TY - JOUR
T1 - Modular Software for Generating and Modeling Diverse Polymer Databases
AU - Santana-Bonilla, Alejandro
AU - López-Ríos de Castro, Raquel
AU - Sun, Peike
AU - Ziolek, Robert M
AU - Lorenz, Christian D
N1 - Funding Information:
We are grateful to the UK Materials and Molecular Modelling Hub, which is partially funded by EPSRC (EP/P020194/1 and EP/T022213/1) and the UK HPC Materials Chemistry Consortium, which is also funded by EPSRC (EP/R029431) for providing us access to computational resources. This work also benefitted from access to the King’s Computational Research, Engineering and Technology Environment (CREATE) at King’s College London.(55) R.L.-R.d.C. acknowledges the support by the Biotechnology and Biological Sciences Research Council (BB/T008709/1) via the London Interdisciplinary Doctoral Programme (LIDo). R.M.Z. and C.D.L. acknowledge the Engineering and Physical Sciences Research Council (EPSRC) for funding (EP/V049771/1). For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence (where permitted by UKRI, “Open Government Licence” or “Creative Commons Attribution No-derivatives (CC BY-ND) public copyright licence” may be stated instead) to any author accepted manuscript version arising.
Funding Information:
We are grateful to the UK Materials and Molecular Modelling Hub, which is partially funded by EPSRC (EP/P020194/1 and EP/T022213/1) and the UK HPC Materials Chemistry Consortium, which is also funded by EPSRC (EP/R029431) for providing us access to computational resources. This work also benefitted from access to the King’s Computational Research, Engineering and Technology Environment (CREATE) at King’s College London. R.L.-R.d.C. acknowledges the support by the Biotechnology and Biological Sciences Research Council (BB/T008709/1) via the London Interdisciplinary Doctoral Programme (LIDo). R.M.Z. and C.D.L. acknowledge the Engineering and Physical Sciences Research Council (EPSRC) for funding (EP/V049771/1). For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence (where permitted by UKRI, “Open Government Licence” or “Creative Commons Attribution No-derivatives (CC BY-ND) public copyright licence” may be stated instead) to any author accepted manuscript version arising.
Publisher Copyright:
© 2023 The Authors. Published by American Chemical Society.
PY - 2023/6/26
Y1 - 2023/6/26
N2 - Machine learning methods offer the opportunity to design new functional materials on an unprecedented scale; however, building the large, diverse databases of molecules on which to train such methods remains a daunting task. Automated computational chemistry modeling workflows are therefore becoming essential tools in this data-driven hunt for new materials with novel properties, since they offer a means by which to create and curate molecular databases without requiring significant levels of user input. This ensures that well-founded concerns regarding data provenance, reproducibility, and replicability are mitigated. We have developed a versatile and flexible software package, PySoftK (Python Soft Matter at King's College London) that provides flexible, automated computational workflows to create, model, and curate libraries of polymers with minimal user intervention. PySoftK is available as an efficient, fully tested, and easily installable Python package. Key features of the software include the wide range of different polymer topologies that can be automatically generated and its fully parallelized library generation tools. It is anticipated that PySoftK will support the generation, modeling, and curation of large polymer libraries to support functional materials discovery in the nanotechnology and biotechnology arenas.
AB - Machine learning methods offer the opportunity to design new functional materials on an unprecedented scale; however, building the large, diverse databases of molecules on which to train such methods remains a daunting task. Automated computational chemistry modeling workflows are therefore becoming essential tools in this data-driven hunt for new materials with novel properties, since they offer a means by which to create and curate molecular databases without requiring significant levels of user input. This ensures that well-founded concerns regarding data provenance, reproducibility, and replicability are mitigated. We have developed a versatile and flexible software package, PySoftK (Python Soft Matter at King's College London) that provides flexible, automated computational workflows to create, model, and curate libraries of polymers with minimal user intervention. PySoftK is available as an efficient, fully tested, and easily installable Python package. Key features of the software include the wide range of different polymer topologies that can be automatically generated and its fully parallelized library generation tools. It is anticipated that PySoftK will support the generation, modeling, and curation of large polymer libraries to support functional materials discovery in the nanotechnology and biotechnology arenas.
KW - Humans
KW - Reproducibility of Results
KW - Software
KW - Databases, Factual
UR - https://www.scopus.com/pages/publications/85164052230
U2 - 10.1021/acs.jcim.3c00081
DO - 10.1021/acs.jcim.3c00081
M3 - Article
C2 - 37288782
SN - 1549-9596
VL - 63
SP - 3761
EP - 3771
JO - JOURNAL OF CHEMICAL INFORMATION AND MODELING
JF - JOURNAL OF CHEMICAL INFORMATION AND MODELING
IS - 12
ER -