The Materials Genome Initiative(MGI)advanced a new paradigm for materials discovery and design,namely that the pace of new materials deployment could be accelerated through complementary efforts in theory,computation,...The Materials Genome Initiative(MGI)advanced a new paradigm for materials discovery and design,namely that the pace of new materials deployment could be accelerated through complementary efforts in theory,computation,and experiment.Along with numerous successes,new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the MGI.In May 2017,the National Science Foundation sponsored the workshop“Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation,Experiment,and Theory:Opening New Frontiers”to review accomplishments that emerged from investments in science and infrastructure under the MGI,identify scientific opportunities in this new environment,examine how to effectively utilize new materials innovation infrastructure,and discuss challenges in achieving accelerated materials research through the seamless integration of experiment,computation,and theory.This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts.展开更多
The complexity and diversity of polymer topologies,or chain architectures,present substantial challenges in predicting and engineering polymer properties.Although machine learning is increasingly used in polymer scien...The complexity and diversity of polymer topologies,or chain architectures,present substantial challenges in predicting and engineering polymer properties.Although machine learning is increasingly used in polymer science,applications to address architecturally complex polymers are nascent.Here,we use a generative machine learning model based on variational autoencoders and data generated from molecular dynamics simulations to design polymer topologies that exhibit target properties.展开更多
Modifying solution viscosity is a key functional application of polymers,yet the interplay of molecular chemistry,polymer architecture,and intermolecular interactions makes tailoring precise rheological responses chal...Modifying solution viscosity is a key functional application of polymers,yet the interplay of molecular chemistry,polymer architecture,and intermolecular interactions makes tailoring precise rheological responses challenging.We introduce a computational framework coupling topology-aware generative machine learning,Gaussian process modeling,and multiparticle collision dynamics to design polymers yielding prescribed shear-rate-dependent viscosity profiles.Targeting thirty rheological profiles of varying difficulty,Bayesian optimization identifies polymers that satisfy all lowand most medium-difficulty targets by modifying topology and solvophobicity,with other variables fixed.In these regimes,wefind and explain design degeneracy,where distinct polymers produce nearidentical rheological profiles.However,satisfying high-difficulty targets requires extrapolation beyond the initial constrained design space;this is rationally guided by physical scaling theories.This integrated framework establishes a data-driven yet mechanistic route to rational polymer design.展开更多
文摘The Materials Genome Initiative(MGI)advanced a new paradigm for materials discovery and design,namely that the pace of new materials deployment could be accelerated through complementary efforts in theory,computation,and experiment.Along with numerous successes,new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the MGI.In May 2017,the National Science Foundation sponsored the workshop“Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation,Experiment,and Theory:Opening New Frontiers”to review accomplishments that emerged from investments in science and infrastructure under the MGI,identify scientific opportunities in this new environment,examine how to effectively utilize new materials innovation infrastructure,and discuss challenges in achieving accelerated materials research through the seamless integration of experiment,computation,and theory.This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts.
基金M.A.W.and A.B.D acknowledge funding from the Princeton Catalysis Initiative for this researchM.A.W.and S.J.also acknowledge support from the donors of ACS Petroleum Research Fund under Doctoral New Investigator Grant 66706-DNI7.
文摘The complexity and diversity of polymer topologies,or chain architectures,present substantial challenges in predicting and engineering polymer properties.Although machine learning is increasingly used in polymer science,applications to address architecturally complex polymers are nascent.Here,we use a generative machine learning model based on variational autoencoders and data generated from molecular dynamics simulations to design polymer topologies that exhibit target properties.
基金supported by the donors of ACS Petroleum Research Fund under Doctoral New Investigator Grant 66706-DNI7Simulations and analyses were performed using resources from Princeton Research Computing at Princeton University, which is a consortium led by the Princeton Institute for Computational Science and Engineering (PICSciE) and Office of Information Technology’s Research Computing. These resources include a GPU-based computing cluster purchased with support from the National Science Foundation (Grant No. NSF-MRI: OAC-2320649).
文摘Modifying solution viscosity is a key functional application of polymers,yet the interplay of molecular chemistry,polymer architecture,and intermolecular interactions makes tailoring precise rheological responses challenging.We introduce a computational framework coupling topology-aware generative machine learning,Gaussian process modeling,and multiparticle collision dynamics to design polymers yielding prescribed shear-rate-dependent viscosity profiles.Targeting thirty rheological profiles of varying difficulty,Bayesian optimization identifies polymers that satisfy all lowand most medium-difficulty targets by modifying topology and solvophobicity,with other variables fixed.In these regimes,wefind and explain design degeneracy,where distinct polymers produce nearidentical rheological profiles.However,satisfying high-difficulty targets requires extrapolation beyond the initial constrained design space;this is rationally guided by physical scaling theories.This integrated framework establishes a data-driven yet mechanistic route to rational polymer design.
