jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: P.N. Rowe | Gábor Cśanyi | Dario Alfè | Angelos Michaelides
Source: Physical review. B./Physical review. B (article, 2018-02-05)
Field: Materials Science — Materials Chemistry

Development of a machine learning potential for graphene We present an accurate interatomic potential for graphene, constructed using the Gaussian approximation potential (GAP) machine learning methodology. This GAP model obtains a faithful representation of a density functional theory (DFT) potential energy surface, facilitating highly accurate (approaching the accuracy of ab initio methods) molecular dynamics simulations. This is achieved at a computational cost which is orders of magnitude lower than that of comparable calculations which directly invoke electronic structure methods. We evaluate the accuracy of our machine learning model alongside that of a number of popular empirical and bond-order potentials, using both experimental and ab initio data as references. We find that whilst significant discrepancies exist between the empirical interatomic potentials and the reference data---and amongst the empirical potentials themselves---the machine learning model introduced here provides exemplary performance in all of the tested areas. The calculated properties include: graphene phonon dispersion curves at 0 K (which we predict with sub-meV accuracy), phonon spectra at finite temperature, in-plane thermal expansion up to 2500 K as compared to NPT ab initio molecular dynamics simulations and a comparison of the thermally induced dispersion of graphene Raman bands to experimental observations. We have made our potential freely available online at [http://www.libatoms.org]. Graphene ; Phonon ; Ab initio ; Interatomic potential ; Machine learning ; Density functional theory ; Statistical physics ; Ab initio quantum chemistry methods ; Artificial intelligence ; Molecular dynamics ; Computer science ; Materials science ; Physics ; Condensed matter physics ; Computational chemistry ; Chemistry ; Quantum mechanics ; Nanotechnology ; Molecule Machine Learning in Materials Science ; Graphene research and applications ; Thermal properties of materials
Authors: Rui Xiong | Huan Chen | Chun Wang | Fengchun Sun
Source: Journal of Cleaner Production (review, 2018-08-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Towards a smarter hybrid energy storage system based on battery and ultracapacitor - A critical review on topology and energy management Energy management ; Battery (electricity) ; Energy storage ; Computer science ; Supercapacitor ; Energy management system ; Implementation ; Key (lock) ; Power management ; Control (management) ; Plug-in ; Systems engineering ; Energy (signal processing) ; Control engineering ; Embedded system ; Power (physics) ; Engineering ; Artificial intelligence ; Software engineering ; Computer security ; Operating system Supercapacitor Materials and Fabrication ; Advanced Battery Technologies Research ; Electric and Hybrid Vehicle Technologies
Authors: Mitchell A. Wood | Aidan P. Thompson
Source: The Journal of Chemical Physics (article, 2018-03-29)
Field: Materials Science — Materials Chemistry

Extending the accuracy of the SNAP interatomic potential form The Spectral Neighbor Analysis Potential (SNAP) is a classical interatomic potential that expresses the energy of each atom as a linear function of selected bispectrum components of the neighbor atoms. An extension of the SNAP form is proposed that includes quadratic terms in the bispectrum components. The extension is shown to provide a large increase in accuracy relative to the linear form, while incurring only a modest increase in computational cost. The mathematical structure of the quadratic SNAP form is similar to the embedded atom method (EAM), with the SNAP bispectrum components serving as counterparts to the two-body density functions in EAM. The effectiveness of the new form is demonstrated using an extensive set of training data for tantalum structures. Similar to artificial neural network potentials, the quadratic SNAP form requires substantially more training data in order to prevent overfitting. The quality of this new potential form is measured through a robust cross-validation analysis. Bispectrum ; Quadratic equation ; Dimension (graph theory) ; Function (biology) ; Extension (predicate logic) ; Artificial neural network ; Stability (learning theory) ; Set (abstract data type) ; Quadratic function Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Nuclear physics research studies
Authors: Yoyo Hinuma | Takashi Toyao | Takashi Kamachi | Zen Maeno | Satoru Takakusagi | Shinya Furukawa | Ichigaku Takigawa | Ken‐ichi Shimizu
Source: The Journal of Physical Chemistry C (article, 2018-11-27)
Field: Materials Science — Materials Chemistry

Density Functional Theory Calculations of Oxygen Vacancy Formation and Subsequent Molecular Adsorption on Oxide Surfaces The surface oxygen vacancy formation energy (EOvac) is an important parameter in determining the catalytic activity of metal oxides. Estimating these energies can therefore lead to data-driven design of promising catalyst candidates. In the present study, we determine EOvac for various insulating and semiconducting oxides. Statistical investigations indicate that the band gap, bulk formation energy, and electron affinity are factors that strongly influence EOvac. Electrons enter defect states after O desorption, and these states can be in the valence band, mid-gap, or in the conduction band. Subsequent adsorption of O2, NO, CO, CO2, and H2 molecules on an O-deficient surface is also investigated. These molecules become preferentially adsorbed at the defect sites, and EOvac is identified as the dominant factor that determines the adsorption mode as well as a descriptor that shows good correlation with the adsorption energy. Adsorption ; Density functional theory ; Band gap ; Oxide ; Desorption ; Molecule ; Vacancy defect ; Oxygen ; Materials science ; Metal ; Chemical physics ; Catalysis ; Valence (chemistry) ; Density of states ; Electron ; Chemistry ; Physical chemistry ; Computational chemistry ; Condensed matter physics ; Crystallography ; Physics ; Optoelectronics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Andriy Zakutayev | Nick Wunder | Marcus Schwarting | John D. Perkins | Robert White | Kristin Munch | William Tumas | Caleb Phillips
Source: Scientific Data (article, 2018-04-03)
Field: Materials Science — Materials Chemistry

An open experimental database for exploring inorganic materials The use of advanced machine learning algorithms in experimental materials science is limited by the lack of sufficiently large and diverse datasets amenable to data mining. If publicly open, such data resources would also enable materials research by scientists without access to expensive experimental equipment. Here, we report on our progress towards a publicly open High Throughput Experimental Materials (HTEM) Database (htem.nrel.gov). This database currently contains 140,000 sample entries, characterized by structural (100,000), synthetic (80,000), chemical (70,000), and optoelectronic (50,000) properties of inorganic thin film materials, grouped in >4,000 sample entries across >100 materials systems; more than a half of these data are publicly available. This article shows how the HTEM database may enable scientists to explore materials by browsing web-based user interface and an application programming interface. This paper also describes a HTE approach to generating materials data, and discusses the laboratory information management system (LIMS), that underpin HTEM database. Finally, this manuscript illustrates how advanced machine learning algorithms can be adopted to materials science problems using this open data resource. Computer science ; Database ; Interface (matter) ; Sample (material) ; Application programming interface ; Resource (disambiguation) ; Open data ; Throughput ; Data science ; World Wide Web ; Chemistry ; Operating system Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Narbe Mardirossian | Martin Head‐Gordon
Source: The Journal of Chemical Physics (article, 2018-06-07)
Field: Materials Science — Materials Chemistry

Survival of the most transferable at the top of Jacob’s ladder: Defining and testing the <i>ω</i>B97M(2) double hybrid density functional A meta-generalized gradient approximation, range-separated double hybrid (DH) density functional with VV10 non-local correlation is presented. The final 14-parameter functional form is determined by screening trillions of candidate fits through a combination of best subset selection, forward stepwise selection, and random sample consensus (RANSAC) outlier detection. The MGCDB84 database of 4986 data points is employed in this work, containing a training set of 870 data points, a validation set of 2964 data points, and a test set of 1152 data points. Following an xDH approach, orbitals from the ωB97M-V density functional are used to compute the second-order perturbation theory correction. The resulting functional, ωB97M(2), is benchmarked against a variety of leading double hybrid density functionals, including B2PLYP-D3(BJ), B2GPPLYP-D3(BJ), ωB97X-2(TQZ), XYG3, PTPSS-D3(0), XYGJ-OS, DSD-PBEP86-D3(BJ), and DSD-PBEPBE-D3(BJ). Encouragingly, the overall performance of ωB97M(2) on nearly 5000 data points clearly surpasses that of all of the tested density functionals. As a Rung 5 density functional, ωB97M(2) completes our family of combinatorially optimized functionals, complementing B97M-V on Rung 3, and ωB97X-V and ωB97M-V on Rung 4. The results suggest that ωB97M(2) has the potential to serve as a powerful predictive tool for accurate and efficient electronic structure calculations of main-group chemistry. Density functional theory ; Hybrid functional ; Outlier ; RANSAC ; Set (abstract data type) ; Atomic orbital ; Mathematics ; Data set ; Basis set ; Selection (genetic algorithm) ; Computer science ; Algorithm ; Physics ; Statistics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: J. Ankney | Adil Muneer | Xian Chen
Source: Annual Review of Analytical Chemistry (review, 2018-06-12)
Field: Chemistry — Spectroscopy

Relative and Absolute Quantitation in Mass Spectrometry–Based Proteomics Mass spectrometry-based quantitative proteomics is a powerful tool for gaining insights into function and dynamics of biological systems. However, peptides with different sequences have different ionization efficiencies, and their intensities in a mass spectrum are not correlated with their abundances. Therefore, various label-free or stable isotope label-based quantitation methods have emerged to assist mass spectrometry to perform comparative proteomic experiments, thus enabling nonbiased identification of thousands of proteins differentially expressed in healthy versus diseased cells. Here, we discuss the most widely used label-free and metabolic-, enzymatic-, and chemical labeling-based proteomic strategies for relative and absolute quantitation. We summarize the specific strengths and weaknesses of each technique in terms of quantification accuracy, proteome coverage, multiplexing capability, and robustness. Applications of each strategy for solving specific biological complexities are also presented. Mass spectrometry ; Quantitative proteomics ; Proteomics ; Proteome ; Label-free quantification ; Computational biology ; Stable isotope labeling by amino acids in cell culture ; Chemistry ; Chromatography ; Biological system ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Quan Zhou | Peizhe Tang | Shenxiu Liu | Jinbo Pan | Qimin Yan | Shou-Cheng Zhang
Source: Proceedings of the National Academy of Sciences (article, 2018-06-26)
Field: Materials Science — Materials Chemistry

Learning atoms for materials discovery Exciting advances have been made in artificial intelligence (AI) during recent decades. Among them, applications of machine learning (ML) and deep learning techniques brought human-competitive performances in various tasks of fields, including image recognition, speech recognition, and natural language understanding. Even in Go, the ancient game of profound complexity, the AI player has already beat human world champions convincingly with and without learning from the human. In this work, we show that our unsupervised machines (Atom2Vec) can learn the basic properties of atoms by themselves from the extensive database of known compounds and materials. These learned properties are represented in terms of high-dimensional vectors, and clustering of atoms in vector space classifies them into meaningful groups consistent with human knowledge. We use the atom vectors as basic input units for neural networks and other ML models designed and trained to predict materials properties, which demonstrate significant accuracy. Computer science ; Data science ; Materials science Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Martin Koller
Source: Fermentation (review, 2018-04-23)
Field: Materials Science — Biomaterials

A Review on Established and Emerging Fermentation Schemes for Microbial Production of Polyhydroxyalkanoate (PHA) Biopolyesters Polyhydroxyalkanoates (PHA) are microbial biopolyesters utilized as “green plastics”. Their production under controlled conditions resorts to bioreactors operated in different modes. Because PHA biosynthesis constitutes a multiphase process, both feeding strategy and bioreactor operation mode need smart adaptation. Traditional PHA production setups based on batch, repeated batch, fed-batch or cyclic fed-batch processes are often limited in productivity, or display insufficient controllability of polyester composition. For highly diluted substrate streams like is the case of (agro) industrial waste streams, fed-batch enhanced by cell recycling has recently been reported as a viable tool to increase volumetric productivity. As an emerging trend, continuous fermentation processes in single-, two- and multi-stage setups are reported, which bring the kinetics of both microbial growth and PHA accumulation into agreement with process engineering and allow tailoring PHA’s molecular structure. Moreover, we currently witness an increasing number of CO2-based PHA production processes using cyanobacteria; these light-driven processes resort to photobioreactors similar to those used for microalgae cultivation and can be operated both discontinuously and continuously. This development is parallel to the emerging use of methane and syngas as abundantly available gaseous substrates, which also calls for bioreactor systems with optimized gas transfer. The review sheds light on the challenges of diverse PHA production processes in different bioreactor types and operational regimes using miscellaneous microbial production strains such as extremophilic Archaea, chemoheterotrophic eubacteria and phototrophic cyanobacteria. Particular emphasis is dedicated to the limitations and promises of different bioreactor–strain combinations and to efforts devoted to upscaling these processes to industrially relevant scales. Polyhydroxyalkanoates ; Production (economics) ; Fermentation ; Biochemical engineering ; Bioplastic ; Biotechnology ; Cupriavidus necator ; Pulp and paper industry ; Business ; Chemistry ; Food science ; Biology ; Bacteria ; Economics ; Engineering ; Ecology biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis ; Additive Manufacturing and 3D Printing Technologies
Authors: Carolina Moretto Carnielli | Carolina Carneiro Soares Macedo | Tatiane De Rossi | Daniela C. Granato | César Rivera | Romênia R. Domingues | Bianca Alves Pauletti | Sami Yokoo | Henry Heberle | Ariane F. Busso‐Lopes | Nilva K. Cervigne | Íris Sawazaki‐Calone | Gabriela Vaz Meirelles | Fábio Albuquerque Marchi | Guilherme P. Telles | Rosane Minghim | Ana Carolina Prado Ribeiro | Thaís Bianca Brandão | Gilberto de Castro | Wilfredo Alejandro González‐Arriagada | Alexandre F. Gomes | Fabio Penteado | Alan Roger Santos‐Silva | Márcio Ajudarte Lopes | Priscila Campioni Rodrigues | Elias Sundquist | Tuula Salo | Sabrina Daniela da Silva | Moulay A. Alaoui‐Jamali | Edgard Graner | Jay W. Fox | Ricardo Della Coletta | Adriana Franco Paes Leme
Source: Nature Communications (article, 2018-08-30)
Field: Chemistry — Spectroscopy

Combining discovery and targeted proteomics reveals a prognostic signature in oral cancer Different regions of oral squamous cell carcinoma (OSCC) have particular histopathological and molecular characteristics limiting the standard tumor-node-metastasis prognosis classification. Therefore, defining biological signatures that allow assessing the prognostic outcomes for OSCC patients would be of great clinical significance. Using histopathology-guided discovery proteomics, we analyze neoplastic islands and stroma from the invasive tumor front (ITF) and inner tumor to identify differentially expressed proteins. Potential signature proteins are prioritized and further investigated by immunohistochemistry (IHC) and targeted proteomics. IHC indicates low expression of cystatin-B in neoplastic islands from the ITF as an independent marker for local recurrence. Targeted proteomics analysis of the prioritized proteins in saliva, combined with machine-learning methods, highlights a peptide-based signature as the most powerful predictor to distinguish patients with and without lymph node metastasis. In summary, we identify a robust signature, which may enhance prognostic decisions in OSCC and better guide treatment to reduce tumor recurrence or lymph node metastasis. Proteomics ; Computational biology ; Cancer ; Signature (topology) ; Bioinformatics ; Biology ; Medicine ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Peptidase Inhibition and Analysis
Authors: Georgeta Serban | Oana Stănăşel | E. Şerban | Sanda Rodica Bota
Source: Drug Design Development and Therapy (review, 2018-05-01)
Field: Chemistry — Organic Chemistry

2-Amino-1,3,4-thiadiazole as a potential scaffold for promising antimicrobial agents Pathogenic microorganisms are causative agents for different types of serious and even lethal infectious diseases. Despite advancements in medication, bacterial and fungal infections continue to be a growing problem in health care. As more and more bacteria become resistant to antibiotics used in therapy and an increasing number of invasive fungal species become resistant to current antifungal medications, there is considerable interest in the development of new compounds with antimicrobial activity. The compounds containing a heterocyclic ring play an important role among organic compounds with biological activity used as drugs in human and veterinary medicine or as insecticides and pesticides in agriculture. Thiadiazoles belong to the classes of nitrogen-sulfur heterocycles with extensive application as structural units of biologically active molecules and as useful intermediates in medicinal chemistry. The potency of the thiadiazole nucleus is demonstrated by the drugs currently used. 1,3,4-Thiadiazoles and some of their derivatives are extensively studied because of their broad spectrum of pharmacological activities. The aim of this review was to highlight the main antimicrobial properties exhibited by derivatives possessing 2-amino-1,3,4-thiadiazole moiety. Many of the reported 2-amino-1,3,4-thiadiazole derivatives can be considered as lead compounds for drug synthesis, and several of them have demonstrated higher antimicrobial activity in comparison to standard drugs. Furthermore, taking into account the reactivity of the amine group in the derivatization process, 2-amino-1,3,4-thiadiazole moiety may be a good scaffold for future pharmacologically active 1,3,4-thiadiazole derivatives. Antimicrobial ; Thiadiazoles ; Moiety ; Chemistry ; Combinatorial chemistry ; Antibiotics ; Antifungal ; Bacteria ; Drug discovery ; Biochemistry ; Biology ; Organic chemistry ; Microbiology Synthesis and biological activity ; Phenothiazines and Benzothiazines Synthesis and Activities ; Synthesis and Biological Evaluation
Authors: Tanveer A. Tabish | Fayyaz Ali Memon | Diego E. Gomez | D. W. Horsell | Shaowei Zhang
Source: Scientific Reports (article, 2018-01-23)
Field: Materials Science — Materials Chemistry

A facile synthesis of porous graphene for efficient water and wastewater treatment The use of two-dimensional graphene-based materials in water treatment has recently gained significant attention due to their unique electronic and thermal mobility, high surface area, high mechanical strength, excellent corrosion resistance and tunable surface chemistry. However, the relatively expensive, poor hydrophobicity, low adsorption capacity and recyclability, and complex post-treatment of the most pristine graphene frameworks limit their practical application. Here, we report a facile scalable method to produce highly porous graphene from reduced graphene oxide via thermal treatment without addition of any catalyst or use of any template. Comparing to conventional graphene counterparts, as-prepared porous graphene nanosheets showed evident improvement in hydrophobicity, adsorption capacity, and recyclability, making them ideal candidate materials for water treatment. Superhydrophobic and superoleophilic porous graphene prepared in this work has been demonstrated as effective absorbents for a broad range of ions, oils and organic solvents, exhibiting high selectivity, good recyclability, and excellent absorption capacities > 90%. The synthesis method of porous graphene reported in this paper is easy to implement, low cost and scalable. These attributes could contribute towards efficient and cost-effective water purification and pollution reduction. Graphene ; Materials science ; Adsorption ; Oxide ; Porosity ; Nanotechnology ; Chemical engineering ; Graphene foam ; Porous medium ; Thermal treatment ; Graphene oxide paper ; Composite material ; Organic chemistry ; Chemistry ; Metallurgy Graphene research and applications ; Graphene and Nanomaterials Applications ; Membrane Separation Technologies
Authors: Chiara Milanese | Torben R. Jensen | Bjørn C. Hauback | Claudio Pistidda | Martin Dornheim | Heena Yang | Loris Lombardo | A. ZUETTEL | Yaroslav Filinchuk | Peter Ngene | Petra E. de Jongh | Craig E. Buckley | Erika Michela Dematteis | Marcello Baricco
Source: International Journal of Hydrogen Energy (article, 2018-12-31)
Field: Materials Science — Materials Chemistry

Complex hydrides for energy storage Hydrogen storage ; Gravimetric analysis ; Hydrogen ; Diffusion ; Stoichiometry ; Energy storage ; Materials science ; Catalysis ; Hydrogen economy ; Hydrogen fuel ; Desorption ; Chemical engineering ; Process engineering ; Chemistry ; Thermodynamics ; Physical chemistry ; Organic chemistry ; Physics Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Hybrid Renewable Energy Systems