Understanding Predictive Materials Science Techniques

PolyID: Artificial Intelligence for Discovering Performance-Advantaged and Sustainable Polymers. https://lnkd.in/efvjiAa4 "A necessary transformation for a sustainable economy is the transition from fossil-derived plastics to polymers derived from biomass and waste resources. While renewable feedstocks can enhance material performance through unique chemical moieties, probing the vast material design space by experiment alone is not practically feasible. Here, we develop a machine-learning-based tool, PolyID, to reduce the design space of renewable feedstocks to enable efficient discovery of performance-advantaged, biobased polymers. PolyID is a multioutput, graph neural network specifically designed to increase accuracy and to enable quantitative structure–property relationship (QSPR) analysis for polymers. It includes a novel domain-of-validity method that was developed and applied to demonstrate how gaps in training data can be filled to improve accuracy. The model was benchmarked with both a 20% held-out subset of the original training data and 22 experimentally synthesized polymers. A mean absolute error for the glass transition temperatures of 19.8 and 26.4 °C was achieved for the test and experimental data sets, respectively. Predictions were made on polymers composed of monomers from four databases that contain biologically accessible small molecules: MetaCyc, MINEs, KEGG, and BiGG. From 1.4 × 106 accessible biobased polymers, we identified five poly(ethylene terephthalate) (PET) analogues with predicted improvements to thermal and transport performance. Experimental validation for one of the PET analogues demonstrated a glass transition temperature between 85 and 112 °C, which is higher than PET and within the predicted range of the PolyID tool. In addition to accurate predictions, we show how the model’s predictions are explainable through analysis of individual bond importance for a biobased nylon. Overall, PolyID can aid the biobased polymer practitioner to navigate the vast number of renewable polymers to discover sustainable materials with enhanced performance." Interesting paper detailing a graph neural network designed to aid in the development of renewable polymers derived from biomass-based chemical feedstocks, by @A. Nolan Wilson and larger team at the National Renewable Energy Laboratory https://lnkd.in/eNvbdDen ,

How can we trust the machine learning (ML) model predictions? To answer this question, we did a systematic uncertainty quantification (UQ) study on the polymer property prediction through various ML and UQ techniques. This work has been published in the Journal of Chemical Information and Modeling, titled "Assessing Uncertainty in Machine Learning for Polymer Property Prediction: A Benchmark Study". Kudos to my PhD students, Hao Tang and Tianle Yue, for leading this interesting study. Machine learning (ML) has emerged as a transformative tool in material science, enabling accelerated discovery and design of novel molecules while reducing experimental costs. Uncertainty quantification (UQ) is crucial for enhancing the reliability of ML predictions, particularly in high-stakes applications, such as functional polymer discovery. In this study, we present a comprehensive evaluation of nine UQ methods in ML─ensemble, Gaussian Process Regression (GPR), Monte Carlo Dropout (MCD), mean-variance estimation (MVE), Bayesian Neural Network based on Variational Inference (BNN-VI) and Markov Chain Monte Carlo (BNN-MCMC), evidential deep learning (EDL), quantile regression (QR), natural gradient boosting (NGBoost)─for predicting key polymer properties, including glass transition temperature (Tg), band gap (Eg), melting temperature (Tm) and decomposition temperature (Td). The models are assessed using three independent metrics, including prediction accuracy (R2), Spearman’s rank correlation coefficient and calibration area, offering a robust framework for evaluating both mean predictions and uncertainty estimates. Our analysis spans data sets of four properties, out-of-distribution (OOD) experimental and molecular dynamics (MD)-derived data, high-Tg polymers and diverse polymer types, providing a holistic perspective on model performance. Our findings reveal that optimal UQ method selection is highly context-dependent. Ensemble method consistently excelled for general in-distribution predictions across four properties. For challenging OOD scenarios, BNN-MCMC offered a strong balance of predictive accuracy and reliable UQ. NGBoost emerged as the top-performing method for high-Tg polymers, effectively balancing accuracy and uncertainty characterization, with Ensemble method also providing excellent accuracy in this case. Furthermore, BNN-VI demonstrated superior and consistent performance across the nine distinct polymer classes evaluated. This comprehensive benchmark underscores the critical importance of selecting tailored UQ strategies to enhance the trustworthiness of ML predictions, optimize experimental validation efforts, and ultimately accelerate the discovery of advanced functional polymers. #polymers #machinelearning #uncertainty UW-Madison Mechanical Engineering UW-Madison College of Engineering https://lnkd.in/g5_cBH-z

The paper below introduces a novel computational framework for understanding&modeling the interaction of molecules through the concept of “molecular holograms” I.e., spatiotemporal representations that encode the quantum&chemical properties of molecules (e.g., electronic distributions and reactive behaviors). The computational approach combines quantum mechanics, ML, & holographic imaging techniques to build a predictive&interpretable model of molecular systems. Molecular holography refers to a high-dimensional representation of molecules that encapsulates their spatial/temporal properties including electronic distributions, spin states, and other quantum mechanical descriptors. Spatiotemporal modeling involves tracking the dynamic behavior of molecules in space&time by integrating quantum mechanical simulations w/data-driven models that account for complex temporal dependencies, such as reaction kinetics. Methods: Time-dependent Density Functional Theory was used to simulate the electronic structure of molecules while molecular dynamics simulations provided insight into temporal evolution. The molecular holograms are generated by encoding wavefunction data into a multidimensional space using Fourier transforms integrating position, momentum, &electronic density. DL models (e.g., graph neural networks, recurrent networks) are trained on holographic data to learn patterns and predict outcomes like reactivity&stability. The molecular holograms enable precise predictions of reaction pathways, transition states, and activation energies. The method facilitates the design of molecules with desired properties by analyzing holograms for stability, reactivity, &functionality. The framework can identify molecular interactions in biological environments, aiding in drug-target binding predictions. The authors demonstrate the effectiveness of the method by applying it to a diverse dataset of molecular systems, including organic reactions, enzyme dynamics, & nanomaterial design. Comparative analysis shows that holographic models outperform traditional descriptors (e.g., molecular fingerprints) in terms of predictive accuracy&interpretability. This framework was able to predict complex non-linear phenomena (e.g., electron delocalization&excited-state dynamics). Molecular holograms provide a visually interpretable & mathematically rigorous framework; The integration of ML accelerates computations without compromising accuracy; The framework is applicable across a wide range of molecular systems. Unfortunately the computational cost remains high for large-scale systems & holographic encoding is sensitive to noise in input data, which may limit accuracy for certain classes of molecules. Future steps: Develop noise-robust holographic encoding algos; Scale up the approach for macromolecular systems (e.g., proteins, polymers); Extend the temporal resolution for ultra-fast processes (e.g., femtosecond reactions).