jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Debasish Koner | Seyedeh Maryam Salehi | Padmabati Mondal | Markus Meuwly
Source: The Journal of Chemical Physics (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

Non-conventional force fields for applications in spectroscopy and chemical reaction dynamics Extensions and improvements of empirical force fields are discussed in view of applications to computational vibrational spectroscopy and reactive molecular dynamics simulations. Particular focus is on quantitative studies, which make contact with experiments and provide complementary information for a molecular-level understanding of processes in the gas phase and in solution. Methods range from including multipolar charge distributions to reproducing kernel Hilbert space approaches and machine learned energy functions based on neural networks. Reaction dynamics ; Phase space ; Molecular dynamics ; Spectroscopy ; Focus (optics) ; Range (aeronautics) ; Force field (fiction) ; Dynamics (music) ; Kernel (algebra) ; Potential energy Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Sabrina Diehn | Boris Zimmermann | Valeria Tafintseva | Murat Bağcıoğlu | Achim Köhler | Mikael Ohlson | Siri Fjellheim | Janina Kneipp
Source: Analytical and Bioanalytical Chemistry (article, 2020-04-29)
Field: Chemistry — Analytical Chemistry

Discrimination of grass pollen of different species by FTIR spectroscopy of individual pollen grains Fourier-transform infrared (FTIR) spectroscopy enables the chemical characterization and identification of pollen samples, leading to a wide range of applications, such as paleoecology and allergology. This is of particular interest in the identification of grass (Poaceae) species since they have pollen grains of very similar morphology. Unfortunately, the correct identification of FTIR microspectroscopy spectra of single pollen grains is hindered by strong spectral contributions from Mie scattering. Embedding of pollen samples in paraffin helps to retrieve infrared spectra without scattering artifacts. In this study, pollen samples from 10 different populations of five grass species (Anthoxanthum odoratum, Bromus inermis, Hordeum bulbosum, Lolium perenne, and Poa alpina) were embedded in paraffin, and their single grain spectra were obtained by FTIR microspectroscopy. Spectra were subjected to different preprocessing in order to suppress paraffin influence on spectral classification. It is shown that decomposition by non-negative matrix factorization (NMF) and extended multiplicative signal correction (EMSC) that utilizes a paraffin constituent spectrum, respectively, leads to good success rates for the classification of spectra with respect to species by a partial least square discriminant analysis (PLS-DA) model in full cross-validation for several species. PLS-DA, artificial neural network, and random forest classifiers were applied on the EMSC-corrected spectra using an independent validation to assign spectra from unknown populations to the species. Variation within and between species, together with the differences in classification results, is in agreement with the systematics within the Poaceae family. The results illustrate the great potential of FTIR microspectroscopy for automated classification and identification of grass pollen, possibly together with other, complementary methods for single pollen chemical characterization. Pollen ; Fourier transform infrared spectroscopy ; Spectroscopy ; Analytical Chemistry (journal) ; Biology ; Botany ; Chemistry ; Optics ; Physics ; Chromatography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Essential Oils and Antimicrobial Activity
Authors: YingXing Cheng | Linggang Zhu | Guanjie Wang | Jian Zhou | Stephen R. Elliott | Zhimei Sun
Source: Computational Materials Science (article, 2020-06-01)
Field: Materials Science — Materials Chemistry

Vacancy formation energy and its connection with bonding environment in solid: A high-throughput calculation and machine learning study Throughput ; Connection (principal bundle) ; Vacancy defect ; Materials science ; Energy (signal processing) ; Computer science ; Chemical physics ; Chemistry ; Physics ; Crystallography ; Mechanical engineering ; Engineering ; Telecommunications Machine Learning in Materials Science ; Electronic Packaging and Soldering Technologies ; X-ray Diffraction in Crystallography
Authors: Marianna Kotzabasaki | Iason Sotiropoulos | Haralambos Sarimveis
Source: RSC Advances (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

QSAR modeling of the toxicity classification of superparamagnetic iron oxide nanoparticles (SPIONs) in stem-cell monitoring applications: an integrated study from data curation to model development The use of <i>in silico</i> approaches for the prediction of biomedical properties of nano-biomaterials (NBMs) can play a significant role in guiding and reducing wetlab experiments. Computational methods, such as data mining and machine learning techniques, can increase the efficiency and reduce the time and cost required for hazard and risk assesment and for designing new safer NBMs. A major obstacle in developing accurate and well-validated <i>in silico</i> models such as Nano Quantitative Structure-Activity Relationships (Nano-QSARs) is that although the volume of data published in the literature is increasing, the data are fragmented in many different publications and are not sufficiently curated for modelling purposes. Moreover, NBMs exhibit high complexity and heterogeneity in their structures, making data collection and curation and QSAR model development more challenging compared to traditional small molecules. The aim of this study was to construct and fully validate a Nano-QSAR model for the prediction of toxicological properties of superparamagnetic iron oxide nanoparticles (SPIONs), focusing on their application as Magnetic Resonance Imaging (MRI) contrast agents for non-invasive stem cell labelling and tracking. To achieve this goal, we first performed an extensive search through the literature for collecting and curating relevant data and we developed a dataset containing both physicochemical and toxicological properties of SPIONs. The data were analysed next, using Automated machine learning (Auto-ML) approaches for optimising the development and validation of nanotoxicity classification QSAR models of SPIONs. Further analysis of relative attribute importances revealed that physicochemical properties such as the size and the magnetic core are the dominant attributes correlated to the toxicity of SPIONs. Our results suggest that as more systematic information from NBM experimental tests becomes available, computational tools could play an important role in supporting the safety-by-design (SbD) concept in regenerative medicine and disease therapeutics. Quantitative structure–activity relationship ; Computer science ; In silico ; Data curation ; Artificial intelligence ; Machine learning ; Data mining ; Chemistry Nanoparticles: synthesis and applications ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Barış Demirbay | Duygu Bayram | Şaziye Uğur
Source: Applied Soft Computing (article, 2020-08-12)
Field: Materials Science — Polymers and Plastics

A Bayesian regularized feed-forward neural network model for conductivity prediction of PS/MWCNT nanocomposite film coatings Mean squared error ; Materials science ; Nanocomposite ; Conductivity ; Artificial neural network ; Composite material ; Biological system ; Machine learning ; Computer science ; Mathematics ; Statistics ; Physics Conducting polymers and applications ; High voltage insulation and dielectric phenomena ; Electrochemical Analysis and Applications
Authors: Troy D. Loeffler | Sukriti Manna | Tarak K. Patra | Henry Chan | Badri Narayanan | Subramanian K. R. S. Sankaranarayanan
Source: ChemCatChem (article, 2020-06-10)
Field: Materials Science — Materials Chemistry

Active Learning A Neural Network Model For Gold Clusters &amp; Bulk From Sparse First Principles Training Data Abstract Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size‐dependent structural motifs and their dynamical evolution has been of longstanding interest. Given the high computational cost of first‐principles calculations, molecular modeling and atomistic simulations such as molecular dynamics (MD) has proven to be an important complementary tool to aid this understanding. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size‐dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well‐trained NN potentials can provide high level of flexibility, transferability and accuracy on‐par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities ( or greater) of training data to ensure that the model adequately samples the energy landscape both near and far‐from‐equilibrium. A highly desirable goal is minimize the number of training data, especially if the underlying reference model is first‐principles based and hence expensive. Here, we introduce an active learning (AL) scheme that trains a NN model on‐the‐fly with minimal amount of first‐principles based training data. Our AL workflow is initiated with a sparse training dataset ( 1 to 5 data points) and is updated on‐the‐fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with 500 total reference calculations. Using an extensive DFT test set of ∼1100 configurations, we show that our AL‐NN is able to accurately predict both the DFT energies and the forces for clusters of a myriad of different sizes. Our NN predictions are within 30 meV/atom and 40 meV/Å of the reference DFT calculations. Moreover, our AL‐NN model also adequately captures the various size‐dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments. We finally show that our AL‐NN model also captures bulk properties reasonably well, even though they were not included in the training data. Training (meteorology) ; Artificial neural network ; Computer science ; Training set ; Artificial intelligence ; Machine learning ; Chemistry ; Nanotechnology ; Materials science ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and ELM
Authors: Yuki Nagai | Masahiko Okumura | Akinori Tanaka
Source: Physical review. B./Physical review. B (article, 2020-03-09)
Field: Materials Science — Materials Chemistry

Self-learning Monte Carlo method with Behler-Parrinello neural networks We propose a general way to construct an effective Hamiltonian in the self-learning Monte Carlo method (SLMC), which speeds up Monte Carlo simulations by training an effective model to propose uncorrelated configurations in the Markov chain. Its applications are, however, limited. This is because it is not obvious to find the explicit form of the effective Hamiltonians. Particularly, it is difficult to make trainable effective Hamiltonians including many-body interactions. In order to overcome this critical difficulty, we introduce the Behler-Parrinello neural networks (BPNNs) as effective Hamiltonian without any prior knowledge, which is used to construct the potential-energy surfaces in interacting many particle systems for molecular dynamics. We combine SLMC with BPNN by focusing on a divisibility of Hamiltonian and propose how to construct the elementwise configurations. We apply it to quantum impurity models. We observed significant improvement of the acceptance ratio from 0.01 (the effective Hamiltonian with the explicit form) to 0.76 (BPNN). This drastic improvement implies that the BPNN effective Hamiltonian includes many-body interaction, which is omitted in the effective Hamiltonian with the explicit forms. The BPNNs make SLMC more promising. Hamiltonian (control theory) ; Monte Carlo method ; Statistical physics ; Hybrid Monte Carlo ; Quantum Monte Carlo ; Markov chain Monte Carlo ; Computer science ; Artificial neural network ; Uncorrelated ; Physics ; Mathematics ; Artificial intelligence ; Mathematical optimization Machine Learning in Materials Science ; Quantum many-body systems ; Quantum, superfluid, helium dynamics
Authors: Alireza Baghban | Sajjad Habibzadeh | Farzin Zokaee Ashtiani
Source: RSC Advances (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Bandgaps of noble and transition metal/ZIF-8 electro/catalysts: a computational study Zeolitic imidazolate frameworks (ZIFs) are designed with metals as center atoms, connected by imidazole-like linkers. The created structures have been employed considerably in the field of advanced energy materials, including catalysis/electrocatalysis and energy storage and harvesting applications. In the present study, the bandgaps of pristine and doped ZIF-8 (using noble and transition metal dopants such as Pd, Pt, Ni, Mn, Co, Cu, Fe, and Ti) are determined. This can result in a promising approach to enhance the corresponding electronic properties while applying noble metal-free dopants. To determine the bandgap values, a quantum mechanical modeling based on density functional theory (DFT) was applied. Then, due to the time-consuming and complicated nature of this approach, the obtained results from the DFT study were then employed to develop the support vector machine (SVM) model to estimate the bandgap of the resulting nanostructure. The outcomes of the proposed model showed its high accuracy, with <i>R</i> <sup>2</sup> of 0.98 and root mean squared error (RMSE) of 0.04. The developed model could have great value in designing various ZIF-8-based nanostructures, particularly when applied in electro/catalytic reactions, <i>e.g.</i>, electrocatalytic hydrogen evolution reaction or catalytic hydrogenation reaction, through a simple approach. Zeolitic imidazolate framework ; Dopant ; Materials science ; Catalysis ; Noble metal ; Electrocatalyst ; Density functional theory ; Band gap ; Transition metal ; Imidazolate ; Nanostructure ; Hydrogen storage ; Doping ; Nanotechnology ; Chemical engineering ; Metal-organic framework ; Electrochemistry ; Physical chemistry ; Metal ; Inorganic chemistry ; Computational chemistry ; Chemistry ; Optoelectronics ; Organic chemistry ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Rebecca L. Greenaway | Kim E. Jelfs
Source: ChemPlusChem (review, 2020-07-28)
Field: Chemistry — Organic Chemistry

High‐Throughput Approaches for the Discovery of Supramolecular Organic Cages The assembly of complex molecules, such as organic cages, can be achieved through supramolecular and dynamic covalent strategies. Their use in a range of applications has been demonstrated, including gas uptake, molecular separations, and in catalysis. However, the targeted design and synthesis of new species for particular applications is challenging, particularly as the systems become more complex. High-throughput computation-only and experiment-only approaches have been developed to streamline the discovery process, although are still not widely implemented. Additionally, combined hybrid workflows can dramatically accelerate the discovery process and lead to the serendipitous discovery and rationalisation of new supramolecular assemblies that would not have been designed based on intuition alone. This Minireview focuses on the advances in high-throughput approaches that have been developed and applied in the discovery of supramolecular organic cages. Supramolecular chemistry ; Nanotechnology ; Throughput ; Computer science ; Business process discovery ; Workflow ; Intuition ; Biochemical engineering ; Chemistry ; Materials science ; Molecule ; Work in process ; Engineering ; Organic chemistry Supramolecular Chemistry and Complexes ; Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications
Authors: Guanghua Wang | Yanxiang Li
Source: China Foundry (article, 2020-03-01)
Field: Materials Science — Materials Chemistry

Thermal conductivity of cast iron -A review This paper gives a brief introduction to the four research methods for the study on thermal conductivity of cast irons, including experimental measurement, statistical analysis, effective medium theory and numerical simulation. Recent studies on the thermal conductivity of various cast irons are reviewed through the influence of alloying elements, structural constituents, and temperature. The addition of alloying elements is the main reason that restricts the thermal conductivity of cast irons, especially spheroidal graphite cast iron. The connectivity of graphite has a significant effect on the thermal conductivity of flake and compacted graphite cast irons, semiquantitative and quantitative analysis of this factor is a key and difficult point in the study of thermal conductivity of cast irons. The thermal conductivities of different types of cast irons show varying degrees of dependence on temperature. This phenomenon is the combination of graphite and matrix, rather than just depending on graphite morphology. The study of the relationship between individual phase and temperature is the focus of future research. These summaries and discussions may provide reference and guidance for the future research and development of high thermal conductivity cast irons. Graphite ; Cast iron ; Thermal conductivity ; Materials science ; Thermal ; Metallurgy ; Composite material ; Conductivity ; Thermodynamics ; Chemistry ; Physics Thermal properties of materials ; Mechanical and Thermal Properties Analysis ; Mechanical Behavior of Composites
Authors: Paolo Decuzzi | Dan Peer | Daniele Di Mascolo | Anna Lisa Palange | Purnima Naresh Manghnani | S. Moein Moghimi | Z. Shadi Farhangrazi | Kenneth A. Howard | Daniel Rosenblum | Tingxizi Liang | Zhaowei Chen | Zejun Wang | Jun‐Jie Zhu | Zhen Gu | Netanel Korin | Didier Letourneur | Cédric Chauvierre | Roy van der Meel | Fabian Kießling | Twan Lammers
Source: Nanotechnology (article, 2020-10-10)
Field: Materials Science — Biomaterials

Roadmap on nanomedicine Since the launch of the Alliance for Nanotechnology in Cancer by the National Cancer Institute in late 2004, several similar initiatives have been promoted all over the globe with the intention of advancing the diagnosis, treatment and prevention of cancer in the wake of nanoscience and nanotechnology. All this has encouraged scientists with diverse backgrounds to team up with one another, learn from each other, and generate new knowledge at the interface between engineering, physics, chemistry and biomedical sciences. Importantly, this new knowledge has been wisely channeled towards the development of novel diagnostic, imaging and therapeutic nanosystems, many of which are currently at different stages of clinical development. This roadmap collects eight brief articles elaborating on the interaction of nanomedicines with human biology; the biomedical and clinical applications of nanomedicines; and the importance of patient stratification in the development of future nanomedicines. The first article reports on the role of geometry and mechanical properties in nanomedicine rational design; the second articulates on the interaction of nanomedicines with cells of the immune system; and the third deals with exploiting endogenous molecules, such as albumin, to carry therapeutic agents. The second group of articles highlights the successful application of nanomedicines in the treatment of cancer with the optimal delivery of nucleic acids, diabetes with the sustained and controlled release of insulin, stroke by using thrombolytic particles, and atherosclerosis with the development of targeted nanoparticles. Finally, the last contribution comments on how nanomedicine and theranostics could play a pivotal role in the development of personalized medicines. As this roadmap cannot cover the massive extent of development of nanomedicine over the past 15 years, only a few major achievements are highlighted as the field progressively matures from the initial hype to the consolidation phase. Nanomedicine ; Nanotechnology ; Cancer therapy ; Engineering ethics ; Cancer ; Medicine ; Materials science ; Engineering ; Nanoparticle ; Internal medicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Yong He | Yiying Zhao | Chu Zhang | Yijian Li | Yidan Bao | Fei Liu
Source: Foods (article, 2020-02-15)
Field: Chemistry — Analytical Chemistry

Discrimination of Grape Seeds Using Laser-Induced Breakdown Spectroscopy in Combination with Region Selection and Supervised Classification Methods The wine-making industry generates a considerable amount of grape pomace. Grape seeds, as an important part of pomace, are rich in bioactive compounds and can be reutilized to produce useful derivatives. The nutritional properties of grape seeds are largely influenced by the cultivar, which calls for effective identification. In the present work, the spectral profiles of grape seeds belonging to three different cultivars were collected by laser-induced breakdown spectroscopy (LIBS). Three conventional supervised classification methods and a deep learning method, a one-dimensional convolutional neural network (CNN), were applied to establish discriminant models to explore the relationship between spectral responses and cultivar information. Interval partial least squares (<i>i</i>PLS) algorithm was successfully used to extract the spectral region (402.74-426.87 nm) relevant for elemental composition in grape seeds. By comparing the discriminant models based on the full spectra and the selected spectral regions, the CNN model based on the full spectra achieved the optimal overall performance, with classification accuracy of 100% and 96.7% for the calibration and prediction sets, respectively. This work demonstrated the reliability of LIBS as a rapid and accurate approach for identifying grape seeds and will assist in the utilization of certain genotypes with desirable nutritional properties essential for production rather than their being discarded as waste. Laser-induced breakdown spectroscopy ; Pomace ; Partial least squares regression ; Linear discriminant analysis ; Artificial intelligence ; Mathematics ; Convolutional neural network ; Pattern recognition (psychology) ; Biological system ; Machine learning ; Computer science ; Spectroscopy ; Chemistry ; Biology ; Food science ; Physics Spectroscopy and Chemometric Analyses ; Laser-induced spectroscopy and plasma ; Spectroscopy Techniques in Biomedical and Chemical Research