Correction to:npj Computational Materials https://doi.org/10.1038/s41524-022-00914-4,published online 04 November 2022 The original version of this Article omitted some contributions in the Author Contributions sectio...Correction to:npj Computational Materials https://doi.org/10.1038/s41524-022-00914-4,published online 04 November 2022 The original version of this Article omitted some contributions in the Author Contributions section and incorrectly read[H.W.:Formal analysis,data visualization,review and editing.]The correct Author Contributions should read:[H.W.:Conceptua-lisation,formal analysis,data visualisation,writing,review and editing;Z.S.and H.W.contributed equally to the conduct of the research and preparation of the manuscript.]This has now been corrected in both the PDF and HTML versions of the Article.展开更多
This work presents a framework governing the development of an efficient,accurate,and transferable coarse-grained(CG)model of a polyether material.The framework combines bottom-up and top-down approaches of coarse-gra...This work presents a framework governing the development of an efficient,accurate,and transferable coarse-grained(CG)model of a polyether material.The framework combines bottom-up and top-down approaches of coarse-grained model parameters by integrating machine learning(ML)with optimization algorithms.In the bottom-up approach,bonded interactions of the CG model are optimized using deep neural networks(DNN),where atomistic bonded distributions are matched.In the top-down approach,optimization of nonbonded parameters is accomplished by reproducing the temperature-dependent experimental density.We demonstrate that developed framework addresses the thermodynamic consistency and transferability issues associated with the classical coarse-graining approaches.The efficiency and transferability of the CG model is demonstrated through accurate predictions of chain statistics,the limiting behavior of the glass transition temperature,diffusion,and stress relaxation,where none were included in the parametrization process.The accuracy of the predicted properties are evaluated in context of molecular theories and available experimental data.展开更多
文摘Correction to:npj Computational Materials https://doi.org/10.1038/s41524-022-00914-4,published online 04 November 2022 The original version of this Article omitted some contributions in the Author Contributions section and incorrectly read[H.W.:Formal analysis,data visualization,review and editing.]The correct Author Contributions should read:[H.W.:Conceptua-lisation,formal analysis,data visualisation,writing,review and editing;Z.S.and H.W.contributed equally to the conduct of the research and preparation of the manuscript.]This has now been corrected in both the PDF and HTML versions of the Article.
基金This research is supported by the Commonwealth of Australia as represented by the Defence Science and Technology Group of the Department of Defence.
文摘This work presents a framework governing the development of an efficient,accurate,and transferable coarse-grained(CG)model of a polyether material.The framework combines bottom-up and top-down approaches of coarse-grained model parameters by integrating machine learning(ML)with optimization algorithms.In the bottom-up approach,bonded interactions of the CG model are optimized using deep neural networks(DNN),where atomistic bonded distributions are matched.In the top-down approach,optimization of nonbonded parameters is accomplished by reproducing the temperature-dependent experimental density.We demonstrate that developed framework addresses the thermodynamic consistency and transferability issues associated with the classical coarse-graining approaches.The efficiency and transferability of the CG model is demonstrated through accurate predictions of chain statistics,the limiting behavior of the glass transition temperature,diffusion,and stress relaxation,where none were included in the parametrization process.The accuracy of the predicted properties are evaluated in context of molecular theories and available experimental data.
