Data-Driven Methods for Accelerating Polymer Design
Open Access
- 28 December 2021
- journal article
- review article
- Published by American Chemical Society (ACS) in ACS Polymers Au
- Vol. 2 (1) , 8-26
Abstract
No abstract availableKeywords
Funding Information
- Anusandhan National Research Foundation
- National Supercomputing Mission, DST, Gov. of India
This publication has 201 references indexed in Scilit:
- Accelerating materials property predictions using machine learningScientific Reports, 2013
- A Coarse-Grained Protein Model in a Water-like SolventScientific Reports, 2013
- How Good Are Coarse‐Grained Polymer Models? A Comparison for Atactic PolystyreneChemphyschem, 2012
- Combinatorial and High-Throughput Screening of Materials Libraries: Review of State of the ArtACS Combinatorial Science, 2011
- A genetic algorithm for predicting the structures of interfaces in multicomponent systemsNature Materials, 2010
- The multiscale coarse-graining method. I. A rigorous bridge between atomistic and coarse-grained modelsThe Journal of Chemical Physics, 2008
- GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular SimulationJournal of Chemical Theory and Computation, 2008
- Predicting Physical−Chemical Properties of Compounds from Molecular Structures by Recursive Neural NetworksJournal of Chemical Information and Modeling, 2006
- GROMACS: Fast, flexible, and freeJournal of Computational Chemistry, 2005
- The Protein Data BankNucleic Acids Research, 2000