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Molecular dynamics (MD) is a computer simulation method for analyzing the physical movements of atoms and molecules. The atoms and molecules are allowed to interact for a fixed period of time, giving a view of the dynamic "evolution" of the system. In the most common version, the trajectories of atoms and molecules are determined by numerically solving Newton's equations of motion for a system of interacting particles, where forces between the particles and their potential energies are often calculated using interatomic potentials or molecular mechanical force fields. The method is applied mostly in chemical physics, materials science, and biophysics.
Molecular mechanics uses classical mechanics to model molecular systems. The Born–Oppenheimer approximation is assumed valid and the potential energy of all systems is calculated as a function of the nuclear coordinates using force fields. Molecular mechanics can be used to study molecule systems ranging in size and complexity from small to large biological systems or material assemblies with many thousands to millions of atoms.
E-Science or eScience is computationally intensive science that is carried out in highly distributed network environments, or science that uses immense data sets that require grid computing; the term sometimes includes technologies that enable distributed collaboration, such as the Access Grid. The term was created by John Taylor, the Director General of the United Kingdom's Office of Science and Technology in 1999 and was used to describe a large funding initiative starting in November 2000. E-science has been more broadly interpreted since then, as "the application of computer technology to the undertaking of modern scientific investigation, including the preparation, experimentation, data collection, results dissemination, and long-term storage and accessibility of all materials generated through the scientific process. These may include data modeling and analysis, electronic/digitized laboratory notebooks, raw and fitted data sets, manuscript production and draft versions, pre-prints, and print and/or electronic publications." In 2014, IEEE eScience Conference Series condensed the definition to "eScience promotes innovation in collaborative, computationally- or data-intensive research across all disciplines, throughout the research lifecycle" in one of the working definitions used by the organizers. E-science encompasses "what is often referred to as big data [which] has revolutionized science... [such as] the Large Hadron Collider (LHC) at CERN... [that] generates around 780 terabytes per year... highly data intensive modern fields of science...that generate large amounts of E-science data include: computational biology, bioinformatics, genomics" and the human digital footprint for the social sciences.
In the context of chemistry and molecular modelling, a force field is a computational method that is used to estimate the forces between atoms within molecules and also between molecules. More precisely, the force field refers to the functional form and parameter sets used to calculate the potential energy of a system of atoms or coarse-grained particles in molecular mechanics, molecular dynamics, or Monte Carlo simulations. The parameters for a chosen energy function may be derived from experiments in physics and chemistry, calculations in quantum mechanics, or both. Force fields are interatomic potentials and utilize the same concept as force fields in classical physics, with the difference that the force field parameters in chemistry describe the energy landscape, from which the acting forces on every particle are derived as a gradient of the potential energy with respect to the particle coordinates.
Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) is a molecular dynamics program from Sandia National Laboratories. LAMMPS makes use of Message Passing Interface (MPI) for parallel communication and is free and open-source software, distributed under the terms of the GNU General Public License.
Car–Parrinello molecular dynamics or CPMD refers to either a method used in molecular dynamics or the computational chemistry software package used to implement this method.
In computational chemistry, a water model is used to simulate and thermodynamically calculate water clusters, liquid water, and aqueous solutions with explicit solvent. The models are determined from quantum mechanics, molecular mechanics, experimental results, and these combinations. To imitate a specific nature of molecules, many types of models have been developed. In general, these can be classified by the following three points; (i) the number of interaction points called site, (ii) whether the model is rigid or flexible, (iii) whether the model includes polarization effects.
Integrated Computational Materials Engineering (ICME) is an approach to design products, the materials that comprise them, and their associated materials processing methods by linking materials models at multiple length scales. Key words are "Integrated", involving integrating models at multiple length scales, and "Engineering", signifying industrial utility. The focus is on the materials, i.e. understanding how processes produce material structures, how those structures give rise to material properties, and how to select materials for a given application. The key links are process-structures-properties-performance. The National Academies report describes the need for using multiscale materials modeling to capture the process-structures-properties-performance of a material.
The George E. Brown, Jr. Network for Earthquake Engineering Simulation (NEES) was created by the National Science Foundation (NSF) to improve infrastructure design and construction practices to prevent or minimize damage during an earthquake or tsunami. Its headquarters were at Purdue University in West Lafayette, Indiana as part of cooperative agreement #CMMI-0927178, and it ran from 2009 till 2014. The mission of NEES is to accelerate improvements in seismic design and performance by serving as a collaboratory for discovery and innovation.
Molecular modeling on GPU is the technique of using a graphics processing unit (GPU) for molecular simulations.
CP2K is a freely available (GPL) quantum chemistry and solid state physics program package, written in Fortran 2008, to perform atomistic simulations of solid state, liquid, molecular, periodic, material, crystal, and biological systems. It provides a general framework for different methods: density functional theory (DFT) using a mixed Gaussian and plane waves approach (GPW) via LDA, GGA, MP2, or RPA levels of theory, classical pair and many-body potentials, semi-empirical and tight-binding Hamiltonians, as well as Quantum Mechanics/Molecular Mechanics (QM/MM) hybrid schemes relying on the Gaussian Expansion of the Electrostatic Potential (GEEP). The Gaussian and Augmented Plane Waves method (GAPW) as an extension of the GPW method allows for all-electron calculations. CP2K can do simulations of molecular dynamics, metadynamics, Monte Carlo, Ehrenfest dynamics, vibrational analysis, core level spectroscopy, energy minimization, and transition state optimization using NEB or dimer method.
Integrated computational materials engineering (ICME) involves the integration of experimental results, design models, simulations, and other computational data related to a variety of materials used in multiscale engineering and design. Central to the achievement of ICME goals has been the creation of a cyberinfrastructure, a Web-based, collaborative platform which provides the ability to accumulate, organize and disseminate knowledge pertaining to materials science and engineering to facilitate this information being broadly utilized, enhanced, and expanded.
Massively Parallel Monte Carlo (MPMC) is a Monte Carlo method package primarily designed to simulate liquids, molecular interfaces, and functionalized nanoscale materials. It was developed originally by Jon Belof and is now maintained by a group of researchers in the Department of Chemistry and SMMARTT Materials Research Center at the University of South Florida. MPMC has been applied to the scientific research challenges of nanomaterials for clean energy, carbon sequestration, and molecular detection. Developed to run efficiently on the most powerful supercomputing platforms, MPMC can scale to extremely large numbers of CPUs or GPUs. Since 2012, MPMC has been released as an open-source software project under the GNU General Public License (GPL) version 3, and the repository is hosted on GitHub.
Interatomic potentials are mathematical functions to calculate the potential energy of a system of atoms with given positions in space. Interatomic potentials are widely used as the physical basis of molecular mechanics and molecular dynamics simulations in computational chemistry, computational physics and computational materials science to explain and predict materials properties. Examples of quantitative properties and qualitative phenomena that are explored with interatomic potentials include lattice parameters, surface energies, interfacial energies, adsorption, cohesion, thermal expansion, and elastic and plastic material behavior, as well as chemical reactions.
Science gateways provide access to advanced resources for science and engineering researchers, educators, and students. Through streamlined, online, user-friendly interfaces, gateways combine a variety of cyberinfrastructure (CI) components in support of a community-specific set of tools, applications, and data collections.: In general, these specialized, shared resources are integrated as a Web portal, mobile app, or a suite of applications. Through science gateways, broad communities of researchers can access diverse resources which can save both time and money for themselves and their institutions. As listed below, functions and resources offered by science gateways include shared equipment and instruments, computational services, advanced software applications, collaboration capabilities, data repositories, and networks.
MBN Explorer is a software package for molecular dynamics simulations, structure optimization and kinetic Monte Carlo simulations. It is designed for multiscale computational analysis of structure and dynamics of atomic clusters and nanoparticles, biomolecules and nanosystems, nanostructured materials, different states of matter and various interfaces. The software has been developed by MBN Research Center.
Computational materials science and engineering uses modeling, simulation, theory, and informatics to understand materials. The main goals include discovering new materials, determining material behavior and mechanisms, explaining experiments, and exploring materials theories. It is analogous to computational chemistry and computational biology as an increasingly important subfield of materials science.
In the context of chemistry and molecular modelling, the Interface force field (IFF) is a force field for classical molecular simulations of atoms, molecules, and assemblies up to the large nanometer scale, covering compounds from across the periodic table. It employs a consistent classical Hamiltonian energy function for metals, oxides, and organic compounds, linking biomolecular and materials simulation platforms into a single platform. The reliability is often higher than that of density functional theory calculations at more than a million times lower computational cost. IFF includes a physical-chemical interpretation for all parameters as well as a surface model database that covers different cleavage planes and surface chemistry of included compounds. The Interface Force Field is compatible with force fields for the simulation of primarily organic compounds and can be used with common molecular dynamics and Monte Carlo codes. Structures and energies of included chemical elements and compounds are rigorously validated and property predictions are up to a factor of 100 more accurate relative to earlier models.
References
- ↑ Tadmor, E.B.; Elliott, R.S.; Sethna, J.P.; Miller, R.E.; Becker, C.A. (2011). "The Potential of Atomistic Simulations and the Knowledgebase of Interatomic Models". JOM. 63 (17): 17. Bibcode:2011JOM....63g..17T. doi:10.1007/s11837-011-0102-6. S2CID 137499012.
- ↑ "Collaborative Research:CDI-Type II: The Knowledge-Base of Interatomic Models (KIM)". National Science Foundation.
- ↑ Elliott RS, Tadmor EB (2011). "Knowledgebase of Interatomic Models (KIM) Application Programming Interface (API)". doi:10.25950/ff8f563a.
{{cite journal}}: Cite journal requires|journal=(help) - ↑ "DataCite Members".
- ↑ "XSEDE Science Gateways".
- ↑ "Designing Materials to Revolutionize and Engineer our Future (DMREF)".
- ↑ "The Materials Project".
- ↑ "eXtreme Science and Engineering Discovery Environment (XSEDE)".
- ↑ "Software and Projects Using KIM". OpenKIM.
- ↑ Wen, Mingjian (March 2022). "KLIFF: A framework to develop physics-based and machine learning interatomic potentials". Computer Physics Communications. 272 (108218): 108218. arXiv: 2108.03523 . doi:10.1016/j.cpc.2021.108218. S2CID 238259734.
- ↑ Karls, Daniel (July 2020). "The OpenKIM processing pipeline: A cloud-based automatic material property computation engine". Journal of Chemical Physics. 153 (64104): 064104. arXiv: 2005.09062 . doi:10.1063/5.0014267. PMID 35287439. S2CID 218684972.
- ↑ Reeve, Samuel (July 2019). "Online simulation powered learning modules for materials science". MRS Advances. 4 (50): 2727–2742. arXiv: 2004.04297 . doi:10.1557/adv.2019.287. S2CID 198818058.
- ↑ Thompson, Aidan (February 2022). "LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales". Computer Physics Communications. 271 (108171): 108171. doi:10.1016/j.cpc.2021.108171. S2CID 240525914.
- ↑ Thompson, Matthew (2022). "Towards molecular simulations that are transparent, reproducible, usable by others, and extensible (TRUE)". Molecular Physics. 118 (9–10).
- ↑ van der Giessen, Erik (March 2020). "Roadmap on multiscale materials modeling". Modelling and Simulation in Materials Science and Engineering. 28 (43001).
- ↑ "Materials Science Community Forum".