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Unfolding the prospects of computational (bio)materials modeling
G J Agur Sevink1, Jozef Adam Liwo2, Pietro Asinari3
1Leiden Institute of Chemistry, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands.
The Journal of Chemical Physics
|September 16, 2020
Summary
Computational (bio)material research faces challenges in validation, data handling, and accessibility. This work proposes solutions to unify frameworks, standardize data formats, manage big data, and promote efficient computational tools for broader use.
Area of Science:
- Computational materials science
- Biomaterials research
- Multiscale modeling
Background:
- The field of computational (bio)material research is rapidly advancing.
- Several key challenges hinder consistent progress and widespread adoption.
Purpose of the Study:
- To address critical challenges in computational (bio)material research.
- To propose solutions for improving validation, data management, and accessibility of computational tools.
Main Methods:
- Perspective communication based on discussions from the Workshop on Multi-scale Modeling.
- Identification and analysis of four major challenges in the field.
Main Results:
- Proposes solutions for a unified framework for testing computational methodologies.
- Recommends a standard data format for simulation data to simplify storage, exchange, and reproduction.
- Addresses the generation, storage, and analysis of massive datasets.
- Highlights the benefits of efficient "core" engines for computational tools.
Conclusions:
- Improving validation, reporting, and reproducibility of computational results is crucial.
- Standardizing data formats will enhance data migration between simulation and analysis tools.
- Promoting coarse-grained and multiscale computational tools will expand their user community.
