- Award ID(s):
- NSF-PAR ID:
- Date Published:
- Journal Name:
- Dalton Transactions
- Page Range / eLocation ID:
- 9369 to 9376
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Neural network potentials (NNPs) trained against density functional theory (DFT) are capable of reproducing the potential energy surface at a fraction of the computational cost. However, most NNP implementations focus on energy and forces. In this work, we modified the NNP model introduced by Behler and Parrinello to predict Fermi energy, band edges, and partial density of states of Cu 2 O. Our NNP can reproduce the DFT potential energy surface and properties at a fraction of the computational cost. We used our NNP to perform molecular dynamics (MD) simulations and validated the predicted properties against DFT calculations. Our model achieved a root mean squared error of 16 meV for the energy prediction. Furthermore, we show that the standard deviation of the energies predicted by the ensemble of training snapshots can be used to estimate the uncertainty in the predictions. This allows us to switch from the NNP to DFT on-the-fly during the MD simulation to evaluate the forces when the uncertainty is high.more » « less
This data set for the manuscript entitled "Design of Peptides that Fold and Self-Assemble on Graphite" includes all files needed to run and analyze the simulations described in the this manuscript in the molecular dynamics software NAMD, as well as the output of the simulations. The files are organized into directories corresponding to the figures of the main text and supporting information. They include molecular model structure files (NAMD psf or Amber prmtop format), force field parameter files (in CHARMM format), initial atomic coordinates (pdb format), NAMD configuration files, Colvars configuration files, NAMD log files, and NAMD output including restart files (in binary NAMD format) and trajectories in dcd format (downsampled to 10 ns per frame). Analysis is controlled by shell scripts (Bash-compatible) that call VMD Tcl scripts or python scripts. These scripts and their output are also included.
Changes versus version 1.0 are the addition of the free energy of folding, adsorption, and pairing calculations (Sim_Figure-7) and shifting of the figure numbers to accommodate this addition.
Conventions Used in These Files
- graph_*.psf or sol_*.psf (original NAMD (XPLOR?) format psf file including atom details (type, charge, mass), as well as definitions of bonds, angles, dihedrals, and impropers for each dipeptide.)
- graph_*.pdb or sol_*.pdb (initial coordinates before equilibration)
- repart_*.psf (same as the above psf files, but the masses of non-water hydrogen atoms have been repartitioned by VMD script repartitionMass.tcl)
- freeTop_*.pdb (same as the above pdb files, but the carbons of the lower graphene layer have been placed at a single z value and marked for restraints in NAMD)
- amber_*.prmtop (combined topology and parameter files for Amber force field simulations)
- repart_amber_*.prmtop (same as the above prmtop files, but the masses of non-water hydrogen atoms have been repartitioned by ParmEd)
Force Field Parameters
CHARMM format parameter files:
- par_all36m_prot.prm (CHARMM36m FF for proteins)
- par_all36_cgenff_no_nbfix.prm (CGenFF v4.4 for graphene) The NBFIX parameters are commented out since they are only needed for aromatic halogens and we use only the CG2R61 type for graphene.
- toppar_water_ions_prot_cgenff.str (CHARMM water and ions with NBFIX parameters needed for protein and CGenFF included and others commented out)
Template NAMD Configuration Files
These contain the most commonly used simulation parameters. They are called by the other NAMD configuration files (which are in the namd/ subdirectory):
- template_min.namd (minimization)
- template_eq.namd (NPT equilibration with lower graphene fixed)
- template_abf.namd (for adaptive biasing force)
Adaptive biasing force calculations
- namd/eabfZRest7_graph_chp1404.1.namd (continuation of eabfZRest7_graph_chp1404.0.namd)
For each NAMD configuration file given in the last two sections, there is a log file with the same prefix, which gives the text output of NAMD. For instance, the output of namd/eabfZRest7_graph_chp1404.0.namd is eabfZRest7_graph_chp1404.0.log.
The simulation output files (which match the names of the NAMD configuration files) are in the output/ directory. Files with the extensions .coor, .vel, and .xsc are coordinates in NAMD binary format, velocities in NAMD binary format, and extended system information (including cell size) in text format. Files with the extension .dcd give the trajectory of the atomic coorinates over time (and also include system cell information). Due to storage limitations, large DCD files have been omitted or replaced with new DCD files having the prefix stride50_ including only every 50 frames. The time between frames in these files is 50 * 50000 steps/frame * 4 fs/step = 10 ns. The system cell trajectory is also included for the NPT runs are output/eq_*.xst.
Files with the .sh extension can be found throughout. These usually provide the highest level control for submission of simulations and analysis. Look to these as a guide to what is happening. If there are scripts with step1_*.sh and step2_*.sh, they are intended to be run in order, with step1_*.sh first.
The directory contents are as follows. The directories Sim_Figure-1 and Sim_Figure-8 include README.txt files that describe the files and naming conventions used throughout this data set.
Sim_Figure-1: Simulations of N-acetylated C-amidated amino acids (Ac-X-NHMe) at the graphite–water interface.
Sim_Figure-2: Simulations of different peptide designs (including acyclic, disulfide cyclized, and N-to-C cyclized) at the graphite–water interface.
Sim_Figure-3: MM-GBSA calculations of different peptide sequences for a folded conformation and 5 misfolded/unfolded conformations.
Sim_Figure-4: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 370 K.
Sim_Figure-5: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 295 K.
Sim_Figure-5_replica: Temperature replica exchange molecular dynamics simulations for the peptide cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) with 20 replicas for temperatures from 295 to 454 K.
Sim_Figure-6: Simulation of the peptide molecule cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) in free solution (no graphite).
Sim_Figure-7: Free energy calculations for folding, adsorption, and pairing for the peptide CHP1404 (sequence: cyc(GTGSGTG-GPGG-GCGTGTG-SGPG)). For folding, we calculate the PMF as function of RMSD by replica-exchange umbrella sampling (in the subdirectory Folding_CHP1404_Graphene/). We make the same calculation in solution, which required 3 seperate replica-exchange umbrella sampling calculations (in the subdirectory Folding_CHP1404_Solution/). Both PMF of RMSD calculations for the scrambled peptide are in Folding_scram1404/. For adsorption, calculation of the PMF for the orientational restraints and the calculation of the PMF along z (the distance between the graphene sheet and the center of mass of the peptide) are in Adsorption_CHP1404/ and Adsorption_scram1404/. The actual calculation of the free energy is done by a shell script ("doRestraintEnergyError.sh") in the 1_free_energy/ subsubdirectory. Processing of the PMFs must be done first in the 0_pmf/ subsubdirectory. Finally, files for free energy calculations of pair formation for CHP1404 are found in the Pair/ subdirectory.
Sim_Figure-8: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) where the peptides are far above the graphene–water interface in the initial configuration.
Sim_Figure-9: Two replicates of a simulation of nine peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 370 K.
Sim_Figure-9_scrambled: Two replicates of a simulation of nine peptide molecules with the control sequence cyc(GGTPTTGGGGGGSGGPSGTGGC) at the graphite–water interface at 370 K.
Sim_Figure-10: Adaptive biasing for calculation of the free energy of the folded peptide as a function of the angle between its long axis and the zigzag directions of the underlying graphene sheet.
Aqueous phase reforming (APR) of sugar alcohol molecules derived from biomass, e.g. , C x H (2x+2) O x (aq) + x H 2 O → x CO 2 (g) + (2 x + 1)H 2 (g), creates hydrogen gas sustainably, making it an important component of future bio-refineries; however, problems with the cost, activity, and selectivity of present precious metal based catalysts impede its broader adoption. Ideally, new catalysts would be designed to optimize activity and selectivity; however, a comprehensive understanding of the APR mechanism is lacking. This is complicated by the fact that the primary biomass-derived sugar alcohols are large molecules (meaning that their reaction networks are large) and because of the presence of liquid water. Water influences catalytic phenomena in multiple ways, including altering the thermodynamics of catalytic surface species and participating in catalytic reactions. Understanding the mechanism of APR requires understanding these various effects; however, computational strategies based solely on density functional theory (DFT) are computationally prohibitive for such large and complicated reaction networks. In this work, we investigate the mechanism of APR reactions in the context of glycerol reforming. To calculate the reaction network, we combine DFT calculations, force-field molecular dynamics (MD) simulations, linear scaling relations (LSRs), transition state scaling (TSS) relationships, and data from the literature into a microkinetic model. The microkinetic model is run under vacuum and aqueous phases in order to learn about the roles of water molecules on the mechanism of glycerol APR. We identify four such roles: providing surface hydroxyl groups, which promote oxidation of surface CO formed in glycerol decomposition; promoting C–H scissions; promoting O–H scissions; and inhibiting the thermodynamics of decarbonylation of C3 intermediates.more » « less
Abstract Magnetic and electronic properties of quantum materials heavily rely on the crystal structure even in the same chemical compositions. In this study, it is demonstrated that a layered tetragonal EuCd 2 Sb 2 structure can be obtained by treating bulk trigonal EuCd 2 Sb 2 under high pressure (6 GPa) and high temperature (600 °C). Magnetization measurements of the newly formed layered tetragonal EuCd 2 Sb 2 confirm an antiferromagnetic ordering with Neel temperature ( T N ) around 16 K, which is significantly higher than that ( T N ≈ 7 K) of trigonal EuCd 2 Sb 2 , consistent with heat capacity measurements. Moreover, bad metal behavior is observed in the temperature dependence of the electrical resistivity and the resistivity shows a dramatic increase around the Neel temperature. Electronic structure calculations with local density approximation dynamic mean–field theory (LDA+DMFT) show that this material is strongly correlated with well‐formed large magnetic moments, due to Hund's coupling, which is known to dramatically suppress the Kondo scale.more » « less
Ordered nanoscale patterns have been observed by atomic force microscopy at graphene–water and graphite–water interfaces. The two dominant explanations for these patterns are that (i) they consist of self-assembled organic contaminants or (ii) they are dense layers formed from atmospheric gases (especially nitrogen). Here we apply molecular dynamics simulations to study the behavior of dinitrogen and possible organic contaminants at the graphene–water interface. Despite the high concentration of N 2 in ambient air, we find that its expected occupancy at the graphene–water interface is quite low. Although dense (disordered) aggregates of dinitrogen have been observed in previous simulations, our results suggest that they are stable only in the presence of supersaturated aqueous N 2 solutions and dissipate rapidly when they coexist with nitrogen gas near atmospheric pressure. On the other hand, although heavy alkanes are present at only trace concentrations (micrograms per cubic meter) in typical indoor air, we predict that such concentrations can be sufficient to form ordered monolayers that cover the graphene–water interface. For octadecane, grand canonical Monte Carlo suggests nucleation and growth of monolayers above an ambient concentration near 6 μg m −3 , which is less than some literature values for indoor air. The thermodynamics of the formation of these alkane monolayers includes contributions from the hydration free-energy (unfavorable), the free-energy of adsorption to the graphene–water interface (highly favorable), and integration into the alkane monolayer phase (highly favorable). Furthermore, the peak-to-peak distances in AFM force profiles perpendicular to the interface (0.43–0.53 nm), agree with the distances calculated in simulations for overlayers of alkane-like molecules, but not for molecules such as N 2 , water, or aromatics. Taken together, these results suggest that ordered domains observed on graphene, graphite, and other hydrophobic materials in water are consistent with alkane-like molecules occupying the interface.more » « less