DOI: 10.5061/dryad.41ns1rnft
title: Dual loop active learning of hydrophobicity of patterned SAMs

description: Hydrophobic interactions drive numerous biological and synthetic
 processes. The materials used in these processes often possess chemically
 heterogeneous surfaces that are characterized by diverse chemical groups
 positioned in close proximity at the nanoscale; examples include
 functionalized nanomaterials and biomolecules like proteins and peptides.
 Nonadditive contributions to the hydrophobicity of such surfaces depend on
 the chemical identities and spatial patterns of polar and nonpolar groups
 in ways that remain poorly understood. Here, we develop a dual-loop active
 learning framework that combines a fast, reduced-accuracy method (a
 convolutional neural network) with a slow, higher-accuracy method
 (molecular dynamics simulations with enhanced sampling) to efficiently
 predict the hydration free energy, a thermodynamic descriptor of
 hydrophobicity, for nearly 200,000 chemically heterogeneous self-assembled
 monolayers (SAMs). Analysis of this data set reveals that SAMs with
 distinct polar groups exhibit substantial variations in hydrophobicity as
 a function of their composition and patterning, but the clustering of
 nonpolar groups is a common signature of highly hydrophobic patterns.
 Further MD analysis relates such clustering to the perturbation of
 interfacial water structure. These results provide new insight into the
 influence of chemical heterogeneity on hydrophobicity via quantitative
 analysis of a large set of surfaces, enabled by the active learning
 approach. Paper title: Identifying Nonadditive Contributions to the
 Hydrophobicity of Chemically Heterogeneous Surfaces via Dual-Loop Active
 LearningAuthors: Atharva Kelkar, Bradley Dallin, Reid Van
 LehnDOI: doi.org/10.1063/5.0072385
description: This folder contains files to reproduce and analyze molecular
 dynamics (MD) trajectories of a large set of patterned self-assembled
 monolayers (SAMs), with patterns of a nonpolar and a polar group (either
 amine, amide, or hydroxyl). The dataset is split into 2 major parts
 - 1. <strong>trajectories</strong> - Tar
 files containing equilibrium and short production trajectories (GROMACS
 xtc files) for INDUS-labelled patterns from 3 different parts of the dual
 loop active learning algorithm method. This folder also contains initial
 configurations, topology files, mdp files, and CHARMM inputs needed to
 reproduce or extend trajectories. The 3 different parts of the dual loop
 active learning process are as follows -<br>     a.
 <strong>Seed runs</strong> - Randomly-chosen patterns used to
 initiate the dual loop active learning process.<br>     b.
 <strong>GPR runs</strong> - Patterns identified during the
 slow loop of the active learning loop<br>     c.
 <strong>Max-dev runs</strong> - Patterns which were predicted
 to have the highest and lowest HFEs for a given polar area fraction,
 identified after the completion of training of the active learning
 loop Each trajectory folder contains a file titled
 "hfe_label.txt" which contains the calculated value of the HFE
 in units of kBT (with T = 300K). All simulations were performed using the
 force field files supplied in the top-level charmm36-jul2017.ff<br>
 directory using the TIP4P/2005 water model and at constant volume and
 temperature (NVT). The name of the polar end group for each SAM is
 specified in the folder name. 2.
 '<strong>collated_histograms.pickle</strong>' -
 Pickle containing a pre-processed dataset with 20x20 oxygen and hydrogen
 number density histograms corresponding to the trajectories in the
 'trajectories/' folder. Each pickle file has the following data
 -<br>     a. '<strong>histograms</strong>' -
 Hydrogen and water density histograms (numpy arrays) of size [n_frames, 2,
 400]<br>     b. '<strong>labels</strong>' -
 INDUS-calculated HFE labels for each of the histograms<br>     c.
 '<strong>ligand</strong>' - Ligands associated with
 each histograms<br>     d.
 '<strong>run_type</strong>' - Classification of
 category of runs (from point 1 above - Seed, GPR, or Max-dev)<br>  
   e. '<strong>folder_name</strong>' - Folder name of
 the trajectory associated with each histogram The
 objective of collated histograms is to enable scientists to load in a
 curated dataset with labels and histograms and apply data-centric tools to
 study the hydrophobicity of a large set of chemically heterogeneous
 surfaces with diverse end group chemistries. All the
 data needed to train the 3D CNN, i.e., idealized SAMs with amine, amide,
 and hydroxyl end groups, referenced in the paper have already been shared
 publicly with our previous publication (<em>Kelkar, Dallin, and Van
 Lehn, J Phys Chem B 124 (41), 2020</em>) at the following link:
 https://zenodo.org/record/4485912. All the codes required to generate results and analyze data using the dual-loop active learning algorithm, with trained GPR models, are uploaded to a git repo: https://gitlab.com/atharva-kelkar/dual-loop-active-learning
description: A README file has been added to the base folder which details the
 data present in the backup folder.