We are pleased to announce the special topic of“AI for Polymers”published in the Chinese Journal of Polymer Science (CJPS).In recent years,the advancements in artificial intelligence (AI) techniques,including machin...We are pleased to announce the special topic of“AI for Polymers”published in the Chinese Journal of Polymer Science (CJPS).In recent years,the advancements in artificial intelligence (AI) techniques,including machine learning(particularly deep learning) and data-driven modeling,are reshaping how we design,synthesize and characterize polymers.In particular,a strong and lively research community in the development and application of AI techniques to polymer science has emerged in China,making great contributions to this important area of research in polymer science.With the growing interest in the application of AI to polymer science,we believe it is the right time to organize a special topic showcasing the activities and achievements of this community.展开更多
Different Ziegler-Natta catalysts were employed to polymerize ethylene. To investigate the influences of reaction parameters, namely Al/Ti molar ratio, hydrogen and processing parameters, i.e. ethylene pressure and te...Different Ziegler-Natta catalysts were employed to polymerize ethylene. To investigate the influences of reaction parameters, namely Al/Ti molar ratio, hydrogen and processing parameters, i.e. ethylene pressure and temperature, a Taguchi experimental design was worked out. An L27 orthogonal array was chosen to take the above-mentioned parameters and relevant interactions into account. Response surface method was the tool used to analyze the experimental design results. Al/Ti, ethylene pressure and temperature were selected as experimental design factors, and catalyst activity and polymerization yield were the response parameters. Increasing pressure, due to an increment in monomer accessibility, and rising Al/Ti, because of higher reduction in the catalysts, cause an increase in both polymerization yield and catalyst activity. Nonetheless, a higher temperature, thanks to reducing ethylene solubility in the slurry medium and partially catalyst destruction, lead to a reduction in both response parameters. A synergistic effect was also observed between temperature and pressure. All catalyst activities will reduce in the presence of hydrogen. Molecular weight also shows a decline in the presence of hydrogen as a transfer agent. However, the polydispersity index remains approximately intact. Using SEM, various morphologies, owing to different catalyst morphologies, were seen for the polyethylene.展开更多
A new paradigm driven by artificial intelligence(AI)and machine learning(ML)is significantly accelerating the iterative pace of polymer materials research.Traditional experimental approaches to polymer discovery have ...A new paradigm driven by artificial intelligence(AI)and machine learning(ML)is significantly accelerating the iterative pace of polymer materials research.Traditional experimental approaches to polymer discovery have long relied on trial and error,requiring extensive time and resources while offering limited access to the vast chemical design space.In contrast,ML-assisted strategies provide a transformative framework for efficiently navigating this complex landscape.This paper focuses specifically on polymer design at the molecular level.By integrating data-driven methodologies,researchers can extract structure−property relationships,predict polymer properties,and optimize molecular architectures with unprecedented speed.ML-driven polymer design follows a structured approach:(1)database construction,(2)structural representation and feature engineering,(3)development of ML-based property prediction models,(4)virtual screening of potential candidates,and(5)validation through experiments and/or numerical simulations.This workflow faces two central challenges.First is the limited availability of high-quality polymer datasets,particularly for advanced materials with specialized properties.Second is the generation of virtual polymer structures.Unlike small-molecule drug discovery,where vast libraries of candidate compounds exist,polymer chemistry lacks an equivalent repository of hypothetical structures.Recent efforts have leveraged rule-based polymerization reactions and generative models to create large-scale databases of hypothetical polymers,significantly expanding the design space.Additionally,the diversity of polymer structures,the broad range of their properties,and the limited availability of training samples add complexity to developing accurate predictive models.Addressing these challenges requires innovative ML techniques,such as transfer learning,multitask learning,and generative models,to extract meaningful insights from sparse data and improve prediction reliability.This data-driven approach has enabled the discovery of novel,high-performance polymers for applications in aerospace,electronics,energy storage,and biomedical engineering.Despite these advancements,several hurdles remain.The interpretability of ML models,particularly deep neural networks,is a pressing concern.While black-box models can achieve remarkable predictive accuracy,understanding their decision-making processes remains challenging.Explainable AI methods are increasingly being explored to provide insights into feature importance,model uncertainty,and the underlying chemistry driving polymer properties.Additionally,the synthesizability and processability of ML-generated candidates must be carefully considered to ensure practical experimental validation and real-world application.In this paper,we review recent progress in ML-assisted molecular design of polymer materials,focusing on database development,feature representation,predictive modeling,and virtual polymer generation.We highlight emerging methodologies,including transformer-based language models,physics-informed neural networks,and closed-loop discovery frameworks,which collectively enhance the efficiency and accuracy of polymer informatics.Finally,we discuss the future outlook of ML-driven polymer research,emphasizing the need for collaborative efforts between data scientists,chemists,and engineers to refine predictive models,integrate experimental validation,and accelerate the development of next-generation polymeric materials.By leveraging the synergy between computational modeling and experimental insights,ML-assisted design is poised to revolutionize polymer discovery,enabling the rapid development of sustainable,high-performance materials tailored for diverse applications.展开更多
Organic polymer materials,as the most abundantly produced materials,possess a flammable nature,making them potential hazards to human casualties and property losses.Target polymer design is still hindered due to the l...Organic polymer materials,as the most abundantly produced materials,possess a flammable nature,making them potential hazards to human casualties and property losses.Target polymer design is still hindered due to the lack of a scientific foundation.Herein,we present a robust,generalizable,yet intelligent polymer discovery framework,which synergizes diverse capabilities,including the in situ burning analyzer,virtual reaction generator,and material genomic model,to achieve results that surpass the sum of individual parts.Notably,the high-throughput analyzer created for the first time,grounded in multiple spectroscopic principles,enables in situ capturing of massive combustion intermediates;then,the created realistic apparatus transforming to the virtual reaction generator acquires exponentially more intermediate information;further,the proposed feature engineering tool,which embedded both polymer hierarchical structures and massive intermediate data,develops the generalizable genomic model with excellent universality(adapting over 20 kinds of polymers)and high accuracy(88.8%),succeeding discovering series of novel polymers.This emerging approach addresses the target polymer design for flame-retardant application and underscores a pivotal role in accelerating polymeric materials discovery.展开更多
The aim of this study was to determine the optimum design mix to produce pre-cast concrete blocks by a completely random experimental design (CRED) with mixture and process variables. The polymerized concrete was stud...The aim of this study was to determine the optimum design mix to produce pre-cast concrete blocks by a completely random experimental design (CRED) with mixture and process variables. The polymerized concrete was studied its composition: Cement, and water defined as the mixture compounds. To choose the best model, all the possible models were assessed through the ANOVA, which tested each possible model. The linear-linear model was preferred, since that do not present evidence of lack of fit, and it is capable of relating how to react the process variables, when are changed the variable mixture condition levels. The optimum experimental condition, obtained for the polymerized concrete, was: The size of the polystyrene beads was 4.8 mm sized polystyrene beads, 5.0% polystyrene that replaced the aggregate, 18.3% cement, 73.4% aggregate and 8.3% water. In this condition, the blocks made with polymerized concrete show a compressive strength above 15 Mpa, allowing its utilization in paving.展开更多
<strong>Background: </strong>Recent decades witnessed a significant growth in terms of phytocompounds based therapeutics, extensively explored for almost all types of existing disorders. They have also bee...<strong>Background: </strong>Recent decades witnessed a significant growth in terms of phytocompounds based therapeutics, extensively explored for almost all types of existing disorders. They have also been widely investigated in Neurodegenerative disorders (NDDs) and Chlorogenic acid (CGA), a polyphenolic compound having potential anti-inflammatory and anti-oxidative properties, emerged as a promising compound in ameliorating NDDs. Owing to its poor stability, bioavailability and release kinetics, CGA needed a suitable nanocarrier based pharmaceutical design for targeting NDDs. <strong>Objective: </strong>The current study is aimed at the <em>in-silico</em> validation of CGA as an effective therapeutic agent targeting various NDDs followed by the fabrication of polymeric nanoparticles-based carrier system to overcome its pharmacological limitations and improve its stability. <strong>Methods:</strong> A successful <em>in-silico</em> validation using molecular docking techniques along with synthesis of CGA loaded polymeric nanoparticles (CGA-NPs) by ionic gelation method was performed. The statistical optimisation of the developed CGA-NPs was done by Box Behnken method and then the optimized formulation of CGA-NPs was characterised using particle size analysis (PSA), Transmission electron microscopy (TEM), Fourier Transform Infrared spectroscopy (FTIR) along with in-vitro release kinetics analysis.<strong> Results & Conclusion:</strong> The results attained exhibited average particle size of 101.9 ± 1.5 nm, Polydispersibility (PDI) score of 0.065 and a ZP of <span style="white-space:nowrap;">−</span>17.4 mV. On a similar note, TEM results showed a size range of CGA-NPs between 90 - 110 nm with a spherical shape of NPs. Also, the data from in-vitro release kinetics showed a sustained release of CGA from the NPs following the first-order kinetics suggesting the appropriate designing of nanoformulation.展开更多
文摘We are pleased to announce the special topic of“AI for Polymers”published in the Chinese Journal of Polymer Science (CJPS).In recent years,the advancements in artificial intelligence (AI) techniques,including machine learning(particularly deep learning) and data-driven modeling,are reshaping how we design,synthesize and characterize polymers.In particular,a strong and lively research community in the development and application of AI techniques to polymer science has emerged in China,making great contributions to this important area of research in polymer science.With the growing interest in the application of AI to polymer science,we believe it is the right time to organize a special topic showcasing the activities and achievements of this community.
文摘Different Ziegler-Natta catalysts were employed to polymerize ethylene. To investigate the influences of reaction parameters, namely Al/Ti molar ratio, hydrogen and processing parameters, i.e. ethylene pressure and temperature, a Taguchi experimental design was worked out. An L27 orthogonal array was chosen to take the above-mentioned parameters and relevant interactions into account. Response surface method was the tool used to analyze the experimental design results. Al/Ti, ethylene pressure and temperature were selected as experimental design factors, and catalyst activity and polymerization yield were the response parameters. Increasing pressure, due to an increment in monomer accessibility, and rising Al/Ti, because of higher reduction in the catalysts, cause an increase in both polymerization yield and catalyst activity. Nonetheless, a higher temperature, thanks to reducing ethylene solubility in the slurry medium and partially catalyst destruction, lead to a reduction in both response parameters. A synergistic effect was also observed between temperature and pressure. All catalyst activities will reduce in the presence of hydrogen. Molecular weight also shows a decline in the presence of hydrogen as a transfer agent. However, the polydispersity index remains approximately intact. Using SEM, various morphologies, owing to different catalyst morphologies, were seen for the polyethylene.
基金support from the Air Force Office of Scientific Research through the Air Force’s Young Investigator Research Program(FA9550-20-1-0183,Program Manager:Dr.Ming-Jen Pan and Capt.Derek Barbee)Air Force Research Laboratory/UES Inc.(FA8650-20-S-5008,PICASSO program)the National Science Foundation(CMMI-2332276,CMMI-2316200,and CAREER-2323108).
文摘A new paradigm driven by artificial intelligence(AI)and machine learning(ML)is significantly accelerating the iterative pace of polymer materials research.Traditional experimental approaches to polymer discovery have long relied on trial and error,requiring extensive time and resources while offering limited access to the vast chemical design space.In contrast,ML-assisted strategies provide a transformative framework for efficiently navigating this complex landscape.This paper focuses specifically on polymer design at the molecular level.By integrating data-driven methodologies,researchers can extract structure−property relationships,predict polymer properties,and optimize molecular architectures with unprecedented speed.ML-driven polymer design follows a structured approach:(1)database construction,(2)structural representation and feature engineering,(3)development of ML-based property prediction models,(4)virtual screening of potential candidates,and(5)validation through experiments and/or numerical simulations.This workflow faces two central challenges.First is the limited availability of high-quality polymer datasets,particularly for advanced materials with specialized properties.Second is the generation of virtual polymer structures.Unlike small-molecule drug discovery,where vast libraries of candidate compounds exist,polymer chemistry lacks an equivalent repository of hypothetical structures.Recent efforts have leveraged rule-based polymerization reactions and generative models to create large-scale databases of hypothetical polymers,significantly expanding the design space.Additionally,the diversity of polymer structures,the broad range of their properties,and the limited availability of training samples add complexity to developing accurate predictive models.Addressing these challenges requires innovative ML techniques,such as transfer learning,multitask learning,and generative models,to extract meaningful insights from sparse data and improve prediction reliability.This data-driven approach has enabled the discovery of novel,high-performance polymers for applications in aerospace,electronics,energy storage,and biomedical engineering.Despite these advancements,several hurdles remain.The interpretability of ML models,particularly deep neural networks,is a pressing concern.While black-box models can achieve remarkable predictive accuracy,understanding their decision-making processes remains challenging.Explainable AI methods are increasingly being explored to provide insights into feature importance,model uncertainty,and the underlying chemistry driving polymer properties.Additionally,the synthesizability and processability of ML-generated candidates must be carefully considered to ensure practical experimental validation and real-world application.In this paper,we review recent progress in ML-assisted molecular design of polymer materials,focusing on database development,feature representation,predictive modeling,and virtual polymer generation.We highlight emerging methodologies,including transformer-based language models,physics-informed neural networks,and closed-loop discovery frameworks,which collectively enhance the efficiency and accuracy of polymer informatics.Finally,we discuss the future outlook of ML-driven polymer research,emphasizing the need for collaborative efforts between data scientists,chemists,and engineers to refine predictive models,integrate experimental validation,and accelerate the development of next-generation polymeric materials.By leveraging the synergy between computational modeling and experimental insights,ML-assisted design is poised to revolutionize polymer discovery,enabling the rapid development of sustainable,high-performance materials tailored for diverse applications.
基金supported by the National Natural Science Foundation of China(51991351,51827803,52103122,and 22375138)the Institutional Research Fund from Sichuan University(no.2021SCUNL201)the Fundamental Research Funds for the Central Universities,and the 111 project(B20001).
文摘Organic polymer materials,as the most abundantly produced materials,possess a flammable nature,making them potential hazards to human casualties and property losses.Target polymer design is still hindered due to the lack of a scientific foundation.Herein,we present a robust,generalizable,yet intelligent polymer discovery framework,which synergizes diverse capabilities,including the in situ burning analyzer,virtual reaction generator,and material genomic model,to achieve results that surpass the sum of individual parts.Notably,the high-throughput analyzer created for the first time,grounded in multiple spectroscopic principles,enables in situ capturing of massive combustion intermediates;then,the created realistic apparatus transforming to the virtual reaction generator acquires exponentially more intermediate information;further,the proposed feature engineering tool,which embedded both polymer hierarchical structures and massive intermediate data,develops the generalizable genomic model with excellent universality(adapting over 20 kinds of polymers)and high accuracy(88.8%),succeeding discovering series of novel polymers.This emerging approach addresses the target polymer design for flame-retardant application and underscores a pivotal role in accelerating polymeric materials discovery.
文摘The aim of this study was to determine the optimum design mix to produce pre-cast concrete blocks by a completely random experimental design (CRED) with mixture and process variables. The polymerized concrete was studied its composition: Cement, and water defined as the mixture compounds. To choose the best model, all the possible models were assessed through the ANOVA, which tested each possible model. The linear-linear model was preferred, since that do not present evidence of lack of fit, and it is capable of relating how to react the process variables, when are changed the variable mixture condition levels. The optimum experimental condition, obtained for the polymerized concrete, was: The size of the polystyrene beads was 4.8 mm sized polystyrene beads, 5.0% polystyrene that replaced the aggregate, 18.3% cement, 73.4% aggregate and 8.3% water. In this condition, the blocks made with polymerized concrete show a compressive strength above 15 Mpa, allowing its utilization in paving.
文摘<strong>Background: </strong>Recent decades witnessed a significant growth in terms of phytocompounds based therapeutics, extensively explored for almost all types of existing disorders. They have also been widely investigated in Neurodegenerative disorders (NDDs) and Chlorogenic acid (CGA), a polyphenolic compound having potential anti-inflammatory and anti-oxidative properties, emerged as a promising compound in ameliorating NDDs. Owing to its poor stability, bioavailability and release kinetics, CGA needed a suitable nanocarrier based pharmaceutical design for targeting NDDs. <strong>Objective: </strong>The current study is aimed at the <em>in-silico</em> validation of CGA as an effective therapeutic agent targeting various NDDs followed by the fabrication of polymeric nanoparticles-based carrier system to overcome its pharmacological limitations and improve its stability. <strong>Methods:</strong> A successful <em>in-silico</em> validation using molecular docking techniques along with synthesis of CGA loaded polymeric nanoparticles (CGA-NPs) by ionic gelation method was performed. The statistical optimisation of the developed CGA-NPs was done by Box Behnken method and then the optimized formulation of CGA-NPs was characterised using particle size analysis (PSA), Transmission electron microscopy (TEM), Fourier Transform Infrared spectroscopy (FTIR) along with in-vitro release kinetics analysis.<strong> Results & Conclusion:</strong> The results attained exhibited average particle size of 101.9 ± 1.5 nm, Polydispersibility (PDI) score of 0.065 and a ZP of <span style="white-space:nowrap;">−</span>17.4 mV. On a similar note, TEM results showed a size range of CGA-NPs between 90 - 110 nm with a spherical shape of NPs. Also, the data from in-vitro release kinetics showed a sustained release of CGA from the NPs following the first-order kinetics suggesting the appropriate designing of nanoformulation.
