Advancing material property prediction: using physics-informed machine learning models for viscosity.

In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially in the material science domain where data is limited. To address this, we integrate physics-informed descriptors from molecular dynamics (MD) simulations to enhance the accuracy and interpretability of ML models. Our current study focuses on accurately predicting viscosity in liquid systems using MD descriptors. In this work, we curated a comprehensive dataset of over 4000 small organic molecules' viscosities from scientific literature, publications, and online databases. This dataset enabled us to develop quantitative structure-property relationships (QSPR) consisting of descriptor-based and graph neural network models to predict temperature-dependent viscosities for a wide range of viscosities. The QSPR models reveal that including MD descriptors improves the prediction of experimental viscosities, particularly at the small data set scale of fewer than a thousand data points. Furthermore, feature importance tools reveal that intermolecular interactions captured by MD descriptors are most important for viscosity predictions. Finally, the QSPR models can accurately capture the inverse relationship between viscosity and temperature for six battery-relevant solvents, some of which were not included in the original data set. Our research highlights the effectiveness of incorporating MD descriptors into QSPR models, which leads to improved accuracy for properties that are difficult to predict when using physics-based models alone or when limited data is available.

Complex regional pain syndrome following vascular access creation for hemodialysis.

Pain and swelling of a limb following creation of arterio-venous fistula (AVF) are often attributable to vascular disorders like venous hypertension or limb ischaemia. Here we report the case of a 70-years old man who developed pain and swelling of upper limb distal to the AVF during second post-operative week due to a complex regional pain syndrome (CRPS). CRPS is characterized by pain that is disproportionate to the invoking injury, autonomic dysfunction, trophic changes and functional impairment of affected limb. Hyperalgesia and allodynia are pain patterns characteristic of CRPS. CRPS has been reported rarely following AVF creation surgery. Prompt diagnosis of CRPS is important to avoid misdiagnosis of vascular disorders and for implementation of right therapeutic strategy.

Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics.

Data-driven methods are receiving increasing attention to accelerate materials design and discovery for organic light-emitting diodes (OLEDs). Machine learning (ML) has enabled high-throughput screening of materials properties to suggest new candidates for organic electronics. However, building reliable predictive ML models requires creating and managing a high volume of data that adequately address the complexity of materials' chemical space. In this regard, active learning (AL) has emerged as a powerful strategy to efficiently navigate the search space by prioritizing the decision-making process for unexplored data. This approach allows a more systematic mechanism to identify promising candidates by minimizing the number of computations required to explore an extensive materials library with diverse variables and parameters. In this paper, we applied a workflow of AL that accounts for multiple optoelectronic parameters to identify materials candidates for hole-transport layers (HTL) in OLEDs. Results of this work pave the way for efficient screening of materials for organic electronics with superior efficiencies before laborious simulations, synthesis, and device fabrication.

Critical Assessment of the Hildebrand and Hansen Solubility Parameters for Polymers.

Solubility parameter models are widely used to select suitable solvents/nonsolvents for polymers in a variety of processing and engineering applications. In this study, we focus on two well-established models, namely, the Hildebrand and Hansen solubility parameter models. Both models are built on the basis of the notion of "like dissolves like" and identify a liquid as a good solvent for a polymer if the solubility parameters of the liquid and the polymer are close to each other. Here we make a critical and quantitative assessment of the accuracy/utility of these two models by comparing their predictions against actual experimental data. Using a data set of 75 polymers, we find that the Hildebrand model displays a predictive accuracy of 60% for solvents and 76% for nonsolvents. The Hansen model leads to a similar performance; on the basis of a data set of 25 polymers for which Hansen parameters are available, we find that it has an accuracy of 67% for solvents and 76% for nonsolvents. The availability of the Hildebrand parameters for a large polymer data set makes it a widely applicable capability, as the Hildebrand parameter for a new polymer may be determined using this data set and machine learning methods as we have done before; the predicted Hildebrand parameter for a new polymer may then be used to determine suitable solvents and nonsolvents. Such predictions are difficult to make with the Hansen model, as the data set of Hansen parameters for polymers is rather small. Nevertheless, the Hildebrand approach must be used with caution. Our analysis shows that while the Hildebrand model has a predictive accuracy of 70-75% for nonpolar polymers, it performs rather poorly for polar polymers (with an accuracy of 57%). Going forward, determination of solvents and nonsolvents for polymers may benefit by developing classification models built directly on the basis of available experimental data sets rather than utilizing the solubility parameter approach, which is limited in versatility and accuracy.

Ferroelectricity, Antiferroelectricity, and Ultrathin 2D Electron/Hole Gas in Multifunctional Monolayer MXene.

The presence of ferroelectric polarization in 2D materials is extremely rare due to the effect of the surface depolarizing field. Here, we use first-principles calculations to show the largest out-of-plane polarization observed in a monolayer in functionalized MXenes (Sc2CO2). The switching of polarization in this new class of ferroelectric materials occurs through a previously unknown intermediate antiferroelectric structure, thus establishing three states for applications in low-dimensional nonvolatile memory. We show that the armchair domain interface acts as an 1D metallic nanowire separating two insulating domains. In the case of the van der Waals bilayer we observe, interestingly, the presence of an ultrathin 2D electron/hole gas (2DEG) on the top/bottom layers, respectively, due to the redistrubution of charge carriers. The 2DEG is nondegenerate due to spin-orbit coupling, thus paving the way for spin-orbitronic devices. The coexistence of ferroelectricity, antiferroelectricity, 2DEG, and spin-orbit splitting in this system suggests that such 2D polar materials possess high potential for device application in a multitude of fields ranging from nanoelectronics to photovoltaics.

Consequences of axon guidance defects on the development of retinotopic receptive fields in the mouse colliculus.

Gradients of molecular factors pattern the developing retina and superior colliculus (SC) and guide retinal ganglion cell (RGC) axons to their appropriate central target perinatally. During and subsequent to this period, spontaneous waves of action potentials sweep across the retina, providing an instructive topographic signal based on the correlations of firing patterns of neighbouring RGCs. How these activity-independent and activity-dependent factors interact during retinotopic map formation remains unclear. A typical phenotype of mutant mice lacking genes for one or more RGC axon guidance molecules is the presence of topographically inappropriate projections or 'ectopic spots'. Here, we examine mice that lack functional bone morphogenetic protein receptors (BMPRs) in the retina. Retinal BMP controls the graded expression of RGC axon guidance molecules, resulting in some dorsal RGCs projecting ectopically to locations in the SC that normally receive input from ventral retina. We examine the consequences of this anatomical phenotype in vivo by studying the receptive field (RF) properties of neurons in the superficial SC. We observe a mixture of physiological phenotypes in BMPR mutant mice; notably we find some neurons with ectopic RFs displaced in elevation, corresponding to the observed anatomical defect. However, in a result not necessarily congruent with the presence of focal ectopic projections, some neurons have split, enlarged and patchy/distorted RFs. These results are consistent with the effects of spontaneous retinal waves acting upon a disrupted molecular template, and they place significant limits on the form of an activity-dependent learning rule for the development of retinocollicular projections.

Developmental homeostasis of mouse retinocollicular synapses.

Spontaneous retinal waves during development are thought to provide an instructive signal for precise retinotopic mapping by correlating the activity of neighboring retinal ganglion cells. In mutant mice (beta2-/-) that lack correlated waves, retinocollicular map refinement is impaired. In vivo recordings reveal that neurons in the superior colliculus of beta2-/- mice have large receptive fields and low peak visual responses, resulting in a conservation of total integrated response. We find that this "response homeostasis" is maintained on a cell-by-cell basis, and argue that it does not depend on regulation from the visual cortex during adulthood. Instead, in vitro recordings show that homeostasis arises from the conservation of total synaptic input from the retina, and that it is maintained via different mechanisms over development. In the absence of correlated retinal waves, beta2-/- neurons sample a larger number of weaker retinal inputs relative to controls after the first postnatal week. Once retinal waves are restored, developmental learning rules and homeostasis drive refinement so that fewer, stronger synapses are retained, as in wild-type mice, but from a larger retinal area. Homeostasis in neurons has been shown previously to regulate the gain of synaptic transmission in response to perturbations of activity. Our results suggest that during the development of sensory maps, a unique consequence of homeostatic mechanisms is the precise shaping of neuronal receptive fields in conjunction with activity-dependent competition.

Evidence for an instructive role of retinal activity in retinotopic map refinement in the superior colliculus of the mouse.

Although it is widely accepted that molecular mechanisms play an important role in the initial establishment of retinotopic maps, it has also long been argued that activity-dependent factors act in concert with molecular mechanisms to refine topographic maps. Evidence of a role for retinal activity in retinotopic map refinement in mammals is limited, and nothing is known about the effect of spontaneous retinal activity on the development of receptive fields in the superior colliculus. Using anatomical and physiological methods with two genetically manipulated mouse models and pharmacological interventions in wild-type mice, we show that spontaneous retinal waves instruct retinotopic map refinement in the superior colliculus of the mouse. Activity-dependent mechanisms may play a preferential role in the mapping of the nasal-temporal axis of the retina onto the colliculus, because refinement is particularly impaired along this axis in mutants without retinal waves. Interfering with both axon guidance cues and activity-dependent cues in the same animal has a dramatic cumulative effect. These experiments demonstrate how axon guidance cues and activity-dependent factors combine to instruct retinotopic map development.