Merge pull request #116 from bc118/add_1_4_cites

Paper updates: Added cites for all of 1st paragraph

paper/paper.bib

@@ -428,7 +428,7 @@ @article{Vermeyen:2023
 @article{Mayne:2013,
 author = {Christopher G. Mayne and Jan Saam and Klaus Schulten and Emad Tajkhorshid and James C. Gumbart},
 title = {Rapid parameterization of small molecules using the Force Field Toolkit},
-journal = {Journal of Computational Chemistry},
+journal = {J Comput Chem.},
 volume = {34},
 issue = {32},
 pages = {2757-2770},
@@ -449,3 +449,209 @@ @article{Schmid:2011
 doi = {10.1007/s00249-011-0700-9},
 URL = {https://doi.org/10.1007/s00249-011-0700-9}
 }
+
+# FF's 1-4 (non-bonded) VDW and electrostatic (charges)
+# cause issues in dihedral transferablity
+@article{Huang:2013,
+author = {Huang, Lei and Roux, Benoît},
+title = {Automated Force Field Parameterization for Nonpolarizable and Polarizable Atomic Models Based on Ab Initio Target Data},
+journal = {Journal of Chemical Theory and Computation},
+volume = {9},
+number = {8},
+pages = {3543-3556},
+year = {2013},
+doi = {10.1021/ct4003477},
+URL = {https://doi.org/10.1021/ct4003477}
+}
+
+# FF's 1-4 (non-bonded) VDW and electrostatic (charges)
+# cause issues in dihedral transferablity
+@article{Mackerell:2004,
+author = {Alexander D. Mackerell Jr.},
+title = {"Empirical force fields for biological macromolecules: Overview and issues"},
+journal = {J Comput Chem.},
+volume = {25},
+issue = {13},
+pages = {1584-1604},
+year = {2004},
+doi = {10.1002/jcc.20082},
+URL = {https://doi.org/10.1002/jcc.20082}
+}
+
+# FF's 1-4 (non-bonded) VDW and electrostatic (charges)
+# cause issues in dihedral transferablity
+@article{Vanommeslaeghe:2010,
+author = {K. Vanommeslaeghe and E. Hatcher and C. Acharya and S. Kundu and S. Zhong and J. Shim and E. Darian and O. Guvench and P. Lopes and I. Vorobyov and A. D. MacKerell, Jr.},
+title = {CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields},
+journal = {J Comput Chem.},
+volume = {31},
+issue = {4},
+pages = {671-690},
+year = {2010},
+doi = {10.1002/jcc.21367},
+URL = {https://doi.org/10.1002/jcc.21367}
+}
+
+# FF's 1-4 (non-bonded) VDW and electrostatic (charges)
+# cause issues in dihedral transferablity
+# and dihedral fitting methods
+@article{Vanommeslaeghe:2014,
+author = {Kenno Vanommeslaeghe and Olgun Guvench and Alexander D. MacKerell, Jr.},
+title = {Molecular Mechanics},
+journal = {Current Pharmaceutical Design},
+volume = {20},
+issue = {20},
+pages = {3281-3292},
+year = {2014},
+doi = {10.2174/13816128113199990600},
+}
+
+# FF's 1-4 (non-bonded) VDW and electrostatic (charges) and 1-n non-bonded interactions
+# cause issues in dihedral transferablity
+@article{Chen:2015,
+author = {Siyan Chen and Shasha Yi and Wenmei Gao and Chuncheng Zuo and Zhonghan Hu},
+title = {Force field development for organic molecules: Modifying dihedral and 1-n pair interaction parameters},
+journal = {J Comput Chem.},
+volume = {36},
+issue = {6},
+pages = {376-384},
+year = {2015},
+doi = {doi.org/10.1002/jcc.23808},
+URL = {https://doi.org/10.1002/jcc.23808}
+}
+
+# molecular simulation is 3rd leg of science
+@Inbook{Mielke:2019,
+author="Mielke, Roland R.
+and Leathrum, James F.
+and Collins, Andrew J.
+and Audette, Michel Albert",
+editor="Nestel, Debra
+and Hui, Joshua
+and Kunkler, Kevin
+and Scerbo, Mark W.
+and Calhoun, Aaron W.",
+title="Overview of Computational Modeling and Simulation",
+bookTitle="Healthcare Simulation Research: A Practical Guide",
+year="2019",
+publisher="Springer International Publishing",
+address="Cham",
+pages="39--47",
+abstract="Scientific research involves the formulation of theory to explain observed phenomena and using experimentation to test and evolve these theories. Over the past two decades, computational modeling and simulation (M{\&}S) has become accepted as the third leg of scientific research because it provides additional insights that often are impractical or impossible to acquire using theoretical and experimental analysis alone. The purpose of this chapter is to explore how M{\&}S is used in system-level healthcare research and to present some practical guidelines for its use. Two modeling approaches commonly used in healthcare research, system dynamics models and agent-based models, are presented and their applications in healthcare research are described. The three simulation paradigms, Monte Carlo simulation, continuous simulation, and discrete event simulation, are defined and the conditions for their use are stated. An epidemiology case study is presented to illustrate the use of M{\&}S in the research process.",
+isbn="978-3-030-26837-4",
+doi="10.1007/978-3-030-26837-4_6",
+url="https://doi.org/10.1007/978-3-030-26837-4_6"
+}
+
+# molecular simulation is 3rd leg of science
+@Inbook{Siegfried:2014,
+author="Siegfried, Robert",
+title="Introduction",
+bookTitle="Modeling and Simulation of Complex Systems: A Framework for Efficient Agent-Based Modeling and Simulation",
+year="2014",
+publisher="Springer Fachmedien Wiesbaden",
+address="Wiesbaden",
+pages="1--8",
+abstract="Simulation is nowadays considered to be the third pillar of science, a peer alongside theory and experimentation [105, p. 12], [55, p. 1], [88], [19, p. 1]. The analysis of many systems, processes and phenomena is often only feasible by developing simulation models and executing them using vast amounts of computing power. Forecasts, decision support and training are further areas that are regularly supported or even made possible by using simulation.",
+isbn="978-3-658-07529-3",
+doi="10.1007/978-3-658-07529-3_1",
+url="https://doi.org/10.1007/978-3-658-07529-3_1"
+}
+
+# high pressure and temp simulations
+@article{Yu:2023,
+author = {Diana Yu and Elke Pahl},
+title = {Melting of atomic materials under high pressures using computer simulations},
+journal = {Advances in Physics: X},
+volume = {8},
+number = {1},
+pages = {2235060},
+year = {2023},
+publisher = {Taylor \& Francis},
+doi = {10.1080/23746149.2023.2235060},
+URL = {https://doi.org/10.1080/23746149.2023.2235060},
+}
+
+# high pressure and temp simulations
+@article{Koneru:2022,
+author = {Koneru, Bhargavi and Swapnalin, Jhilmil and Banerjee, P. and Naidu, Kadiyala Chandra Babu and Kumar, N. Suresh},
+title = {Materials under extreme pressure: combining theoretical and experimental techniques},
+journal = {The European Physical Journal Special Topics},
+volume = {231},
+issue = {24},
+pages = {4221},
+year = {2022},
+doi = {10.1140/epjs/s11734-022-00569-8},
+URL = {https://doi.org/10.1140/epjs/s11734-022-00569-8},
+}
+
+# high pressure and temp simulations oil gas drilling
+@article{Swai:2020,
+author = {Swai, Rogers Evarist},
+title = {A review of molecular dynamics simulations in the designing of effective shale inhibitors: application for drilling with water-based drilling fluids},
+journal = {Journal of Petroleum Exploration and Production Technology},
+volume = {10},
+issue = {8},
+pages = {3515},
+year = {2020},
+doi = {10.1007/s13202-020-01003-2},
+URL = {https://doi.org/10.1007/s13202-020-01003-2},
+}
+
+# get insight and difficult to achieve without simulations
+@article{Hollingsworth:2018,
+author = {Scott A. Hollingsworth and Ron O. Dror},
+title = {Molecular Dynamics Simulation for All},
+journal = {Neuron},
+volume = {99},
+issue = {6},
+pages = {1129-1143},
+year = {2018},
+doi = {10.1016/j.neuron.2018.08.011},
+URL = {https://doi.org/10.1016/j.neuron.2018.08.011},
+}
+
+# get insight and difficult to achieve without simulations
+@article{Hirst:2014,
+author = {Jonathan D Hirst and David R Glowacki and Marc Baaden},
+title = {Molecular simulations and visualization: introduction and overview},
+journal = {Faraday Discussions},
+volume = {169},
+pages = {9-22},
+year = {2014},
+doi = {10.1039/c4fd90024c },
+}
+
+# get insight and difficult to achieve without simulations
+@Inbook{Kumar:2022,
+author="Kumar, Gaurav
+and Mishra, Radha Raman
+and Verma, Akarsh",
+editor="Verma, Akarsh
+and Mavinkere Rangappa, Sanjay
+and Ogata, Shigenobu
+and Siengchin, Suchart",
+title="Introduction to Molecular Dynamics Simulations",
+bookTitle="Forcefields for Atomistic-Scale Simulations: Materials and Applications",
+year="2022",
+publisher="Springer Nature Singapore",
+address="Singapore",
+pages="1--19",
+abstract="The invention of novel functional materials and their investigation at the molecular level are vital in today's nanotechnology era. Atomistic modelling approaches are cost-effective and time-consuming alternatives to expensive and time-consuming experimental methods, and they are precise enough to predict the mechanical characteristics of materials. The current chapter goes through the many steps involved in a molecular dynamic's investigation. The various types of interatomic potentials and their applicability to various materials have been thoroughly examined. Following that, the integration algorithm for solving a set of Newton's equations, as well as the radius cut-off distance and temperature control, was addressed. Afterwards, many types of ensembles and boundary conditions were addressed, which helped in simulating real-world experimental settings. The approaches for minimizing energy have also been briefly explored. Finally, the limitations of molecular dynamics have been examined, as well as their applicability.",
+isbn="978-981-19-3092-8",
+doi="10.1007/978-981-19-3092-8_1",
+url="https://doi.org/10.1007/978-981-19-3092-8_1"
+}
+
+# get discovery, insight and difficult to achieve without simulations
+@article{Louie:2021,
+author = {Louie, Steven G. and Chan, Yang-Hao and da Jornada, Felipe H. and Li, Zhenglu and Qiu, Diana Y.},
+title = {Discovering and understanding materials through computation},
+journal = {Nature Materials},
+volume = {20},
+issue = {6},
+pages = {728},
+year = {2021},
+doi = {10.1038/s41563-021-01015-1},
+URL = {https://doi.org/10.1038/s41563-021-01015-1},
+}

paper/paper.md

@@ -60,7 +60,7 @@ bibliography: paper.bib
 
 # Summary
 
-Molecular Mechanics (MM) simulations (molecular dynamics and Monte Carlo) provide a third method of scientific discovery, simulation modeling, adding to the traditional theoretical and experimental scientific methods.  These molecular simulations provide visualizations and calculated properties that are difficult, too expensive, or unattainable by conventional methods.  Additionally, molecular simulations can be used to obtain insights and properties on chemicals or materials that do not currently exist, are not easily attainable, or require hard-to-achieve conditions (i.e., very high pressures and temperatures).  However, these MM models require force field parameters to be determined ideally from Quantum Mechanics (QM) simulations or other methods, including the vibrational spectrum and machine learning methods [@Kania:2021; @Friederich:2018; @Vermeyen:2023; @Mayne:2013; @Schmid:2011], where the MM proper dihedrals (i.e., dihedrals) are challenging to obtain if they don't currently exist for the chosen force field or they do not properly scale-up to larger molecules or moiety combinations from their separately derived parameters using small molecules [@Kania:2021; @Mayne:2013]. While the same QM simulations can be used to fit the dihedrals in all of the force field types, these MM dihedrals are also not easily transferable between different force fields due to the differing parameters and formulas used in each force field (e.g., Combining rules and 1-4 scaling factors).
+Molecular Mechanics (MM) simulations (e.g., molecular dynamics and Monte Carlo) provide a third method of scientific discovery, adding to the traditional theoretical and experimental scientific methods [@Mielke:2019; @Siegfried:2014].  These molecular simulations provide visualizations and calculated properties that are difficult, too expensive, or unattainable by conventional methods [@Hollingsworth:2018; @Hirst:2014].  Additionally, molecular simulations can be used to obtain insights and properties on chemicals or materials that do not currently exist, are not easily attainable, or require hard-to-achieve conditions, such as very high pressures and temperatures [@Yu:2023; @Koneru:2022; @Swai:2020; @Kumar:2022; @Louie:2021].  However, these MM models require force field parameters to be determined ideally from Quantum Mechanics (QM) simulations or other methods, including the vibrational spectrum and machine learning methods [@Kania:2021; @Friederich:2018; @Vermeyen:2023; @Mayne:2013; @Schmid:2011; @Vanommeslaeghe:2014], where the MM proper dihedrals (i.e., dihedrals) are challenging to obtain if they don't currently exist for the chosen force field or they do not properly scale-up to larger molecules or moiety combinations from their separately derived parameters using small molecules [@Kania:2021; @Mayne:2013]. While the same QM simulations can be used to fit the dihedrals in all of the force field types, these MM dihedrals are also not easily transferable between different force fields due to the differing parameters and formulas (e.g., combining rules and 1-4 scaling factors) used in each force field [@Huang:2013; @Vanommeslaeghe:2010; @Vanommeslaeghe:2014; @Chen:2015].
 
 
 The `MoSDeF-Dihedral-Fit` [@Crawford:2023b] library lets users quickly calculate the MM proper dihedrals (dihedrals) directly from the QM simulations for several force fields (OPLS, TraPPE, AMBER, Mie, and Exp6) [@Jorgensen:1996; @Martin:1998; @Weiner:1984; @Weiner:1986; @Potoff:2009; @Hemmen:2015; @Errington:1999].  The user simply has to generate or use an existing Molecular Simulation Design Framework (MoSDeF) force field XML file [@Cummings:2021; @Summers:2020; @GMSO:2019; @forcefield-utilities:2022], provide Gaussian 16 or Gaussian-style Quantum Mechanics (QM) simulation files that cover the dihedral rotation (typically, 0-360 degrees), and provide the molecular structure information in a mol2 format [@Gaussian16:2016].  The `MoSDeF-Dihedral-Fit` software uses the QM and MM data to fit the dihedral for the specific force field, fitting the constants for the OPLS dihedral equation form with the correct combining rules and 1-4 scaling factors, as specified in the MoSDeF XML force field file. This software also accounts for multiple instances of the dihedral and the molecular symmetry in the molecule, and automatically removes all the unusable cosine power series values due to this symmetry.  The user can set other dihedral energies in the molecule to zero, allowing for a more flexible and accurate dihedral fit; this allows the multiple dihedral's conformational energies to be calculated from a single dihedral angle, a strategy that was used in some of the original OPLS dihedral fits.  The `MoSDeF-Dihedral-Fit` software analytically calculates the Ryckaert-Bellemans (RB)-torsions and the periodic dihedral from the OPLS dihedral.  If another form of the dihedral equation that is not currently supported is needed, the software outputs the raw data points to enable users to fit any other dihedral form.  Therefore, the `MoSDeF-Dihedral-Fit` software allows the fitting of any dihedral form, provided the force fields and software it utilizes are supported by MoSDeF and MoSDeF-GOMC (which uses GPU Optimized Monte Carlo - GOMC) [@Crawford:2023a; @Crawford:2022; @Crawford:2023b; @Nejahi:2019; @Nejahi:2021], vmd-python [@vmd-python:2016] (a derivative or modified version of the original VMD software [@Humphrey:1996; @Stone:2001]), and the QM data is provided as a Gaussian output file, or a generalized Gaussian-style output form [@Gaussian16:2016].