An artificial intelligence platform for automated PFAS subgroup classification: A discovery tool for PFAS screening.

Since structural analyses and toxicity assessments have not been able to keep up with the discovery of unknown per- and polyfluoroalkyl substances (PFAS), there is an urgent need for effective categorization and grouping of PFAS. In this study, we presented PFAS-Atlas, an artificial intelligence-based platform containing a rule-based automatic classification system and a machine learning-based grouping model. Compared with previously developed classification software, the platform's classification system follows the latest Organization for Economic Co-operation and Development (OECD) definition of PFAS and reduces the number of uncategorized PFAS.

Connecting Vibrational Spectroscopy to Atomic Structure via Supervised Manifold Learning: Beyond Peak Analysis.

Vibrational spectroscopy is a nondestructive technique commonly used in chemical and physical analyses to determine atomic structures and associated properties. However, the evaluation and interpretation of spectroscopic profiles based on human-identifiable peaks can be difficult and convoluted. To address this challenge, we present a reliable protocol based on supervised manifold learning techniques meant to connect vibrational spectra to a variety of complex and diverse atomic structure configurations.

Data analytics-guided rational design of antimicrobial nanomedicines against opportunistic, resistant pathogens.

Nanoparticle carriers can improve antibiotic efficacy by altering drug biodistribution. However, traditional screening is impracticable due to a massive dataspace. A hybrid informatics approach was developed to identify polymer, antibiotic, and particle determinants of antimicrobial nanomedicine activity against Burkholderia cepacia, and to model nanomedicine performance.

RNA Nanovaccine Protects against White Spot Syndrome Virus in Shrimp.

In the last 15 years, crustacean fisheries have experienced billions of dollars in economic losses, primarily due to viral diseases caused by such pathogens as white spot syndrome virus (WSSV) in the Pacific white shrimp and Asian tiger shrimp . To date, no effective measures are available to prevent or control disease outbreaks in these animals, despite their economic importance. Recently, double-stranded RNA-based vaccines have been shown to provide specific and robust protection against WSSV infection in cultured shrimp.

Arterial and venous thrombotic stroke after ChAdOx1 nCoV-19 vaccine.

Vaccine-induced thrombosis with thrombocytopenia (VITT) is a recently-described condition associated with arterial and venous thrombosis following vaccination with the ChAdOx1 nCoV-19 (AstraZeneca) vaccine. This report describes two cases of stroke caused by arterial and venous thromboses presenting within 28 days of receiving the AstraZeneca vaccine. The patients were otherwise young and healthy with minimal risk factors for thrombosis yet developed a rapid, ultimately fatal neurological deterioration.

Classification of apatite structures via topological data analysis: a framework for a 'Materials Barcode' representation of structure maps.

This paper introduces the use of topological data analysis (TDA) as an unsupervised machine learning tool to uncover classification criteria in complex inorganic crystal chemistries. Using the apatite chemistry as a template, we track through the use of persistent homology the topological connectivity of input crystal chemistry descriptors on defining similarity between different stoichiometries of apatites. It is shown that TDA automatically identifies a hierarchical classification scheme within apatites based on the commonality of the number of discrete coordination polyhedra that constitute the structural building units common among the compounds.

Deep Learning Model for Identifying Critical Structural Motifs in Potential Endocrine Disruptors.

This paper aims to identify structural motifs within a molecule that contribute the most toward a chemical being an endocrine disruptor. We have developed a deep neural network-based toolkit toward this aim. The trained model can virtually assess a synthetic chemical's potential to be an endocrine disruptor using machine-readable molecular representation, simplified molecular input line entry system (SMILES).

Quantum signatures for screening metavalent solids.

The objective of this paper is to describe a new data-driven framework for computational screening and discovery of a class of materials termed "metavalent" solids. "Metavalent" solids possess characteristics that are nominally associated with metallic and covalent bonding (in terms of conductivity and coordination numbers) but are distinctly different from both because they show anomalously large response properties and a unique bond-breaking mechanism that is not observed in either covalent or metallic solids. The paper introduces the use of Hirshfeld surface analysis to provide quantum level descriptors that can be used for rapid screening of crystallographic data to identify potentially new "metavalent" solids with novel and emergent properties.

A database framework for rapid screening of structure-function relationships in PFAS chemistry.

This paper describes a database framework that enables one to rapidly explore systematics in structure-function relationships associated with new and emerging PFAS chemistries. The data framework maps high dimensional information associated with the SMILES approach of encoding molecular structure with functionality data including bioactivity and physicochemical property. This 'PFAS-Map' is a 3-dimensional unsupervised visualization tool that can automatically classify new PFAS chemistries based on current PFAS classification criteria.

Deep Learning Based Prediction of Perovskite Lattice Parameters from Hirshfeld Surface Fingerprints.

This Letter describes the use of deep learning methods on Hirshfeld surface representations of crystal structure, as an automated means of predicting lattice parameters in cubic inorganic perovskites. While Hirshfeld Surface Analysis is a well-established tool in organic crystallography, we also introduce modified computational protocols for Hirshfeld Surface Analysis tailored specifically to account for nuanced but important differences dealing with inorganic crystals. We demonstrate how two-dimensional Hirshfeld surface fingerprints can serve as a rich "database" of information encoding the complexity of relationships between chemical bonding and bond geometry characteristics of perovskites.

Data Analytics Approach for Rational Design of Nanomedicines with Programmable Drug Release.

Drug delivery vehicles can improve the functional efficacy of existing antimicrobial therapies by improving biodistribution and targeting. A critical property of such nanomedicine formulations is their ability to control the release kinetics of their payloads. The combination of (and interactions among) polymer, drug, and nanoparticle properties gives rise to nonlinear behavioral relationships and large data space.

Probabilistic Assessment of Glass Forming Ability Rules for Metallic Glasses Aided by Automated Analysis of Phase Diagrams.

The use of machine learning techniques to expedite the discovery and development of new materials is an essential step towards the acceleration of a new generation of domain-specific highly functional material systems. In this paper, we use the test case of bulk metallic glasses to highlight the key issues in the field of high throughput predictions and propose a new probabilistic analysis of rules for glass forming ability using rough set theory. This approach has been applied to a broad range of binary alloy compositions in order to predict new metallic glass compositions.

Wearable Intrinsically Soft, Stretchable, Flexible Devices for Memories and Computing.

A recent trend in the development of high mass consumption electron devices is towards electronic textiles (e-textiles), smart wearable devices, smart clothes, and flexible or printable electronics. Intrinsically soft, stretchable, flexible, Wearable Memories and Computing devices (WMCs) bring us closer to sci-fi scenarios, where future electronic systems are totally integrated in our everyday outfits and help us in achieving a higher comfort level, interacting for us with other digital devices such as smartphones and domotics, or with analog devices, such as our brain/peripheral nervous system. WMC will enable each of us to contribute to open and big data systems as individual nodes, providing real-time information about physical and environmental parameters (including air pollution monitoring, sound and light pollution, chemical or radioactive fallout alert, network availability, and so on).

Functionalization promotes pathogen-mimicking characteristics of polyanhydride nanoparticle adjuvants.

Rational design of adjuvants and delivery systems will promote development of next-generation vaccines to control emerging and re-emerging diseases. To accomplish this, understanding the immune-enhancing properties of new adjuvants relative to those induced by natural infections can help with the development of pathogen-mimicking materials that will effectively initiate innate immune signaling cascades. In this work, the surfaces of polyanhydride nanoparticles composed of sebacic acid (SA) and 1,6-bis(p-carboxyphenoxy) hexane were decorated with an ethylene diamine spacer partially modified with either a glycolic acid linker or an α-1,2-linked di-mannopyranoside (di-mannose) to confer "pathogen-like" properties and enhance adjuvanticity.

Combination Nanovaccine Demonstrates Synergistic Enhancement in Efficacy against Influenza.

H5N1 influenza virus has the potential to become a significant global health threat, and next generation vaccine technologies are needed. In this work, the combined efficacy of two nanoadjuvant platforms (polyanhydride nanoparticles and pentablock copolymer-based hydrogels) to induce protective immunity against H5N1 influenza virus was examined. Mice received two subcutaneous vaccinations (day 0 and 21) containing 10 μg of H5 hemagglutinin trimer alone or in combination with the nanovaccine platforms.

Silver nanoparticle ink technology: state of the art.

Printed electronics will bring to the consumer level great breakthroughs and unique products in the near future, shifting the usual paradigm of electronic devices and circuit boards from hard boxes and rigid sheets into flexible thin layers and bringing disposable electronics, smart tags, and so on. The most promising tool to achieve the target depends upon the availability of nanotechnology-based functional inks. A certain delay in the innovation-transfer process to the market is now being observed.

Mapping Chemical Selection Pathways for Designing Multicomponent Alloys: an informatics framework for materials design.

A data driven methodology is developed for tracking the collective influence of the multiple attributes of alloying elements on both thermodynamic and mechanical properties of metal alloys. Cobalt-based superalloys are used as a template to demonstrate the approach. By mapping the high dimensional nature of the systematics of elemental data embedded in the periodic table into the form of a network graph, one can guide targeted first principles calculations that identify the influence of specific elements on phase stability, crystal structure and elastic properties.

Mining information from atom probe data.

Whilst atom probe tomography (APT) is a powerful technique with the capacity to gather information containing hundreds of millions of atoms from a single specimen, the ability to effectively use this information creates significant challenges. The main technological bottleneck lies in handling the extremely large amounts of data on spatial-chemical correlations, as well as developing new quantitative computational foundations for image reconstruction that target critical and transformative problems in materials science. The power to explore materials at the atomic scale with the extraordinary level of sensitivity of detection offered by atom probe tomography has not been not fully harnessed due to the challenges of dealing with missing, sparse and often noisy data.

Extracting features buried within high density atom probe point cloud data through simplicial homology.

Feature extraction from Atom Probe Tomography (APT) data is usually performed by repeatedly delineating iso-concentration surfaces of a chemical component of the sample material at different values of concentration threshold, until the user visually determines a satisfactory result in line with prior knowledge. However, this approach allows for important features, buried within the sample, to be visually obscured by the high density and volume (~10(7) atoms) of APT data. This work provides a data driven methodology to objectively determine the appropriate concentration threshold for classifying different phases, such as precipitates, by mapping the topology of the APT data set using a concept from algebraic topology termed persistent simplicial homology.

Automated voxelization of 3D atom probe data through kernel density estimation.

Identifying nanoscale chemical features from atom probe tomography (APT) data routinely involves adjustment of voxel size as an input parameter, through visual supervision, making the final outcome user dependent, reliant on heuristic knowledge and potentially prone to error. This work utilizes Kernel density estimators to select an optimal voxel size in an unsupervised manner to perform feature selection, in particular targeting resolution of interfacial features and chemistries. The capability of this approach is demonstrated through analysis of the γ / γ' interface in a Ni-Al-Cr superalloy.

Informatics derived materials databases for multifunctional properties.

In this review, we provide an overview of the development of quantitative structure-property relationships incorporating the impact of data uncertainty from small, limited knowledge data sets from which we rapidly develop new and larger databases. Unlike traditional database development, this informatics based approach is concurrent with the identification and discovery of the key metrics controlling structure-property relationships; and even more importantly we are now in a position to build materials databases based on design 'intent' and not just design parameters. This permits for example to establish materials databases that can be used for targeted multifunctional properties and not just one characteristic at a time as is presently done.

Hemagglutinin-based polyanhydride nanovaccines against H5N1 influenza elicit protective virus neutralizing titers and cell-mediated immunity.

H5N1 avian influenza is a significant global concern with the potential to become the next pandemic threat. Recombinant subunit vaccines are an attractive alternative for pandemic vaccines compared to traditional vaccine technologies. In particular, polyanhydride nanoparticles encapsulating subunit proteins have been shown to enhance humoral and cell-mediated immunity and provide protection upon lethal challenge.

Electronically driven structural transitions in A10(PO4)6F2 apatites (A = Ca, Sr, Pb, Cd and Hg).

It is shown that there is a dynamic lattice instability in the aristotype P63/m structure of A10(PO4)6F2 apatites containing divalent A-site Cd or Hg cations with (n - 1)d(10)ns(0) electronic configurations. The distortion to a low-symmetry P\bar{1} triclinic structure is driven by an electronic mechanism rather than from ionic size mismatch. Our theoretical work provides key insights into the role of the electronic configurations of A cations in fluorapatites.

Informatics guided discovery of surface structure-chemistry relationships in catalytic nanoparticles.

A data driven discovery strategy based on statistical learning principles is used to discover new correlations between electronic structure and catalytic activity of metal surfaces. From the quantitative formulations derived from this informatics based model, a high throughput computational framework for predicting binding energy as a function of surface chemistry and adsorption configuration that bypasses the need for repeated electronic structure calculations has been developed.