位間建立一線性迴歸方程，分析其線性相依性。可從添加劑分子之EHOMO(Gas)由線性關係推得OP(Solvent)，並附上誤差項；亦可由ELUMO(Gas)推得RP(Solvent)。由於帶電荷溶液態分子結構優化過程不易達收斂，計算過程耗時，由這些線性關係可簡化獲得添加劑分子氧化還原電位的計算過程，從電中性分子即可推估OP(Solvent)與RP(Solvent)，可針對鋰電池系統適合的OP(Solvent)、RP(Solvent)值進行初步篩選，提高實驗效率。
本研究第二部分欲分析分子結構因子與氧化還原電位之間的關係。建立一類神經網路模型，將第一部分中計算之添加劑分子列為類神經網路學習樣本，針對不同學習參數進行測試，建立一完整類神經網路架構，並將網路輸出值與目標值進行迴歸分析以檢驗網路學習效果。由於類神經網路運作，其各項結構因子加權值的大小可解析成該結構因子對目標值之影響強弱；因此，可從分子結構中推得OP(Solvent)、RP(Solvent)之預估值，亦可提高實驗效率。

Recently, a great deal of research regarding energy density and voltage of lithium ion batteries (LIBs) which are being developed for serving grid support in many applications have gained increasing interest. However, high voltage application in LIBs makes it unstable and lowers performance crucially.
Using various solvent formulas and functional additives can address the issue with stabilizing the electrolytes from decomposition. The selection of the best additive among phosphides, sulfonate esters and lactam, etc., which perfectly fits the electrodes, can accelerate and improve the developments in LIBs. Several parameters, including energy of lowest unoccupied molecular orbital (LUMO), highest occupied molecular orbital (HOMO), oxidation potentials (OPs), reduction potentials (RPs) can be used for first screening of additive selection. In this study, first-principle density functional theory calculations were utilized and the solvent effect has been considered. It is inferred that there are certain correlations between EHOMO with OP and ELUMO with RP.
An artificial neural network (ANN) model is developed and the calculation efficiency of the entire scheme can be improved. It is demonstrated that systematic machine learning of molecular structure factors is capable of describing the EHOMO and ELUMO. Furthermore, the developed model can also be used for estimating the EHOMO and ELUMO of newly designed electrolyte additives from the structure factors with high accuracy.

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