Leptospirosis in India: insights on circulating serovars, research lacunae and proposed strategies to control through one health approach.

Leptospirosis is one of the most neglected zoonotic infections of public health concern worldwide and a remerging infection in tropical countries such as India. The infection least explored disease and the epidemiological and other critical data are scarce for the disease rate reported and to control the infection. Leptospirosis as sapronosis is as underrated as the infection itself, and this article aims to explore the significance of this aspect of the disease. The research review aimed at the epidemiological understanding of the infection to control the negative impact of the disease. A mixed review and analysis were carried out to understand the knowledge published on the critical and understudied areas like epidemiology, transmission, diagnosis, treatment, and control of infection. A systematic analysis was carried out to extract information about the reported circulating strains, and research lacunae in India with the published data available in PubMed. The article elaborately discusses crucial inference areas of infection transmission and addresses lacunae in critically unacclaimed areas of infection to control the spread of infection using one health approach (OHA), and strategies to control leptospiral infection are proposed. The article also reviewed how and why Leptospirosis can be best studied and controlled by "One health approach" in India.

Distinguishing Elements at the Sub-Nanometer Scale on the Surface of a High Entropy Alloy.

Materials in crystalline form possess translational symmetry (TS) when the unit cell is repeated in real space with long- and short-range orders. The periodic potential in the crystal regulates the electron wave function and results in unique band structures, which further define the physical properties of the materials. Amorphous materials lack TS due to the randomization of distances and arrangements between atoms, causing the electron wave function to lack a well-defined momentum. High entropy materials provide another way to break the TS by randomizing the potential strength at periodic atomic sites. The local elemental distribution has a great impact on physical properties in high entropy materials. It is critical to distinguish elements at the sub-nanometer scale to uncover the correlations between the elemental distribution and the material properties. Here, the use of synchrotron X-ray scanning tunneling microscopy (SX-STM) with sub-nm scale resolution in identifying elements on a high entropy alloy (HEA) surface is demonstrated. By examining the elementally sensitive X-ray absorption spectra with an STM tip to enhance the spatial resolution, the elemental distribution on an HEA's surface at a sub-nm scale is extracted. These results open a pathway towards quantitatively understanding high entropy materials and their material properties.

Electronic and Lattice Distortions Induce Elastic Softening in Refractory Multicomponent Borides.

Borides are extensively employed in applications demanding exceptionally high hardness, which arises from the unique and strong crystallographic arrangement of boron atoms therein. Addition of multiprincipal elements in borides is expected to enhance their structural properties due to lattice distortion and high configurational entropy. In contrast, we unravel a phenomenon of elastic softening in refractory multicomponent borides from first-principle predictions, which concur with experimentally determined metrics in their single-phase multiprincipal element counterparts. The reductions in the bulk and Young's modulus of these compounds are attributed to the lengthening and distortion of the boron-boron bonds and angles, but more critically to the perturbation in the charge densities arising from the different cations and the consequential increase in statistical weights of the d5 configuration states of the transition metals present in the boride..

Predictive Modeling of Molecular Mechanisms in Hydrogen Production and Storage Materials.

Hydrogen has been widely considered to hold promise for solving challenges associated with the increasing demand for green energy. While many chemical and biochemical processes produce molecular hydrogen as byproducts, electrochemical approaches using water electrolysis are considered to be a predominant method for clean and green hydrogen production. We discuss the current state-of-the-art in molecular hydrogen production and storage and, more significantly, the increasing role of computational modeling in predictively designing and deriving insights for enhancing hydrogen storage efficiency in current and future materials of interest. One of the key takeaways of this review lies in the continued development and implementation of large-scale atomistic simulations to enable the use of designer electrolyzer-electrocatalysts operating under targeted thermophysical conditions for increasing green hydrogen production and improving hydrogen storage in advanced materials, with limited tradeoffs for storage efficiency.

Advances and Challenges in Predictive Modeling for Additive Manufacturing of Dissimilar Metals and Complex Alloys.

We present a scrutiny on the state of the art and applicability of predictive methods for additive manufacturing (AM) of metals, alloys, and compositionally complex metallic materials, to provide insights from the computational models for AM process optimization. Our work emphasizes the importance of manufacturing parameters on the thermal profiles evinced during processing, and the fundamental insights offered by the models used to simulate metal AM mechanisms. We discuss the methods and assumptions necessary for an educated tradeoff between the efficacy and accuracy of the computational approaches that incorporate multi-physics required to mimic the associated fluid flow phenomena as well as the resulting microstructures. Finally, the current challenges in the existing approaches are summarized and future scopes identified.

Examining sialic acid derivatives as potential inhibitors of SARS-CoV-2 spike protein receptor binding domain.

Severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) has been the primary reason behind the COVID-19 global pandemic which has affected millions of lives worldwide. The fundamental cause of the infection is the molecular binding of the viral spike protein receptor binding domain (SP-RBD) with the human cell angiotensin-converting enzyme 2 (ACE2) receptor. The infection can be prevented if the binding of RBD-ACE2 is resisted by utilizing certain inhibitors or drugs that demonstrate strong binding affinity towards the SP RBD. Sialic acid based glycans found widely in human cells and tissues have notable propensity of binding to viral proteins of the coronaviridae family. Recent experimental literature have used N-acetyl neuraminic acid (Sialic acid) to create diagnostic sensors for SARS-CoV-2, but a detailed interrogation of the underlying molecular mechanisms is warranted. Here, we perform all atom molecular dynamics (MD) simulations for the complexes of certain Sialic acid-based molecules with that of SP RBD of SARS CoV-2. Our results indicate that Sialic acid not only reproduces a binding affinity comparable to the RBD-ACE2 interactions, it also assumes the longest time to dissociate completely from the protein binding pocket of SP RBD. Our predictions corroborate that a combination of electrostatic and van der Waals energies as well the polar hydrogen bond interactions between the RBD residues and the inhibitors influence free energy of binding.Communicated by Ramaswamy H. Sarma.

BindingSite-AugmentedDTA: enabling a next-generation pipeline for interpretable prediction models in drug repurposing.

While research into drug-target interaction (DTI) prediction is fairly mature, generalizability and interpretability are not always addressed in the existing works in this field. In this paper, we propose a deep learning (DL)-based framework, called BindingSite-AugmentedDTA, which improves drug-target affinity (DTA) predictions by reducing the search space of potential-binding sites of the protein, thus making the binding affinity prediction more efficient and accurate. Our BindingSite-AugmentedDTA is highly generalizable as it can be integrated with any DL-based regression model, while it significantly improves their prediction performance. Also, unlike many existing models, our model is highly interpretable due to its architecture and self-attention mechanism, which can provide a deeper understanding of its underlying prediction mechanism by mapping attention weights back to protein-binding sites. The computational results confirm that our framework can enhance the prediction performance of seven state-of-the-art DTA prediction algorithms in terms of four widely used evaluation metrics, including concordance index, mean squared error, modified squared correlation coefficient ($r^2_m$) and the area under the precision curve. We also contribute to three benchmark drug-traget interaction datasets by including additional information on 3D structure of all proteins contained in those datasets, which include the two most commonly used datasets, namely Kiba and Davis, as well as the data from IDG-DREAM drug-kinase binding prediction challenge. Furthermore, we experimentally validate the practical potential of our proposed framework through in-lab experiments. The relatively high agreement between computationally predicted and experimentally observed binding interactions supports the potential of our framework as the next-generation pipeline for prediction models in drug repurposing.

Spatial heterogeneity of risk factors associated with HIV prevalence among men who inject drugs in India: An analysis of the data from the integrated bio-behavioral surveillance, India.

People who inject drugs (PWID) are India's third-largest vulnerable population to human immunodeficiency virus (HIV) infection. PWID in India are confined to certain geographic locations and exhibit varying injecting and sexual risk behaviors, contributing considerably to increasing HIV trends in specific regions. Spatial heterogeneity in risk factors among vulnerable PWID influences HIV prevalence, transmission dynamics, and disease management. Stratified analysis of HIV prevalence based on risk behaviors and geographic locations of PWID will be instrumental in strategic interventions. To stratify the male PWID based on their risk behaviors in each state and determine the HIV prevalence for each stratum. The behavioral data and HIV prevalence of the national integrated biological and behavioural surveillance (IBBS), a nationwide cross-sectional community-based study conducted in 2014 to 2015, was analyzed. Data from 19,902 men who inject drugs across 53 domains in 29 states of India were included. Women who inject drugs were excluded at the time of IBBS, and hence PWID in this study refers to only men who inject drugs. PWID were categorized based on their risk profile, and the corresponding HIV prevalence for each state was determined. HIV prevalence was the highest (29.6%) in Uttar Pradesh, with a high prevalence of risk behaviors among PWID. High HIV prevalence ranging between 12.1% and 22.4% was observed in a few states in East and North-East India and most states in central and North India. Unsafe injecting and sexual practices were significantly (P < .05) associated with higher HIV prevalence and more significantly in National Capital Territory of Delhi (P < .001). Unsafe injecting practices among PWID were proportionally higher in Western and Central India, whereas unsafe sexual behaviors were widespread among most states. Unsafe sexual practices among male PWID were common. The high prevalence of unsafe injecting had significant HIV infection and transmission risks in Western and Central India. The results emphasize the need for stratified, region-specific interventions and combination approaches for harm reduction among PWID. Strengthening the measures that facilitate the reduction of high-risk behaviors, adoption of safe practices, and utilization of HIV services will positively impact HIV prevention measures among PWID.

Modeling Oxidation of AlCoCrFeNi High-Entropy Alloy Using Stochastic Cellular Automata.

Together with the thermodynamics and kinetics, the complex microstructure of high-entropy alloys (HEAs) exerts a significant influence on the associated oxidation mechanisms in these concentrated solid solutions. To describe the surface oxidation in AlCoCrFeNi HEA, we employed a stochastic cellular automata model that replicates the mesoscale structures that form. The model benefits from diffusion coefficients of the principal elements through the native oxides predicted by using molecular simulations. Through our examination of the oxidation behavior as a function of the alloy composition, we corroborated that the oxide scale growth is a function of the complex chemistry and resultant microstructures. The effect of heat treatment on these alloys is also simulated by using reconstructed experimental micrographs. When they are in a single-crystal structure, no segregation is noted for α-Al2O3 and Cr2O3, which are the primary scale-forming oxides. However, a coexistent separation between Al2O3 and Cr2O3 oxide scales with the Al-Ni- and Cr-Fe-rich regions is predicted when phase-separated microstructures are incorporated into the model.

Injecting drug use & HIV prevalence among female sex workers: Evidence from the National Integrated Biological & Behavioural Surveillance, India.

Background & objectives: Female sex workers (FSWs) who inject drugs (FSW-IDs) have a higher risk of HIV infection and transmission. Understanding the socio-demographic characteristics and other risk behaviours among FSW-IDs will help in strengthening targeted interventions for HIV prevention and management. In the present study, the HIV prevalence, associated socio-demographic characteristics and risk behaviours among FSWs who injected drugs (FSW-IDs) and those who did not ID (FSW-NIDs) was determined in India. Methods: The national cross-sectional, community-based, integrated biological and behavioural surveillance was conducted in 2014-2015 at 73 randomly selected FSW domains across 28 States and Union Territories in India. The sample size was fixed at 400 for each domain, and a probability-based sampling method was followed. The data were analyzed by logistic regression methods. Results: Data from 27,007 FSWs were included in the analysis, of which 802 (3%) were FSW-IDs. HIV prevalence among FSW-IDs was significantly higher than that in FSW-NIDs (4.5 vs. 1.9%). Univariate analysis showed that factors significantly associated with higher HIV prevalence among FSW-IDs were older age, sex work as the only source of income, dissolved marriage, living with a sex worker, urban locality of sex work and consumption of alcohol or oral drugs. In multivariable analysis, factors such as older age of FSW-IDs (35 yr and above), having a dissolved marriage and sex work being the only source of income were observed to be independently and significantly associated with higher HIV prevalence. Interpretation & conclusions: Scaling up the HIV preventive interventions for FSW-IDs, such as facilitating awareness and improved access to needle and syringe exchange programme (NSEP) and opioid substitution therapy (OST), encouraging safe sex and injecting practices, educating on the harmful effects of alcohol and drugs and providing alternative vocation options to secure their financial needs are several strategies that may reduce HIV transmission among FSWs.

Direct Observations of Uniform Bulk Heterojunctions and the Energy Level Alignments in Nonfullerene Organic Photovoltaic Active Layers.

State-of-the-art organic photovoltaic (OPV) cells rely on the engineering of the energy levels of the organic molecules as well as the bulk-heterojunction nanomorphology to achieve high performance. However, both are difficult to measure inside the active layer where the electron donor and acceptor molecules are mingled. While the energy level alignments of the lowest unoccupied molecular orbital (LUMO) and highest occupied molecular orbital (HOMO) between the electron donors and acceptors may be altered in the mixed active layer compared to their pure forms, the nanomorphology of the donor and acceptor molecular domains is mostly studied in indirect means. Here, we present the direct observations of the nanomorphology of the molecular domains as well as the energy level alignments in the active layer of a nonfullerene-based OPV (donor: PBDB-T-2F and acceptor: IT-4Cl) using cross-sectional scanning tunneling microscopy and spectroscopy (XSTM/S). It is revealed that (1) the bulk-heterojunction (BHJ) structures are homogeneous and uniform throughout the ∼1.2 µm thick active layer; (2) the energy alignments between the donor-rich and acceptor-rich domains are directly observed; (3) there exist the intermixing domains at the boundaries of the donor-rich and acceptor-rich domains with thickness in the nm scale; (4) the exciton binding energies in PBDB-T-2F and IT-4Cl are estimated to be 0.74 and 0.32 eV, respectively; and (5) there is an ∼0.7 V loss in the open circuit voltage. The results provide a nanoscale understanding of the OPV active layers to guide further improvement of the OPV performance.

An Analysis of Levels and Trends in HIV Prevalence Among Pregnant Women Attending Antenatal Clinics in Karnataka, South India, 2003-2019.

BACKGROUND AND OBJECTIVE: Periodic tracking of the trends and the levels of HIV prevalence at regional and district levels helps to strengthen a state's HIV/AIDS response. HIV prevalence among pregnant women is crucial for the HIV prevalence estimation of the general population. Karnataka is one of the high HIV prevalence states in India. Probing regional and district levels and trends of HIV prevalence provides critical insights into district-level epidemic patterns. This paper analyzes the region- and district-wise levels and trends of HIV prevalence among pregnant women attending the antenatal clinics (ANC) from 2003 to 2019 in Karnataka, South India. METHODS: HIV prevalence data collected from pregnant women in Karnataka during HIV Sentinel Surveillance (HSS) between 2003 and 2019 was used for trend analysis. The consistent sites were grouped into four zones (Bangalore, Belgaum, Gulbarga and Mysore regions), totaling 60 sites, including 30 urban and 30 rural sites. Regional and district-level HIV prevalence was calculated; trend analysis using Chi-square trend test and spatial analysis using QGIS software was done. For the last three HSS rounds, HIV prevalence based on sociodemographic variables was calculated to understand the factors contributing to HIV positivity in each region. RESULTS: In total, 254,563 pregnant women were recruited. HIV prevalence in Karnataka was 0.22 (OR: 0.15 95% CI: 0.16 - 0.28) in 2019. The prevalence was 0.24, 0.32, 0.17 and 0.14 in Bangalore, Belgaum, Gulbarga, and Mysore regions, respectively. HIV prevalence had significantly (P< 0.05) declined in 26 districts. CONCLUSION AND GLOBAL HEALTH IMPLICATIONS: HIV prevalence among pregnant women was comparatively higher in Bangalore and Belgaum regions. Analysis of contextual factors associated with the transmission risk and evidence-based targeted interventions will strengthen HIV management in Karnataka. Regionalized, disaggregated, sub-national analyses will help identify emerging pockets of infections, concentrated epidemic zones and contextual factors driving the disease transmission.

Machine learning assisted prediction of the Young's modulus of compositionally complex alloys.

We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young's modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young's modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.

How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future.

The clinical severity, rapid transmission and human losses due to coronavirus disease 2019 (Covid-19) have led the World Health Organization to declare it a pandemic. Traditional epidemiological tools are being significantly complemented by recent innovations especially using artificial intelligence (AI) and machine learning. AI-based model systems could improve pattern recognition of disease spread in populations and predictions of outbreaks in different geographical locations. A variable and a minimal amount of data are available for the signs and symptoms of Covid-19, allowing a composite of maximum likelihood algorithms to be employed to enhance the accuracy of disease diagnosis and to identify potential drugs. AI-based forecasting and predictions are expected to complement traditional approaches by helping public health officials to select better response and preparedness measures against Covid-19 cases. AI-based approaches have helped address the key issues but a significant impact on the global healthcare industry is yet to be achieved. The capability of AI to address the challenges may make it a key player in the operation of healthcare systems in future. Here, we present an overview of the prospective applications of the AI model systems in healthcare settings during the ongoing Covid-19 pandemic.

Transfer Learned Designer Polymers For Organic Solar Cells.

Organic photovoltaic (OPV) materials have been examined extensively over the past two decades for solar cell applications because of the potential for device flexibility, low-temperature solution processability, and negligible environmental impact. However, discovery of new candidate OPV materials, especially polymer-based electron donors, that demonstrate notable power conversion efficiencies (PCEs), is nontrivial and time-intensive exercise given the extensive set of possible chemistries. Recent progress in machine learning accelerated materials discovery has facilitated to address this challenge, with molecular line representations, such as Simplified Molecular-Input Line-Entry Systems (SMILES), gaining popularity as molecular fingerprints describing the donor chemical structures. Here, we employ a transfer learning based recurrent neural (LSTM) model, which harnesses the SMILES molecular fingerprints as an input to generate novel designer chemistries for OPV devices. The generative model, perfected on a small focused OPV data set, predicts new polymer repeat units with potentially high PCE. Calculations of the similarity coefficient between the known and the generated polymers corroborate the accuracy of the model predictability as a function of the underlying chemical specificity. The data-enabled framework is sufficiently generic for use in accelerated machine learned materials discovery for various chemistries and applications, mining the hitherto available experimental and computational data.

Accelerating computational modeling and design of high-entropy alloys.

High-entropy alloys, with N elements and compositions {cν = 1,N} in competing crystal structures, have large design spaces for unique chemical and mechanical properties. Here, to enable computational design, we use a metaheuristic hybrid Cuckoo search (CS) to construct alloy configurational models on the fly that have targeted atomic site and pair probabilities on arbitrary crystal lattices, given by supercell random approximates (SCRAPs) with S sites. Our Hybrid CS permits efficient global solutions for large, discrete combinatorial optimization that scale linearly in a number of parallel processors, and linearly in sites S for SCRAPs. For example, a four-element, 128-site SCRAP is found in seconds-a more than 13,000-fold reduction over current strategies. Our method thus enables computational alloy design that is currently impractical. We qualify the models and showcase application to real alloys with targeted atomic short-range order. Being problem-agnostic, our Hybrid CS offers potential applications in diverse fields.

Substance use and risk of HIV infection among Men who have Sex with Men in India: Analysis of National IBBS data, India.

HIV prevalence is higher among Men who have Sex with Men (MSM), owing to their unsafe sexual behavior. Further, MSM indulge in behaviors such as consumption of alcohol/oral drugs and/or injecting during/before sex that poses the risk of unsafe behaviors, thereby increasing their vulnerability to HIV. The study aims to analyze the factors associated with HIV infection among the multi-risk MSM using any substances with those MSM who do not use substances.Community-based cross-sectional survey design using probability-based sampling between October 2014 and November 2015.For the nation-wide Integrated Biological and Behavioral Surveillance (IBBS), 23,081 MSM were recruited from 4067 hotspots in 108 districts across India. Information on demographics, sexual behaviors, substance use, sexual partners, and awareness on HIV and its management was collected from the consented respondents using computer-assisted personal interview (CAPI) by trained personnel. Blood samples were tested for HIV. Statistical analyses were done, to study the associations between substance use and its influence on high-risk sexual behaviors and HIV infection.One in 3 MSM (33.88%) in India were substance users, thus exhibiting "multi-risk" (MR) behaviors. Significantly higher HIV prevalence (3.8%, P < .05) was reported among MR-MSM, despite 97.2% of them being aware of HIV. Higher HIV prevalence among MSM exhibiting homosexual behavior for ≤1 year is of specific concern, as this accounts to recent infections and indicates the increased vulnerability of the infection among the new entrants.Substance-use resulting in high-risk sexual behavior was significantly associated with higher HIV prevalence among MR-MSM. Integrated targeted interventions focusing on safe sex and safe-IDU practices among MR-MSM are required to end the disease transmission.

Elasto-morphology of P3HT:PCBM bulk heterojunction organic solar cells.

Predicting the mechanical properties of organic semiconductors is important when using these materials in flexible electronics applications. For instance, knowledge of the mechanical and thermal stability of thin film organic solar cells (OSCs) is critical for the roll-to-roll production of photovoltaic devices and their use under various operating conditions. Here, we examine the thermal and elasto-mechanical properties of the conjugated donor polymer poly-(3-hexylthiophene) (P3HT) and the interpenetrating mixtures of P3HT and phenyl-C61-butyric acid methyl (PCBM) ester bulk heterojunction (BHJ) active layers under the application of unidirectional tensile deformation using coarse-grained molecular dynamics (CGMD) simulations. The predictions are validated against previous experimental reports as well as with earlier modeling results derived using different intermolecular force fields. Our results reveal that PCBM molecules behave as anti-plasticizers when mixed with P3HT and tend to increase the tensile modulus and glass transition temperature, while decreasing the crack-onset strain relative to pure P3HT. The variations in the mechanical properties with the composition of the BHJ active layer suggest that, in the presence of small oligomers as additives in the BHJ, the P3HT:PCBM mixture resists the anti-plasticizing effect of PCBM molecules due to the low tensile modulus of the short polymer chains.

Nonclinical safety evaluation of erenumab, a CGRP receptor inhibitor for the prevention of migraine.

Calcitonin gene-related peptide (CGRP) and its receptor have been implicated as a key mediator in the pathophysiology of migraine. Thus, erenumab, a monoclonal antibody antagonist of the CGRP receptor, administered as a once monthly dose of 70 or 140 mg has been approved for the preventive treatment of migraine in adults. Due to the species specificity of erenumab, the cynomolgus monkey was used in the pharmacology, pharmacokinetics, and toxicology studies to support the clinical program. There were no effects of erenumab on platelets in vitro (by binding, activation or phagocytosis assays). Specific staining of human tissues with erenumab did not indicated any off-target binding. There were no erenumab-related findings in a cardiovascular safety pharmacology study in cynomolgus monkeys or in vitro in human isolated coronary arteries. Repeat-dose toxicology studies conducted in cynomolgus monkeys at dose levels up to 225 mg/kg (1 month) or up to 150 mg/kg (up to 6 months) with twice weekly subcutaneous (SC) doses showed no evidence of erenumab-mediated adverse toxicity. There were no effects on pregnancy, embryo-fetal or postnatal growth and development in an enhanced pre-postnatal development study in the cynomolgus monkey. There was evidence of placental transfer of erenumab based on measurable serum concentrations in the infants up to 3 months post birth. The maternal and developmental no-observed-effect level (NOEL) was the highest dose tested (50 mg/kg SC Q2W). These nonclinical data in total indicate no safety signal of concern to date and provide adequate margins of exposure between the observed safe doses in animals and clinical dose levels.

Solution Processing Dependent Bulk Heterojunction Nanomorphology of P3HT/PCBM Thin Films.

Mixtures of poly(3-hexyl-thiophene) (P3HT) and phenyl-C61-butyric acid methyl ester (PCBM) have been widely employed as donor and acceptor materials, respectively, for the active layer of the bulk heterojunction (BHJ) organic solar cells. Experiments are able to provide only limited insights on the dynamics of blend morphology of these organic materials because of the challenges in extracting microstructural characterization amidst the poor contrast in electron microscopy. We present results from coarse-grained molecular dynamics simulations (CGMD) describing the morphological evolution of P3HT/PCBM active layer under solution processing in chlorobenzene (CB). We examine the impact of various processing parameters such as weight ratio, degree of polymerization (DOP), thermal annealing, and preheating on the BHJ active layers using morphological characterizations from atomic trajectories. Simulated diffraction patterns are compared with experimental results of X-ray diffraction and Small Angle X-ray Scattering (SAXS). Both simulated scattering and experimental X-ray diffraction and X-ray scattering measurements reveal increase in crystallinity for P3HT upon annealing until PCBM weight fraction ∼50%. The solubility of PCBM being greater in CB than that of P3HT facilitates the phase separation of the polymer during early stages of solvent evaporation. An increase in the average size of the P3HT domain relative to the preannealed morphology, is due to phase segregation and crystallization of the polymer upon annealing. Percolation for PCBM remains unchanged until PCBM constitutes at least one-half of the composition. Although 1.0:2.0 weight ratio is predicted to be ideal for balanced charge transport, 1.0:1.0 weight ratio is the most beneficial of overall power conversion based on exciton generation and charge separation at the interface. DOP of P3HT molecules is another important design variable as larger P3HT molecules tend to entangle more often deteriorating molecular order of P3HT phase in the active layer. Preheating the ternary mixture of P3HT, PCBM, and CB modifies the structural order and morphology of the BHJ due to changes in PCBM diffusion into the P3HT phase.