Investigating the Effect of Coarse-Graining on Chemical Compound Space

Abstract

Chemical structure-property relationships are essential for the development of new materials used in all facets of life. Practically, this process amounts to projecting regions of the chemical compound space (CCS) onto certain descriptors related to the property of interest, allowing the structure-property relationship to be inferred. The challenge in constructing these relationships usually stems from a lack of data, as their accuracy and transferability will depend on how well-sampled CCS is with respect to the chosen descriptors. High-throughput screening, in which the properties of compounds are determined in an automated fashion, is one strategy used to overcome this problem. However, for many properties of soft-matter systems, this approach is difficult to implement computationally. This difficulty arises due to the large costs associated with adequately sampling complex free energy landscapes at atomistic resolutions using established tools such as molecular dynamics (MD) simulations. Coarse-grained (CG) models, parameterized at lower resolutions compared to their atomistic counterparts, provide a means to circumvent these costs. However, many of these models are constructed in order to specifically reproduce the properties of a small number of compounds, making it difficult to generalize across CCS. In this work, we demonstrate that the coarse-grained Martini model reduces the size of CCS, and can be used in computational high-throughput screening methods to efficiently construct chemical structure-property relationships over wide ranges of CCS. We find that this reduction of CCS is due to a limited number of Martini interaction types, with multiple atomistic chemical fragments mapping to the same CG interaction type. We then investigate the relationship between unsupervised machine learning and coarse-graining, yielding strategies for parameterizing chemically transferable CG models from both a top-down and bottom-up perspective. We employ these data-driven techniques to parameterize new top-down CG models and quantify their transferability and accuracy as a function of the number of CG interaction types for each model. Finally, we develop a method that uses unsupervised machine learning in combination with the bottom-up multiscale coarse-graining technique to generate chemically-transferable CG models with high structural accuracy. We examine the limitations of both top-down and bottom-up approaches and make recommendations for the future development of these methodologies. Overall, our work demonstrates the means by which chemically-transferable CG models can be both constructed and utilized to efficiently infer chemical structure-property relationships for materials discovery.