Aramco Americas - Boston Research Center, 400 Technology Square, Cambridge, MA, 02139, USA.
Department of Chemical and Biological Engineering, Tufts University, Medford, MA, 02155, USA.
Sci Rep. 2021 Feb 25;11(1):4534. doi: 10.1038/s41598-021-82816-x.
Simulation of conformationally complicated molecules requires multiple levels of theory to obtain accurate thermodynamics, requiring significant researcher time to implement. We automate this workflow using all open-source code (XTBDFT) and apply it toward a practical challenge: diphosphinoamine (PNP) ligands used for ethylene tetramerization catalysis may isomerize (with deleterious effects) to iminobisphosphines (PPNs), and a computational method to evaluate PNP ligand candidates would save significant experimental effort. We use XTBDFT to calculate the thermodynamic stability of a wide range of conformationally complex PNP ligands against isomeriation to PPN (ΔG), and establish a strong correlation between ΔG and catalyst performance. Finally, we apply our method to screen novel PNP candidates, saving significant time by ruling out candidates with non-trivial synthetic routes and poor expected catalytic performance.
模拟构象复杂的分子需要多层次的理论来获得准确的热力学性质，这需要研究人员花费大量时间来实现。我们使用全开源代码（XTBDFT）来自动化这个工作流程，并将其应用于一个实际挑战中：用于乙烯四聚化催化的二膦亚胺（PNP）配体可能会异构化为亚膦酰胺（PPN）（产生有害影响），而一种评估 PNP 配体候选物的计算方法将节省大量的实验工作。我们使用 XTBDFT 来计算广泛的构象复杂的 PNP 配体相对于异构化为 PPN 的热力学稳定性（ΔG），并建立了 ΔG 与催化剂性能之间的强相关性。最后，我们将我们的方法应用于筛选新型 PNP 候选物，通过排除具有复杂合成路线和预期催化性能不佳的候选物，节省了大量时间。
