jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Logan Ward | Naveen K. Dandu | Ben Blaiszik | Badri Narayanan | Rajeev S. Assary | Paul C. Redfern | Ian Foster | Larry A. Curtiss
Source: The Journal of Physical Chemistry A (article, 2021-06-30)
Field: Materials Science — Materials Chemistry

Graph-Based Approaches for Predicting Solvation Energy in Multiple Solvents: Open Datasets and Machine Learning Models The solvation properties of molecules, often estimated using quantum chemical simulations, are important in the synthesis of energy storage materials, drugs, and industrial chemicals. Here, we develop machine learning models of solvation energies to replace expensive quantum chemistry calculations with inexpensive-to-compute message-passing neural network models that require only the molecular graph as inputs. Our models are trained on a new database of solvation energies for 130,258 molecules taken from the QM9 dataset computed in five solvents (acetone, ethanol, acetonitrile, dimethyl sulfoxide, and water) via an implicit solvent model. Our best model achieves a mean absolute error of 0.5 kcal/mol for molecules with nine or fewer non-hydrogen atoms and 1 kcal/mol for molecules with between 10 and 14 non-hydrogen atoms. We make the entire dataset of 651,290 computed entries openly available and provide simple web and programmatic interfaces to enable others to run our solvation energy model on new molecules. This model calculates the solvation energies for molecules using only the SMILES string and also provides an estimate of whether each molecule is within the domain of applicability of our model. We envision that the dataset and models will provide the functionality needed for the rapid screening of large chemical spaces to discover improved molecules for many applications. Solvation ; Molecule ; Computer science ; Implicit solvation ; Molecular graph ; Artificial neural network ; Molecular descriptor ; Cheminformatics ; Quantum chemical ; Chemistry ; Computational chemistry ; Machine learning ; Graph ; Quantitative structure–activity relationship ; Theoretical computer science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Rahamatullah Shaikh | Saeed Shirazian | Gavin Walker
Source: Neural Computing and Applications (article, 2021-04-09)
Field: Materials Science — Materials Chemistry

Application of artificial neural network for prediction of particle size in pharmaceutical cocrystallization using mechanochemical synthesis Particle size ; Excipient ; Materials science ; Cocrystal ; Polyvinylpyrrolidone ; Microcrystalline cellulose ; Polyethylene glycol ; Artificial neural network ; Chemical engineering ; Ball mill ; Active ingredient ; Biological system ; Chromatography ; Composite material ; Computer science ; Chemistry ; Machine learning ; Organic chemistry ; Cellulose ; Bioinformatics ; Polymer chemistry ; Hydrogen bond ; Engineering Crystallization and Solubility Studies ; Crystallography and molecular interactions ; Drug Solubulity and Delivery Systems
Authors: Jin Hyun Chang | Peter Bjørn Jørgensen | Simon Loftager | Arghya Bhowmik | Juan María García Lastra | Tejs Vegge
Source: Electrochimica Acta (article, 2021-05-07)
Field: Chemistry — Electrochemistry

On-the-fly assessment of diffusion barriers of disordered transition metal oxyfluorides using local descriptors Disorder plays an increasingly important role in the design and development of high-performance battery materials and other clean energy materials like thermoelectrics and catalysts. However, conventional computational design approaches based on the thermodynamic properties of statistically averaged structures are unable to predict the accessible energy and power densities of such materials. Kinetic properties like ionic diffusion within locally resolved atomic structures is needed to perform longer time and length scale simulations like kinetic Monte Carlo in order to accurately estimate kinetic properties like power densities in battery electrodes. Here, we present and demonstrate a fast, on-the-fly, approach to calculate local diffusion barrier as a function of only the local atomic structure using machine learning and cluster expansion, particularly for Li-ions in lithium-rich transition metal oxyfluorides and the disordered rock salt (DRS) Li2−xVO2F electrodes. Transition metal ; Diffusion ; On the fly ; Materials science ; Chemistry ; Inorganic chemistry ; Thermodynamics ; Physics ; Computer science ; Organic chemistry ; Catalysis Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Rui Xin | Edirisuriya M. Dilanga Siriwardane | Yuqi Song | Yong Zhao | Steph-Yves Louis | Alireza Nasiri | Jianjun Hu
Source: The Journal of Physical Chemistry C (article, 2021-07-20)
Field: Materials Science — Materials Chemistry

Active-Learning-Based Generative Design for the Discovery of Wide-Band-Gap Materials Active learning has been increasingly applied to screening functional materials from existing materials databases with desired properties. However, the number of known materials deposited in the popular materials databases such as ICSD and Materials Project is extremely limited and consists of just a tiny portion of the vast chemical design space. Herein, we present an active generative inverse design method that combines active learning with a deep autoencoder neural network and a generative adversarial deep neural network model to discover new materials with a target property in the whole chemical design space. The application of this method has allowed us to discover new thermodynamically stable materials with high band gap (SrYF5) and semiconductors with specified band gap ranges (SrClF3, CaClF5, YCl3, SrC2F3, AlSCl, As2O3), all of which are verified by the first-principles DFT calculations. Our experiments show that while active learning itself may sample chemically infeasible candidates, these samples help to train effective screening models for filtering out materials with desired properties from the hypothetical materials created by the generative model. The experiments show the effectiveness of our active generative inverse design approach. The source code can be freely downloaded from https://github.com/glard/Active-Generative-Design. Autoencoder ; Generative grammar ; Computer science ; Chemical space ; Generative Design ; Artificial neural network ; Code (set theory) ; Deep learning ; Artificial intelligence ; Generative model ; Space (punctuation) ; Inverse ; Machine learning ; Materials science ; Mathematics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface and Thin Film Phenomena
Authors: Mohammed Suhaan Dar | Khush Bakhat Akram | Ayesha Sohail | Fatima Arif | Fatemeh Zabihi | Shengyuan Yang | Shamsa Munir | Meifang Zhu | Мuhammad Abid | Muhammad Nauman
Source: RSC Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Heat induction in two-dimensional graphene–Fe<sub>3</sub>O<sub>4</sub> nanohybrids for magnetic hyperthermia applications with artificial neural network modeling We report the synthesis and characterization of graphene functionalized with iron (Fe<sup>3+</sup>) oxide (G-Fe<sub>3</sub>O<sub>4</sub>) nanohybrids for radio-frequency magnetic hyperthermia application. We adopted the wet chemical procedure, using various contents of Fe<sub>3</sub>O<sub>4</sub> (magnetite) from 0-100% for making two-dimensional graphene-Fe<sub>3</sub>O<sub>4</sub> nanohybrids. The homogeneous dispersal of Fe<sub>3</sub>O<sub>4</sub> nanoparticles decorated on the graphene surface combined with their biocompatibility and high thermal conductivity make them an excellent material for magnetic hyperthermia. The morphological and magnetic properties of the nanohybrids were studied using scanning electron microscopy (SEM) and a vibrating sample magnetometer (VSM), respectively. The smart magnetic platforms were exposed to an alternating current (AC) magnetic field of 633 kHz and of strength 9.1 mT for studying their hyperthermic performance. The localized antitumor effects were investigated with artificial neural network modeling. A neural net time-series model was developed for the assessment of the best nanohybrid composition to serve the purpose with an accuracy close to 100%. Six Nonlinear Autoregressive with External Input (NARX) models were obtained, one for each of the components. The assessment of the accuracy of the predicted results has been done on the basis of Mean Squared Error (MSE). The highest Mean Squared Error value was obtained for the nanohybrid containing 45% magnetite and 55% graphene (F<sub>45</sub>G<sub>55</sub>) in the training phase <i>i.e.</i>, 0.44703, which is where the model achieved optimal results after 71 epochs. The F<sub>45</sub>G<sub>55</sub> nanohybrid was found to be the best for hyperthermia applications in low dosage with the highest specific absorption rate (SAR) and mean squared error values. Graphene ; Artificial neural network ; Induction heating ; Materials science ; Hyperthermia ; Electromagnetic induction ; Nanotechnology ; Chemical engineering ; Computer science ; Physics ; Artificial intelligence ; Engineering ; Electromagnetic coil ; Quantum mechanics Graphene research and applications ; Stochastic Gradient Optimization Techniques ; Electrical and Bioimpedance Tomography
Authors: Peyman Ziyaee | Vahid Farzand Ahmadi | Pourya Bazyar | Eugenio Cavallo
Source: Agronomy (article, 2021-04-29)
Field: Chemistry — Analytical Chemistry

Comparison of Different Image Processing Methods for Segregation of Peanut (Arachis hypogaea L.) Seeds Infected by Aflatoxin-Producing Fungi Fungi such as Aspergillus flavus and Aspergillus parasiticus are molds infecting food and animal feed, are responsible for aflatoxin contamination, and cause a significant problem for human and animal health. The detection of aflatoxin and aflatoxigenic fungi on raw material is a major concern to protect health, secure food and feed, and preserve their value. The effectiveness of image processing, combined with computational techniques, has been investigated to detect and segregate peanut (Arachis hypogaea L.) seeds infected with an aflatoxin producing fungus. After inoculation with Aspergillus flavus, images of peanuts seeds were taken using various lighting sources (LED, UV, and fluorescent lights) on two backgrounds (black and white) at 0, 48, and 72 h after inoculation. Images were post-processed with three different machine learning tools: the artificial neural network (ANN), the support vector machine (SVM), and the adaptive neuro-fuzzy inference system (ANFIS) to detect the Aspergillus flavus growth on peanuts. The results of the study show that the combination of LED light and a white background with ANN had 99.7% accuracy in detecting fungal growth on peanuts 72 h from infection with Aspergillus. Additionally, UV lights and a black background with ANFIS achieve 99.9% accuracy in detecting fungal growth on peanuts 48 h after their infection with Aspergillus. Aflatoxin ; Aspergillus flavus ; Arachis hypogaea ; Biology ; Aspergillus ; Aspergillus parasiticus ; Inoculation ; Horticulture ; Food science ; Botany Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Listeria monocytogenes in Food Safety
Authors: Natalie Boehnke | Paula T. Hammond
Source: JACS Au (review, 2021-11-23)
Field: Materials Science — Biomaterials

Power in Numbers: Harnessing Combinatorial and Integrated Screens to Advance Nanomedicine Nanocarriers have significant potential to advance personalized medicine through targeted drug delivery. However, to date, efforts to improve nanoparticle accumulation at target disease sites have largely failed to translate clinically, stemming from an incomplete understanding of nano-bio interactions. While progress has been made to evaluate the effects of specific physical and chemical nanoparticle properties on trafficking and uptake, there is much to be gained from controlling these properties singularly and in combination to determine their interactions with different cell types. We and others have recently begun leveraging library-based nanoparticle screens to study structure-function relationships of lipid- and polymer-based drug delivery systems to guide nanoparticle design. These combinatorial screening efforts are showing promise in leading to the successful identification of critical characteristics that yield improved and specific accumulation at target sites. However, there is a crucial need to equally consider the influence of biological complexity on nanoparticle delivery, particularly in the context of clinical translation. For example, tissue and cellular heterogeneity presents an additional dimension to nanoparticle trafficking, uptake, and accumulation; applying imaging and screening tools as well as bioinformatics may further expand our understanding of how nanoparticles engage with cells and tissues. Given recent advances in the fields of omics and machine learning, there is substantial promise to revolutionize nanocarrier development through the use of integrated screens, harnessing the combinatorial parameter space afforded both by nanoparticle libraries and clinically annotated biological data sets in combination with high throughput <i>in vivo</i> studies. Nanocarriers ; Context (archaeology) ; Nanomedicine ; Nanotechnology ; Computer science ; Drug delivery ; Nanoparticle ; Computational biology ; Biology ; Materials science Nanoparticle-Based Drug Delivery ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanoparticles: synthesis and applications
Authors: Clemens Hall | Benoît Creton | Bastian Rauch | Uwe Bauder | Manfred Aigner
Source: Energy & Fuels (article, 2021-12-10)
Field: Chemistry — Organic Chemistry

Probabilistic Mean Quantitative Structure–Property Relationship Modeling of Jet Fuel Properties We present a novel probabilistic mean quantitative structure–property relationship (M-QSPR) method for the prediction of jet fuel properties considering two-dimensional gas chromatography measurements. Fuels are represented as one mean pseudo-structure that is inferred by a weighted average over structures of 1866 molecules that could be present in the individual fuel. The method allows training of models on both data of pure components and of fuels and does not require mixing rules for the calculation of the bulk property. This drastically increases the number of available training data and allows the direct learning of the mixing behavior. For the modeling, we use a Monte-Carlo dropout neural network, a probabilistic machine learning algorithm, that estimates prediction uncertainties due to possible unidentified isomers and dissimilarity of training and test data. Models are developed to predict the freezing point, flash point, net heat of combustion, and temperature-dependent properties such as density, viscosity, and surface tension. We investigate the effect of the presence of fuels in the training data on the predictions for up to 82 conventional fuels and 50 synthetic fuels. The results of the predictions are compared on three metrics that quantify accuracy, precision, and reliability. These metrics allow a comprehensive estimation of the predictive capability of the models. For the prediction of density, surface tension, and net heat of combustion, the M-QSPR method yields highly accurate results even without the presence of fuels in the training data. For properties with nonlinear behavior over temperature and complex fuel component interactions, like viscosity and freezing point, the presence of fuels in the training data was found to be essential for the method. Combustion ; Flash point ; Probabilistic logic ; Monte Carlo method ; Artificial neural network ; Jet fuel ; Viscosity ; Uncertainty quantification ; Surface tension ; Biological system ; Computer science ; Machine learning ; Chemistry ; Thermodynamics ; Artificial intelligence ; Mathematics ; Statistics Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics ; Petroleum Processing and Analysis
Authors: Bence Paul | Kai Kysenius | James B. Hilton | Michael W. Jones | Robert Hutchinson | Daniel D. Buchanan | Christophe Rosty | Fred Fryer | Ashley I. Bush | Janet Hergt | Jon Woodhead | David Bishop | Philip Doble | Michelle M. Hill | Peter J. Crouch | Dominic J. Hare
Source: Chemical Science (article, 2021-01-01)
Field: Chemistry — Spectroscopy

An integrated mass spectrometry imaging and digital pathology workflow for objective detection of colorectal tumours by unique atomic signatures Tumours are abnormal growths of cells that reproduce by redirecting essential nutrients and resources from surrounding tissue. Changes to cell metabolism that trigger the growth of tumours are reflected in subtle differences between the chemical composition of healthy and malignant cells. We used LA-ICP-MS imaging to investigate whether these chemical differences can be used to spatially identify tumours and support detection of primary colorectal tumours in anatomical pathology. First, we generated quantitative LA-ICP-MS images of three colorectal surgical resections with case-matched normal intestinal wall tissue and used this data in a Monte Carlo optimisation experiment to develop an algorithm that can classify pixels as tumour positive or negative. Blinded testing and interrogation of LA-ICP-MS images with micrographs of haematoxylin and eosin stained and Ki67 immunolabelled sections revealed Monte Carlo optimisation accurately identified primary tumour cells, as well as returning false positive pixels in areas of high cell proliferation. We analysed an additional 11 surgical resections of primary colorectal tumours and re-developed our image processing method to include a random forest regression machine learning model to correctly identify heterogenous tumours and exclude false positive pixels in images of non-malignant tissue. Our final model used over 1.6 billion calculations to correctly discern healthy cells from various types and stages of invasive colorectal tumours. The imaging mass spectrometry and data analysis methods described, developed in partnership with clinical cancer researchers, have the potential to further support cancer detection as part of a comprehensive digital pathology approach to cancer care through validation of a new chemical biomarker of tumour cells. Digital pathology ; Colorectal cancer ; Pathology ; Haematoxylin ; Pixel ; Mass spectrometry imaging ; Surgical pathology ; Medicine ; Cancer ; Radiology ; Computer science ; Artificial intelligence ; Chemistry ; Staining ; Mass spectrometry ; Internal medicine Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Nuclear Physics and Applications
Authors: Albert Queraltó | Juri Banchewski | Adrià Pacheco | Kapil Gupta | Lavinia Saltarelli | Diana Moreno García | Núria Alcalde-Herraiz | Cristian Mocuta | S. Ricart | Flavio Pino | X. Obradors | Teresa Puig
Source: ACS Applied Materials & Interfaces (article, 2021-02-12)
Field: Materials Science — Materials Chemistry

Combinatorial Screening of Cuprate Superconductors by Drop-On-Demand Inkjet Printing Combinatorial and high-throughput experimentation (HTE) is achieving more relevance in material design, representing a turning point in the process of accelerated discovery, development, and optimization of materials based on data-driven approaches. The versatility of drop-on-demand inkjet printing (IJP) allows performing combinatorial studies through fabrication of compositionally graded materials with high spatial precision, here by mixing superconducting REBCO precursor solutions with different rare earth (RE) elements. The homogeneity of combinatorial Y<sub>1-<i>x</i></sub>Gd<i><sub>x</sub></i>Ba<sub>2</sub>Cu<sub>3</sub>O<sub>7</sub> samples was designed with computational methods and confirmed by energy-dispersive X-ray spectroscopy (EDX) and high-resolution X-ray diffraction (XRD). We reveal the advantages of this strategy in the optimization of the epitaxial growth of high-temperature REBCO superconducting films using the novel transient liquid-assisted growth method (TLAG). Advanced characterization methods, such as in situ synchrotron growth experiments, are tailored to suit the combinatorial approach and demonstrated to be essential for HTE schemes. The experimental strategy presented is key for the attainment of large datasets for the implementation of machine learning backed material design frameworks. Materials science ; Synchrotron ; Superconductivity ; Nanotechnology ; Fabrication ; Inkjet printing ; Drop (telecommunication) ; Process engineering ; Computer science ; Inkwell ; Composite material ; Optics Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; ZnO doping and properties
Authors: Julian Arnold | Juan Carlos San Vicente Veliz | Debasish Koner | Narendra Singh | Raymond J. Bemish | Markus Meuwly
Source: The Journal of Chemical Physics (article, 2021-12-16)
Field: Materials Science — Materials Chemistry

Machine learning product state distributions from initial reactant states for a reactive atom–diatom collision system A machine-learned model for predicting product state distributions from specific initial states (state-to-distribution or STD) for reactive atom-diatom collisions is presented and quantitatively tested for the N(<sup>4</sup>S) + O<sub>2</sub>(X<sup>3</sup>Σ<sub>g</sub> <sup>-</sup>) → NO(X<sup>2</sup>Π) + O(<sup>3</sup>P) reaction. The reference dataset for training the neural network consists of final state distributions determined from quasi-classical trajectory (QCT) simulations for ∼2000 initial conditions. Overall, the prediction accuracy as quantified by the root-mean-squared difference (∼0.003) and the R<sup>2</sup> (∼0.99) between the reference QCT and predictions of the STD model is high for the test set, for off-grid state-specific initial conditions, and for initial conditions drawn from reactant state distributions characterized by translational, rotational, and vibrational temperatures. Compared with a more coarse grained distribution-to-distribution (DTD) model evaluated on the same initial state distributions, the STD model shows comparable performance with the additional benefit of the state resolution in the reactant preparation. Starting from specific initial states also leads to a more diverse range of final state distributions, which requires a more expressive neural network compared with DTD. A direct comparison between QCT simulations, the STD model, and the widely used Larsen-Borgnakke (LB) model shows that the STD model is quantitative, whereas the LB model is qualitative at best for rotational distributions P(j') and fails for vibrational distributions P(v'). As such, the STD model can be well-suited for simulating nonequilibrium high-speed flows, e.g., using the direct simulation Monte Carlo method. Trajectory ; State (computer science) ; Monte Carlo method ; Statistical physics ; Probability distribution ; Distribution (mathematics) ; Artificial neural network ; Chemistry ; Physics ; Computer science ; Mathematics ; Algorithm ; Statistics ; Artificial intelligence ; Mathematical analysis ; Quantum mechanics Catalytic Processes in Materials Science ; Gas Dynamics and Kinetic Theory ; Advanced Chemical Physics Studies
Authors: Haiyang Luo | Bijaya B. Karki | Dipta B. Ghosh | Huiming Bao
Source: Geochimica et Cosmochimica Acta (article, 2021-04-23)
Field: Materials Science — Materials Chemistry

Deep neural network potentials for diffusional lithium isotope fractionation in silicate melts Albite ; Isotope fractionation ; Silicate ; Thermal diffusivity ; Diffusion ; Equilibrium fractionation ; Geology ; Mineralogy ; Silicate minerals ; Lithium (medication) ; Chemistry ; Analytical Chemistry (journal) ; Thermodynamics ; Fractionation ; Quartz X-ray Diffraction in Crystallography ; Radioactive element chemistry and processing ; Chemical Synthesis and Characterization