Research graph
References from Machine learning for predicting the glass transition and melting temperatures of polymers: molecular representations, model architectures, and interpretability. Local targets link to admitted publications; unresolved targets remain external evidence.
Machine learning-assisted prediction of polymer glass transition temperature: a structural feature approach
10.1007/s10118-025-3361-3 · 2025 · External reference
Explainable machine learning algorithms for predicting glass transition temperatures
10.1016/j.actamat.2020.01.047 · 2020 · External reference
A graph representation of molecular ensembles for polymer property prediction
10.1039/d2sc02839e · 2022 · External reference
Workflow for investigating thermodynamic, structural, and energy properties of condensed polymer systems
2021 · External reference
Representing polymers as periodic graphs with learned descriptors for accurate polymer property predictions
10.1021/acs.jcim.2c00875 · 2022 · External reference
Machine-learning-based prediction of the glass transition temperature of organic compounds using experimental data
10.1021/acsomega.3c00139 · 2023 · External reference
Prediction of dielectric constant in series of polymers by quantitative structure-property relationship (QSPR)
10.3390/polym16192731 · 2024 · External reference
Machine learning with physically grounded, interpretable descriptors for polymer property prediction and monomer design
10.1016/j.ces.2026.123732 · 2026 · External reference
Explainability and extrapolation of machine learning models for predicting the glass transition temperature of polymers
10.1002/pol.20230714 · 2024 · External reference
Emerging materials intelligence ecosystems propelled by machine learning
10.1038/s41578-020-00255-y · 2021 · External reference
Molecular dynamics simulations for glass transition temperature predictions of polyhydroxyalkanoate biopolymers
10.1039/d0cp03163a · 2020 · External reference
Composition and configuration dependence of glass-transition temperature in binary copolymers and blends of polyhydroxyalkanoate biopolymers
10.1021/acs.macromol.1c00135 · 2021 · External reference
Machine learning for melting temperature predictions and design in polyhydroxyalkanoate-based biopolymers
10.1021/acs.jpcb.1c08354 · External reference
Predicting the mechanical response of polyhydroxyalkanoate biopolymers using molecular dynamics simulations
10.3390/polym14020345 · External reference
Ensuring structural fidelity of generated polymers with PoGE
10.1039/d6me00054a · 2026 · External reference
Autonomous chemical research with large language models
10.1038/s41586-023-06792-0 · 2023 · External reference
Augmenting large language models with chemistry tools
10.1038/s42256-024-00832-8 · 2024 · External reference
Polymer informatics method for fast and accurate prediction of the glass transition temperature from chemical structure
10.1021/acs.macromol.5c00178 · 2025 · External reference
A mobile robotic chemist
10.1038/s41586-020-2442-2 · 2020 · External reference
Machine learning analysis of a large set of homopolymers to predict glass transition temperatures
10.1038/s42004-024-01305-0 · 2024 · External reference
Uncertainty quantification with graph neural networks for efficient molecular design
10.1038/s41467-025-58503-0 · 2025 · External reference
Frequency-dependent dielectric constant prediction of polymers using machine learning
10.1038/s41524-020-0333-6 · 2020 · External reference
Predicting polymers’ glass transition temperature by a chemical language processing model
10.3390/polym13121898 · 2021 · External reference
Machine learning-based predictive analysis and characterisation of functional group development and shade depth in esterase-modified polyester
10.1007/s10965-025-04692-7 · 2025 · External reference
Machine-learning predictions of polymer properties with polymer genome
10.1063/5.0023759 · 2020 · External reference
Self-supervised graph neural networks for polymer property prediction
10.1039/d4me00088a · 2024 · External reference
The tensile, thermal and flame-retardant properties of polyetherimide and polyetherketoneketone processed via fused filament fabrication
10.3390/polym16030336 · 2024 · External reference
Automatic chemical design using a data-driven continuous representation of molecules
10.1021/acscentsci.7b00572 · 2018 · External reference
Deep learning based approach for prediction of glass transition temperature in polymers
10.1016/j.matpr.2021.02.730 · 2021 · External reference
Polymer informatics at scale with multitask graph neural networks
10.1021/acs.chemmater.2c02991 · 2023 · External reference
Knowledge-embedded message-passing neural networks: improving molecular property prediction with human knowledge
10.1021/acsomega.1c03839 · 2021 · External reference
Recent advances and challenges in experiment-oriented polymer informatics
10.1038/s41428-022-00734-9 · 2023 · External reference
RadonPy: automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics
10.1038/s41524-022-00906-4 · 2022 · External reference
Prediction of the glass-transition temperatures of linear homo/heteropolymers and cross-linked epoxy resins
10.1021/acsapm.9b00198 · 2019 · External reference
Uncertainty quantification using neural networks for molecular property prediction
10.1021/acs.jcim.0c00502 · 2020 · External reference
Prediction and interpretability of glass transition temperature of homopolymers by data-augmented graph convolutional neural networks
10.1021/acsami.3c13698 · 2023 · External reference
Accelerated materials property predictions and design using motif-based fingerprints
10.1103/physrevb.92.014106 · 2015 · External reference
Enhancing copolymer property prediction through the weighted-chained-SMILES machine learning framework
10.1021/acsapm.3c02715 · 2024 · External reference
Unified multimodal multidomain polymer representation for property prediction
10.1038/s41524-025-01652-z · 2025 · External reference
Prediction and interpretability study of the glass transition temperature of polyimide based on machine learning and molecular dynamics simulations
10.3390/polym17152083 · 2025 · External reference
Deformation modeling of polyamide 6 and the effect of water content using molecular dynamics simulation
10.1007/s10965-019-1815-4 · 2019 · External reference
NIMS polymer database PoLyInfo (I): an overarching view of half a million data points
10.1080/27660400.2024.2354649 · External reference
NIMS polymer database PoLyInfo (II): machine-readable standardization of polymer knowledge expression
10.1080/27660400.2024.2354651 · External reference
A physics-enforced neural network to predict polymer melt viscosity
10.1038/s41524-025-01532-6 · 2025 · External reference
Impact of dataset uncertainties on machine learning model predictions: the example of polymer glass transition temperatures
10.1088/1361-651x/aaf791 · 2019 · External reference
Message-passing neural networks for high-throughput polymer screening
10.1063/1.5099132 · 2019 · External reference
Predicting glass transition of amorphous polymers by application of cheminformatics and molecular dynamics simulations
10.1016/j.polymer.2021.123495 · 2021 · External reference
Polymer genome: a data-powered polymer informatics platform for property predictions
10.1021/acs.jpcc.8b02913 · 2018 · External reference
Open Macromolecular genome: generative design of synthetically accessible polymers
10.1021/acspolymersau.3c00003 · 2023 · External reference
polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics
10.1038/s41467-023-39868-6 · 2023 · External reference
Copolymer informatics with multitask deep neural networks
10.1021/acs.macromol.1c00728 · External reference
Polymer informatics with multi-task learning
10.1016/j.patter.2021.100238 · External reference
Bioplastic design using multitask deep neural networks
10.1038/s43246-022-00319-2 · 2022 · External reference
Combinatorial polycation synthesis and causal machine learning reveal divergent polymer design rules for effective pDNA and ribonucleoprotein delivery
10.1021/jacsau.1c00467 · 2022 · External reference
PolyMetriX: an ecosystem for digital polymer chemistry
10.1038/s41524-025-01823-y · 2025 · External reference
Comparison of machine learning methods towards developing interpretable polyamide property prediction
10.3390/polym13213653 · 2021 · External reference
Coarse-grained molecular dynamics simulation on strain-hardening and fracture behaviors of polycarbonate: effect of polydispersity and spatial distribution
10.1007/s10965-023-03785-5 · 2023 · External reference
Machine learning-enabled parallel prediction and co-design of polyimides for tailored glass transition, dielectric, and bandgap properties
10.1021/acsomega.5c09078 · 2025 · External reference
BigSMILES: a structurally-based line notation for describing macromolecules
10.1021/acscentsci.9b00476 · 2019 · External reference
Artificial neural network prediction of glass transition temperature of polymers
10.1007/s00396-009-2035-y · 2009 · External reference
High-temperature polymer dielectrics designed using an invertible molecular graph generative model
10.1021/acs.jcim.3c01572 · 2023 · External reference
Open polymer challenge: post-competition report
10.48550/arxiv.2512.08896 · 2025 · External reference
Large-scale glass-transition temperature prediction with an equivariant neural network for screening polymers
10.1021/acsomega.3c06843 · 2024 · External reference
A unified approach to interpreting model predictions
2017 · External reference
Effect of crosslinking rate on the glass transition temperature of polyimide cross-linked silica aerogels
10.1007/s10965-020-02082-9 · 2020 · External reference
Machine learning-based high-throughput screening for high-stability polyimides
10.1021/acs.iecr.4c03379 · 2024 · External reference
Evaluating polymer representations via quantifying structure-property relationships
10.1021/acs.jcim.9b00358 · 2019 · External reference
A self-driving laboratory advances the Pareto front for material properties
10.1038/s41467-022-28580-6 · 2022 · External reference
Estimation and prediction of the polymers’ physical characteristics using the machine learning models
10.3390/polym16010115 · 2024 · External reference
Machine learning strategy for accelerated design of polymer dielectrics
10.1038/srep20952 · 2016 · External reference
Emerging trends in machine learning: a polymer perspective
10.1021/acspolymersau.2c00053 · 2023 · External reference
From chemical structure to quantitative polymer properties prediction through convolutional neural networks
10.1016/j.polymer.2020.122341 · 2020 · External reference
A machine learning framework for predicting the glass transition temperature of homopolymers
10.1021/acs.iecr.2c01302 · 2022 · External reference
Introducing conformal prediction in predictive modeling: a transparent and flexible alternative to applicability domain determination
10.1021/ci5001168 · 2014 · External reference
Relationships between melting temperature, maximum crystallization temperature and glass transition temperature
10.1016/0032-3861(90)90067-9 · 1990 · External reference
Unresolved reference
External reference
PoLyInfo: polymer database for polymeric materials design
2011 · External reference
Prediction and interpretation of polymer properties using the graph convolutional network
10.1021/acspolymersau.1c00050 · 2022 · External reference
Featurization strategies for polymer sequence or composition design by machine learning
10.1039/d1me00160d · 2022 · External reference
100th anniversary of macromolecular science viewpoint: opportunities in the physics of sequence-defined polymers
10.1021/acsmacrolett.0c00002 · 2020 · External reference
Machine-learning-based predictive modeling of glass transition temperatures: a case of polyhydroxyalkanoate homopolymers and copolymers
10.1021/acs.jcim.9b00807 · 2019 · External reference
Data-driven strategies for accelerated materials design
10.1021/acs.accounts.0c00785 · 2021 · External reference
Elucidating the physicochemical basis of the glass transition temperature in linear polyurethane elastomers with machine learning
10.1021/acs.jpcb.0c06439 · 2020 · External reference
Accelerating materials discovery using artificial intelligence, high performance computing and robotics
10.1038/s41524-022-00765-z · 2022 · External reference
Why should I trust you? explaining the predictions of any classifier
2016 · External reference
NIMS polymer database PoLyInfo (III): modularizing ShEx schemas for descriptors and properties in PoLyInfoRDF
10.1080/27660400.2025.2544516 · 2025 · External reference
E(n) equivariant graph neural networks
2021 · External reference
Generative BigSMILES: an extension for polymer informatics, computer simulations and ML/AI
10.1039/d3dd00147d · 2024 · External reference
Polymer informatics beyond homopolymers
10.1557/s43577-023-00561-0 · 2024 · External reference
Autonomous experimentation systems for materials development: a community perspective
10.1016/j.matt.2021.06.036 · 2021 · External reference
Predicting homopolymer and copolymer solubility through machine learning
10.1039/d4dd00290c · 2025 · External reference
Axiomatic attribution for deep networks
2017 · External reference
Benchmarking machine learning models for polymer informatics: an example of glass transition temperature
10.1021/acs.jcim.1c01031 · External reference
Machine learning discovery of high-temperature polymers
10.1016/j.patter.2021.100225 · External reference
Machine learning strategies for the structure-property relationship of copolymers
10.1016/j.isci.2022.104585 · External reference
Unified machine learning protocol for copolymer structure-property predictions
10.1016/j.xpro.2022.101875 · External reference
Discovery of multi-functional polyimides through high-throughput screening using explainable machine learning
10.1016/j.cej.2023.142949 · 2023 · External reference
The index of ideality of correlation: hierarchy of Monte Carlo models for glass transition temperatures of polymers
10.1007/s10965-018-1618-z · 2018 · External reference
Interpretable machine learning framework to predict the glass transition temperature of polymers
10.3390/polym16081049 · 2024 · External reference
Advances in natural fiber polymer and PLA composites through artificial intelligence and machine learning integration
10.1007/s10965-025-04282-7 · 2025 · External reference
Molecular simulation of structural and dynamic properties of polymer nanoparticles composed of linear and cyclic polyethylene
10.1007/s10965-019-1797-2 · 2019 · External reference
Predicting crystallization tendency of polymers using multifidelity information fusion and machine learning
10.1021/acs.jpcb.0c01865 · 2020 · External reference
Inverse design of copolymers including stoichiometry and chain architecture
10.1039/d4sc05900j · 2025 · External reference
Machine learning with enormous synthetic data sets: predicting glass transition temperature of polyimides using graph convolutional neural networks
10.1021/acsomega.2c04649 · 2022 · External reference
Inverse design of materials by machine learning
10.3390/ma14247642 · 2021 · External reference
Targeted sequence design within the coarse-grained polymer genome
10.1126/sciadv.abc6216 · 2020 · External reference
PolyID: artificial intelligence for discovering performance-advantaged and sustainable polymers
10.1021/acs.macromol.3c00994 · 2023 · External reference
Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm
10.1038/s41524-019-0203-2 · 2019 · External reference
TransPolymer: a Transformer-based language model for polymer property predictions
10.1038/s41524-023-01016-5 · 2023 · External reference
Prediction of the glass transition temperatures for polymers with artificial neural network
10.1142/s0219633608004416 · 2008 · External reference
Benchmarking study of deep generative models for inverse polymer design
10.1039/d4dd00395k · 2025 · External reference
Machine learning glass transition temperature of styrenic random copolymers
10.1016/j.jmgm.2020.107796 · 2021 · External reference
Property prediction and structural feature extraction of polyimide materials based on machine learning
10.1021/acs.jcim.3c00326 · External reference
Machine-learning-enabled framework in engineering plastics discovery: a case study of designing polyimides with desired glass-transition temperature
10.1021/acsami.3c05376 · External reference
Polymer-unit graph: advancing interpretability in graph neural network machine learning for organic polymer semiconductor materials
10.1021/acs.jctc.3c01385 · External reference
Prediction and interpretability study of the glass transition temperature of polyimide based on machine learning with quantitative structure-property relationship (Tg-QSPR)
10.1021/acs.jpcb.4c00756 · External reference
