jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zeren Shui | George Karypis
Source:  (article, 2020-11-01)
Field: Materials Science — Materials Chemistry

Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties As they carry great potential for modeling complex interactions, graph neural network (GNN)-based methods have been widely used to predict quantum mechanical properties of molecules. Most of the existing methods treat molecules as molecular graphs in which atoms are modeled as nodes. They characterize each atom's chemical environment by modeling its pairwise interactions with other atoms in the molecule. Although these methods achieve a great success, limited amount of works explicitly take many-body interactions, i.e., interactions between three and more atoms, into consideration. In this paper, we introduce a novel graph representation of molecules, heterogeneous molecular graph (HMG) in which nodes and edges are of various types, to model many-body interactions. HMGs have the potential to carry complex geometric information. To leverage the rich information stored in HMGs for chemical prediction problems, we build heterogeneous molecular graph neural networks (HMGNN) on the basis of a neural message passing scheme. HMGNN incorporates global molecule representations and an attention mechanism into the prediction process. The predictions of HMGNN are invariant to translation and rotation of atom coordinates, and permutation of atom indices. Our model achieves state-of-the-art performance in 9 out of 12 tasks on the QM9 dataset. Molecular graph ; Computer science ; Theoretical computer science ; Artificial neural network ; Leverage (statistics) ; Graph ; Pairwise comparison ; Biological system ; Artificial intelligence ; Topology (electrical circuits) ; Algorithm ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Pranay Chakraborty | Yida Liu | Tengfei Ma | Xixi Guo | Lei Cao | Run Hu | Yan Wang
Source: ACS Applied Materials & Interfaces (article, 2020-01-29)
Field: Materials Science — Materials Chemistry

Quenching Thermal Transport in Aperiodic Superlattices: A Molecular Dynamics and Machine Learning Study Random multilayer (RML) structures, or aperiodic superlattices, can localize coherent phonons and therefore exhibit drastically reduced lattice thermal conductivity compared to their superlattice counterparts. The optimization of RML structures is essential for obtaining ultralow thermal conductivity, which is critical for various applications such as thermoelectrics and thermal barrier coatings. A higher degree of disorder in RMLs will lead to stronger phonon localization and, correspondingly, a lower lattice thermal conductivity. In this work, we identified several essential parameters for quantifying the disorder in layer thicknesses of RMLs. We were able to correlate these disorder parameters with thermal conductivity, as confirmed by classical molecular dynamics simulations of conceptual Lennard-Jones RMLs. Moreover, we have shown that these parameters are effective as features for physics-based machine learning models to predict the lattice thermal conductivity of RMLs with improved accuracy and efficiency. Thermal conductivity ; Superlattice ; Materials science ; Aperiodic graph ; Phonon ; Thermoelectric materials ; Condensed matter physics ; Lattice (music) ; Molecular dynamics ; Thermal ; Thermodynamics ; Optoelectronics ; Physics ; Composite material ; Quantum mechanics ; Mathematics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Anna M. Hiszpanski | Brian Gallagher | Karthik Chellappan | Peggy Li | Shusen Liu | Hyojin Kim | Jinkyu Han | Bhavya Kailkhura | David Buttler | T. Yong-Jin Han
Source: Journal of Chemical Information and Modeling (article, 2020-04-14)
Field: Materials Science — Materials Chemistry

Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge Nanomaterials of varying compositions and morphologies are of interest for many applications from catalysis to optics, but the synthesis of nanomaterials and their scale-up are most often time-consuming and Edisonian processes. Information gleaned from the scientific literature can help inform and accelerate nanomaterials development, but again, searching the literature and digesting the information are time-consuming manual processes for researchers. To help address these challenges, we developed scientific article-processing tools that extract and structure information from the text and figures of nanomaterials articles, thereby enabling the creation of a personalized knowledgebase for nanomaterials synthesis that can be mined to help inform further nanomaterials development. Starting with a corpus of ∼35k nanomaterials-related articles, we developed models to classify articles according to the nanomaterial composition and morphology, extract synthesis protocols from within the articles' text, and extract, normalize, and categorize chemical terms within synthesis protocols. We demonstrate the efficiency of the proposed pipeline on an expert-labeled set of nanomaterials synthesis articles, achieving 100% accuracy on composition prediction, 95% accuracy on morphology prediction, 0.99 AUC on protocol identification, and up to a 0.87 F1-score on chemical entity recognition. In addition to processing articles' text, microscopy images of nanomaterials within the articles are also automatically identified and analyzed to determine the nanomaterials' morphologies and size distributions. To enable users to easily explore the database, we developed a complementary browser-based visualization tool that provides flexibility in comparing across subsets of articles of interest. We use these tools and information to identify trends in nanomaterials synthesis, such as the correlation of certain reagents with various nanomaterial morphologies, which is useful in guiding hypotheses and reducing the potential parameter space during experimental design. Computer science ; Categorization ; Visualization ; Nanomaterials ; Flexibility (engineering) ; Identification (biology) ; Structuring ; Data science ; Nanotechnology ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Monzure-Khoda Kazi | Fadwa Eljack | E. Mahdi
Source: Composite Structures (article, 2020-06-29)
Field: Materials Science — Polymers and Plastics

Optimal filler content for cotton fiber/PP composite based on mechanical properties using artificial neural network Hyperparameter ; Artificial neural network ; Python (programming language) ; Composite number ; Polypropylene ; Filler (materials) ; Materials science ; Composite material ; Fiber ; Computer science ; Artificial intelligence ; Machine learning Textile materials and evaluations ; Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites
Authors: April M. Cooper | Johannes Kästner | Alexander Urban | Nongnuch Artrith
Source: npj Computational Materials (article, 2020-05-13)
Field: Materials Science — Materials Chemistry

Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide Abstract Artificial neural network (ANN) potentials enable the efficient large-scale atomistic modeling of complex materials with near first-principles accuracy. For molecular dynamics simulations, accurate energies and interatomic forces are a prerequisite, but training ANN potentials simultaneously on energies and forces from electronic structure calculations is computationally demanding. Here, we introduce an efficient alternative method for the training of ANN potentials on energy and force information, based on an extrapolation of the total energy via a Taylor expansion. By translating the force information to approximate energies, the quadratic scaling with the number of atoms exhibited by conventional force-training methods can be avoided, which enables the training on reference datasets containing complex atomic structures. We demonstrate for different materials systems, clusters of water molecules, bulk liquid water, and a lithium transition-metal oxide that the proposed force-training approach provides substantial improvements over schemes that train on energies only. Including force information for training reduces the size of the reference datasets required for ANN potential construction, increases the transferability of the potential, and generally improves the force prediction accuracy. For a set of water clusters, the Taylor-expansion approach achieves around 50% of the force error improvement compared to the explicit training on all force components, at a much smaller computational cost. The alternative force-training approach thus simplifies the construction of general ANN potentials for the prediction of accurate energies and interatomic forces for diverse types of materials, as demonstrated here for water and a transition-metal oxide. Extrapolation ; Force field (fiction) ; Scaling ; Quadratic equation ; Interatomic potential ; Artificial neural network ; Water model ; Set (abstract data type) ; Potential energy Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Crystallography and molecular interactions
Authors: Orkun Furat | Donal P. Finegan | David R. Diercks | Francois L. E. Usseglio‐Viretta | Kandler Smith | Volker Schmidt
Source: Journal of Power Sources (article, 2020-11-16)
Field: Materials Science — Surfaces, Coatings and Films

Mapping the architecture of single lithium ion electrode particles in 3D, using electron backscatter diffraction and machine learning segmentation Accurately quantifying the architecture of lithium ion electrode particles in 3D is critical to understanding sub-particle lithium transport, rate limitations, and degradation mechanisms within lithium ion batteries. Most commercial positive electrode materials consist of polycrystalline particles, where intra-particle grains have a range of morphologies and orientations. Here, focused ion beam slicing in sequence with electron backscatter diffraction is used to accurately quantify intra-particle grain morphologies in 3D. The intra-particle grains are identified using convolution neural network segmentation and distinctly labeled. Efficient morphological characterization of the grain architectures is achieved. Bivariate probability density maps are developed to show correlative relationships between morphological grain descriptors. The implication of morphological features on cell performance, as well as the extension of this dataset to guide artificial generation of realistic particle architectures for 3D multi-physics models, is discussed. Electron backscatter diffraction ; Materials science ; Lithium (medication) ; Electrode ; Particle (ecology) ; Range (aeronautics) ; Ion ; Segmentation ; Computer science ; Artificial intelligence ; Physics ; Composite material ; Microstructure ; Geology Electron and X-Ray Spectroscopy Techniques ; Non-Destructive Testing Techniques ; Advanced Neural Network Applications
Authors: Geng Sun | Anastassia N. Alexandrova | Philippe Sautet
Source: ACS Catalysis (article, 2020-04-10)
Field: Materials Science — Materials Chemistry

Structural Rearrangements of Subnanometer Cu Oxide Clusters Govern Catalytic Oxidation Sub-nanometer metal oxide clusters are very important materials that are widely used, for example, in catalysis or electronic devices such as sensors. Hence, it is critical to understand the atomic structures and properties of sub-nanometer metal oxide clusters under a reactive gas environment, such as O2. We consider here experimentally accessible precise-size Cu clusters (Cu4) supported on partially hydroxylated amorphous alumina and show that such clusters can access, in catalytic conditions at high temperature under a pressure of O2, a large ensemble of oxidized structures, representing a large variety of oxygen content, of geometries in link with the support, and of catalytic activities for oxidation reactions, as seen from their reducibilities. A grand canonical basin hopping method based on first-principles energy reveals an ensemble of 24 configurations for the Cu4Ox cluster of low free energy, less than 0.8 eV above the global minimum. The low free energy ensemble consists of clusters of different stoichiometries, which are mainly Cu4O3 and Cu4O4 in the temperature range of 200–400 °C and under a pressure of 0.5 bar of O2. The presence of several competitive isomers at each composition implies that cluster fluxionality impacts the phase diagram, which should be ensemble-averaged. In terms of catalytic oxidation activity, Cu4O3 isomers present highly variable O abstraction energy: the most stable isomers are inactive for alkane oxidative dehydrogenation, but isomerization to metastable isomers, that proceed with low barrier, enable to create active configurations with low O abstraction energy. O atoms with the lowest anionic character, and thus of more electrophilic nature, present the best oxidation capability. In contrast, all Cu4O4 isomers show a low O abstraction energy and a high potential catalytic activity. This manuscript demonstrates the unique structural and electronic properties of sub-nano Cu oxide clusters and illustrates the critical roles of configuration ensembles and rearrangement to highly reactive metastable cluster isomers in nanocatalysis. Isomerization ; Catalysis ; Dehydrogenation ; Chemical physics ; Chemistry ; Oxide ; Cluster (spacecraft) ; Metastability ; Stoichiometry ; Heterogeneous catalysis ; Alkane ; Computational chemistry ; Physical chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Nanocluster Synthesis and Applications ; Copper-based nanomaterials and applications
Authors: Syed Salman Shafqat | Showkat Ahmad Bhawani | Salma Bakhtiar | Mohamad Nasir Mohamad Ibrahim
Source: BMC Chemistry (article, 2020-04-03)
Field: Chemistry — Analytical Chemistry

Synthesis of molecularly imprinted polymer for removal of Congo red Abstract Congo red (CR) is an anionic azo dye widely used in many industries including pharmaceutical, textile, food and paint industries. The disposal of huge amount of CR into the various streams of water has posed a great threat to both human and aquatic life. Therefore, it has become an important aspect of industries to remove CR from different water sources. Molecular imprinting technology is a very slective method to remove various target pollutant from environment. In this study a precipitation polymerization was employed for the effective and selective removal of CR from contaminated aqueous media. A series of congo red molecularly imprinted polymers (CR-MIPs) of uniform size and shape was developed by changing the mole ratio of the components. The optimum ratio (0.1:4: 20, template, functional monomer and cross-linking monomer respectively) for CR1-MIP from synthesized polymers was able to rebind about 99.63% of CR at the optimum conditions of adsorption parameters (contact time 210 min, polymer dosage 0.5 g, concentration 20 ppm and pH 7). The synthesized polymers were characterized by various techniques such as Fourier Infra-red spectroscopy (FTIR), scanning electron microscopy (SEM), Thermogravimetric analysis (TGA), energy-dispersive X-ray spectroscopy (EDX), and Brumauer–Emmett–Teller (BET). The polymer particles have successfully removed CR from different aqueous media with an efficiency of about ~ 90%. Congo red ; Thermogravimetric analysis ; Polymer ; Aqueous solution ; Molecularly imprinted polymer ; Fourier transform infrared spectroscopy ; Adsorption ; Monomer ; Materials science ; Chemical engineering ; Precipitation polymerization ; Nuclear chemistry ; Polymerization ; Scanning electron microscope ; Precipitation ; Chemistry ; Radical polymerization ; Organic chemistry ; Catalysis ; Selectivity ; Composite material Analytical chemistry methods development ; Dye analysis and toxicity ; Electrochemical sensors and biosensors
Authors: Anna O. Conrad | Wei Li | Da-Young Lee | Guo‐Liang Wang | Luis Rodriguez‐Saona | Pierluigi Bonello
Source: Plant Phenomics (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Machine Learning-Based Presymptomatic Detection of Rice Sheath Blight Using Spectral Profiles Early detection of plant diseases, prior to symptom development, can allow for targeted and more proactive disease management. The objective of this study was to evaluate the use of near-infrared (NIR) spectroscopy combined with machine learning for early detection of rice sheath blight (ShB), caused by the fungus <i>Rhizoctonia solani</i>. We collected NIR spectra from leaves of ShB-susceptible rice (<i>Oryza sativa</i> L.) cultivar, Lemont, growing in a growth chamber one day following inoculation with <i>R. solani</i>, and prior to the development of any disease symptoms. Support vector machine (SVM) and random forest, two machine learning algorithms, were used to build and evaluate the accuracy of supervised classification-based disease predictive models. Sparse partial least squares discriminant analysis was used to confirm the results. The most accurate model comparing mock-inoculated and inoculated plants was SVM-based and had an overall testing accuracy of 86.1% (<i>N</i> = 72), while when control, mock-inoculated, and inoculated plants were compared the most accurate SVM model had an overall testing accuracy of 73.3% (<i>N</i> = 105). These results suggest that machine learning models could be developed into tools to diagnose infected but asymptomatic plants based on spectral profiles at the early stages of disease development. While testing and validation in field trials are still needed, this technique holds promise for application in the field for disease diagnosis and management. Artificial intelligence ; Support vector machine ; Machine learning ; Algorithm ; Oryza sativa ; Inoculation ; Rhizoctonia solani ; Linear discriminant analysis ; Computer science ; Mathematics ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Plant Pathogens and Fungal Diseases ; Plant Disease Management Techniques
Authors: Thomas J. Struble | Connor W. Coley | Klavs F. Jensen
Source: Reaction Chemistry & Engineering (article, 2020-01-01)
Field: Chemistry — Organic Chemistry

Multitask prediction of site selectivity in aromatic C–H functionalization reactions Aromatic C–H functionalization reactions are an important part of the synthetic chemistry toolbox. Surface modification ; Toolbox ; Selectivity ; Chemistry ; Combinatorial chemistry ; Organic chemistry ; Computer science ; Catalysis ; Physical chemistry Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions ; Synthesis and Catalytic Reactions
Authors: Kelly Fraser | Vamsi Kodali | Naveena Yanamala | M. Eileen Birch | Lorenzo Cena | Gary S. Casuccio | Kristin Bunker | Traci L. Lersch | Douglas E. Evans | Aleksandr B. Stefaniak | Mary Ann Hammer | Michael L. Kashon | Theresa Boots | Tracy Eye | John Hubczak | Sherri Friend | Matthew M. Dahm | Mary K. Schubauer‐Berigan | Katelyn J. Siegrist | David T. Lowry | Alison K. Bauer | Linda M. Sargent | Aaron Erdely
Source: Particle and Fibre Toxicology (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

Physicochemical characterization and genotoxicity of the broad class of carbon nanotubes and nanofibers used or produced in U.S. facilities Distributions of physical dimensions provided more consistent grouping of CNT/F with respect to toxicity outcomes compared to means only. All CNT/F induced some level of genotoxicity in human epithelial cells. The severity of toxicity was dependent on the sample containing a proportion of tubes with greater nominal lengths and diameters. Micronucleus test ; Genotoxicity ; Agglomerate ; Carbon nanotube ; Toxicity ; Breakage ; Nanofiber ; Materials science ; Nanotechnology ; DNA damage ; Nanotoxicology ; Characterization (materials science) ; Micronucleus ; Oxidative stress ; Biophysics ; Nanoparticle ; DNA ; Chemistry ; Composite material ; Biology ; Biochemistry Nanoparticles: synthesis and applications ; Carcinogens and Genotoxicity Assessment ; Microplastics and Plastic Pollution
Authors: Sara R. Savage | Bing Zhang
Source: Clinical Proteomics (review, 2020-07-11)
Field: Chemistry — Spectroscopy

Using phosphoproteomics data to understand cellular signaling: a comprehensive guide to bioinformatics resources Mass spectrometry-based phosphoproteomics is becoming an essential methodology for the study of global cellular signaling. Numerous bioinformatics resources are available to facilitate the translation of phosphopeptide identification and quantification results into novel biological and clinical insights, a critical step in phosphoproteomics data analysis. These resources include knowledge bases of kinases and phosphatases, phosphorylation sites, kinase inhibitors, and sequence variants affecting kinase function, and bioinformatics tools that can predict phosphorylation sites in addition to the kinase that phosphorylates them, infer kinase activity, and predict the effect of mutations on kinase signaling. However, these resources exist in silos and it is challenging to select among multiple resources with similar functions. Therefore, we put together a comprehensive collection of resources related to phosphoproteomics data interpretation, compared the use of tools with similar functions, and assessed the usability from the standpoint of typical biologists or clinicians. Overall, tools could be improved by standardization of enzyme names, flexibility of data input and output format, consistent maintenance, and detailed manuals. Phosphoproteomics ; Computational biology ; Proteomics ; Computer science ; Bioinformatics ; Phosphorylation ; Kinase ; Identification (biology) ; Protein phosphorylation ; Biology ; Protein kinase A ; Biochemistry Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Biomedical Text Mining and Ontologies
Authors: Lei Wang | J.J. Hoyt | Nan Wang | Nikolas Provatas | Chad W. Sinclair
Source: Nature Communications (article, 2020-02-05)
Field: Materials Science — Materials Chemistry

Controlling solid-liquid interfacial energy anisotropy through the isotropic liquid Although the anisotropy of the solid-liquid interfacial free energy for most alloy systems is very small, it plays a crucial role in the growth rate, morphology and crystallographic growth direction of dendrites. Previous work posited a dendrite orientation transition via compositional additions. In this work we examine experimentally the change in dendrite growth behaviour in the Al-Sm (Samarium) system as a function of solute concentration and study its interfacial properties using molecular dynamics simulations. We observe a dendrite growth direction which changes from [Formula: see text] to [Formula: see text] as Sm content increases. The observed change in dendrite orientation is consistent with the simulation results for the variation of the interfacial free energy anisotropy and thus provides definitive confirmation of a conjecture in previous works. In addition, our results provide physical insight into the atomic structural origin of the concentration dependent anisotropy, and deepen our fundamental understanding of solid-liquid interfaces in binary alloys. Anisotropy ; Materials science ; Isotropy ; Dendrite (mathematics) ; Surface energy ; Crystallography ; Thermodynamics ; Physics ; Chemistry ; Geometry ; Composite material Solidification and crystal growth phenomena ; nanoparticles nucleation surface interactions ; Theoretical and Computational Physics