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Related Experiment Video

Updated: Aug 28, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

MISPR: an open-source package for high-throughput multiscale molecular simulations.

Rasha Atwi¹, Matthew Bliss¹, Maxim Makeev¹

¹Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA.

Scientific Reports

|September 21, 2022

Summary

This summary is machine-generated.

We developed MISPR, a computational infrastructure that integrates molecular dynamics (MD) and density functional theory (DFT) simulations. This tool automates materials informatics workflows, enabling efficient structure-property relationship analysis for optimal material design.

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Last Updated: Aug 28, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

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Area of Science:

Computational materials science
Materials informatics
Computational chemistry

Background:

Designing materials with specific functionalities is challenging due to complex computational workflows, data heterogeneity, and the need for extensive exploration of chemical spaces.
Existing methods often struggle with integrating diverse computational tools and managing large datasets, hindering the derivation of reliable structure-property relationships.

Purpose of the Study:

To introduce MISPR, a high-throughput, multi-scale computational infrastructure designed to streamline materials design.
To address challenges in automated workflow management, data provenance, and the integration of classical molecular dynamics (MD) and density functional theory (DFT) simulations.

Main Methods:

Developed MISPR, a robust infrastructure integrating MD and DFT simulations for automated, high-performance data analytics.
Implemented automated DFT workflows for calculating properties like nuclear magnetic resonance chemical shift, binding energy, and redox potential.
Included MD workflows for characterizing large-scale ensemble properties in liquid solutions.

Main Results:

MISPR seamlessly integrates classical MD and DFT, enabling automated simulations and data analysis.
The infrastructure supports automated calculation of diverse molecular and ensemble properties, ensuring reproducibility and transparency.
Fully automated DFT workflows compute properties relevant to electron transfer and proton-coupled electron transfer reactions.

Conclusions:

MISPR provides a powerful platform for materials informatics, facilitating the understanding and prediction of structure-property relationships.
This infrastructure is crucial for accelerating the design of novel materials for diverse scientific and engineering applications.
By automating complex workflows and ensuring data integrity, MISPR overcomes key barriers in computational materials design.