SEAMM: A Simulation Environment for Atomistic and Molecular Modeling

Abstract

The simulation environment for atomistic and molecular modeling (SEAMM) is an open-source software package written in Python that provides a graphical interface for setting up, executing, and analyzing molecular and materials simulations. The graphical interface reduces the entry barrier for the use of new simulation tools, facilitating the interoperability of a wide range of simulation tools available to solve complex scientific and engineering problems in computational molecular science. Workflows are represented graphically by user-friendly flowcharts, which are shareable and reproducible. When a flowchart is executed within the SEAMM environment, all results, as well as metadata describing the workflow and codes used, are saved in a datastore that can be viewed using a browser-based dashboard, which allows collaborators to view the results and use the flowcharts to extend the results. SEAMM is a powerful productivity and collaboration tool that enables interoperability between simulation codes and ensures reproducibility and transparency in scientific research. We illustrate the flexibility and productivity of SEAMM with three examples: a simple molecular dynamics calculation to provide an overview; exploring the rearrangement of methylisocyanide to acetonitrile using a wide range of quantum codes and force fields; and using SEAMM for industrial research on battery materials with simulations of the diffusivity and ionic conductivity of electrolytes and the density, thermal expansion, and thermal conductivity of cathode materials as a function of lithiation.

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Number of citations , (left panel) and patents (right panel) filed using significant CMS codes.

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A sample flowchart for predicting the density of liquid ethanol via NPT MD simulation.

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PACKMOL parameter configuration dialogue window in SEAMM.

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A sample input for PACKMOL.

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Subflowchart for LAMMPS.

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Parameters for LAMMPS.

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Summary output from the LAMMPS step.

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Line plots of the density (left) and the autocorrelation function (right) versus time, generated by the LAMMPS step in SEAMM. Note the horizontal line which SEAMM automatically creates on the density plot to represent the mean density value on the plateu.

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Components of the SEAMM environment.

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High-level software stack view of SEAMM core component.

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Database schema for molecular structures.

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Star schema for system properties.

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Simulation of transport properties of lithium ion battery liquid electrolytes, EC:EMC (3:7 w/w) and EC:DMC (1:1 w/w), at various molar concentrations (c) of LiPF₆ salt at 25 °C. Panels (a−d) show the MD density, ionic diffusivity, ionic conductivity, and cationic transference number, respectively, which are obtained from MD simulation and compared with experimental results performed at 25 °C. The experimental densities in (a) were taken from refs. and . The experimental results for diffusivity, conductivity and transference numbers in (b−d) were taken from ref. .

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Calculated radial distribution function, g(r), and corresponding coordination number integrals, CN­(r), of (a) Li−F (PF₆ ⁻), Li−O (EC), Li−O (EMC) in EC:EMC (3:7 w/w) with 1 M LiPF₆, and (b) Li−F (PF₆ ⁻), Li−O (EC), Li−O (DMC) in EC:DMC (1:1 w/w) with 1 M LiPF₆.

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Thermal expansion in LiCoO₂. (a) The experimental layered (R3m) crystal structure of LiCoO₂ used in the MD simulation. The Li, Co and O atoms are represented as green, blue and red spheres. (b) Lattice parameter expansion and change in density as a function of temperature in LiCoO₂.

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Thermal conductivity simulation of LiCoO₂ using equilibrium MD at T = 298 K. Sample calculation of (a) heat flux autocorrelation function, (b) thermal conductivity fittings of Green−Kubo integrals, (c) thermal conductivity fitting of Helfand derivatives, and (d) Helfand moments as a function of time in ps.

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Calculated thermal conductivity, k (W/m K) of Li _x CoO₂ as a function of Li concentrations at T = 298 K from the MD simulation. The inset shows a convex hull diagram calculated using ab initio ground state formation energies to predict the lowest energy Li vacancy structures.