jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Philippe Schwaller | Benjamin Hoover | Jean‐Louis Reymond | Hendrik Strobelt | Teodoro Laino
Source: ChemRxiv (preprint, 2020-05-14)
Field: Materials Science — Materials Chemistry

Unsupervised Attention-Guided Atom-Mapping Knowing how atoms rearrange during a chemical transformation is fundamental to numerous applications aiming to accelerate organic synthesis and molecular discovery. This labelling is known as atom-mapping and is an NP-hard problem. Current solutions use a combination of graph-theoretical approaches, heuristics, and rule-based systems. Unfortunately, the existing mappings and algorithms are often prone to errors and quality issues, which limit the effectiveness of supervised approaches. Self-supervised neural networks called Transformers, on the other hand, have recently shown tremendous potential when applied to textual representations of different domain-specific data, such as chemical reactions. Here we demonstrate that attention weights learned by a Transformer, without supervision or human labelling, encode atom rearrangement information between products and reactants. We build a chemically agnostic attention-guided reaction mapper that shows a remarkable performance in terms of accuracy and speed, even for strongly imbalanced reactions. Our work suggests that unannotated collections of chemical reactions contain all the relevant information to construct coherent sets of reaction rules. This finding provides the missing link between data-driven and rule-based approaches and will stimulate machine-assisted discovery in the chemical domain.&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;Code is available at: https://github.com/rxn4chemistry/rxnmapper&lt;/div&gt; Computer science ; Heuristics ; ENCODE ; Transformer ; Theoretical computer science ; Artificial intelligence ; Machine learning ; Data mining ; Algorithm ; Chemistry Machine Learning in Materials Science ; Advanced Text Analysis Techniques ; Computational Drug Discovery Methods
Authors: Abhishek Srivastava | C Abadie | G Tcherkez | G Kolluru | X Shen | C Kevil | J Fukuto | L Ignarro | P Nagy | D Wink | M Feelisch | M Cortese-Krott | C Bianco | Y Kumagai | A Hobbs | T Litwin | A Czlonkowska | P Socha | H Chang | A Xu | Z Chen | Y Zhang | F Tian | T Li | S Kianoush | M Mood | S Mousavi | N Sisombath | F Lalilehvand | A Schell | Q Wu | M Shannon | M Townsens | X Liu | W Ding | Y Wu | C Zeng | Z Luo | H Fu | Y Hong | S Jo | J Park | J Yang | P Panahi | M Hasanzadeh | R Keihan | A Jullien | C Gateau | C Lebrun | I Kieffer | D Testemale | P Delangle | X Zhang | Q Liu | Z Wang | H Xu | F An | Q Huang | H Song | Y Wang | M Layton | P Jones | J Alldersea | R Strange | A Fryer | P Dawes | D Mattey | R Omondi | O Stephen | S Ojwach | D Jaganyi | R Naik | A Srivastava | A Asthana | T Iioka | Takahashi | Y Yoshida | Y Matsumura | S Hiraoka | H Sato | A Verma | S Yadav | R Singh | S Prasad | V Sharma | A Prajapati | N Srivastava | R Rastogi
Source: Biointerface Research in Applied Chemistry (article, 2020-12-11)
Field: Chemistry — Spectroscopy

Quantitative Estimation of D-Penicillamine in Pure and Pharmaceutical Samples Using Inhibitory Kinetic Spectrophotometric Method Sulfur is the key element in a large number of drugs and bioactive molecules. Organo-sulfur compounds inhibit the catalytic efficiency of Hg2+ by forming a stable complex with it. The Hg2+ catalyzed exchange rate of cyanide with pyrazine from [Ru(CN)6]4- will be reduced by the addition of the sulfur-containing drug, D-penicillamine (D-PCN). This inhibitory property of D-PCN can be employed for its micro-level kinetic determination. Optimum reaction condition viz. Temperature = 45.0 ± 0.1 o C, I = 0.1 M (KCl), [Hg+2] = 1.5 × 10-4 M, [pyrazine] = 7.5 × 10-4 M, pH = 4.0 ± 0.02, and [Ru(CN)64-] = 5.25 × 10-5 M were utilized to investigate the kinetic measurements at 370 nm (λmax of [Ru(CN)5 Pz]3- complex). To acknowledge the inhibition induced by D-PCN on Hg2+ catalyzed substitution of cyanide with pyrazine from [Ru(CN)6]4-, a modified mechanistic scheme has been proposed. D-PCN can be quantitatively determined up to 1.0 × 10-6 M level by the proposed analytical method. The methodology can be economically and effectively employed for the quantitative determination of D-PCN in different samples. This methodology can also be convincingly adopted for the quick determination of D-PCN in the pharmaceutical samples with good accuracy and reproducibility. The addition of common excipients in pharmaceuticals even up to 1000 times with [D-PCN] does not interfere significantly in the estimation of D-PCN. Pyrazine ; Sulfur ; Chemistry ; Cyanide ; Catalysis ; Kinetic energy ; Nuclear chemistry ; Stereochemistry ; Inorganic chemistry ; Organic chemistry Molecular Sensors and Ion Detection ; Analytical Methods in Pharmaceuticals ; Drug Transport and Resistance Mechanisms
Authors: Ailiang Kang | Muqin Tian | Chuanyang Li | Jiancheng Song | Simone Vincenzo Suraci | Wei Li | Lingyan Lin | Zhipeng Lei | Davide Fabiani
Source: High Voltage (article, 2020-02-05)
Field: Materials Science — Materials Chemistry

Development and pattern identification of end‐winding discharge under effect of relative humidity and temperature for HV motors During operation of high voltage (HV) motors, different types of discharges, e.g. surface discharge, corona discharge and bar‐to‐bar discharge can occur at the same time, increasing the difficulty of partial discharge (PD) sources determination and PDs pattern identification. In this study, the end‐winding of a 10 kV motor coil was artificially aged and the related PD was measured. The initiation and variation of different PDs under multi‐factor stresses were studied. The localised fingerprints coexisting with multiple PDs were identified and analysed. The results confirm that the end‐winding discharge process was significantly influenced by the experiment relative humidity (RH) and temperature. The bar‐to‐bar discharge was easily recognised at a lower voltage since the identification of bar‐to‐bar discharge pattern would be affected by the corona discharge pattern at a higher voltage. It is shown that it is more difficult for the corona discharge to be detected when RH exceeded 80%, while the surface discharge dominated at the higher RH. In addition, the PDs were more easily identified with the rise of the temperature. This study can provide a reference in PD identification test and be useful for the PD online monitoring of HV motors. Partial discharge ; Bar (unit) ; Relative humidity ; Corona discharge ; Voltage ; Materials science ; Electromagnetic coil ; Corona (planetary geology) ; Analytical Chemistry (journal) ; Humidity ; Electrical engineering ; Mechanics ; Chemistry ; Thermodynamics ; Meteorology ; Physics ; Engineering ; Chromatography High voltage insulation and dielectric phenomena ; Electrostatic Discharge in Electronics ; Power Transformer Diagnostics and Insulation
Authors: Ben Wealer | Christian von Hirschhausen
Source: SSRN Electronic Journal (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Nuclear Power as a System Good: Organizational Models for Production along the Value-Added Chain Production (economics) ; Value (mathematics) ; Industrial organization ; Chain (unit) ; Business ; Power (physics) ; Microeconomics ; Economics ; Mathematics ; Physics ; Statistics Graphite, nuclear technology, radiation studies ; Risk Perception and Management
Authors: Aravind Krishnamoorthy | Pankaj Rajak | Sungwook Hong | Kenichi Nomura | Subodh Tiwari | Rajiv K. Kalia | Aiichiro Nakano | Priya Vashishta
Source: Journal of Physics Conference Series (article, 2020-03-01)
Field: Materials Science — Materials Chemistry

Reactive molecular dynamics simulations and machine learning Abstract Machine learning (ML) is revolutionizing scientific and engineering disciplines owing to its ability to capture hidden patterns in large amounts of data. The recent success of ML can be attributed to increasing amount of data, simulation resources, and improving understanding of statistical inference. For these reasons computational materials science is undergoing a paradigm shift. The main reason is that trial-and-error approach to materials design is inefficient: laboratory trials require a lot of time, and the results of previous trials are not utilized in a systematic fashion. A data-driven approach, which draws upon all relevant data from experiments, and reactive and quantum molecular dynamics simulations, can address these issues. The MAGICS (Materials Genome Innovation for Computational Software) Center develops to aid the synthesis of stacked layered materials by chemical vapor deposition, exfoliation, and intercalation. The identification of different phases is a classification problem and can be solved using ML techniques. We have used feed-forward neural network with three hidden layers to identify the different phases present during computational synthesis of MoSe2. Our goal is to carry out exascale MD simulations using forces for multimillion-atom quantum dynamics simulations from neural networks and deep learning using small QMD simulations. Computer science ; Identification (biology) ; Artificial neural network ; Molecular dynamics ; Artificial intelligence ; Inference ; Machine learning ; Computational science ; Chemistry Machine Learning in Materials Science ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Scott Broderick | Krishna Rajan
Source: JOM (article, 2020-10-09)
Field: Materials Science — Materials Chemistry

Designing a Periodic Table for Alloy Design: Harnessing Machine Learning to Navigate a Multiscale Information Space Periodic table ; Table (database) ; Visualization ; Alloy ; Space (punctuation) ; Computer science ; Process (computing) ; Track (disk drive) ; Theoretical computer science ; Artificial intelligence ; Materials science ; Machine learning ; Data mining ; Physics ; Metallurgy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Materials Characterization Techniques
Authors: Anders S. Christensen | O. Anatole von Lilienfeld
Source: Machine Learning Science and Technology (preprint, 2020-09-21)
Field: Materials Science — Materials Chemistry

On the role of gradients for machine learning of molecular energies and forces The accuracy of any machine learning potential can only be as good as the data used in the fitting process. The most efficient model therefore selects the training data that will yield the highest accuracy compared to the cost of obtaining the training data. We investigate the convergence of prediction errors of quantum machine learning models for organic molecules trained on energy and force labels, two common data types in molecular simulations. When training and predicting on different geometries corresponding to the same single molecule, we find that the inclusion of atomic forces in the training data increases the accuracy of the predicted energies and forces 7-fold, compared to models trained on energy only. Surprisingly, for models trained on sets of organic molecules of varying size and composition in non-equilibrium conformations, inclusion of forces in the training does not improve the predicted energies of unseen molecules in new conformations. Predicted forces, however, also improve about 7-fold. For the systems studied, we find that force labels and energy labels contribute equally per label to the convergence of the prediction errors. Choosing to include derivatives such as atomic forces in the training set or not should thus depend on, not only on the computational cost of acquiring the force labels for training, but also on the application domain, the property of interest, and the desirable size of the machine learning model. Based on our observations we describe key considerations for the creation of datasets for potential energy surfaces of molecules which maximize the efficiency of the resulting machine learning models. Convergence (economics) ; Computer science ; Training set ; Artificial intelligence ; Machine learning ; Set (abstract data type) ; Energy (signal processing) ; Quantum ; Property (philosophy) ; Process (computing) ; Potential energy ; Statistical physics ; Mathematics ; Physics ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Cheng‐Wei Ju | Hanzhi Bai | Bo Li | Rizhang Liu
Source: ChemRxiv (preprint, 2020-08-03)
Field: Materials Science — Materials Chemistry

Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields The predictions of photophysical parameters are of crucial practical importance for the development of functional organic fluorescent materials, whereas the expense of quantum mechanical calculations and the relatively low universality of QSAR models have challenged the task. New avenues opened up by machine learning (ML), we establish a database of solvated organic fluorescent dyes and develop highly efficient ML models for the predictions of maximum emission/absorption wavelength and photoluminescence quantum yields, providing a reliable and efficient approach to high-throughput screenings. Various combinations of ML algorithms and molecular fingerprints were investigated. For emission wavelengths, TD-DFT accuracy was achieved under real-world conditions. Reliable identification of strong fluorescent materials was also demonstrated. We show that the easily obtainable consensus fingerprint inputs combined with proper ML algorithms enables efficient re-training based on additional datapoints whereby systematic improvements of our ML models can be achieved. Fluorescence ; Photoluminescence ; Wavelength ; Quantum ; Biological system ; Computer science ; Absorption (acoustics) ; Quantum efficiency ; Materials science ; Chemistry ; Optoelectronics ; Physics ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Optical Imaging and Spectroscopy Techniques ; Computational Drug Discovery Methods
Authors: Saeid Atashrouz | Amir M. Rahmani | Zahra Balzade | Bahram Nasernejad
Source: SN Applied Sciences (article, 2020-02-06)
Field: Chemistry — Organic Chemistry

Mathematical modeling of ethylene polymerization over advanced multisite catalysts: an artificial intelligence approach Artificial neural network ; Petrochemical ; Polyethylene ; Polymerization ; Metallocene ; Robustness (evolution) ; Materials science ; Computer science ; Artificial intelligence ; Organic chemistry ; Chemistry ; Polymer Organometallic Complex Synthesis and Catalysis ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: B. R. Jany | A. Janas | W. Piskorz | K. Szajna | A. Kryshtal | G. Cempura | P. Indyka | A. Kruk | A. Czyrska-Filemonowicz | F. Krok
Source: Nanoscale (article, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Towards the understanding of the gold interaction with AIII-BV semiconductors at the atomic level AIII-BV semiconductors have been considered to be a promising material for decades in overcoming the limitations of silicon semiconductor devices. One of the important aspects within the AIII-BV semiconductor technology is gold-semiconductor interactions on the nanoscale. We report on the investigations into the basic chemical interactions of Au atoms with AIII-BV semiconductor crystals by the investigation of the nanostructure formation in the process of thermally-induced Au self-assembly on various AIII-BV surfaces by means of atomically resolved High Angle Annular Dark Field (HAADF) Scanning Transmission Electron Microscopy (STEM) measurements. We have found that the formation of nanostructures is a consequence of the surface diffusion and nucleation of adatoms produced by Au induced chemical reactions on AIII-BV semiconductor surfaces. Only for InSb crystals we have found that there is efficient diffusion of Au atoms into the bulk, which we experimentally studied by Machine Learning HAADF STEM image quantification and theoretically by Density Functional Theory (DFT) calculations with the inclusion of finite temperature effects. Furthermore, the effective number of Au atoms needed to release one AIII metal atom has been estimated. The experimental finding reveals a difference in the Au interactions with the In- and Ga-based groups of AIII-BV semiconductors. Our comprehensive and systematic studies uncover the details of the Au interactions with the AIII-BV surface at the atomic level with chemical sensitivity and shed new light on the fundamental Au/AIII-BV interactions at the atomic scale. Semiconductor ; Nucleation ; Silicon ; Nanostructure ; Atom (system on chip) ; Scanning transmission electron microscopy ; Diffusion ; Density functional theory ; Semiconductor nanostructures Copper Interconnects and Reliability ; Semiconductor materials and interfaces ; Metal and Thin Film Mechanics
Authors: Manyi Yang | Luigi Bonati | Daniela Polino | Michele Parrinello
Source: arXiv (Cornell University) (preprint, 2020-11-23)
Field: Materials Science — Materials Chemistry

Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water The study of chemical reactions in aqueous media is very important for its implications in several fields of science, from biology to industrial processes. Modelling these reactions is however difficult when water directly participates in the reaction. Since it requires a fully quantum mechanical description of the system, $\textit{ab-initio}$ molecular dynamics is the ideal candidate to shed light on these processes. However, its scope is limited by a high computational cost. A popular alternative is to perform molecular dynamics simulations powered by machine learning potentials, trained on an extensive set of quantum mechanical calculations. Doing so reliably for reactive processes is difficult because it requires including very many intermediate and transition state configurations. In this study, we used an active learning procedure accelerated by enhanced sampling to harvest such structures and to build a neural-network potential to study the urea decomposition process in water. This allowed us to obtain the free energy profiles of this important reaction in a wide range of temperatures, to discover a number of novel metastable states and to improve the accuracy of the kinetic rates calculations. Furthermore, we found that the formation of the zwitterionic intermediate has the same probability of occurring via an acidic or a basic pathway, which could be the cause of the insensitivity of reaction rates to the pH solution. Metadynamics ; Metastability ; Artificial neural network ; Computer science ; Quantum ; Molecular dynamics ; Ab initio ; Biological system ; Decomposition ; Reaction rate ; Statistical physics ; Chemistry ; Computational chemistry ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Hong‐Jian Feng | Kan Wu | Zun‐Yi Deng
Source: Cell Reports Physical Science (article, 2020-10-01)
Field: Materials Science — Materials Chemistry

Predicting Inorganic Photovoltaic Materials with Efficiencies &gt;26% via Structure-Relevant Machine Learning and Density Functional Calculations Photovoltaic system ; Materials science ; Density functional theory ; Engineering physics ; Computer science ; Nanotechnology ; Chemistry ; Engineering ; Computational chemistry ; Electrical engineering Machine Learning in Materials Science
Authors: Mads Koerstz | Anders S. Christensen | Kurt V. Mikkelsen | Mogens Brøndsted Nielsen | Jan H. Jensen
Source: ChemRxiv (preprint, 2020-10-13)
Field: Materials Science — Materials Chemistry

High Throughput Virtual Screening of 230 Billion Molecular Solar Heat Battery Candidates The dihydroazulene/vinylheptafulvene (DHA/VHF) thermocouple is a promising candidate for thermal heat batteries that absorb and store solar energy as chemical energy without the need for insulation. However, in order to be viable the energy storage capacity and lifetime of the high energy form (i.e. the free energy barrier to the back reaction) of the canonical parent compound must be increased significantly to be of practical use. We use semiempirical quantum chemical methods, machine learning, and density func- tional theory to virtually screen over 230 billion substituted DHA molecules to identify promising candidates. We identify a molecule with a predicted energy density of 0.38 kJ/g, which is significantly larger than the 0.14 kJ/g computed for the parent compound. The free energy barrier to the back reaction is 11 kJ/mol higher than the parent com- pound, which should correspond to a half-life of about 10 days - 4 months. This is considerably longer than the 3-39 hours (depending on solvent) observed for the parent compound and sufficiently long for many practical applications. Our paper makes two main important contributions: 1) a novel and generally applicable methodological approach that makes screening of huge libraries for properties involving chemical reactivity with modest computational resources, and 2) a clear demonstration that the storage capacity of the DHA/VHF thermocouple cannot be increased to >0.5 kJ/g by combining simple substituents. Energy density ; Virtual screening ; Energy storage ; Reactivity (psychology) ; Molecule ; Battery (electricity) ; Thermal energy ; Solar energy ; Density functional theory ; Throughput ; Chemistry ; Materials science ; Computer science ; Engineering physics ; Thermodynamics ; Electrical engineering ; Physics ; Computational chemistry ; Organic chemistry ; Telecommunications ; Engineering Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Molecular Junctions and Nanostructures
Authors: Catherine Groschner | Christina Choi | Mary Scott
Source:  (article, 2020-01-14)
Field: Materials Science — Surfaces, Coatings and Films

Methodologies for Successful Segmentation of HRTEM Images via Neural Network. High throughput analysis of samples has been a topic increasingly discussed in both light and electron microscopy. Deep learning can help implement high throughput analysis by segmenting images in a pixel-by-pixel fashion and classifying these regions. However, to date, relatively little has been done in the realm of automated high resolution transmission electron microscopy (HRTEM) micrograph analysis. Neural networks for HRTEM have, so far, focused on identification of single atomic columns in single materials systems. For true high throughput analysis, networks will need to not only recognize atomic columns but also segment out regions of interest from background for a wide variety of materials. We therefore analyze the requirements for achieving a high performance convolutional neural network for segmentation of nanoparticle regions from amorphous carbon in HRTEM images. We also examine how to achieve generalizability of the neural network to a range of materials. We find that networks trained on micrographs of a single material system result in worse segmentation outcomes than one which is trained on a variety of materials' micrographs. Our final network is able to segment nanoparticle regions from amorphous background with 91% pixelwise accuracy. High-resolution transmission electron microscopy ; Artificial intelligence ; Segmentation ; Artificial neural network ; Computer science ; Pixel ; Convolutional neural network ; Throughput ; Pattern recognition (psychology) ; Materials science ; Amorphous solid ; Image segmentation ; Computer vision ; Nanotechnology ; Transmission electron microscopy ; Telecommunications ; Chemistry ; Crystallography Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science