Conductivity prediction model for ionic liquids using machine learning.

Ionic liquids (ILs) are salts, composed of asymmetric cations and anions, typically existing as liquids at ambient temperatures. They have found widespread applications in energy storage devices, dye-sensitized solar cells, and sensors because of their high ionic conductivity and inherent thermal stability. However, measuring the conductivity of ILs by physical methods is time-consuming and expensive, whereas the use of computational screening and testing methods can be rapid and effective. In this study, we used experimentally measured and published data to construct a deep neural network capable of making rapid and accurate predictions of the conductivity of ILs. The neural network is trained on 406 unique and chemically diverse ILs. This model is one of the most chemically diverse conductivity prediction models to date and improves on previous studies that are constrained by the availability of data, the environmental conditions, or the IL base. Feature engineering techniques were employed to identify key chemo-structural characteristics that correlate positively or negatively with the ionic conductivity. These features are capable of being used as guidelines to design and synthesize new highly conductive ILs. This work shows the potential for machine-learning models to accelerate the rate of identification and testing of tailored, high-conductivity ILs.

Comparative Analysis of Clinical and Biochemical Profile of Exertional Heat Related Illness Among Cadets in a Military Training Centre in South India: A Single Centre Experience.

BACKGROUND: Heat-related illnesses includes a range of manifestation starting from minor illness like heat rashes/ heat cramps to more complicated illness like heat exhaustion and the most severe heat stroke. Often derangements in biochemical parameters including metabolic acidosis, respiratory alkalosis, electrolytes, transminitis and renal dysfunction are noticed in patients with heat stroke. Objective: The present study was an attempt to compare the clinical and changes in biochemical parameters in exertional heat exhaustion and heat stroke patients among cadets from a military training centre admitted to an Armed forces hospital in South India. MATERIAL AND METHODS: The present study was carried out as a cross sectional comparative study among patients with heat exhaustion (n=30) and heat stroke (n=30) in a tertiary level Armed forces hospital located in Chennai. Simple random sampling technique was used to select study participants. Clinical and biochemical parameters of the study participants were examined. Statistical analysis: Means and proportions were calculated for continuous and categorical variables respectively. Difference in proportions were tested using chi square test and a p value <0.05 was considered statistically significant. RESULTS: On examination most the patients had tachycardia, blood pressure and respiratory rate in normal ranges. Most of the patients were found having elevated liver enzymes (>90%). Hyponatremia was the most common electrolyte abnormality. Other abnormal biochemical parameters noted were hypokalemia and deranged renal parameters. Higher proportion of patients with heat stroke were found to have tachycardia, transaminitis and abnormal electrolyte and biochemical parameters as compared to those with heat exhaustion. CONCLUSION: Tachycardia, transaminitis and hyponatremia was widely observed in patients with heat related illness and these changes occur at higher rates in patients in heat stroke as compared to heat exhaustion.

Physically informed artificial neural networks for atomistic modeling of materials.

Large-scale atomistic computer simulations of materials heavily rely on interatomic potentials predicting the energy and Newtonian forces on atoms. Traditional interatomic potentials are based on physical intuition but contain few adjustable parameters and are usually not accurate. The emerging machine-learning (ML) potentials achieve highly accurate interpolation within a large DFT database but, being purely mathematical constructions, suffer from poor transferability to unknown structures. We propose a new approach that can drastically improve the transferability of ML potentials by informing them of the physical nature of interatomic bonding. This is achieved by combining a rather general physics-based model (analytical bond-order potential) with a neural-network regression. This approach, called the physically informed neural network (PINN) potential, is demonstrated by developing a general-purpose PINN potential for Al. We suggest that the development of physics-based ML potentials is the most effective way forward in the field of atomistic simulations.

Brain stem death certification protocol.

Transplantation of Human Organs is guided by laid down specific Laws in India. The organs which are targeted to be transplanted are liver, kidney and cornea. The waiting list is enormous but the donor pool is meagre. This document has been made with a view that the donor pool can be enlarged by identifying patients who are 'Brain Dead' while still not having 'Cardiac Death'. The steps include the prerequisite conditions which must be satisfied by patients who have suspicion of being brain dead, detailed examination of the patient, confirmation of the Brain Death and Counselling of the relatives for organ donation.

Hydroxyapatite substituted by transition metals: experiment and theory.

Bioceramics are versatile materials for hard tissue engineering. Hydroxyapatite (HA) is a widely studied biomaterial for bone grafting and tissue engineering applications. The crystal structure of HA allows for a wide range of substitutions, which allows for tailoring materials properties. Transition metals and lanthanides are of interest since substitution in HA can result in magnetic properties. In this study, experimental results were compared to theoretical calculations of HA substituted with a transition metal. Calculation of a 10 atomic percent substitution of a transition metal ion Mn(2+), Fe(2+), and Co(2+) substituted HA samples lead to magnetic moments of 5, 4, and 3 Bohr magnetons, respectively. Hydroxyapatite substituted by transition metals (MHA) was fabricated through an ion exchange procedure and characterized with X-ray diffraction, Fourier transform infra-red spectroscopy (FTIR), X-ray photoelectron spectroscopy, and vibrating sample magnetometer, and results were compared to theoretical calculations. All the substitutions resulted in phase-pure M(2+)HA with lattice parameters and FTIR spectra in good agreement with calculations. Magnetic measurements revealed that the substitution of Mn(2+) has the greatest effect on the magnetic properties of HA followed by the substitution of Fe(2+) and then Co(2+). The present work underlines the power of synergistic theoretical-experimental work in guiding the rational design of materials.

Machine learning bandgaps of double perovskites.

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. The developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.

An atomistic description of the high-field degradation of dielectric polyethylene.

A microscopic mechanism governing the initiating step in the high-field aging of crystalline polyethylene is proposed, based on density functional calculations and ab initio molecular dynamics simulations. It is assumed that electrons, holes, and excitons are present in the system. While the additional individual electrons or holes are not expected to lead to significant degradation, the presence of triplet excitons are concluded to be rather damaging. The electron and hole states of the exciton localize on a distorted region of polyethylene, significantly weakening nearby C-H bonds and facilitating C-H bond scission. The barrier to cleavage of the weakened C-H bonds is estimated and is comparable to the thermal energy, suggesting that this mechanism may be responsible for the degradation of polyethylene when placed under electrical stress, e.g., in high-voltage cables.

New Group IV chemical motifs for improved dielectric permittivity of polyethylene.

An enhanced dielectric permittivity of polyethylene and related polymers, while not overly sacrificing their excellent insulating properties, is highly desirable for various electrical energy storage applications. In this computational study, we use density functional theory (DFT) in combination with modified group additivity based high throughput techniques to identify promising chemical motifs that can increase the dielectric permittivity of polyethylene. We consider isolated polyethylene chains and allow the CH2 units in the backbone to be replaced by a number of Group IV halides (viz., SiF2, SiCl2, GeF2, GeCl2, SnF2, or SnCl2 units) in a systematic, progressive, and exhaustive manner. The dielectric permittivity of the chemically modified polyethylene chains is determined by employing DFT computations in combination with the effective medium theory for a limited set of compositions and configurations. The underlying chemical trends in the DFT data are first rationalized in terms of various tabulated atomic properties of the constituent atoms. Next, by parametrizing a modified group contribution expansion using the DFT data set, we are able to predict the dielectric permittivity and bandgap of nearly 30,000 systems spanning a much larger part of the configurational and compositional space. Promising motifs which lead to simultaneously large dielectric constant and band gap in the modified polyethylene chains have been identified. Our theoretical work is expected to serve as a possible motivation for future experimental efforts.

New perspective on formation energies and energy levels of point defects in nonmetals.

We propose a powerful scheme to accurately determine the formation energy and thermodynamic charge transition levels of point defects in nonmetals. Previously unknown correlations between defect properties and the valence-band width of the defect-free host material are identified allowing for a determination of the former via an accurate knowledge of the latter. These correlations are identified through a series of hybrid density-functional theory computations and an unbiased exploration of the parameter space that defines the Hyde-Scuseria-Ernzerhof family of hybrid functionals. The applicability of this paradigm is demonstrated for point defects in Si, Ge, ZnO, and ZrO2.

Early predictors of success of non-invasive positive pressure ventilation in hypercapnic respiratory failure.

BACKGROUND: Non-invasive positive pressure ventilation (NIPPV) has emerged as a significant advancement in the management of acute hypercapnic respiratory failure. METHOD: Patients with hypercapnic respiratory failure requiring ventilation therapy (respiratory rate [RR] of > 30 breaths per minutes, PaCO2 > 55 mmHg and arterial pH < 7.35) were included in the study. Baseline clinical parameters and arterial blood gas (ABG) were recorded before initiating NIPPV. Clinical parameters including heart rate (HR), RR, oxygen saturation and ABG were revaluated at 1, 4, and 24 hours after initiation of NIPPV. Change in these parameters and need for intubation was evaluated. RESULTS: Of the 100 patients, 76 (76%) showed improvement in clinical parameters and ABG. There was improvement in HR and RR, pH, and PCO2 within the first hour in the success group and these parameters continued to improve even after four and 24 hours of NIPPV treatment. Out of 24 (24%) patients who failed to respond, 13 (54%) needed endotracheal intubation within one hour. The failure group had higher baseline HR than the success group. CONCLUSION: Improvement in HR, RR, pH, and PCO2 one hour after putting the patient on NIPPV predicts success of non-invasive positive pressure ventilation in hypercapnic respiratory failure.