Simulation of deep eutectic solvents: Progress to promises
Simulation of deep eutectic solvents: Progress to promises
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DOI：
10.1002/wcms.1598
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Caroline Velez;O. Acevedo
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文献类型：
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作者：
Caroline Velez;O. Acevedo
深共晶溶剂（DESs）是二元或三元化合物的混合物，相对于纯分离组分具有显著的熔点下降。DESs的发现是多个领域的重大突破，其低成本和可调的物理化学性质使其受益。然而，考虑到进行大规模实验测量所需的费用和时间，通过几乎无限的合成组合为特定应用量身定制DESs可能是一种障碍，也可能是一种好处。这强调了对快速计算工具的需求，能够从分子结构中准确预测DES的物理化学性质。然而，考虑到这些系统极其不理想的行为、组件的不对称以及分子静电相互作用的复杂性，在原子水平上对它们进行建模或模拟并非易事。尽管存在挑战，以量子力学（QM）方法为特征的计算报告已经为熔点降低与DESs中存在的独特而复杂的氢键网络之间的关系提供了重要的理解。经典分子动力学（MD）方法研究了体相溶剂组织与热力学和输运性质的关系。机器学习算法作为结构属性预测工具已经显示出巨大的潜力。总的来说，这篇综述强调了有意义地推进了我们对DESs的理解的计算成就，并努力让读者了解所采用方法的总体优势和缺点，同时暗示了进步的承诺。
Deep eutectic solvents (DESs) are binary or ternary mixtures of compounds that possess significant melting point depressions relative to the pure isolated components. The discovery of DESs has been a major breakthrough with multiple fields benefitting from their low cost and tunable physiochemical properties. However, tailoring DESs for specific applications through their practically unlimited synthetic combinations can be as much a hindrance as a benefit given the expense and time‐required to perform large‐scale experimental measurements. This emphasizes the need for fast computational tools capable of making accurate predictions of DES physiochemical properties exclusively from molecular structure. Yet, these systems are not trivial to model or simulate at the atomic level given their exceedingly nonideal behaviors, asymmetry of components, and the complexity of their molecular electrostatic interactions. Despite the challenge, computational reports featuring quantum mechanical (QM) methods have provided significant understanding into the relationship between the melting point depression and the unique and complex hydrogen bond network present in DESs. Classical molecular dynamics (MD) methods have examined bulk‐phase solvent organization in conjunction with thermodynamic and transport properties. Machine learning (ML) algorithms have shown great potential as structure–property prediction tools. Overall, this review highlights computational accomplishments that have meaningfully advanced our understanding of DESs and strives to give the reader a sense of the overall strengths and drawbacks of the methodologies employed while hinting at promises of advances to come.