Enhancing Multifunctionality: Optimal Properties of Iron-Oxide-Reinforced Polyvinylidene Difluoride Unveiled Through Full Atom Molecular Dynamics Simulations.

Nanocomposites made of magnetite (Fe3O4) nanoparticles (NP)s with different surface chemistry and polyvinyl difluoride (PVDF) polymer were investigated using full atom molecular dynamics (MD) simulation. NPs with hydroxyl (OH), hexanoic, and oleic acid terminations were considered in this study. The effect of each surface chemistry was investigated in terms of the mechanical properties, the distribution of the internal energy around the NP, and the chain polarization gradient from the interface to the bulk. From this investigation, we find that oleic acid termination, although the most popular, is less favorable for interfacial interaction and local polarization. The OH-terminated NP results in the best configuration for the properties investigated. The hexanoic acid-grafted NP presents a good compromise. Hydrogen bonding governs the induced response of the nanocomposites. Although the hexanoic acid grafted NP presents less hydrogen bonding than the OH-terminated case, the conformation of the hexanoic acid acts as a mobility flow inhibitor, leading to a performance comparable to that of the OH-terminated NP composite. This work led to investigating routes to make nanocomposite materials with optimized properties. These results shed light on the multiple combinations offered by nanocomposites that go beyond the conventional effects of size.

Advancing abiotic stress monitoring in plants with a wearable non-destructive real-time salicylic acid laser-induced-graphene sensor.

Drought and salinity stresses present significant challenges that exert a severe impact on crop productivity worldwide. Understanding the dynamics of salicylic acid (SA), a vital phytohormone involved in stress response, can provide valuable insights into the mechanisms of plant adaptation to cope with these challenging conditions. This paper describes and tests a sensor system that enables real-time and non-invasive monitoring of SA content in avocado plants exposed to drought and salinity. By using a reverse iontophoretic system in conjunction with a laser-induced graphene electrode, we demonstrated a sensor with high sensitivity (82.3 nA/[µmol L-1⋅cm-2]), low limit of detection (LOD, 8.2 µmol L-1), and fast sampling response (20 s). Significant differences were observed between the dynamics of SA accumulation in response to drought versus those of salt stress. SA response under drought stress conditions proved to be faster and more intense than under salt stress conditions. These different patterns shed light on the specific adaptive strategies that avocado plants employ to cope with different types of environmental stressors. A notable advantage of the proposed technology is the minimal interference with other plant metabolites, which allows for precise SA detection independent of any interfering factors. In addition, the system features a short extraction time that enables an efficient and rapid analysis of SA content.

Laser-induced graphene electrochemical sensor for quantitative detection of phytotoxic aluminum ions (Al3+) in soils extracts.

Aluminum in its Al3+ form is a metal that inhibits plant growth, especially in acidic soils (pH < 5.5). Rapid and accurate quantitative detection of Al3+ in agricultural soils is critical for the timely implementation of remediation strategies. However, detecting metal ions requires time-consuming preparation of samples, using expensive instrumentation and non-portable spectroscopic techniques. As an alternative, electrochemical sensors offer a cost-effective and minimally invasive approach for in situ quantification of metal ions. Here, we developed and validated an electrochemical sensor based on bismuth-modified laser-induced graphene (LIG) electrodes for Al3+ quantitative detection in a range relevant to agriculture (1-300 ppm). Our results show a linear Al3+ detection range of 1.07-300 ppm with a variation coefficient of 5.3%, even in the presence of other metal ions (Pb2+, Cd2+, and Cu2+). The sensor offers a limit of detection (LOD) of 0.34 ppm and a limit of quantification (LOQ) of 1.07 ppm. We compared its accuracy for soil samples with pH < 4.8 to within 89-98% of spectroscopic methods (ICP-OES) and potentiometric titration. This technology's portability, easy to use, and cost-effectiveness make it a promising candidate for in situ quantification and remediation of Al3+ in agricultural soils and other complex matrices.

Artificial intelligence for cervical cancer screening: Scoping review, 2009-2022.

BACKGROUND: The intersection of artificial intelligence (AI) with cancer research is increasing, and many of the advances have focused on the analysis of cancer images. OBJECTIVES: To describe and synthesize the literature on the diagnostic accuracy of AI in early imaging diagnosis of cervical cancer following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). SEARCH STRATEGY: Arksey and O'Malley methodology was used and PubMed, Scopus, and Google Scholar databases were searched using a combination of English and Spanish keywords. SELECTION CRITERIA: Identified titles and abstracts were screened to select original reports and cross-checked for overlap of cases. DATA COLLECTION AND ANALYSIS: A descriptive summary was organized by the AI algorithm used, total of images analyzed, data source, clinical comparison criteria, and diagnosis performance. MAIN RESULTS: We identified 32 studies published between 2009 and 2022. The primary sources of images were digital colposcopy, cervicography, and mobile devices. The machine learning/deep learning (DL) algorithms applied in the articles included support vector machine (SVM), random forest classifier, k-nearest neighbors, multilayer perceptron, C4.5, Naïve Bayes, AdaBoost, XGboots, conditional random fields, Bayes classifier, convolutional neural network (CNN; and variations), ResNet (several versions), YOLO+EfficientNetB0, and visual geometry group (VGG; several versions). SVM and DL methods (CNN, ResNet, VGG) showed the best diagnostic performances, with an accuracy of over 97%. CONCLUSION: We concluded that the use of AI for cervical cancer screening has increased over the years, and some results (mainly from DL) are very promising. However, further research is necessary to validate these findings.

Characterization of Rice Yield Based on Biomass and SPAD-Based Leaf Nitrogen for Large Genotype Plots.

The use of Unmanned Aerial Vehicle (UAV) images for biomass and nitrogen estimation offers multiple opportunities for improving rice yields. UAV images provide detailed, high-resolution visual information about vegetation properties, enabling the identification of phenotypic characteristics for selecting the best varieties, improving yield predictions, and supporting ecosystem monitoring and conservation efforts. In this study, an analysis of biomass and nitrogen is conducted on 59 rice plots selected at random from a more extensive trial comprising 400 rice genotypes. A UAV acquires multispectral reflectance channels across a rice field of subplots containing different genotypes. Based on the ground-truth data, yields are characterized for the 59 plots and correlated with the Vegetation Indices (VIs) calculated from the photogrammetric mapping. The VIs are weighted by the segmentation of the plants from the soil and used as a feature matrix to estimate, via machine learning models, the biomass and nitrogen of the selected rice genotypes. The genotype IR 93346 presented the highest yield with a biomass gain of 10,252.78 kg/ha and an average daily biomass gain above 49.92 g/day. The VIs with the highest correlations with the ground-truth variables were NDVI and SAVI for wet biomass, GNDVI and NDVI for dry biomass, GNDVI and SAVI for height, and NDVI and ARVI for nitrogen. The machine learning model that performed best in estimating the variables of the 59 plots was the Gaussian Process Regression (GPR) model with a correlation factor of 0.98 for wet biomass, 0.99 for dry biomass, and 1 for nitrogen. The results presented demonstrate that it is possible to characterize the yields of rice plots containing different genotypes through ground-truth data and VIs.

Non-invasive in-vivo glucose-based stress monitoring in plants.

Plant stress responses involve a suite of genetically encoded mechanisms triggered by real-time interactions with their surrounding environment. Although sophisticated regulatory networks maintain proper homeostasis to prevent damage, the tolerance thresholds to these stresses vary significantly among organisms. Current plant phenotyping techniques and observables must be better suited to characterize the real-time metabolic response to stresses. This impedes practical agronomic intervention to avoid irreversible damage and limits our ability to breed improved plant organisms. Here, we introduce a sensitive, wearable electrochemical glucose-selective sensing platform that addresses these problems. Glucose is a primary plant metabolite, a source of energy produced during photosynthesis, and a critical molecular modulator of various cellular processes ranging from germination to senescence. The wearable-like technology integrates a reverse iontophoresis glucose extraction capability with an enzymatic glucose biosensor that offers a sensitivity of 22.7 nA/(µM·cm2), a limit of detection (LOD) of 9.4 µM, and a limit of quantification (LOQ) of 28.5 µM. The system's performance was validated by subjecting three different plant models (sweet pepper, gerbera, and romaine lettuce) to low-light and low-high temperature stresses and demonstrating critical differential physiological responses associated with their glucose metabolism. This technology enables non-invasive, non-destructive, real-time, in-situ, and in-vivo identification of early stress response in plants and provides a unique tool for timely agronomic management of crops and improving breeding strategies based on the dynamics of genome-metabolome-phenome relationships.

Predicted Three-Dimensional Structure of the GCR1 Putative GPCR in Arabidopsis thaliana and Its Binding to Abscisic Acid and Gibberellin A1.

GCR1 has been proposed as a plant analogue to animal G-protein-coupled receptors that can promote or regulate several physiological processes by binding different phytohormones. For instance, abscisic acid (ABA) and gibberellin A1 (GA1) have been shown to promote or regulate germination and flowering, root elongation, dormancy, and biotic and abiotic stresses, among others. They may act through binding to GCR1, which would put GCR1 at the heart of key signaling processes of agronomic importance. Unfortunately, this GPCR function has yet to be fully validated due to the lack of an X-ray or cryo-EM 3D atomistic structure for GCR1. Here, we used the primary sequence data from Arabidopsis thaliana and the GEnSeMBLE complete sampling method to examine 13 trillion possible packings of the 7 transmembrane helical domains corresponding to GCR1 to downselect an ensemble of 25 configurations likely to be accessible to the binding of ABA or GA1. We then predicted the best binding sites and energies for both phytohormones to the best GCR1 configurations. To provide the basis for the experimental validation of our predicted ligand-GCR1 structures, we identify several mutations that should improve or weaken the interactions. Such validations could help establish the physiological role of GCR1 in plants.

Molecular Level Sucrose Quantification: A Critical Review.

Sucrose is a primary metabolite in plants, a source of energy, a source of carbon atoms for growth and development, and a regulator of biochemical processes. Most of the traditional analytical chemistry methods for sucrose quantification in plants require sample treatment (with consequent tissue destruction) and complex facilities, that do not allow real-time sucrose quantification at ultra-low concentrations (nM to pM range) under in vivo conditions, limiting our understanding of sucrose roles in plant physiology across different plant tissues and cellular compartments. Some of the above-mentioned problems may be circumvented with the use of bio-compatible ligands for molecular recognition of sucrose. Nevertheless, problems such as the signal-noise ratio, stability, and selectivity are some of the main challenges limiting the use of molecular recognition methods for the in vivo quantification of sucrose. In this review, we provide a critical analysis of the existing analytical chemistry tools, biosensors, and synthetic ligands, for sucrose quantification and discuss the most promising paths to improve upon its limits of detection. Our goal is to highlight the criteria design need for real-time, in vivo, highly sensitive and selective sucrose sensing capabilities to enable further our understanding of living organisms, the development of new plant breeding strategies for increased crop productivity and sustainability, and ultimately to contribute to the overarching need for food security.

The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization.

The OMICAS alliance is part of the Colombian government's Scientific Ecosystem, established between 2017-2018 to promote world-class research, technological advancement and improved competency of higher education across the nation. Since the program's kick-off, OMICAS has focused on consolidating and validating a multi-scale, multi-institutional, multi-disciplinary strategy and infrastructure to advance discoveries in plant science and the development of new technological solutions for improving agricultural productivity and sustainability. The strategy and methods described in this article, involve the characterization of different crop models, using high-throughput, real-time phenotyping technologies as well as experimental tissue characterization at different levels of the omics hierarchy and under contrasting conditions, to elucidate epigenome-, genome-, proteome- and metabolome-phenome relationships. The massive data sets are used to derive in-silico models, methods and tools to discover complex underlying structure-function associations, which are then carried over to the production of new germplasm with improved agricultural traits. Here, we describe OMICAS' R&D trans-disciplinary multi-project architecture, explain the overall strategy and methods for crop-breeding, recent progress and results, and the overarching challenges that lay ahead in the field.

Performance of electrochemical immunoassays for clinical diagnostics of SARS-CoV-2 based on selective nucleocapsid N protein detection: Boron-doped diamond, gold and glassy carbon evaluation.

The 21st century has already brought us a plethora of new threats related to viruses that emerge in humans after zoonotic transmission or drastically change their geographic distribution or prevalence. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first spotted at the end of 2019 to rapidly spread in southwest Asia and later cause a global pandemic, which paralyzes the world since then. We have designed novel immunosensors targeting conserved protein sequences of the N protein of SARS-CoV-2 based on lab-produced and purified anti-SARS-CoV-2 nucleocapsid antibodies that are densely grafted onto various surfaces (diamond/gold/glassy carbon). Titration of antibodies shows very strong reactions up to 1:72 900 dilution. Next, we showed the mechanism of interactions of our immunoassay with nucleocapsid N protein revealing molecular recognition by impedimetric measurements supported by hybrid modeling results with both density functional theory and molecular dynamics methods. Biosensors allowed for a fast (in less than 10 min) detection of SARS-CoV-2 virus with a limit of detection from 0.227 ng/ml through 0.334 ng/ml to 0.362 ng/ml for glassy carbon, boron-doped diamond, and gold surfaces, respectively. For all tested surfaces, we obtained a wide linear range of concentrations from 4.4 ng/ml to 4.4 pg/ml. Furthermore, our sensor leads to a highly specific response to SARS-CoV-2 clinical samples versus other upper respiratory tract viruses such as influenza, respiratory syncytial virus, or Epstein-Barr virus. All clinical samples were tested simultaneously on biosensors and real-time polymerase chain reactions.

Corrigendum: The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization.

[This corrects the article DOI: 10.3389/fpls.2022.992663.].

Understanding the Origin of Enhanced Piezoelectric Response in PVDF Matrices with Embedded ZnO Nanoparticles, from Polarizable Molecular Dynamics Simulations.

The pervasive use of portable electronic devices, powered from rechargeable batteries, represents a significant portion of the electricity consumption in the world. A sustainable and alternative energy source for these devices would require unconventional power sources, such as harvesting kinetic/potential energy from mechanical vibrations, ultrasound waves, and biomechanical motion, to name a few. Piezoelectric materials transform mechanical deformation into electric fields or, conversely, external electric fields into mechanical motion. Therefore, accurate prediction of elastic and piezoelectric properties of materials, from the atomic structure and composition, is essential for studying and optimizing new piezogenerators. Here, we demonstrate the application of harmonic-covalent and reactive force fields (FF), Dreiding and ReaxFF, respectively, coupled to the polarizable charge equilibration (PQEq) model for predicting the elastic moduli and piezoelectric response of crystalline zinc oxide (ZnO) and polyvinylidene difluoride (PVDF). Furthermore, we parametrized the ReaxFF atomic interactions for Zn-F in order to characterize the interfacial effects in hybrid PVDF matrices with embedded ZnO nanoparticles (NPs). We capture the nonlinear piezoelectric behavior of the PVDF-ZnO system at different ZnO concentrations and the enhanced response that was recently observed experimentally, between 5 and 7 wt % ZnO concentrations. From our simulation results, we demonstrate that the origin of this enhancement is due to an increase in the total atomic stress distribution at the interface between the two materials. This result provides valuable insight into the design of new and improved piezoelectric nanogenerators and demonstrates the practical value of these first-principles based modeling methods in materials science.

Coarse-grained force-field for large scale molecular dynamics simulations of polyacrylamide and polyacrylamide-gels based on quantum mechanics.

We developed a new coarse-grained (CG) molecular dynamics force field for polyacrylamide (PAM) polymer based on fitting to the quantum mechanics (QM) equation of state (EOS). In this method, all nonbond interactions between representative beads are parameterized using a series of QM-EOS, which significantly improves the accuracy in comparison to common CG methods derived from atomistic molecular dynamics. This CG force-field has both higher accuracy and improved computational efficiency with respect to the OPLS atomistic force field. The nonbond components of the EOS were obtained from cold-compression curves on PAM crystals with rigid chains, while the covalent terms that contribute to the EOS were obtained using relaxed chains. For describing PAM gels we developed water-PAM interaction parameters using the same method. We demonstrate that the new CG-PAM force field reproduces the EOS of PAM crystals, isolated PAM chains, and water-PAM systems, while successfully predicting such experimental quantities as density, specific heat capacity, thermal conductivity and melting point.

Understanding Hypervelocity Sampling of Biosignatures in Space Missions.

The atomic-scale fragmentation processes involved in molecules undergoing hypervelocity impacts (HVIs; defined as >3 km/s) are challenging to investigate via experiments and still not well understood. This is particularly relevant for the consistency of biosignals from small-molecular-weight neutral organic molecules obtained during solar system robotic missions sampling atmospheres and plumes at hypervelocities. Experimental measurements to replicate HVI effects on neutral molecules are challenging, both in terms of accelerating uncharged species and isolating the multiple transition states over very rapid timescales (<1 ps). Nonequilibrium first-principles-based simulations extend the range of what is possible with experiments. We report on high-fidelity simulations of the fragmentation of small organic biosignature molecules over the range v = 1-12 km/s, and demonstrate that the fragmentation fraction is a sensitive function of velocity, impact angle, molecular structure, impact surface material, and the presence of surrounding ice shells. Furthermore, we generate interpretable fragmentation pathways and spectra for velocity values above the fragmentation thresholds and reveal how organic molecules encased in ice grains, as would likely be the case for those in "ocean worlds," are preserved at even higher velocities than bare molecules. Our results place ideal spacecraft encounter velocities between 3 and 5 km/s for bare amino and fatty acids and within 4-6 km/s for the same species encased in ice grains and predict the onset of organic fragmentation in ice grains at >5 km/s, both consistent with recent experiments exploring HVI effects using impact-induced ionization and analysis via mass spectrometry and from the analysis of Enceladus organics in Cassini Data. From nanometer-sized ice Ih clusters, we establish that HVI energy is dissipated by ice casings through thermal resistance to the impact shock wave and that an upper fragmentation velocity limit exists at which ultimately any organic contents will be cleaved by the surrounding ice-this provides a fundamental path to characterize micrometer-sized ice grains. Altogether, these results provide quantifiable insights to bracket future instrument design and mission parameters.

SenSARS: A Low-Cost Portable Electrochemical System for Ultra-Sensitive, Near Real-Time, Diagnostics of SARS-CoV-2 Infections.

A critical path to solving the SARS-CoV-2 pandemic, without further socioeconomic impact, is to stop its spread. For this to happen, pre- or asymptomatic individuals infected with the virus need to be detected and isolated opportunely. Unfortunately, there are no current ubiquitous (i.e., ultra-sensitive, cheap, and widely available) rapid testing tools capable of early detection of SARS-CoV-2 infections. In this article, we introduce an accurate, portable, and low-cost medical device and bio-nanosensing electrode dubbed SenSARS and its experimental validation. SenSARS' device measures the electrochemical impedance spectra of a disposable bio-modified screen-printed carbon-based working electrode (SPCE) to the changes in the concentration of SARS-CoV-2 antigen molecules ("S" spike proteins) contained within a sub-microliter fluid sample deposited on its surface. SenSARS offers real-time diagnostics and viral load tracking capabilities. Positive and negative control tests were performed in phosphate-buffered saline (PBS) at different concentrations (between 1 and 50 fg/mL) of SARS-CoV-2(S), Epstein-Barr virus (EBV) glycoprotein gp350, and Influenza H1N1 M1 recombinant viral proteins. We demonstrate that SenSARS is easy to use, with a portable and lightweight (< 200 g) instrument and disposable test electrodes (

A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops.

Traditional methods to measure spatio-temporal variations in biomass rely on a labor-intensive destructive sampling of the crop. In this paper, we present a high-throughput phenotyping approach for the estimation of Above-Ground Biomass Dynamics (AGBD) using an unmanned aerial system. Multispectral imagery was acquired and processed by using the proposed segmentation method called GFKuts, that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo based K-means, and a guided image filtering. Accurate plot segmentation results enabled the extraction of several canopy features associated with biomass yield. Machine learning algorithms were trained to estimate the AGBD according to the growth stages of the crop and the physiological response of two rice genotypes under lowland and upland production systems. Results report AGBD estimation correlations with an average of r = 0.95 and R2 = 0.91 according to the experimental data. We compared our segmentation method against a traditional technique based on clustering. A comprehensive improvement of 13% in the biomass correlation was obtained thanks to the segmentation method proposed herein.

Effect of surface oxidation on the electronic transport properties of phosphorene gas sensors: a computational study.

The potential for phosphorene-based devices has been compromised by the material's fast degradation under ambient conditions. Its tendency to fully oxidize under O2-rich and humid environments, leads to the loss of its appealing semiconducting properties. However, partially-oxidized phosphorene (po-phosphorene), has been demonstrated to remain stable over significantly longer periods of time, thereby enabling its use in sensing applications. Here, we present a computational study of po-phosphorene-based gas sensors, using the Density-Functional-based Tight Binding (DFTB) method. We show that DFTB accurately predicts the bandgap for the pristine material and po-phosphorene, the electronic transport properties of po-phosphorene at different surface oxygen concentrations, and the appropriate trends in Density-of-States (DOS) contributions caused by adsorbed gas molecules, to demonstrate its potential application in the development of gas sensors. Results are compared against the more traditional and expensive Density Functional Theory (DFT) method using generalized gradient approximation (GGA) exchange-correlation functionals, which significantly underestimates the material's bandgap.

Ligand-Modified Boron-Doped Diamond Surface: DFT Insights into the Electronic Properties of Biofunctionalization.

With the increasing power of computation systems, theoretical calculations provide a means for quick determination of material properties, laying out a research plan, and lowering material development costs. One of the most common is Density Functional Theory (DFT), which allows us to simulate the structure of chemical molecules or crystals and their interaction. In developing a new generation of biosensors, understanding the nature of functional linkers, antibodies, and ligands become essential. In this study, we used DFT to model a bulk boron-doped diamond slab, modified by a functional linker and a surrogate proteins ligand. DTF calculations enable the prediction of electronic transport properties in an electrochemical sensor setup, composed of a boron-doped diamond electrode functionalized by 4-amino benzoic acids and a target surrogated protein-ligand for influenza. Electron conduction pathways and other signatures associated with the detection and measurement of the target analyte are revealed.

Partially-oxidized phosphorene sensor for the detection of sub-nano molar concentrations of nitric oxide: a first-principles study.

The development of new techniques or instruments for detecting and accurately measuring biomarker concentrations in living organisms is essential for early diagnosis of diseases, and for tracking the effectiveness of treatments. In chronic diseases, such as asthma, precise phenotyping can help predict the response of patients to treatments and reduce the risk of complications. Fractional exhaled nitric oxide (FeNO) is a positive biomarker for eosinophilic asthma in humans, and it can be directly detected in the respiratory tract, at very low and volatile concentrations, which makes real-time measurement a challenge. This work describes the first-principles design and characterization of a molecular- and back-gated electronic field-effect transistor device for the detection and measurement of ultra-low FeNO concentrations (pM-nM) from a person' s exhaled breath, as a cost-efficient alternative to the slower and more expensive techniques based on off-line sputum characterization via mass spectrometry. The proposed device uses a partially oxidized phosphorene semiconducting channel material for FeNO detection, allowing nM L-1 concentration measurements of this analyte in an array configuration with an effective sensing surface area of 8.775 µm2, which results in a predicted limit of detection (LOD) of 19 nM L-1. In spite of the limited stability of phosphorene in oxygen-rich and humid environments, the proposed device would be practical for mobile applications with disposable sensors.

First principles-based multiscale atomistic methods for input into first principles nonequilibrium transport across interfaces.

This issue of PNAS features "nonequilibrium transport and mixing across interfaces," with several papers describing the nonequilibrium coupling of transport at interfaces, including mesoscopic and macroscopic dynamics in fluids, plasma, and other materials over scales from microscale to celestial. Most such descriptions describe the materials in terms of the density and equations of state rather than specific atomic structures and chemical processes. It is at interfacial boundaries where such atomistic information is most relevant. However, there is not yet a practical way to couple these phenomena with the atomistic description of chemistry. The starting point for including such information is the quantum mechanics (QM). However, practical QM calculations are limited to a hundred atoms for dozens of picoseconds, far from the scales required to inform the continuum level with the proper atomistic description. To bridge this enormous gap, we need to develop practical methods to extend the scale of the atomistic simulation by several orders of magnitude while retaining the level of QM accuracy in describing the chemical process. These developments would enable continuum modeling of turbulent transport at interfaces to incorporate the relevant chemistry. In this perspective, we will focus on recent progress in accomplishing these extensions in first principles-based atomistic simulations and the strategies being pursued to increase the accuracy of very large scales while dramatically decreasing the computational effort.