jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christoph Bauer | Gisbert Schneider | Andreas H. Göller
Source: Journal of Cheminformatics (article, 2019-09-11)
Field: Materials Science — Materials Chemistry

Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies We present machine learning (ML) models for hydrogen bond acceptor (HBA) and hydrogen bond donor (HBD) strengths. Quantum chemical (QC) free energies in solution for 1:1 hydrogen-bonded complex formation to the reference molecules 4-fluorophenol and acetone serve as our target values. Our acceptor and donor databases are the largest on record with 4426 and 1036 data points, respectively. After scanning over radial atomic descriptors and ML methods, our final trained HBA and HBD ML models achieve RMSEs of 3.8 kJ mol<sup>-1</sup> (acceptors), and 2.3 kJ mol<sup>-1</sup> (donors) on experimental test sets, respectively. This performance is comparable with previous models that are trained on experimental hydrogen bonding free energies, indicating that molecular QC data can serve as substitute for experiment. The potential ramifications thereof could lead to a full replacement of wetlab chemistry for HBA/HBD strength determination by QC. As a possible chemical application of our ML models, we highlight our predicted HBA and HBD strengths as possible descriptors in two case studies on trends in intramolecular hydrogen bonding. Hydrogen bond ; Intramolecular force ; Acceptor ; Molecule ; Hydrogen ; Proton ; Training set ; Chemistry ; Computational chemistry ; Computer science ; Artificial intelligence ; Physics ; Stereochemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Sayak Subhra Panda | Kirill Shmilovich | Andrew L. Ferguson | John D. Tovar
Source: Langmuir (article, 2019-09-30)
Field: Materials Science — Biomaterials

Controlling Supramolecular Chirality in Peptide−π-Peptide Networks by Variation of the Alkyl Spacer Length Self-assembled supramolecular organic materials with π-functionalities are of great interest because of their applications as biocompatible nanoelectronics. A detailed understanding of molecular parameters to modulate the formation of hierarchical structures can inform design principles for materials with engineered optical and electronic properties. In this work, we combine molecular-level characterization techniques with all-atom molecular simulations to investigate the subtle relationship between the chemical structure of peptide-π-peptide molecules and the emergent supramolecular chirality of their spontaneously self-assembled nanoaggregates. We demonstrate through circular dichroism measurements that we can modulate the chirality by incorporating alkyl spacers of various lengths in between the peptides and thienylene-phenylene π-system chromophores: even numbers of alkyl carbons in the spacer units (0, 2) induce M-type helical character whereas odd numbers (1, 3) induce P-type. Corroborating molecular dynamics simulations and explicating machine learning analysis techniques identify hydrogen bonding and hydrophobic packing to be the principal discriminants of the observed chirality switches. Our results present a molecular-level design rule to engineer chirality into optically and electronically active nanoaggregates of these peptidic building blocks by exploiting systematic variations in the alkyl spacer length. Chirality (physics) ; Supramolecular chemistry ; Alkyl ; Supramolecular chirality ; Circular dichroism ; Peptide ; Chromophore ; Chemistry ; Molecule ; Self-assembly ; Materials science ; Nanotechnology ; Crystallography ; Organic chemistry ; Physics Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Luminescence and Fluorescent Materials
Authors: Anuradha Beniwal | Ritesh Dadhich | Alankar Alankar
Source: Materialia (article, 2019-08-13)
Field: Materials Science — Metals and Alloys

Deep learning based predictive modeling for structure-property linkages Interpretability ; Homogenization (climate) ; Convolutional neural network ; Artificial intelligence ; Materials science ; Artificial neural network ; Minification ; Isotropy ; Property (philosophy) ; Deep learning ; Computer science ; Multiscale modeling ; Curse of dimensionality ; Machine learning ; Finite element method ; Structural engineering Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Non-Destructive Testing Techniques
Authors: Zeynep Gülsoy | Kutay B. Sezginel | Alper Uzun | Seda Keskın | Ramazan Yıldırım
Source: ACS Combinatorial Science (article, 2019-03-01)
Field: Chemistry — Inorganic Chemistry

Analysis of CH<sub>4</sub>Uptake over Metal–Organic Frameworks Using Data-Mining Tools A database containing 2224 data points for CH<sub>4</sub> storage or delivery in metal-organic frameworks (MOFs) was analyzed using machine-learning tools to extract knowledge for generalization. The database was first reviewed to observe the basic trends and patterns. It was then analyzed using decision trees and artificial neural networks (ANN) to extract hidden information and develop rules and heuristics for future studies. Five-fold cross validations were used in each analysis to test the validity of the models with data not seen before. Decision-tree analyses were carried out using six user-defined descriptors and two structural properties, separately. The crystal structure and the total degree of unsaturation were found to be the effective user-defined descriptors, whereas the pore volume and maximum pore diameter, as structural properties, were sufficient to determine the MOFs having high CH<sub>4</sub>-storage capacity. Moreover, a high pore volume is always required, as expected. In ANN analyses, models were also developed by using user-defined descriptors and structural properties separately. It was observed that the user-defined descriptors were not sufficient to describe the CH<sub>4</sub>-storage capacities of MOFs, whereas the structural properties in particular led to accurate CH<sub>4</sub>-storage predictions with an RMSE of 26.8 and an R<sup>2</sup> of 0.92 for testing. Heuristics ; Chemistry ; Generalization ; Artificial neural network ; Volume (thermodynamics) ; Data mining ; Metal-organic framework ; Decision tree ; Molecular descriptor ; Biological system ; Computer science ; Artificial intelligence ; Quantitative structure–activity relationship ; Mathematics ; Thermodynamics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon dioxide utilization in catalysis
Authors: Christine K. Payne
Source: The Journal of Chemical Physics (article, 2019-10-01)
Field: Materials Science — Biomaterials

A protein corona primer for physical chemists Nanoparticles present in any biological environment are exposed to extracellular proteins. These proteins adsorb on the surface of the nanoparticle forming a "protein corona." These proteins control the interaction of nanoparticles with cells. The interaction of proteins with the nanoparticle surface is governed by physical chemistry. Understanding this process requires spectroscopy, microscopy, and computational tools that are familiar to physical chemists. This perspective provides an overview of the protein corona along with two future directions: first, the need for new computational approaches, including machine learning, to predict corona formation and second, the extension of protein corona studies to more complex environments ranging from lung fluids to waste water treatment. Primer (cosmetics) ; Corona (planetary geology) ; Chemistry ; Nanotechnology ; Materials science ; Physics ; Astrobiology ; Organic chemistry Nanoparticle-Based Drug Delivery ; Biosensors and Analytical Detection ; Polymer Surface Interaction Studies
Authors: Meng Huang | Xueyao Zhou | Yaolong Zhang | Linsen Zhou | M. Alducin | Bin Jiang | Hua Guo
Source: Physical review. B./Physical review. B (article, 2019-11-25)
Field: Materials Science — Materials Chemistry

Adiabatic and nonadiabatic energy dissipation during scattering of vibrationally excited CO from Au(111) A high-dimensional potential energy surface (PES) for CO interaction with the Au(111) surface is developed using a machine-learning algorithm. Including both molecular and surface coordinates, this PES enables the simulations of the recent experiment on scattering of vibrationally excited CO from Au(111). Trapping in a physisorption well is observed to increase with decreasing incidence energy. While the energy dissipation of physisorbed CO is slow, due to weak coupling with both the phonons and electron-hole pairs, the access of the impinging CO to the chemisorption well facilitates its fast vibrational relaxation through nonadiabatic coupling with surface electron-hole pairs. The latter is proposed as a mechanism for the experimentally observed fast as well as slow components of the $\mathrm{CO}(\ensuremath{\nu}=1)$ product. Excited state ; Chemisorption ; Adiabatic process ; Physisorption ; Vibronic coupling ; Dissipation ; Atomic physics ; Scattering ; Relaxation (psychology) ; Vibrational energy relaxation ; Coupling (piping) ; Molecular physics ; Chemistry ; Materials science ; Physics ; Physical chemistry ; Adsorption ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Greg L. Hura | Curtis D. Hodge | Daniel J. Rosenberg | Dmytro Guzenko | José M. Duarte | Bohdan Monastyrskyy | Sergei Grudinin | Andriy Kryshtafovych | John A. Tainer | Krzysztof Fidelis | Susan E. Tsutakawa
Source: Proteins Structure Function and Bioinformatics (article, 2019-10-07)
Field: Materials Science — Materials Chemistry

Small angle X‐ray scattering‐assisted protein structure prediction in CASP13 and emergence of solution structure differences Abstract Small angle X‐ray scattering (SAXS) measures comprehensive distance information on a protein's structure, which can constrain and guide computational structure prediction algorithms. Here, we evaluate structure predictions of 11 monomeric and oligomeric proteins for which SAXS data were collected and provided to predictors in the 13th round of the Critical Assessment of protein Structure Prediction (CASP13). The category for SAXS‐assisted predictions made gains in certain areas for CASP13 compared to CASP12. Improvements included higher quality data with size exclusion chromatography‐SAXS (SEC‐SAXS) and better selection of targets and communication of results by CASP organizers. In several cases, we can track improvements in model accuracy with use of SAXS data. For hard multimeric targets where regular folding algorithms were unsuccessful, SAXS data helped predictors to build models better resembling the global shape of the target. For most models, however, no significant improvement in model accuracy at the domain level was registered from use of SAXS data, when rigorously comparing SAXS‐assisted models to the best regular server predictions. To promote future progress in this category, we identify successes, challenges, and opportunities for improved strategies in prediction, assessment, and communication of SAXS data to predictors. An important observation is that, for many targets, SAXS data were inconsistent with crystal structures, suggesting that these proteins adopt different conformation(s) in solution. This CASP13 result, if representative of PDB structures and future CASP targets, may have substantive implications for the structure training databases used for machine learning, CASP, and use of prediction models for biology. Small-angle X-ray scattering ; CASP ; Scattering ; Protein Data Bank (RCSB PDB) ; Protein Data Bank ; Crystallography ; Protein structure ; Computer science ; Protein structure prediction ; Biology ; Chemistry ; Physics ; Biochemistry ; Optics Enzyme Structure and Function ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Na Wang | Gaoyi Han | Yunzhen Chang | Wenjing Hou | Yaoming Xiao | Honggang Li
Source: Electrochimica Acta (article, 2019-06-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Preparing Ni3S2 composite with neural network-like structure for high-performance flexible asymmetric supercapacitors Supercapacitor ; Graphene ; Capacitance ; Electrode ; Materials science ; Oxide ; Electrochemistry ; Nanotechnology ; Chemical engineering ; Composite number ; Composite material ; Chemistry ; Metallurgy Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Conducting polymers and applications
Authors: Emad Al Ibrahim | Aamir Farooq
Source: Energy & Fuels (article, 2019-10-22)
Field: Chemistry — Analytical Chemistry

Octane Prediction from Infrared Spectroscopic Data A model for the prediction of research octane number (RON) and motor octane number (MON) of hydrocarbon mixtures and gasoline–ethanol blends has been developed based on infrared spectroscopy data of pure components. Infrared spectra for 61 neat hydrocarbon species were used to generate spectra of 148 hydrocarbon blends by averaging the spectra of their pure components on a molar basis. The spectra of 38 FACE (fuels for advanced combustion engines) gasoline blends were calculated using PIONA (paraffin, isoparaffin, olefin, naphthene, and aromatic) class averages of the pure components. The study sheds light on the significance of dimensional reduction of spectra and shows how it can be used to extract scores with linear correlations to the following important features: molecular weight, paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic −CH═CH2 groups, naphthenic CH–CH2 groups, aromatic C–CH groups, ethanolic OH groups, and branching index. Both scores and features can be used as input to predict octane numbers through nonlinear regression. Artificial neural network (ANN) was found to be the optimal method where the mean absolute error on a randomly selected test set was within the experimental uncertainty of RON, MON, and octane sensitivity. Octane ; Gasoline ; Hydrocarbon ; Octane rating ; Chemistry ; Olefin fiber ; Infrared spectroscopy ; Branching (polymer chemistry) ; Hydrocarbon mixtures ; Combustion ; Aromatic hydrocarbon ; Analytical Chemistry (journal) ; Fraction (chemistry) ; Infrared ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Combustion Engine Technologies ; Biodiesel Production and Applications
Authors: Vsevolod Ya. Iakovlev | Dmitry V. Krasnikov | Eldar M. Khabushev | Аlena A. Alekseeva | Artem K. Grebenko | Alexey P. Tsapenko | Boris Yu. Zabelich | Julia V. Kolodiazhnaia | Albert G. Nasibulin
Source: Chemical Engineering Journal (article, 2019-10-09)
Field: Materials Science — Materials Chemistry

Fine-tuning of spark-discharge aerosol CVD reactor for single-walled carbon nanotube growth: The role of ex situ nucleation Carbon nanotube ; Materials science ; Raman spectroscopy ; Nanotechnology ; Nanoparticle ; Nucleation ; Aerosol ; Chemical engineering ; Chemical vapor deposition ; Nanotube ; Chemistry ; Organic chemistry ; Optics Carbon Nanotubes in Composites ; Graphene research and applications ; Thermal properties of materials
Authors: Vishnu H Nair | Philippe Schwaller | Teodoro Laino
Source: CHIMIA International Journal for Chemistry (article, 2019-12-18)
Field: Materials Science — Materials Chemistry

Data-driven Chemical Reaction Prediction and Retrosynthesis The synthesis of organic compounds, which is central to many areas such as drug discovery, material synthesis and biomolecular chemistry, requires chemists to have years of knowledge and experience. The development of technologies with the potential to learn and support experts in the design of synthetic routes is a half-century-old challenge with an interesting revival in the last decade. In fact, the renewed interest in artificial intelligence (AI), driven mainly by data availability, is profoundly changing the landscape of computer-aided chemical reaction prediction and retrosynthetic analysis. In this article, we briefly review different approaches to predict forward reactions and retrosynthesis, with a strong focus on data-driven ones. While data-driven technologies still need to demonstrate their full potential compared to expert rule-based systems in synthetic chemistry, the acceleration experienced in the last decade is a convincing sign that where we use software today, there will be AI tomorrow. This revolution will help and empower bench chemists, driving the transformation of chemistry towards a high-tech business over the next decades. Retrosynthetic analysis ; Computer science ; Data science ; Software ; Artificial intelligence ; Engineering ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Manuel V. C. Morais | A.I. Oliva-Avilés | Miguel A.S. Matos | Vito L. Tagarielli | S.T. Pinho | Christof Hübner | Frank Henning
Source: Carbon (article, 2019-04-27)
Field: Materials Science — Materials Chemistry

On the effect of electric field application during the curing process on the electrical conductivity of single-walled carbon nanotubes–epoxy composites Single-walled carbon nanotube (SWCNT)/epoxy composites were cured under external electric fields and the influence of the processing parameters (electric field magnitude and frequency, SWCNT concentration and curing temperature) on the electrical response of the system was evaluated. A mold for the electric field application was designed and manufactured, allowing in situ measurements of the electrical resistivity of the composite, during and after the curing process. The resulting electrical properties revealed a strong dependence on the processing parameters. By rising the curing temperature, the solid bulk resistivity was decreased by one order of magnitude. Further reduction was observed with electric fields, up to an additional order of magnitude. Such improvements can be related with the decrease in viscosity and improvement of interconnected-nanotube paths within the polymer matrix. The effect of the electric field on the rotation and interconnection of the SWCNTs was investigated using a classical mechanics model based on the dielectrophoretic theory for the liquid state. The influence of inter-nanotube distances on the bulk electrical properties was calculated at different particle concentrations, using finite element models of the microstructure. This processing technique presents promising results for enhancing the electrical conductivity of polymer composites with carbon-based nanoparticles. Materials science ; Carbon nanotube ; Composite material ; Electrical resistivity and conductivity ; Curing (chemistry) ; Epoxy ; Electric field ; Composite number ; Microstructure ; Electrical engineering Carbon Nanotubes in Composites ; Conducting polymers and applications ; Smart Materials for Construction
Authors: Daniel U. Campos-Delgado | Omar Gutiérrez-Navarro | Jose J. Rico-Jimenez | Elvis Duran-Sierra | Himar Fabelo | Samuel Ortega | Gustavo M. Callicó | Javier A. Jo
Source: IEEE Access (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Extended Blind End-Member and Abundance Extraction for Biomedical Imaging Applications In some applications of biomedical imaging, a linear mixture model can represent the constitutive elements (end-members) and their contributions (abundances) per pixel of the image. In this work, the extended blind end-member and abundance extraction (EBEAE) methodology is mathematically formulated to address the blind linear unmixing (BLU) problem subject to positivity constraints in optical measurements. The EBEAE algorithm is based on a constrained quadratic optimization and an alternated least-squares strategy to jointly estimate end-members and their abundances. In our proposal, a local approach is used to estimate the abundances of each end-member by maximizing their entropy, and a global technique is adopted to iteratively identify the end-members by reducing the similarity among them. All the cost functions are normalized, and four initialization approaches are suggested for the end-members matrix. Synthetic datasets are used first for the EBEAE validation at different noise types and levels, and its performance is compared to state-of-the-art algorithms in BLU. In a second stage, three experimental biomedical imaging applications are addressed with EBEAE: m-FLIM for chemometric analysis in oral cavity samples, OCT for macrophages identification in post-mortem artery samples, and hyper-spectral images for <i>in-vivo</i> brain tissue classification and tumor identification. In our evaluations, EBEAE was able to provide a quantitative analysis of the samples with none or minimal a priori information. Computer science ; Initialization ; Pattern recognition (psychology) ; Artificial intelligence ; A priori and a posteriori ; Pixel ; End-to-end principle ; Blind signal separation ; Algorithm ; Channel (broadcasting) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification
Authors: Rahul Nigam | Rojalin Tripathy | Sujay Kumar Dutta | Nita Bhagia | Rohit Nagori | K. Chandrasekar | Rajsi Kot | Bimal K. Bhattacharya | Susan L. Ustin
Source: Current Science (article, 2019-04-10)
Field: Chemistry — Analytical Chemistry

Crop Type Discrimination and Health Assessment using Hyperspectral Imaging Advancements in hyperspectral remote sensing technology have opened new avenues to explore innovative ways to map crops in terms of area and health. To study precise mapping of agriculture and horticulture crops along with biophysical and biochemical constituents at field scale, an airborne AVIRIS-NG hyperspectral imaging has been conducted in various agro-climatic regions representing diverse agricultural types of India. Crop classification with available and developed algorithms has been applied over homogeneous and heterogeneous agriculture and horticulture cropped areas. The spectral angle mapper and maximum likelihood algorithms showed classification accuracy of 77%-94% for AVIRI-NG and 42%-55% for LISS IV. The customized deep neural network and maximum noise function (MNF)-based classification schemes showed an accuracy of 93% and 86% for mapping of agriculture and horticulture crops respectively. The forward and inversion of canopy radiative transfer model protocol was deve-loped for retrieval of crop parameters such as leaf area index (LAI) and chlorophyll content (C ab ) using AVIRIS-NG narrow bands. The retrieved LAI and C ab showed 19%-27% and 23%-29% deviation from measured mean for homogeneous and heterogeneous agricultural areas respectively. Red edge position index-based empirical model and multivariate linear regression of multiple indices showed maximum correlation of 0.62 and 0.93 respectively, to map leaf nitrogen content. Water condition index was developed using vegetation and water indices to distinguish crop water-based abiotic stress. Wheat yellow rust disease has been identified at field scale using absorption band depth analysis at 662-702 and 2155-2175 nm, and further applied to AVIRIS-NG data to detect biotic stress at spatial scale. This study establishes that such missions have the potential to boost accurate mapping of economically valuable minor crops and generate health indicators to distinguish biotic and abiotic stresses at field scale. Hyperspectral imaging ; Crop ; Environmental science ; Remote sensing ; Biology ; Geography ; Agronomy Spectroscopy and Chemometric Analyses