jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pinghui Mo | Mengchao Shi | Wenze Yao | Jie Liu
Source: IEEE Electron Device Letters (article, 2020-02-06)
Field: Materials Science — Materials Chemistry

Transfer Learning of Potential Energy Surfaces for Efficient Atomistic Modeling of Doping and Alloy This letter proposes a transfer learning (TL) method to generate neural network (NN) database to model doping and alloy. By leveraging the valuable potential energy surface (PES) information already available in source system and similarities between source and target systems, the proposed TL successfully reduces computational cost by several orders of magnitude, while keeping ab-initio level high accuracy. We show that it is generally applicable to model <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> -type, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> -type, and alloy atomic substitutions. Doping ; Alloy ; Materials science ; Energy transfer ; Engineering physics ; Nanotechnology ; Optoelectronics ; Condensed matter physics ; Electronic engineering ; Metallurgy ; Engineering ; Physics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Fuel Cells and Related Materials
Authors: Victor Lantz | Najmeh Abiri | G. Carlsson | Mats‐Erik Pistol
Source: International Journal of Quantum Chemistry (article, 2020-12-31)
Field: Materials Science — Materials Chemistry

Deep learning for inverse problems in quantum mechanics Abstract Inverse problems are important in quantum mechanics and involve such questions as finding which potential give a certain spectrum or which arrangement of atoms give certain properties to a molecule or solid. Inverse problems are typically very hard to solve and tend to be very compute intense. We here show that neural networks can easily solve inverse problems in quantum mechanics. It is known that a neural network can compute the spectrum of a given potential, a result which we reproduce. We find that the (much harder) inverse problem of computing the correct potential that gives a prescribed spectrum is equally easy for a neural network. We extend previous work where neural networks were used to find the electronic many‐particle density given a potential by considering the inverse problem. That is, we show that neural networks can compute the potential that gives a prescribed many‐electron density. Inverse problem ; Artificial neural network ; Inverse ; Computer science ; Quantum ; Spectrum (functional analysis) ; Statistical physics ; Quantum mechanics ; Physics ; Mathematics ; Artificial intelligence ; Mathematical analysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Muhammad Ardiansyah | Kurt R. Brorsen
Source: The Journal of Physical Chemistry A (article, 2020-10-27)
Field: Materials Science — Materials Chemistry

Mixed Quantum–Classical Dynamics with Machine Learning-Based Potentials via Wigner Sampling Machine learning-based approaches for surface hopping (SH) offer the prospect of SH simulations with <i>ab initio</i> accuracy, but with a computational cost more similar to classical molecular dynamics simulations. However, such approaches in the adiabatic basis are difficult due to the need to fit a machine learning model to reproduce the nonadiabatic coupling, which rapidly changes in the vicinity of a conical intersection. Previous approaches have typically dealt with this difficulty by either computing the hopping probabilities using methods that do not require the explicit nonadiabatic coupling or by employing adaptive sampling. In this study, we introduce a new approach using a simple modification of Wigner sampling to generate appropriate training data. Test SH simulations on the two-state spin-boson Hamiltonian system show that Wigner sampling with an appropriately selected data set can reduce the size of the training data set by up to a factor of 7.5 per degree of freedom compared to previously linear sampling-based approaches. Surface hopping ; Computer science ; Conical intersection ; Sampling (signal processing) ; Hamiltonian (control theory) ; Quantum ; Adiabatic process ; Statistical physics ; Quantum dynamics ; Coupling (piping) ; Algorithm ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics ; Mathematical optimization ; Engineering Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Ali Raza | Árni Sturluson | Cory M. Simon | Xiaoli Z. Fern
Source: ChemRxiv (preprint, 2020-05-14)
Field: Chemistry — Inorganic Chemistry

Message Passing Neural Networks for Partial Charge Assignment to Metal-Organic Frameworks Virtual screenings can accelerate and reduce the cost of discovering metal-organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas adsorption/diffusion in MOFs, the adsorbate-MOF electrostatic interaction is typically modeled by placing partial point charges on the atoms of the MOF. For the virtual screening of large libraries of MOFs, it is critical to develop computationally inexpensive methods to assign atomic partial charges to MOFs that accurately reproduce the electrostatic potential in their pores. Herein, we design and train a message passing neural network (MPNN) to predict the atomic partial charges on MOFs under a charge neutral constraint. A set of ca. 2,250 MOFs labeled with high-fidelity partial charges, derived from periodic electronic structure calculations, serves as training examples. In an end-to-end manner, from charge-labeled crystal graphs representing MOFs, our MPNN machine-learns features of the local bonding environments of the atoms and learns to predict partial atomic charges from these features. Our trained MPNN assigns high-fidelity partial point charges to MOFs with orders of magnitude lower computational cost than electronic structure calculations. To enhance the accuracy of virtual screenings of large libraries of MOFs for their adsorption-based applications, we make our trained MPNN model and MPNN-charge-assigned computation-ready, experimental MOF structures publicly available.&lt;br&gt; Partial charge ; Charge (physics) ; Artificial neural network ; Fidelity ; Computer science ; Computation ; Constraint (computer-aided design) ; Chemical physics ; Set (abstract data type) ; Electrostatics ; Point (geometry) ; Partial oxidation ; Nanotechnology ; Materials science ; Chemistry ; Physics ; Algorithm ; Artificial intelligence ; Physical chemistry ; Quantum mechanics ; Mathematics ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Tanmay Sarkar | Molla Salauddin | Sudipta Hazra | Runu Chakraborty
Source: ES Food & Agroforestry (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Comparative Classification Efficiency of Self-organizing Map, Principal Component Analysis, and Hierarchical Cluster Analysis for Normal Dairy and Differently Pineapple Fortified Rasgulla Rasgulla, the popular Indian dairy-based dessert, was fortified by using pineapple to improve its nutritional and different physical parameters like color, texture and sensory attributes. Being a highly nutritious, palatable fruit with excellent flavor and visual color characteristics, pineapple fortification provides an edge over normal dairy rasgulla. Comparative analysis of nutritional, X-ray diffraction, texture and sensory characteristics for normal rasgulla sample and pineapple with different drying process (pulp (PP), convective (PH), microwave (PMW), microwave convective (PMWC), and freeze-dried (PF)) fortified rasgulla samples were done. PH was found with maximum carbohydrate (40.602.04) and minimum protein content (5.910.95). PMWC was found with the maximum total color difference (E = 48.793.33). Pineapple fortification did not alter the crystal characteristics of the rasgulla samples. Normal rasgulla showed the lowest hardness value while PMWC was the hardest one. The results obtained from the analysis were further comparatively segregated with the help of principal component analysis, hierarchical cluster analysis and self-organizing map to optimize the analysis frequency towards a more accurate result. It was found that more or less the same trends in results were obtained by all three analysis tools. It can be concluded that machine learning-based self-organizing map can be more sophistically used for classification between different parameters even in case of complex non-linear problems also. Among the entire samples microwave, convective dried pineapple rasgullawas found to be the best in terms of all the observed parameters. Principal component analysis ; Fortification ; Mathematics ; Food science ; Flavor ; Statistics ; Chemistry ; Computer science Spectroscopy and Chemometric Analyses ; Food Drying and Modeling ; Biochemical Analysis and Sensing Techniques
Authors: Hossein Rajabi Kuyakhi | Ramin Tahmasebi Boldaji | Meysam Azadian
Source: Petroleum Science and Technology (article, 2020-12-24)
Field: Chemistry — Analytical Chemistry

Light hydrocarbons solvents solubility modeling in bitumen using learning approaches In this paper, two types of machine learning, namely neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP), have been studied to model light hydrocarbons’ solubility solvent in bitumen. The 268 number of experimental data is used in this work from different articles. The input parameters are Temperature (T), pressure (P), and molecular weight (MW) of hydrocarbons. The result shows the high performance of the MLP model with a two layers to predict the experimental values. The estimated values were investigated by statistical parameters such as R2, MSE, and MARD%. According to, statistical parameters, the values of 0.99, 0.00081, and 0.68 for MLP, and 0.96, 0.0029, and 0.78 for ANFIS indicate the high performance of the MLP model. Comparison between established models and previous work indicates that the developed model can be a suitable technique for solubility modeling Solubility ; Adaptive neuro fuzzy inference system ; Multilayer perceptron ; Asphalt ; Biological system ; Solvent ; Hildebrand solubility parameter ; Statistical parameter ; Computer science ; Inference system ; Artificial neural network ; Artificial intelligence ; Materials science ; Chemistry ; Fuzzy logic ; Mathematics ; Statistics ; Fuzzy control system ; Organic chemistry Petroleum Processing and Analysis ; Asphalt Pavement Performance Evaluation ; Spectroscopy and Chemometric Analyses
Authors: Kohei Miyamoto | Hajime Mizuno | Eiji Sugiyama | Toshimasa Toyo’oka | Kenichiro Todoroki
Source: Journal of Pharmaceutical and Biomedical Analysis (article, 2020-11-20)
Field: Chemistry — Spectroscopy

Machine learning guided prediction of liquid chromatography–mass spectrometry ionization efficiency for genotoxic impurities in pharmaceutical products Chemistry ; Support vector machine ; Quantitative structure–activity relationship ; Mass spectrometry ; Molecular descriptor ; Chromatography ; Linear regression ; Mean squared error ; Feature selection ; Coefficient of determination ; Correlation coefficient ; Artificial intelligence ; Analytical Chemistry (journal) ; Machine learning ; Computer science ; Mathematics ; Statistics Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Pesticide Residue Analysis and Safety
Authors: Leila Fard Soleimani | Mohammadreza Khanmohammadi | Shima Zandbaaf | Ghasem Rezanejade Bardajee | Peyman Noor | Keyvan Ghasemi
Source: Infrared Physics & Technology (article, 2020-10-05)
Field: Chemistry — Analytical Chemistry

Determination of micropore volumes of ZSM-5 zeolite samples by diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy using back-propagation artificial neural network (BP-ANN) and non-negative matrix factorization -alternating least squares (NMF-ALS) as chemometric approaches Chemometrics ; Diffuse reflectance infrared fourier transform ; Spectroscopy ; Microporous material ; Analytical Chemistry (journal) ; Fourier transform infrared spectroscopy ; Partial least squares regression ; Diffuse reflection ; Mathematics ; Matrix (chemical analysis) ; Materials science ; Chemistry ; Optics ; Statistics ; Physics ; Chromatography Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Zeolite Catalysis and Synthesis
Authors: Moslem Ghobadi | Davood Zaarei | Reza Naderi | Najmeh Asadi | Seyed Ramin Seyedi | Mehdi Ravan Avard
Source: Progress in Organic Coatings (article, 2020-12-16)
Field: Materials Science — Materials Chemistry

Improvement the protection performance of lanolin based temporary coating using benzotriazole and cerium (III) nitrate: Combined experimental and computational analysis Corrosion ; Lanolin ; Materials science ; Coating ; Cerium nitrate ; Dielectric spectroscopy ; Benzotriazole ; Fourier transform infrared spectroscopy ; Cerium ; Chemical engineering ; Adaptive neuro fuzzy inference system ; Composite material ; Metallurgy ; Electrochemistry ; Chromatography ; Chemistry ; Electrode ; Computer science Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Metal and Thin Film Mechanics
Authors: Yu Ning | Huimin Zhang | Qiang Zhang | Xueru Zhang
Source: Vibrational Spectroscopy (article, 2020-07-15)
Field: Chemistry — Analytical Chemistry

Rapid identification and quantitative pit mud by near infrared Spectroscopy with chemometrics Partial least squares regression ; Chemometrics ; Support vector machine ; Linear discriminant analysis ; Calibration ; Pattern recognition (psychology) ; Coefficient of determination ; Least squares support vector machine ; Principal component analysis ; Artificial intelligence ; Mathematics ; Computer science ; Statistics ; Machine learning Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Geochemistry and Geologic Mapping
Authors: Abd El-Aziz | A. S. Fouda | Eslam Haleem | A Chirkunov | D Chugunov | G Red'kina | Y Kuznetsov | S Marzorati | L Verotta | P Trasatti | T Harvey | F Walsh | A Nahl | B | C Rao | P | R Keyikoglu | O Can | A Aygun | A Tek | G Zhang | L Zhou | F Li | S Xia | L Yu | S Nwanonenyi | H Obasi | E Oguzie | I Chukwujike | C Anyiam | R Hassan | S Ibrahim | S Salman | H Takagi | Z Liu | Y Tang | N Liao | P Yang | J Wysocka | S Krakowiak | J Ryl | K Ramakrishnan | S Karthikeyan | D Rajagopal | M Brodowski | M Kowalski | W Lipinska | P Niedzialkowski | C Verma | E Ebenso | I Bahadur | M Quraishi | A Mahmmod | I Kazarinov | A Khadom | H Mahood | M Benarioua | A Mihi | N Bouzeghaia | M Naoun | V Grudi | S Martinez | B Kneevi | I Bokovi | B Xu | C Chen | E Ma | Z Wei | X Li | Reena Kumari | P Kumari | D | T Mariami | B Antoko | S Soim | E Noor | A Al-Moubaraki | A Al-Ghamdi | P Arumugam | S Subbiah | K Kannusamy | K Kalirajan | L Muravnik | O Kostina | A Shavarda | I Sile | E Romane | S Reinsone | B Maurina | D Tirzite | M Dambrova | K Shalabi | Y Abdallah | A Fouda | M Norani | M.-T Ebadi
Source: Biointerface Research in Applied Chemistry (article, 2020-06-01)
Field: Materials Science — Materials Chemistry

Tussilago Farfara Extract (TFE) as Green Corrosion Inhibitor for Aluminum in Hydrochloric Acid Solution The dissolution of aluminum in 2 M HCl medium in the absence and presence of Tussilago Farfara Extract (TFE) was examined utilizing Tafel polarization (TP), electrochemical impedance spectroscopy (EIS), electrochemical frequency modulation (EFM), gasometry and mass reduction techniques. The outcomes of these procedures illustrated that the inhibiting effect of this inhibitor depends on its concentration and chemical composition. The inhibitive impact of TFE illustrated the blocking of the Al surface by adsorption of its components through the reacted atoms contained in its molecules. The adsorption model was obeyed to Langmuir isotherm. The influence of temperature on the dissolution rate in the non-existence and existence of TFE was observed. Tafel polarization indicated that TFE acts as a mixed kind inhibitor. Impedance outcomes illustrated that the dissolution of Al is monitored by charge transfer mode at all concentrations of the extract. Varied surface examinations like XPS, FTIR, and AFM were checked to affirm the presence of the defensive film on the Al surface. All outcomes measured from all procedures are in perfect conformity. Tafel equation ; Dissolution ; Dielectric spectroscopy ; Adsorption ; X-ray photoelectron spectroscopy ; Hydrochloric acid ; Polarization (electrochemistry) ; Electrochemistry ; Langmuir adsorption model ; Chemistry ; Analytical Chemistry (journal) ; Corrosion ; Corrosion inhibitor ; Inorganic chemistry ; Materials science ; Nuclear chemistry ; Chemical engineering ; Electrode ; Chromatography ; Physical chemistry ; Organic chemistry Corrosion Behavior and Inhibition
Authors: Cyrille Lavigne | Gabriel dos Passos Gomes | Robert Pollice | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2020-09-29)
Field: Materials Science — Materials Chemistry

Automatic discovery of chemical reactions using imposed activation Computational power and quantum chemical methods have improved immensely since computers were first applied to the study of reactivity, but the de novo prediction of chemical reactions has remained challenging. We show that complex reactions can be efficiently and autonomously predicted using chemical activation imposed by simple geometrical constraints. Our approach is demonstrated on realistic and challenging chemistry, such as a triple cyclization cascade involved in the total synthesis of a natural product and several oxidative addition reactions of complex drug-like molecules. Notably and in contrast with traditional hand-guided computational chemistry calculations, our method requires minimal human involvement and no prior knowledge of products or mechanisms. Imposed activation can be a transformational tool to screen for chemical reactivity and mechanisms as well as to study byproduct formation and decomposition. Reactivity (psychology) ; Computer science ; Chemical reaction ; Drug discovery ; Chemistry ; Biochemical engineering ; Natural product ; Quantum chemical ; Decomposition ; Combinatorial chemistry ; Molecule ; Biological system ; Computational chemistry ; Organic chemistry ; Biochemistry ; Engineering Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Computational Drug Discovery Methods
Authors: G. J. A. Sevink | Adam Liwo | Pietro Asinari | Dónal MacKernan | Giuseppe Milano | Ignacio Pagonabarraga
Source: The Journal of Chemical Physics (article, 2020-09-08)
Field: Materials Science — Materials Chemistry

Unfolding the prospects of computational (bio)materials modeling In this perspective communication, we briefly sketch the current state of computational (bio)material research and discuss possible solutions for the four challenges that have been increasingly identified within this community: (i) the desire to develop a unified framework for testing the consistency of implementation and physical accuracy for newly developed methodologies, (ii) the selection of a standard format that can deal with the diversity of simulation data and at the same time simplifies data storage, data exchange, and data reproduction, (iii) how to deal with the generation, storage, and analysis of massive data, and (iv) the benefits of efficient "core" engines. Expressed viewpoints are the result of discussions between computational stakeholders during a Lorentz center workshop with the prosaic title Workshop on Multi-scale Modeling and are aimed at (i) improving validation, reporting and reproducibility of computational results, (ii) improving data migration between simulation packages and with analysis tools, (iii) popularizing the use of coarse-grained and multi-scale computational tools among non-experts and opening up these modern computational developments to an extended user community. Viewpoints ; Computer science ; Sketch ; Scale (ratio) ; Data science ; Consistency (knowledge bases) ; Computational model ; Simulation ; Artificial intelligence ; Algorithm Machine Learning in Materials Science
Authors: Saedeh Zamani | Mehdi Shafeie‐Ardestani | Ahmad Bitarafan‐Rajabi | Ali Khalaj | Omid Sabzevari
Source: IET Nanobiotechnology (article, 2020-09-01)
Field: Materials Science — Polymers and Plastics

Synthesis, radiolabelling, and biological assessment of folic acid‐conjugated G‐3 <sup>99m</sup> Tc‐dendrimer as the breast cancer molecular imaging agent Hence, in this study, the authors aimed to develop a dendrimer-based imaging agent comprised of poly(ethylene glycol) (PEG)-citrate, technetium-99 m (<sup>99m</sup>Tc), and folic acid. The dendrimer-G3 was synthesised and conjugated with folic acid, which confirmed by Fourier transform infrared, proton nuclear magnetic resonance, dynamic light scattering, and transition electron microscopy. 2,3-bis-(2-methoxy-4-nitro-5-sulfophenyl)-2H-Tetrazolium-5-Carboxanilide cytotoxicity assay kit was used to measure the cellular toxicity of dendrimer. Imaging and biodistribution studies were conducted on the mice bearing tumour. The results showed that the fabricated dendrimer-G3 has a size of 90 ± 3 nm, which was increased to 100 ± 4 nm following the conjugation with folic acid. The radiostablity investigation showed that the fabricated dendrimers were stable in the human serum at various times. Toxicity assessment confirmed no cellular toxicity against HEK-293 cells at 0.25, 0.5, 1, 2, 4, and 8 mg/μl concentrations. The in vivo studies demonstrated that the synthesised dendrimers were able to provide a bright SPECT image applicable for tumour detection. In conclusion, the authors' study documented the positive aspects of PEG-citrate dendrimer conjugated with folic acid as the SPECT contrast agent for breast cancer detection. Dendrimer ; Biodistribution ; Chemistry ; Imaging agent ; Nuclear chemistry ; Ethylene glycol ; Conjugated system ; Toxicity ; Cytotoxicity ; In vivo ; Radiochemistry ; Biochemistry ; In vitro ; Organic chemistry ; Biology Dendrimers and Hyperbranched Polymers ; Radiopharmaceutical Chemistry and Applications ; Nanoparticle-Based Drug Delivery