Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Resolution limit of data-driven coarse-grained models spanning chemical
  space   Increasing the efficiency of materials design and discovery remains a
significant challenge, especially given the prohibitively large size of
chemical compound space. The use of a chemically transferable coarse-grained
model enables different molecular fragments to map to the same bead type, while
also reducing computational expense. These properties further increase
screening efficiency, as many compounds are screened through the use of a
single coarse-grained simulation, effectively reducing the size of chemical
compound space. Here, we propose new criteria for the rational design of
coarse-grained models that allows for the optimization of their chemical
transferability and evaluate the Martini model within this framework. We
further investigate the scope of this chemical transferability by
parameterizing three Martini-like models, in which the number of bead types
ranges from five to sixteen for the different force fields. We then implement a
Bayesian approach to determining which chemical groups are more likely to be
present on fragments corresponding to specific bead types for each model. We
demonstrate that a level of performance and accuracy comparable to Martini can
be obtained by using a force field with fewer bead types. However, the
advantage of including more bead types is a reduction of uncertainty with
respect to back-mapping these bead types to specific chemistries. Just as
reducing the size of the coarse-grained particles leads to a finer mapping of
conformational space, increasing the number of bead types yields a finer
mapping of chemical compound space. Finally, we note that, due to the
relatively large size of the chemical fragments that map to a single martini
bead, a clear resolution limit arises when using the water/octanol partition
free energy as the only descriptor when coarse-graining chemical compound
space.

23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
16
The categories of this paper are: stat