PolyNC: a natural and chemical language model for the prediction of unified polymer properties.

Language models exhibit a profound aptitude for addressing multimodal and multidomain challenges, a competency that eludes the majority of off-the-shelf machine learning models. Consequently, language models hold great potential for comprehending the intricate interplay between material compositions and diverse properties, thereby accelerating material design, particularly in the realm of polymers. While past limitations in polymer data hindered the use of data-intensive language models, the growing availability of standardized polymer data and effective data augmentation techniques now opens doors to previously uncharted territories.

Exploration of the Polarization Curve for Proton-Exchange Membrane Fuel Cells.

The polarization curve is the most important profile to evaluate the performance of proton-exchange membrane fuel cells (PEMFCs). To explore the important thermodynamic parameters and their correlation with the composition, fabrication, and operational settings, a comprehensive data set consisting of 446 polarization curves from 191 perfluorosulfonate and 255 sulfonated hydrocarbon-based PEMs is collected. Then, a Markov chain Monte Carlo simulation within the Bayesian frame provides higher than 93% confidence to extract six important thermodynamic parameters including open-circuit potential, the transfer coefficient, the current loss, the reference exchange current density, the internal resistance, and the limiting current density.

Anomalous Dynamics of Water in Polyamide Matrix.

Water in polymer matrixes is likely to show anomalous dynamics, a problem that has not been well understood yet. Here, we performed atomistic molecular dynamics simulations to study the water dynamics in a polyamide (PA) matrix, the bulk phase of well-known reverse osmosis membranes. For time-dependent ensemble average, water molecules experienced ballistic diffusion at a shorter time scale, followed by a crossover from subdiffusion to Brownian diffusion at a time scale ∼10 ns, and non-Gaussian diffusion, an indication of anomalous dynamics, sticks on even in the Brownian diffusion region.

Determining electrospun morphology from the properties of protein-polymer solutions.

Integrating natural macromolecules, e.g. proteins, is a progressive trend in the fabrication of biocompatible sub-micrometer fibers with tunable diameters using the electrospinning technique.

Predictive models for tyrosinase inhibitors: Challenges from heterogeneous activity data determined by different experimental protocols.

Quantitative Structure-Activity Relationship (QSAR) models of tyrosinase inhibitors were built using Random Forest (RF) algorithm and evaluated by the out-of-bag estimation (R) and 10-fold cross validation (Q). We found that the performances of QSAR models were closely correlated with the systematic errors of inhibitory activities of tyrosinase inhibitors arising from the different measuring protocols. By defining ERR, outliers with larger errors can be efficiently identified and removed from heterogeneous activity data.

Assembled Structures of Perfluorosulfonic Acid Ionomers Investigated by Anisotropic Modeling and Simulations.

Nafion, a classic of perfluorosulfonic acid ionomers, has broad applications in proton conduction, attributed from the unique structures. However, a satisfactory structure model from theoretical calculation and simulation that can match with the well-known experimental observations is still absent. We performed GPU-accelerated molecular dynamics simulations to investigate the assembled structures of Nafion at different water contents based on an anisotropic coarse-grained model equipped with Gay-Berne potential.

Construction of explicit models to correlate the structure and the inhibitory activity of aldose reductase: Flavonoids and sulfonyl-pyridazinones as inhibitors.

The correlation between binding energies and bioactivities is the core of structure-based computer-aided drug design. However, many models to address this correlation are still strongly system-dependent at current stage. We constructed two explicit models to correlate the binding energies with the inhibitory activities of flavonoids and sulfonyl-pyridazinones as inhibitors of aldose reductase.

GPU-Accelerated Molecular Dynamics Simulation to Study Liquid Crystal Phase Transition Using Coarse-Grained Gay-Berne Anisotropic Potential.

Gay-Berne (GB) potential is regarded as an accurate model in the simulation of anisotropic particles, especially for liquid crystal (LC) mesogens. However, its computational complexity leads to an extremely time-consuming process for large systems. Here, we developed a GPU-accelerated molecular dynamics (MD) simulation with coarse-grained GB potential implemented in GALAMOST package to investigate the LC phase transitions for mesogens in small molecules, main-chain or side-chain polymers.