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Meeting MS&T21: Materials Science & Technology
Symposium AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
Presentation Title Machine Learning Polymer Property Prediction Models with Polymers Represented as Natural Language
Author(s) Christopher Benjamin Kuenneth, Rampi Ramprasad
On-Site Speaker (Planned) Christopher Benjamin Kuenneth
Abstract Scope Polymer informatics tools have been recently gaining ground to design and discover polymers that meet specific application needs. A critical component of such tools is the conversion of polymers to machine readable representations (so-called fingerprints). The fingerprinting process has so far been based on handcrafted approaches that capture key chemical and structural features. Recently, within the domain of natural language processing, transformer-based ML models have demonstrated a new, fully ML based path to obtain fingerprints of language. Here, we view SMILES strings as a language representation of polymers, and use them to train a transformer based ML model using more than 100 million SMILES strings. The performance of the so-derived fingerprints are compared with traditional fingerprints using a large polymer property data set. Our new approach has a similar prediction performance compared to the existing state-of-the-art methods, but is faster, more flexible, and allows us to create fully-autonomous ML pipelines.
Proceedings Inclusion? Undecided

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Deep Generative Model for Parametric EBSD Pattern Simulation
Aluminum Alloy Design Using Physics Informed Machine Learning
De Novo Inverse Design of Nanoporous Materials by Machine Learning
Deep Learning and Uncertainty Quantification for Automated Experiments
Discovery of Novel Crystal Structures via Generative Adversarial Networks
Improving EBM NIR Image Analysis for Component Qualification a Statistical Learning Approach
Machine-learning Based Algorithms for 4D X-ray Microtomographic Analysis
Machine Learning for Automated Experiment in Scanning Probe and Electron Microscopy
Machine Learning Polymer Property Prediction Models with Polymers Represented as Natural Language
Non-iterative Deep Learning for High-fidelity Microscopic Tomography
Optimizing the Training of Convolutional Neural Networks for Image Segmentation
Prediction of Dynamic Properties of LiF and FLiBe Molten Salts with DeepPot Network Potentials
Refinements to the Production of Machine Learning Interatomic Potentials
Semantic Segmentation of Porosity in In-situ X-ray Tomography Data Using FCNs
Tuning Optoelectronic Properties of Semiconductors with First Principles Modeling and Machine Learning
Understanding the Composition–property Relationship of Glasses Using Interpretable Machine Learning

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