The phrase "atomistic methodology" is correct and usable in written English.
It can be used in contexts discussing approaches that break down complex systems into their individual components for analysis.
Example: "The research employed an atomistic methodology to better understand the interactions at the molecular level."
Alternatives: "reductionist approach" or "component-based methodology".
As a Miller Fellow working with Prof. Daniel Rokhsar at UC Berkeley, Prof. Pande extended this methodology to examine atomistic protein models, laying the foundations for his later work at Stanford University.
We propose a new methodology that uses atomistic simulation and only carbon pore size distribution as the input to predict bed deactivation caused by natural-gas alkanes.
We not only present a detailed description of the simulation methodology coupled to the atomistic sp3d5s∗ tight-binding band model, but also validate the suggested methodology with a focus on a proof of principle on small GaAs quantum dots (QDs).
Here, a mimetic methodology is developed to generate atomistic models of CDCs using Molecular Dynamics and the Environment Dependent Interaction Potential.
Using multiple methodologies, over 500 μs of atomistic simulation time has been collected for a large ensemble of hairpins (sequence 5′-GGGC[GCAA]GCCU-3′), allowing characterization of rare events not previously observable in simulation.
Recent improvements in methodology and increased computer power now allow atomistic computer simulations of protein folding.
In this work, a methodology has been developed for construction of atomistic models of crosslinked polymer networks.
A novel design methodology combining phase field crystal method and atomistic simulations is proposed to solve the inverse problem of finding the optimized distribution and type of topological defects that make a graphene sheet conform to a targeted arbitrary three dimensional (3D) surface.
The availability of methodologies that assist in this selection of relevant atomistic protein structural models from low-resolution X-ray data will lead to an enhanced understanding of biological structure and function.
Computational methodology recently provided vital insights, due to its ability to provide atomistic level information on a wide range of time-scales.
