arXiv subjects

Lois Smith

Publications and source records attributed to Lois Smith.

3 recordsLinked to original sources

arXiv2026

PolyRapid: Automated High-Throughput Screening of Polymers Using a Computational Workflow

High-throughput computational screening of polymers offers a powerful way to address the imbalance between the vast number of polymers synthesised for diverse applications and the relatively small subset that can be studied using atomistic simulations. This work presents PolyRapid: an automatic workflow designed to enable the rapid and efficient screening of an extensive polymer library. In this work it is deployed on a library of 103 homopolymers. The workflow integrates an automated annealing protocol with adaptive control, allowing for reproducible simulations with minimal human intervention and minimisation of the computational cost. To this end, we test a number of quantitative conformational and energetic convergence metrics. It achieves equilibration in 95% of systems within four 30 ns annealing cycles, and 100% within nine annealing cycles saving, for our dataset, more than 2500 hours of compute compared to a fixed equilibration protocol. The simulation results are compared with experimental data and compiled into a publicly available repository which reports polymer temperature, tacticity and crystallinity. The availability of a homogenous large set of simulations enables the adoption of machine learning approaches for a variety of tasks. We exemplify this possibility by proposing rapid machine-learning-based method to predict the (computed) polymer density (F1-score = 0.91) and (experimental) glass transition temperature (F1-score = 0.76), using both chemical fingerprint representations and physically meaningful MD-derived descriptors.

cond-mat.mtrl-sci↗

arXiv2025

Active Learning for Predicting Polymer/Plasticizer Phase Behaviour

Plasticisers (PLs) are small additives commonly incorporated into polymer composites to enhance processability and improve mechanical properties. Their effectiveness depends heavily on their miscibility within the polymer melt, yet isolating the influence of plasticiser properties, such as flexibility and geometry, remains challenging. This difficulty stems from the time consuming nature of experimental work and also from the presence of impurities and inconsistencies that often arise during synthesis and testing. Atomistic simulations face similar difficulties as phase separation can occur over microsecond timescales, which can be computationally expensive. In this work, we use a coarse-grained bead-and-spring model to screen plasticisers of varying flexibilities and geometries to build a pool-based active learning procedure which characterizes their design space and its effect on polymer/plasticiser miscibility. We perform an active learning cycle with a random forest model and an uncertainty/random hybrid query strategy to determine the miscibility behaviour of queried molecules. This is evaluated through computationally expensive, coarse-grained polymer/plasticiser simulations of a cis-(1,4)-polyisoprene melt filled with small hydrocarbon additives of varying sizes and rigidities. Through this, we are able to efficiently improve model performance in order to make predictions on the entire PL design space. Such findings enable us to determine a new set of general plasticiser design rules, suitable for non-polar molecules, which expands on our previous work. To further prove this, we perform atomistic simulations of polyisoprene/plasticiser systems which are approximately back-mapped from their coarse-grained equivalents. Our findings indicate that the polyisoprene/plasticiser phase behaviour, observed using the coarse-grained model for PLs in the absence of side chains, is valid.

cond-mat.soft↗

arXiv2024

A Framework for a High Throughput Screening Method to Assess Polymer/Plasticizer Miscibility

Polymer composite materials require softening to reduce their glass transition temperature and improve processability. To this end, plasticizers, which are small organic molecules, are added to the polymer matrix. The miscibility of these plasticizers has a large impact on their effectiveness and therefore their interactions with the polymer matrix must be carefully considered. Many plasticizer characteristics, including their size, topology and flexibility, can impact their miscibility and, because of the exponentially large numbers of plasticizers, the current trial-and-error approach is very ineffective. In this work we show that using molecular simulations of a small dataset of 48 plasticizers, it is possible to identify topological and thermodynamic descriptors that are proxy for their miscibility. Using ad-hoc molecular dynamics simulation set-ups that are relatively computationally inexpensive, we establish correlations between the plasticizers' topology, internal flexibility, thermodynamics of aggregation and their degree of miscibility and use these descriptors to classify the molecules as miscible or immiscible. With all available data we also construct a decision tree model which achieves a F1 score of 0.86 +/- 0.01 with repeated, stratified 5-fold cross-validation, indicating that this machine learning method is a promising route to fully automate the screening. By evaluating the individual performance of the descriptors, we show this procedure enables a 10-fold reduction of the test space and provides the basis for the development of workflows which can efficiently screen thousands of plasticizers with a variety of features.

cond-mat.soft↗