Principal Publications
(1)
Cummings, P. T.; McCabe, C.; Iacovella, C.
R.; Ledeczi, A.; Jankowski, E.; Jayaraman, A.; Palmer, J. C.; Maginn, E. J.; Glotzer, S. C.;
Anderson, J. A.; Siepmann, J. I.; Potoff, J.; Matsumoto, R. A.; Gilmer, J. B.; DeFever, R. S.;
Singh, R.; Crawford, B. Open-Source Molecular Modeling Software in Chemical Engineering Focusing on
the Molecular Simulation Design Framework. AIChE Journal 2021, 67 (3), e17206.
https://doi.org/10.1002/aic.17206.
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(2)
Thompson, M. W.; Gilmer, J. B.; Matsumoto,
R. A.; Quach, C. D.; Shamaprasad, P.; Yang, A. H.; Iacovella, C. R.; McCabe, C.; Cummings, P. T.
Towards Molecular Simulations That Are Transparent, Reproducible, Usable by Others, and Extensible
(TRUE). Molecular Physics 2020, 118 (9–10), e1742938. https://doi.org/10.1080/00268976.2020.1742938.
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(3)
Klein, C.; Summers, A. Z.; Thompson, M.
W.; Gilmer, J. B.; McCabe, C.; Cummings, P. T.; Sallai, J.; Iacovella, C. R. Formalizing Atom-Typing
and the Dissemination of Force Fields with Foyer. Computational Materials Science 2019,
167, 215–227. https://doi.org/10.1016/j.commatsci.2019.05.026.
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(4)
Klein, C.; Sallai, J.; Jones, T. J.;
Iacovella, C. R.; McCabe, C.; Cummings, P. T. A Hierarchical, Component Based Approach to Screening
Properties of Soft Matter. In Foundations of Molecular Modeling and Simulation: Select Papers
from FOMMS 2015; Snurr, R. Q., Adjiman, C. S., Kofke, D. A., Eds.; Molecular Modeling and
Simulation; Springer Singapore: Singapore, 2016; pp 79–92. https://doi.org/10.1007/978-981-10-1128-3_5.
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Related Software Publications
(1)
DeFever, R. S.; Matsumoto, R. A.; Dowling,
A. W.; Cummings, P. T.; Maginn, E. J. MoSDeF Cassandra: A Complete Python Interface for the
Cassandra Monte Carlo Software. Journal of Computational Chemistry 2021 42 (18). https://doi.org/10.1002/jcc.26544.
(2)
Jankowski, E.; Ellyson, N.; Fothergill, J.
W.; Henry, M. M.; Leibowitz, M. H.; Miller, E. D.; Alberts, M.; Chesser, S.; Guevara, J. D.; Jones,
C. D.; Klopfenstein, M.; Noneman, K. K.; Singleton, R.; Uriarte-Mendoza, R. A.; Thomas, S.;
Estridge, C. E.; Jones, M. L. Perspective on Coarse-Graining, Cognitive Load, and Materials
Simulation. Computational Materials Science 2020, 171, 109129. https://doi.org/10.1016/j.commatsci.2019.109129.
(3)
Ramasubramani, V.; Dice, B. D.; Harper, E.
S.; Spellings, M. P.; Anderson, J. A.; Glotzer, S. C. Freud: A Software Suite for High Throughput
Analysis of Particle Simulation Data. Computer Physics Communications 2020, 254,
107275. https://doi.org/10.1016/j.cpc.2020.107275.
(4)
Glaser, J;
Anderson, J. A.; Glotzer, S. C. HOOMD-blue: A Python package for high-performance molecular dynamics and hard particle Monte Carlo simulations.
Comput. Mater. Sci.,
2019, 173 109363. https://doi.org/10.1016/j.commatsci.2019.109363.
(5)
Adorf, D. S.; Dodd, P. M.; Ramasubramani, V.; Glotzer, S. C.
Simple data and workflow management with the signac framework.
Comput. Mater. Sci.
2018, 146, 220-229. https://doi.org/10.1016/j.commatsci.2018.01.035.
(6)
Cummings, P. T.; Gilmer, J. B. Open-Source
Molecular Modeling Software in Chemical Engineering. Current Opinion in Chemical Engineering
2019, 23, 99–105. https://doi.org/10.1016/j.coche.2019.03.008.
(7)
Albooyeh, M.; Jones, C.; Barrett,
R.; Jankowski, E. FlowerMD: Flexible Library of Organic Workflows and Extensible Recipes for Molecular Dynamics. Journal of Open Source Software 2023 8 (92), 5989. https://doi.org/10.21105/joss.05989.
(8)
Crawford, B.; Timalsina, U.; Quach,
C. D.; Craven, N. C.; Gilmer, J. B.; McCabe, C.; Cummings, P. T.; Potoff, J. J. MoSDeF-GOMC: Python Software for the Creation of Scientific Workflows for the Monte Carlo Simulation Engine GOMC. Journal of Chemical Information and Modeling 2023 63 (4), 1218-1228. https://doi.org/10.1021/acs.jcim.2c01498.
Machine Learnings and Screening Publications
(1)
Summers, A. Z.; Gilmer, J. B.; Iacovella,
C. R.; Cummings, P. T.; McCabe, C. MoSDeF, a Python Framework Enabling Large-Scale Computational
Screening of Soft Matter: Application to Chemistry-Property Relationships in Lubricating Monolayer
Films. J. Chem. Theory Comput. 2020, 16 (3), 1779–1793. https://doi.org/10.1021/acs.jctc.9b01183.
(2)
Thompson, M. W.; Matsumoto, R.; Sacci, R.
L.; Sanders, N. C.; Cummings, P. T. Scalable Screening of Soft Matter: A Case Study of Mixtures of
Ionic Liquids and Organic Solvents. J. Phys. Chem. B 2019, 123 (6), 1340–1347.
https://doi.org/10.1021/acs.jpcb.8b11527.
(3)
Nehil-Puleo, K.; Quach,
C. D.; Craven, N. C.; McCabe, C.; Cummings, P. T. E(n) Equivariant Graph Neural Network for Learning Interactional Properties of Molecules. J. Phys. Chem. B 2024, Article ASAP. https://doi.org/10.1021/acs.jpcb.3c07304.
(4)
Quach,
C. D.; Gilmer, J. B.; Pert, D..; Mason-Hogans, A.; Iacovella C. R.; Cummings, P.T.; McCabe, C.;
High-throughput screening of tribological properties of monolayer films using molecular dynamics and machine learning. J. Chem. Phys. 2022, 156, 15490. https://doi.org/10.1063/5.0080838.
Publications for Other Applications
(1)
Craven, N. C.; Gilmer, J. B.; Spindel, C.
J.; Summers, A. Z.; Iacovella, C. R.; McCabe, C. Examining the Self-Assembly of Patchy
Alkane-Grafted Silica Nanoparticles Using Molecular Simulation. J. Chem. Phys. 2021,
154 (3), 034903. https://doi.org/10.1063/5.0032658.
(2)
Henry, M. M.; Thomas, S.; Alberts, M.;
Estridge, C. E.; Farmer, B.; McNair, O.; Jankowski, E. General-Purpose Coarse-Grained Toughened
Thermoset Model for 44DDS/DGEBA/PES. Polymers 2020, 12 (11), 2547. https://doi.org/10.3390/polym12112547.
(3)
Kapoor, U.; Kulshreshtha, A.; Jayaraman,
A. Development of Coarse-Grained Models for Poly(4-Vinylphenol) and Poly(2-Vinylpyridine): Polymer
Chemistries with Hydrogen Bonding. Polymers 2020, 12 (11), 2764. https://doi.org/10.3390/polym12112764.
(4)
Matsumoto, R.; Thompson, M. W.; Cummings,
P. T. Ion Pairing Controls Physical Properties of Ionic Liquid-Solvent Mixtures. J. Phys. Chem.
B 2019, 123 (46), 9944–9955. https://doi.org/10.1021/acs.jpcb.9b08509.
(5)
Kulshreshtha, A.; Modica, K. J.;
Jayaraman, A. Impact of Hydrogen Bonding Interactions on Graft–Matrix Wetting and Structure in
Polymer Nanocomposites. Macromolecules 2019, 52 (7), 2725–2735. https://doi.org/10.1021/acs.macromol.8b02666.
(6)
Taylor P. A.; Kloxin A. M.; Jayaraman A. E. (n)
Impact of collagen-like peptide (CLP) heterotrimeric triple helix design on helical thermal stability and hierarchical assembly: a coarse-grained molecular dynamics simulation study.
Soft Matter 2022, 18 (16), 3177-3192.
https://doi.org/10.1039/D2SM00087C.
(7)
Lin, X. B.; Tee, S. R.; Searles, D. J.; Cummings, P. T.
Molecular insights on optimizing nanoporous carbon-based supercapacitors with various electrolytes.
Electrochimica Acta 2024, 474 143500.
https://doi.org/10.1016/j.electacta.2023.143500.