BACKGROUND The traditional radial approach(RA)is recommended as the standard method for coronary angiography(CAG),while a distal RA(DRA)has been recently used for CAG.AIM To assess the efficacy and safety of the DRA v...BACKGROUND The traditional radial approach(RA)is recommended as the standard method for coronary angiography(CAG),while a distal RA(DRA)has been recently used for CAG.AIM To assess the efficacy and safety of the DRA vs RA during CAG.METHODS The following databases were searched through December 2020:MEDLINE,the Cochrane Central Register of Controlled Trials,EMBASE,the World Health Organization International Clinical Trials Platform Search Portal,and Clinical-Trials.gov.Individual randomized-controlled trials for adult patients undergoing cardiac catheterization were included.The primary outcomes were the successful cannulation rate and the incidence of radial artery spasm(RAS)and radial artery occlusion(RAO).Study selection,data abstraction and quality assessment were independently performed using the Grading of Recommendations,Assessment,Development,and Evaluation approach.RESULTS Three randomized control trials and 13 registered trials were identified.The two approaches showed similar successful cannulation rates[risk ratio(RR)0.90,95%confidence interval(CI):0.72-1.13].The DRA did not decrease RAS(RR 0.43,95%CI:0.08-2.49)and RAO(RR 0.48,95%CI:0.18-1.29).Patients with the DRA had a shorter hemostasis time in comparison to those with the RA(mean difference-6.64,95%CI:-10.37 to-2.90).The evidence of certainty was low.CONCLUSION For CAG,the DRA would be safer than the RA with comparable cannulation rates.Given the limited data,additional research,including studies with standard protocols,is necessary.展开更多
Next-generation power electronics require efficient heat dissipation management,and molecular design guidelines are needed to develop polymers with high thermal conductivity.Polymer materials have considerably lower t...Next-generation power electronics require efficient heat dissipation management,and molecular design guidelines are needed to develop polymers with high thermal conductivity.Polymer materials have considerably lower thermal conductivity than metals and ceramics due to phonon scattering in the amorphous region.The spontaneous orientation of the molecular chains of liquid crystalline polymers could potentially give rise to high thermal conductivity,but the molecular design of such polymers remains largely empirical.In this study,wedeveloped amachine learningmodel that predicts with more than 96%accuracy whether liquid crystalline states will form based on the chemical structure of the polymer.By exploring the inverse mapping of this model,we identified a comprehensive set of chemical structures for liquid crystalline polyimides.The polymers were then experimentally synthesized,and the results confirmed that they form liquid crystalline phases,with all polymers exhibiting calculated thermal conductivities within the range of 0.722–1.26Wm^(−1)K^(−1).展开更多
To address the challenge of limited experimental materials data,extensive physical property databases are being developed based on high-throughput computational experiments,such as molecular dynamics simulations.Previ...To address the challenge of limited experimental materials data,extensive physical property databases are being developed based on high-throughput computational experiments,such as molecular dynamics simulations.Previous studies have shown that fine-tuning a predictor pretrained on a computational database to a real system can result in models with outstanding generalization capabilities compared to learning from scratch.This study demonstrates the scaling law of simulationto-real(Sim2Real)transfer learning for several machine learning tasks in materials science.Case studies of three prediction tasks for polymers and inorganic materials reveal that the prediction error on real systems decreases according to a power-law as the size of the computational data increases.Observing the scaling behavior offers various insights for database development,such as determining the sample size necessary to achieve a desired performance,identifying equivalent sample sizes for physical and computational experiments,and guiding the design of data production protocols for downstream real-world tasks.展开更多
Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems.First-principles calculations and other computer experiments have been integrated into mate...Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems.First-principles calculations and other computer experiments have been integrated into material design pipelines to address the lack of experimental data and the limitations of interpolative machine learning predictors.However,the enormous computational costs and technical challenges of automatingcomputer experiments for polymeric materials have limited the availability of open-source automated polymer design systems that integrate molecular simulations and machine learning.We developed SPACIER,an open-source software program that incorporates RadonPy,a Python library for fully automated polymer physical property calculations based on allatom classical molecular dynamics,into a Bayesian optimization-based polymer design system to overcome these challenges.As a proof-of-concept study,we synthesized optical polymers that surpass the Pareto boundary formed by the tradeoff between the refractive index and the Abbe number.展开更多
The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials.However,its practical benefits still remain unproven in real-world applications,par...The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials.However,its practical benefits still remain unproven in real-world applications,particularly in polymer science.We demonstrate the successful discovery of new polymers with high thermal conductivity,inspired by machine-learning-assisted polymer chemistry.This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data,expertise from laboratory synthesis and advanced technologies for thermophysical property measurements.Using a molecular design algorithm trained to recognize quantitative structure—property relationships with respect to thermal conductivity and other targeted polymeric properties,we identified thousands of promising hypothetical polymers.From these candidates,three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications.The synthesized polymers reached thermal conductivities of 0.18–0.41 W/mK,which are comparable to those of state-of-the-art polymers in non-composite thermo-plastics.展开更多
The spread of data-driven materials research has increased the need for systematically designed materials property databases.However,the development of polymer databases has lagged far behind other material systems.We...The spread of data-driven materials research has increased the need for systematically designed materials property databases.However,the development of polymer databases has lagged far behind other material systems.We present RadonPy,an open-source library that can automate the complete process of all-atom classical molecular dynamics(MD)simulations applicable to a wide variety of polymeric materials.Herein,15 different properties were calculated for more than 1000 amorphous polymers.The MD-calculated properties were systematically compared with experimental data to validate the calculation conditions;the bias and variance in the MD-calculated properties were successfully calibrated by a machine learning technique.During the high-throughput data production,we identified eight amorphous polymers with extremely high thermal conductivity(>0.4 W∙m^(–1)∙K^(–1))and their underlying mechanisms.Similar to the advancement of materials informatics since the advent of computational property databases for inorganic crystals,database construction using RadonPy will promote the development of polymer informatics.展开更多
Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations.Generally,this requires repeated fi...Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations.Generally,this requires repeated first-principles energy calculations,which is often impractical for large crystalline systems.Here,we present significant progress toward solving the crystal structure prediction problem:we performed noniterative,single-shot screening using a large library of virtually created crystal structures with a machine-learning energy predictor.This shotgun method(ShotgunCSP)has two key technical components:transfer learning for accurate energy prediction of pre-relaxed crystalline states,and two generative models based on element substitution and symmetry-restricted structure generation to produce promising and diverse crystal structures.First-principles calculations were performed only to generate the training samples and to refine a few selected pre-relaxed crystal structures.The ShotunCSP method is less computationally intensive than conventional methods and exhibits exceptional prediction accuracy,reaching 93.3%in benchmark tests with 90 different crystal structures.展开更多
文摘BACKGROUND The traditional radial approach(RA)is recommended as the standard method for coronary angiography(CAG),while a distal RA(DRA)has been recently used for CAG.AIM To assess the efficacy and safety of the DRA vs RA during CAG.METHODS The following databases were searched through December 2020:MEDLINE,the Cochrane Central Register of Controlled Trials,EMBASE,the World Health Organization International Clinical Trials Platform Search Portal,and Clinical-Trials.gov.Individual randomized-controlled trials for adult patients undergoing cardiac catheterization were included.The primary outcomes were the successful cannulation rate and the incidence of radial artery spasm(RAS)and radial artery occlusion(RAO).Study selection,data abstraction and quality assessment were independently performed using the Grading of Recommendations,Assessment,Development,and Evaluation approach.RESULTS Three randomized control trials and 13 registered trials were identified.The two approaches showed similar successful cannulation rates[risk ratio(RR)0.90,95%confidence interval(CI):0.72-1.13].The DRA did not decrease RAS(RR 0.43,95%CI:0.08-2.49)and RAO(RR 0.48,95%CI:0.18-1.29).Patients with the DRA had a shorter hemostasis time in comparison to those with the RA(mean difference-6.64,95%CI:-10.37 to-2.90).The evidence of certainty was low.CONCLUSION For CAG,the DRA would be safer than the RA with comparable cannulation rates.Given the limited data,additional research,including studies with standard protocols,is necessary.
基金The synchrotron radiation experiments were performed at the BL40B2 beamline of SPring-8 with the approval of the Japan Synchrotron Radiation Research Institute (JASRI) (Proposal No. 2022B1131), with support from Dr. Noboru Ohta (JASRI) and Prof. Tomoyasu Hirai (Osaka Institute of Technology)This work was supported by the Japan Science and Technology Agency (JST) under the CREST program (Grant Number JPMJCR19I3) (J.M., T.H., R.Y., M.T., R.M.)+2 种基金Additionally, we acknowledge the Japan Society for the Promotion of Science (JSPS) for their support through the KAKENHI program (Grant Number 21K04828) (Y.N.)the Ministry of Education, Culture, Sports, Science and Technology for “Program for Promoting Researches on the Supercomputer Fugaku” (project ID: hp210264) (R.Y.)HM and RM were supported by JST SPRING (Grant No. JPMJSP2106).
文摘Next-generation power electronics require efficient heat dissipation management,and molecular design guidelines are needed to develop polymers with high thermal conductivity.Polymer materials have considerably lower thermal conductivity than metals and ceramics due to phonon scattering in the amorphous region.The spontaneous orientation of the molecular chains of liquid crystalline polymers could potentially give rise to high thermal conductivity,but the molecular design of such polymers remains largely empirical.In this study,wedeveloped amachine learningmodel that predicts with more than 96%accuracy whether liquid crystalline states will form based on the chemical structure of the polymer.By exploring the inverse mapping of this model,we identified a comprehensive set of chemical structures for liquid crystalline polyimides.The polymers were then experimentally synthesized,and the results confirmed that they form liquid crystalline phases,with all polymers exhibiting calculated thermal conductivities within the range of 0.722–1.26Wm^(−1)K^(−1).
基金support from MEXT as“Program for Promoting Researches on the Supercomputer Fugaku”(project ID:hp210264)JST CREST(Grant Numbers JPMJCR19I3,JPMJCR22O3,JPMJCR2332)+5 种基金MEXT/JSPS KAKENHI Grant-in-Aid for Scientific Research on Innovative Areas(19H05820)Grant-in-Aid for Scientific Research(A)(19H01132)Grant-in-Aid for Research Activity Start-up(23K19980)Grant-in-Aid for Scientific Research(C)(22K11949)Computational resources were provided by Fugaku at the RIKEN Center for Computational Science,Kobe,Japan(hp210264)the supercomputer at the Research Center for Computational Science,Okazaki,Japan(project:23-IMS-C113,24-IMS-C107).
文摘To address the challenge of limited experimental materials data,extensive physical property databases are being developed based on high-throughput computational experiments,such as molecular dynamics simulations.Previous studies have shown that fine-tuning a predictor pretrained on a computational database to a real system can result in models with outstanding generalization capabilities compared to learning from scratch.This study demonstrates the scaling law of simulationto-real(Sim2Real)transfer learning for several machine learning tasks in materials science.Case studies of three prediction tasks for polymers and inorganic materials reveal that the prediction error on real systems decreases according to a power-law as the size of the computational data increases.Observing the scaling behavior offers various insights for database development,such as determining the sample size necessary to achieve a desired performance,identifying equivalent sample sizes for physical and computational experiments,and guiding the design of data production protocols for downstream real-world tasks.
基金support from MEXT as“Program for Promoting Researches on the Supercomputer Fugaku”(Project ID:hp210264)JST CREST(Grant Numbers JPMJCR19I3,JPMJCR22O3,JPMJCR2332)+4 种基金MEXT/JSPS KAKENHI Grant-in-Aid for Scientific Research on Innovative Areas(19H05820)Grant-in-Aid for Scientific Research(A)(19H01132)Grant-in-Aid for Scientific Research(C)(22K11949)Computational resources were provided by Fugaku at the RIKEN Center for Computational Science,Kobe,Japan(hp210264)the supercomputer at the Research Center for Computational Science,Okazaki,Japan(project:23-IMS-C113,24-IMS-C107).
文摘Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems.First-principles calculations and other computer experiments have been integrated into material design pipelines to address the lack of experimental data and the limitations of interpolative machine learning predictors.However,the enormous computational costs and technical challenges of automatingcomputer experiments for polymeric materials have limited the availability of open-source automated polymer design systems that integrate molecular simulations and machine learning.We developed SPACIER,an open-source software program that incorporates RadonPy,a Python library for fully automated polymer physical property calculations based on allatom classical molecular dynamics,into a Bayesian optimization-based polymer design system to overcome these challenges.As a proof-of-concept study,we synthesized optical polymers that surpass the Pareto boundary formed by the tradeoff between the refractive index and the Abbe number.
基金This work was supported in part by the“Materials Research by Information Integration”Initiative(MI2I)project of the Support Program for Starting Up Innovation Hub from Japan Science and Technology Agency(JST)and a Grant-in-Aid for Scientific Research(B)15H02672 from the Japan Society for the Promotion of Science(JSPS)S.W.gratefully acknowledges financial support from JSPS KAKENHI Grant Number JP18K18017+3 种基金K.H.gratefully acknowledges financial support from JSPS KAKENHI Grant Number JP17K17762a Grant-in-Aid for Scientific Research on Innovative Areas(16H06439)and PRESTO(JPMJPR16NA)C.S.gratefully acknowledges financial support from the Ministry of Education and Science of the Russian Federation(Grant 14.Y26.31.0019)J.M.acknowledges partial financial support by JSPS KAKENHI Grant Number JP16K06768.
文摘The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials.However,its practical benefits still remain unproven in real-world applications,particularly in polymer science.We demonstrate the successful discovery of new polymers with high thermal conductivity,inspired by machine-learning-assisted polymer chemistry.This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data,expertise from laboratory synthesis and advanced technologies for thermophysical property measurements.Using a molecular design algorithm trained to recognize quantitative structure—property relationships with respect to thermal conductivity and other targeted polymeric properties,we identified thousands of promising hypothetical polymers.From these candidates,three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications.The synthesized polymers reached thermal conductivities of 0.18–0.41 W/mK,which are comparable to those of state-of-the-art polymers in non-composite thermo-plastics.
基金The numerical calculations were conducted on the five supercomputer systems,Fugaku at the RIKEN Center for Computational Science,Kobe,Japanthe supercomputer at the Research Center for Computational Science,Okazaki,Japan(Project:21-IMS-C126,22-IMS-C125)+7 种基金the supercomputer Ohtaka at the Supercomputer Center,the Institute for Solid State Physics,the University of Tokyo,Tokyo,Japanthe supercomputer TSUBAME3.0 at the Tokyo Institute of Technology,Tokyo,Japanthe supercomputer ABCI at the National Institute of Advanced Industrial Science and Technology,Tsukuba,JapanThis work was supported by the following five grants:a JST CREST(Grant Number JPMJCR19I3 to J.M.and R.Y.)the MEXT as“Program for Promoting Researches on the Supercomputer Fugaku”(Project ID:hp210264 to R.Y.)the Grant-in-Aid for Scientific Research(A)from the Japan Society for the Promotion of Science(19H01132 to R.Y.)the Grant-in-Aid for Scientific Research(C)from the Japan Society for the Promotion of Science(22K11949 to Y.H.)the HPCI System Research Project(Project ID:hp210213 to Y.H.).
文摘The spread of data-driven materials research has increased the need for systematically designed materials property databases.However,the development of polymer databases has lagged far behind other material systems.We present RadonPy,an open-source library that can automate the complete process of all-atom classical molecular dynamics(MD)simulations applicable to a wide variety of polymeric materials.Herein,15 different properties were calculated for more than 1000 amorphous polymers.The MD-calculated properties were systematically compared with experimental data to validate the calculation conditions;the bias and variance in the MD-calculated properties were successfully calibrated by a machine learning technique.During the high-throughput data production,we identified eight amorphous polymers with extremely high thermal conductivity(>0.4 W∙m^(–1)∙K^(–1))and their underlying mechanisms.Similar to the advancement of materials informatics since the advent of computational property databases for inorganic crystals,database construction using RadonPy will promote the development of polymer informatics.
基金supported in part by a Ministry of Education,Culture,Sports,Science and Technology(MEXT)KAKENHI Grant-in-Aid for Scientific Research on Innovative Areas(grant number 19H05820)Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(A)(grant number 19H01132)+2 种基金Early-Career Scientists(grant number 23K16955)JST CREST(grant numbers JPMJCR19I3,JPMJCR22O3,and JPMJCR2332)Computational resources were partly provided by the supercomputer at the Research Center for Computational Science,Okazaki,Japan(projects 23-IMSC113 and 24-IMS-C107).
文摘Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations.Generally,this requires repeated first-principles energy calculations,which is often impractical for large crystalline systems.Here,we present significant progress toward solving the crystal structure prediction problem:we performed noniterative,single-shot screening using a large library of virtually created crystal structures with a machine-learning energy predictor.This shotgun method(ShotgunCSP)has two key technical components:transfer learning for accurate energy prediction of pre-relaxed crystalline states,and two generative models based on element substitution and symmetry-restricted structure generation to produce promising and diverse crystal structures.First-principles calculations were performed only to generate the training samples and to refine a few selected pre-relaxed crystal structures.The ShotunCSP method is less computationally intensive than conventional methods and exhibits exceptional prediction accuracy,reaching 93.3%in benchmark tests with 90 different crystal structures.
