jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rongxin Xia | Sabre Kais
Source: RePEc: Research Papers in Economics (article, 2018-03-27)
Field: Materials Science — Materials Chemistry

Quantum Machine Learning for Electronic Structure Calculations Considering recent advancements and successes in the development of efficient quantum algorithms for electronic structure calculations --- alongside impressive results using machine learning techniques for computation --- hybridizing quantum computing with machine learning for the intent of performing electronic structure calculations is a natural progression. Here we report a hybrid quantum algorithm employing a restricted Boltzmann machine to obtain accurate molecular potential energy surfaces. By exploiting a quantum algorithm to help optimize the underlying objective function, we obtained an efficient procedure for the calculation of the electronic ground state energy for a small molecule system. Our approach achieves high accuracy for the ground state energy for H$_2$, LiH, H$_2$O at a specific location on its potential energy surface with a finite basis set. With the future availability of larger-scale quantum computers, quantum machine learning techniques are set to become powerful tools to obtain accurate values for electronic structures. Quantum machine learning ; Computer science ; Quantum computer ; Restricted Boltzmann machine ; Quantum ; Electronic structure ; Quantum algorithm ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Algorithm ; Deep learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Markus Meuwly
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2018-08-24)
Field: Chemistry — Physical and Theoretical Chemistry

Reactive molecular dynamics: From small molecules to proteins The current status of reactive molecular dynamics (MD) simulations is summarized. Both, methodological aspects and applications to problems ranging from gas phase reaction dynamics to ligand‐binding in solvated proteins are discussed, focusing on extracting information from simulations that cannot easily be obtained from experiments alone. One specific example is the structural interpretation of the ligand rebinding time scales extracted from state‐of‐the art time‐resolved experiments. Atomistic simulations employing validated reactive interaction potentials are capable of providing structural information about the time scales involved. Both, merits and shortcomings of the various methods are discussed and the outlook summarizes possible future avenues such as reactive potentials based on machine learning techniques. Molecular dynamics ; Dynamics (music) ; Chemistry ; Computational biology ; Biophysics ; Biology ; Computational chemistry ; Psychology Various Chemistry Research Topics ; Free Radicals and Antioxidants ; thermodynamics and calorimetric analyses
Authors: Yuanchao Yang | Dezhen Xue | Ruihao Yuan | Yumei Zhou | Turab Lookman | Xiangdong Ding | Xiaobing Ren | Jun Sun
Source: SSRN Electronic Journal (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Doping Effects of Point Defects in Shape Memory Alloys Doping ; Materials science ; Shape-memory alloy ; Crystallographic defect ; Condensed matter physics ; Point (geometry) ; Engineering physics ; Optoelectronics ; Metallurgy ; Physics ; Geometry ; Mathematics Shape Memory Alloy Transformations
Authors: Grace X. Gu | Chun‐Teh Chen | Deon J. Richmond | Markus J. Buehler
Source: The Royal Society of Chemistry (article, 2018-07-01)
Field: Materials Science — Materials Chemistry

Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment Biomimicry, adapting and implementing nature's designs provides an adequate first-order solution to achieving superior mechanical properties. However, the design space is too vast even using biomimetic designs as prototypes for optimization. Here, we propose a new approach to design hierarchical materials using machine learning, trained with a database of hundreds of thousands of structures from finite element analysis, together with a self-learning algorithm for discovering high-performing materials where inferior designs are phased out for superior candidates. Results show that our approach can create microstructural patterns that lead to tougher and stronger materials, which are validated through additive manufacturing and testing. We further show that machine learning can be used as an alternative method of coarse-graining – analyzing and designing materials without the use of full microstructural data. This novel paradigm of smart additive manufacturing can aid in the discovery and fabrication of new material designs boasting orders of magnitude increase in computational efficacy over conventional methods. Biomimetics ; Computer science ; Granularity ; 3D printing ; Machine learning ; Artificial intelligence ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies
Authors: Eitam Vinegrad | Uri Hananel | Gil Markovich | Ori Cheshnovsky
Source: arXiv (Cornell University) (preprint, 2018-08-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Determination of Handedness in a Single Chiral Nanocrystal via\n Circularly Polarized Luminescence The occurrence of biological homochirality is attributed to symmetry breaking\nmechanisms which are still debatable1. Studies of symmetry breaking require\ntools for monitoring the population ratios of individual chiral nano-objects,\nsuch as molecules, polymers or nanocrystals. Moreover, mapping their spatial\ndistributions may elucidate on the symmetry breaking mechanism. Recently,\nresearchers have utilized differential scattering or circular dichroism\nmicroscopy to identify chirality on individual plasmonic nanostructures and\ninorganic nanocrystals. However, these measurements are prone to optical system\nartifacts. While luminescence is preferred for detecting single particle\nchirality, the typical low optical activity of chromophores limits its\napplicability. Here, we report on handedness determination of single chiral\nlanthanide based nanocrystals, using circularly polarized luminescence, with\ntotal photon count of $2\\times 10^4$. We also utilize a machine learning\napproach for correlative microscopy to determine and spatially map handedness\nof individual nanocrystals with high accuracy. This technique may become\ninvaluable in studies of spontaneous symmetry breaking in chiral materials.\n Luminescence ; Chirality (physics) ; Symmetry breaking ; Homochirality ; Nanocrystal ; Materials science ; Circular dichroism ; Chromophore ; Chiral symmetry breaking ; Chemical physics ; Molecular physics ; Scattering ; Population ; Nanotechnology ; Optoelectronics ; Crystallography ; Physics ; Optics ; Chemistry ; Photochemistry ; Quantum mechanics ; Enantiomer ; Stereochemistry Metamaterials and Metasurfaces Applications ; Photoreceptor and optogenetics research
Authors: Zhen Yang | Xingsheng Gu | Changjian Ling | Xiaoyi Liang
Source: Journal of Mathematical Chemistry (article, 2018-05-12)
Field: Chemistry — Analytical Chemistry

Computational and experimental approaches for investigating membranes diffusion behavior in model diesel fuel Polyvinyl chloride ; Genetic algorithm ; Diesel fuel ; Diffusion ; Polyvinyl alcohol ; Dibenzothiophene ; Computer science ; Biological system ; Materials science ; Flue-gas desulfurization ; Engineering ; Chemical engineering ; Machine learning ; Composite material ; Waste management ; Physics ; Thermodynamics Petroleum Processing and Analysis ; Thermochemical Biomass Conversion Processes ; Catalysis and Hydrodesulfurization Studies
Authors: Mary Jo Ondrechen | Timothy A. Coulther | Lisa Ngu | Penny J. Beuning
Source: The FASEB Journal (article, 2018-04-01)
Field: Materials Science — Materials Chemistry

Electrostatic interactions in natural enzymes: What can we learn for enzyme design? The goal of this work is to understand the intrinsic properties that give natural enzymes their catalytic power and to learn how to build these properties in silico, for the design and expression of artificial enzymes that catalyze reactions that do not occur in nature. Partial Order Optimum Likelihood (POOL) is a machine learning method developed by us to predict residues important for function, using the 3D structure of the query protein. The input features to POOL are based on computed electrostatic and chemical properties from THEMATICS. These input features are effectively measures of the strength of coupling between protonation events. POOL is used to characterize the properties of natural enzymes that are necessary for efficient catalysis. Catalytic sites in proteins are characterized by networks of strongly coupled protonation states; these networks impart the necessary electrostatic and proton‐transfer properties to the active residues in the first layer around the reacting substrate molecule(s). Typically these networks include first‐, second‐, and sometimes third‐layer residues. POOL‐predicted, multi‐layer active sites with significant participation by distal residues have been verified experimentally by single‐point site‐directed mutagenesis and kinetics assays for Ps. putida nitrile hydratase, human phosphoglucose isomerase, E. coli replicative DNA polymerase Pol III, E. coli Y family DNA polymerase DinB, and E. coli ornithine transcarbamoylase. In designed enzymes, such as retroaldolases, the residue‐specific input features to POOL – measures of the strength of coupling between protonation equilibria – rise as the enzymes evolve to higher rates of catalytic turnover. An approach to build these properties into the initial designs is proposed. Support or Funding Information NSF MCB‐1517290 (MJO &amp; PJB) and a National Institute of Justice Fellowship (TAC) This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal . Chemistry ; Protonation ; Enzyme ; Active site ; Enzyme kinetics ; Directed evolution ; Enzyme catalysis ; Biochemistry ; Stereochemistry ; Organic chemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Machine Learning in Bioinformatics
Authors: Ming Fang | Jing-An Lei
Source: Proceedings of the 2nd International Conference on Computer Science and Application Engineering (article, 2018-10-18)
Field: Chemistry — Analytical Chemistry

Field Corn Stalk Identification Method Based on Machine Learning and Spectral Characteristics Using1 the combination of spectral characteristics and machine learning methods, the corn stalk in the field is identified in natural light. First, in the natural light, the reflection spectra of corn stalk, soil, leaves and other field materials in the band of 199~995 nm are obtained. Then the spectral data is normalized to eliminate the effects of light intensity, sampling height, etc. Finally, the SVM (Support Vector Machine) was used to analyze and train the data to identify corn stalk from various substances in the field. The experiment shows that the field corn stalk identification method based on machine learning and spectral characteristics can better identify the corn stalk and other field materials. The average error rate is less than 2%. Compared with the traditional image processing methods, it can effectively improve recognition accuracy. Stalk ; Support vector machine ; Field (mathematics) ; Artificial intelligence ; Identification (biology) ; Computer science ; Reflection (computer programming) ; Machine learning ; Biological system ; Mathematics ; Botany ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Water Quality Monitoring and Analysis
Authors: Kim A. Nicoli | Pan Kessel | Michael Gastegger | Kristof T. Schütt
Source: arXiv (Cornell University) (preprint, 2018-10-23)
Field: Materials Science — Materials Chemistry

Analysis of Atomistic Representations Using Weighted Skip-Connections In this work, we extend the SchNet architecture by using weighted skip connections to assemble the final representation. This enables us to study the relative importance of each interaction block for property prediction. We demonstrate on both the QM9 and MD17 dataset that their relative weighting depends strongly on the chemical composition and configurational degrees of freedom of the molecules which opens the path towards a more detailed understanding of machine learning models for molecules. Computer science ; Statistical physics ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Enzyme Structure and Function
Authors: Guillaume Fraux | Siwar Chibani | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2018-12-03)
Field: Chemistry — Inorganic Chemistry

Modelling of framework materials at multiple scales: current practices and open questions The last decade has seen an explosion of the family of framework materials and their study, both from the experimental and computational point of view. We propose here a short highlight of the current state of methodologies for modelling framework materials at multiple scales, putting together a brief review of new methods and recent endeavours in this area, as well as outlining some of the open challenges in this field. We will detail advances in atomistic simulation methods, the development of materials databases, and the growing use of machine learning for properties prediction. Field (mathematics) ; Computer science ; Current (fluid) ; Data science ; Point (geometry) ; Management science ; Open research ; Systems engineering ; Engineering ; Mathematics ; World Wide Web Metal-Organic Frameworks: Synthesis and Applications ; Advanced Condensed Matter Physics ; Machine Learning in Materials Science
Authors: Guillaume Fraux | Siwar Chibani | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2018-11-12)
Field: Chemistry — Inorganic Chemistry

Modelling of framework materials at multiple scales: current practices and open questions The last decade has seen an explosion of the family of framework materials and their study, both from the experimental and computational point of view. We propose here a short highlight of the current state of methodologies for modelling framework materials at multiple scales, putting together a brief review of new methods and recent endeavours in this area, as well as outlining some of the open challenges in this field. We will detail advances in atomistic simulation methods, the development of materials databases, and the growing use of machine learning for properties prediction. Current (fluid) ; Field (mathematics) ; Data science ; Computer science ; Open research ; Point (geometry) ; Management science ; Engineering ; World Wide Web ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Advanced Condensed Matter Physics ; Machine Learning in Materials Science
Authors: Guillaume Fraux | Siwar Chibani | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2018-11-12)
Field: Chemistry — Inorganic Chemistry

Modelling of framework materials at multiple scales: current practices and open questions The last decade has seen an explosion of the family of framework materials and their study, both from the experimental and computational point of view. We propose here a short highlight of the current state of methodologies for modelling framework materials at multiple scales, putting together a brief review of new methods and recent endeavours in this area, as well as outlining some of the open challenges in this field. We will detail advances in atomistic simulation methods, the development of materials databases, and the growing use of machine learning for properties prediction. Current (fluid) ; Data science ; Field (mathematics) ; Computer science ; Point (geometry) ; Management science ; Open research ; Systems engineering ; Engineering ; World Wide Web ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Advanced Condensed Matter Physics ; Machine Learning in Materials Science
Authors: Ashraf A. A. Abdel‐Fattah
Source: International Journal of Sciences (article, 2018-03-01)
Field: Chemistry — Analytical Chemistry

MATHEMATICAL SIMULTANEOUS DETERMINATION OF ROSUVASTATIN CALCIUM AND PROPRANOLOL HYDROCHLORIDE USING DIFFERENT CHEMOMETRIC TECHNIQUES Abstract: Introduction : Simultaneous mathematical determination of rosuvastatin calcium and propranolol hydrochloride using different methods were described. The mathematical determination involves different chemometric models, namely, partial least square (PLS-1) as traditional model and artificial neural network as advanced model with and without variable selection procedure (genetic algorithm). Experimental : A two factor, 5-level experimental design was established resulting in 25 mixtures with variable ratios of the drugs. Thirteen mixtures were used as a calibration set and the last twelve mixtures were used as a validation set to validate the ability of the prediction. Conclusion : The methods were used for quantitative analysis of the drugs in raw materials and pharmaceutical dosage form. The validity of the proposed methods was assessed using the standard addition technique. Keywords : Chemometric technique; partial least squares; genetic algorithm; artificial neural network. Partial least squares regression ; Artificial neural network ; Rosuvastatin Calcium ; Chemometrics ; Calibration ; Propranolol Hydrochloride ; Computer science ; Genetic algorithm ; Biological system ; Chromatography ; Mathematics ; Chemistry ; Rosuvastatin ; Machine learning ; Statistics ; Propranolol Analytical Methods in Pharmaceuticals ; Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography
Authors: Strаhinjа Kоvаčеvić | Milica Karadžić Banjac | Sanja Podunavac-Kuzmаnоvić | Lidija R. Jevrić | Evica R. Ivanović | Matilda Vojnović
Source: Acta chimica slovenica (article, 2018-09-15)
Field: Chemistry — Organic Chemistry

Electrostatic and Topological Features as Predictors of Antifungal Potential of Oxazolo Derivatives as Promising Compounds in Treatment of Infections Caused by Candida albicans The results presented in this study include the prediction of the antifungal activity of 24 oxazolo derivatives based on their topological and electrostatic molecular descriptors, derived from the 2D molecular structures. The artificial neural network (ANN) method was applied as a regression tool. The input data for ANN modeling were selected by stepwise selection (SS) procedure. The ANN modeling resulted in three networks with the outstanding statistical characteristics. High predictivity of the established networks was confirmed by comparisons of the predicted and experimental data and by the residuals analysis. The obtained results indicate the usefulness of the formed ANNs in precise prediction of minimum inhibitory concentrations of the analyzed compounds towards Candida albicans. The Sum of Ranking Differences (SRD) method was used in this study to reveal possible grouping of the compounds in the space of the variables used in ANN modeling. The obtained results can be considered to be a contribution to development of new antifungal drugs structurally based on oxazole core, particularly nowadays when there is a lack of highly efficient antimycotics. Antifungal ; Candida albicans ; Artificial neural network ; Ranking (information retrieval) ; Quantitative structure–activity relationship ; Oxazole ; Computational biology ; Biological system ; Corpus albicans ; Stepwise regression ; Linear regression ; Mathematics ; Computer science ; Biology ; Chemistry ; Artificial intelligence ; Machine learning ; Stereochemistry ; Microbiology Synthesis and biological activity ; Computational Drug Discovery Methods ; Fungal Plant Pathogen Control
Authors: Xueyang Li | Christian C. Roth | Dirk Mohr
Source: EPJ Web of Conferences (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Large Deformation Behavior of High Strength Steel Under Extreme Loading Conditions: High Temperature and High Strain Rate Experiments and Modeling Plasticity and fracture experiments are carried out on flat smooth and notched tensile specimens extracted from DP800 steel sheets. A split Hopkinson pressure bar testing system equipped with a load inversion device is utilized to reach high strain rates. Temperature dependent experiments ranging from 20°C to 300°C are performed at quasi-static strain rates. The material exposes a monotonic strain hardening behaviour with a non-monotonic temperature dependency. The rate-independent material behaviour at room-temperature is described with a non-associated Hill’48 plasticity model and an Swift-Voce strain hardening. A machine learning based model is used multiplicatively to capture the rate and temperature responses. A good agreement between measured and simulated force-displacement curves as well as local surface is obtained. The loading paths to fracture are then extracted to facilitate further development of a temperature dependent fracture initiation model. Materials science ; Plasticity ; Split-Hopkinson pressure bar ; Strain rate ; Ultimate tensile strength ; Strain hardening exponent ; Composite material ; Hardening (computing) ; Monotonic function ; Viscoplasticity ; Fracture (geology) ; Deformation (meteorology) ; Structural engineering ; Constitutive equation ; Engineering ; Mathematics High-Velocity Impact and Material Behavior ; Microstructure and mechanical properties ; Metallurgy and Material Forming
Authors: Didarul Islam
Source: DigitalCommons@UTEP (The University of Texas at El Paso) (article, 2018-01-01)
Field: Materials Science — Polymers and Plastics

Predictive Modeling On The Piezoelectric Properties Of 3D Printed Functional Nanocomposites Using The Data Analytics Approach This paper presents research done on prediction modeling using a data analytics approach to determine various factors affecting the piezoelectric properties of the 3D printed pressure sensors. Previously, the material extrusion 3D printing technique was used to fabricate pressure sensors composed of multiwall carbon nanotubes (CNT), barium titanate (BT), and polyvinylidene fluoride (PVDF) using simple fabrication and low-cost methods. This sensor produced a voltage output of 725 mV (0.13 pC/N) which is enough for pressure sensing applications. However, a holistic study to determine impacts of all factors was not carried out in the previous model. In this study, the design of experiments (DOE) approach was conducted on two set of variables of the fabrication process: material percentages and printing parameters. The weight percentage (wt.%) of BT and CNT in the PVDF matrix and the temperature of the printing bed and the extrusion nozzle were examined. Acquired data were analyzed through regression analysis and neural network (NN) to find the most significant factors as well as relevant interactions between the factors. It was found that (wt.%) of BT is the most significant factor to increase the piezoelectric properties among all parameters. Additionally, CNT nanoparticles introduced to the nanocomposite system as the stress reinforcing agent has substantially enhanced the piezoelectric properties by interacting with BT. It was observed that an increase in heating bed temperature has a negative impact on the piezoelectric properties. The extrusion nozzle temperatures did not have any individual impact. However, an increase in the extrusion temperature has a negative interaction with the BT for output response. This study also provides a prediction model to estimate sensor capabilities based on the combination of materials and printing parameters. Among the regression and the NN approaches, NN provides a model with higher prediction accuracy. Analytics ; Data analysis ; Computer science ; Data science ; Data mining Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials ; Additive Manufacturing and 3D Printing Technologies