Materials Precursor Score: Modeling Chemists’ Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors

Abstract

Computation is increasingly being used to try to accelerate the discovery of new materials. One specific example of this is porous molecular materials, specifically porous organic cages, where the porosity of the materials predominantly comes from the internal cavities of the molecules themselves. The computational discovery of novel structures with useful properties is currently hindered by the difficulty in transitioning from a computational prediction to synthetic realization. Attempts at experimental validation are often time-consuming, expensive, and frequently, the key bottleneck of material discovery. In this work, we developed a computational screening workflow for porous molecules that includes consideration of the synthetic difficulty of material precursors, aimed at easing the transition between computational prediction and experimental realization. We trained a machine learning model by first collecting data on 12,553 molecules categorized either as “easy-to-synthesize” or “difficult-to-synthesize” by expert chemists with years of experience in organic synthesis. We used an approach to address the class imbalance present in our data set, producing a binary classifier able to categorize easy-to-synthesize molecules with few false positives. We then used our model during computational screening for porous organic molecules to bias toward precursors whose easier synthesis requirements would make them promising candidates for experimental realization and material development. We found that even by limiting precursors to those that are easier-to-synthesize, we are still able to identify cages with favorable, and even some rare, properties.

Rigidity and Flexibility in Rotaxanes and Their Relatives; On Being Stubborn and Easy-Going

“…Similarly, illustrative examples have been selected rather than providing exhaustive coverage of the literature. Strategies to rigidify individual macrocycles and threads based on covalent bonding have already been extensively described by others ( Greenaway et al, 2019 ; Turcani et al, 2019 ; Bennett et al, 2021 ) and is outside the scope of this Review . We will end by providing examples of how rotaxane rigidity and flexibility can play a role in molecular technologies including molecular switches, molecular machines, and bulk materials at the macroscopic scale.…”

Unlocking the computational design of metal–organic cages

“…A common issue for both approaches that the user must be aware of is that arbitrarily combining components can diminish synthesisability. 77,78 The top-down approach benefits from being based on templates, which inherently encodes design rules into the process and the building blocks used.…”

“…Rational precursor selection should be an integral part of material design to avoid synthesisability issues. 78 4 Predicting topology and configuration of metal-organic cages Much of the computational effort spent on MOC studies, so far, has been on trying to determine or rationalise the outcome of a self-assembly process. Assuming the thermodynamic product is formed, the connectivity of the building blocks and their stoichiometric ratios define the possible self-assembly outcomes (topologies).…”

“…Rational precursor selection should be an integral part of material design to avoid synthesisability issues. 78 …”

Roles and opportunities for machine learning in organic molecular crystal structure prediction and its applications

“…al. 72 developed a binary classification model for the synthetic difficulty of porous organic cage precursors, learning the responses from experienced synthetic chemists to the question, "Can you make 1 g of the compound in under five steps?" Limiting the number of reaction steps works to reduce the overall yield loss during synthesis.…”