Deep learning combined with IAST to screen thermodynamically feasible MOFs for adsorption-based separation of multiple binary mixtures
Deep learning combined with IAST to screen thermodynamically feasible MOFs for adsorption-based separation of multiple binary mixtures
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DOI：
10.1063/5.0048736
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发表时间：
2021-06-21
影响因子：
4.4
通讯作者：
Gomez-Gualdron, Diego A.
中科院分区：
文献类型：
--
作者：
Anderson, Ryther;Gomez-Gualdron, Diego A.
金属-有机骨架(MOF)的结构可以调整，以重复地产生吸附性能，从而使这些材料能够在固定吸附床中用于非热分离。然而，对于数以百万计的可能的MOF结构，挑战是找到具有最佳吸附性能的MOF来分离给定的混合物。因此，为了找出值得进一步研究的有前景的MOF结构，有必要进行计算筛选，而不是实验筛选，这是一个传统上使用分子模拟完成的过程。然而，当筛选一个扩展的MOF数据库时，即使是分子模拟也可能变得困难，因为它们在多于几个组成、温度和压力组合下的分离性质。在这里，我们展示了一种替代计算框架的进展，该框架可以在各种条件下有效地识别用于分离各种气体混合物的最高性能的MOF，并且所需的计算成本是分子模拟的一小部分。该框架使用了一个多用途多层感知器(MLP)模型，该模型可以预测各种小吸附物的单组分吸附，与理想吸附溶液理论(IAST)相结合，可以预测多种组成和压力下Xe/Kr、CH4/CH6、N-2/CH4和Ar/Kr等混合物的二元吸附。为了让这个MLP+IAST框架以足够的精度工作，我们发现MLP在低压(0.01-0.1bar)下做出准确的预测是至关重要的。用这种能力训练模型后，我们发现由MLP+IAST计算确定的分离性能第95和第90个百分位数的MOF分别为65%和87%，与不同条件下几个混合物的模拟预测的第95个百分位数的MOF相同(平均)。在验证了我们的MLP+IAST框架之后，我们使用了一个集群算法来识别在多个条件下的多个分离的高性能的“特权”MOF。作为一个例子，我们重点研究了在工业上相关的1巴80/20 Xe/Kr和5巴80/20 N-2/CH4分离的高性能MOF。最后，我们使用MOF自由能(在我们的整个数据库上计算)来识别特权MOF，这些MOF也可能是可合成的，至少从热力学的角度来看是这样。
The structures of metal-organic frameworks (MOFs) can be tuned to reproducibly create adsorption properties that enable the use of these materials in fixed-adsorption beds for non-thermal separations. However, with millions of possible MOF structures, the challenge is to find the MOF with the best adsorption properties to separate a given mixture. Thus, computational, rather than experimental, screening is necessary to identify promising MOF structures that merit further examination, a process traditionally done using molecular simulation. However, even molecular simulation can become intractable when screening an expansive MOF database for their separation properties at more than a few composition, temperature, and pressure combinations. Here, we illustrate progress toward an alternative computational framework that can efficiently identify the highest-performing MOFs for separating various gas mixtures at a variety of conditions and at a fraction of the computational cost of molecular simulation. This framework uses a "multipurpose" multilayer perceptron (MLP) model that can predict single component adsorption of various small adsorbates, which, upon coupling with ideal adsorbed solution theory (IAST), can predict binary adsorption for mixtures such as Xe/Kr, CH4/CH6, N-2/CH4, and Ar/Kr at multiple compositions and pressures. For this MLP+IAST framework to work with sufficient accuracy, we found it critical for the MLP to make accurate predictions at low pressures (0.01-0.1 bar). After training a model with this capability, we found that MOFs in the 95th and 90th percentiles of separation performance determined from MLP+IAST calculations were 65% and 87%, respectively, the same as MOFs in the simulation-predicted 95th percentile across several mixtures at diverse conditions (on average). After validating our MLP+IAST framework, we used a clustering algorithm to identify "privileged" MOFs that are high performing for multiple separations at multiple conditions. As an example, we focused on MOFs that were high performing for the industrially relevant separations 80/20 Xe/Kr at 1 bar and 80/20 N-2/CH4 at 5 bars. Finally, we used the MOF free energies (calculated on our entire database) to identify privileged MOFs that were also likely synthetically accessible, at least from a thermodynamic perspective.