jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Saket Mishra | Piyush Tagade
Source: arXiv (Cornell University) (preprint, 2018-12-22)
Field: Materials Science — Materials Chemistry

Learning formation energy of inorganic compounds using matrix variate deep Gaussian process Future advancement of engineering applications is dependent on design of novel materials with desired properties. Enormous size of known chemical space necessitates use of automated high throughput screening to search the desired material. The high throughput screening uses quantum chemistry calculations to predict material properties, however, computational complexity of these calculations often imposes prohibitively high cost on the search for desired material. This critical bottleneck is resolved by using deep machine learning to emulate the quantum computations. However, the deep learning algorithms require a large training dataset to ensure an acceptable generalization, which is often unavailable a-priory. In this paper, we propose a deep Gaussian process based approach to develop an emulator for quantum calculations. We further propose a novel molecular descriptor that enables implementation of the proposed approach. As demonstrated in this paper, the proposed approach can be implemented using a small dataset. We demonstrate efficacy of our approach for prediction of formation energy of inorganic molecules. Random variate ; Process (computing) ; Matrix (chemical analysis) ; Gaussian ; Energy (signal processing) ; Gaussian process ; Statistical physics ; Computer science ; Mathematics ; Chemistry ; Computational chemistry ; Physics ; Statistics ; Chromatography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and ELM
Authors: Andreas Singraber
Source:  (article, 2018-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Designing and training neural network potentials for molecular dynamics simulations Die vorliegende Doktorarbeit befasst sich mit der Entwicklung, dem Training und der Anwendung von Neuronalen-Netzwerk-Potentialen (NNPs), welche in Molekulardynamiksimulationen zum Einsatz kommen. Im Gegensatz zu weitverbreiteten empirischen Potentialen sind NNPs nicht durch eine fixe funktionale Form eingeschrankt, sondern nutzen die Fahigkeit kunstlicher neuronaler Netzwerke, Ab-Initio-Potentialhyperflachen genau zu reproduzieren. Korrekt trainierte NNPs ermoglichen die Berechnung potentieller Energien und daraus analytisch abgeleiteter Krafte mit geringen Abweichungen zur Ab-Initio-Referenzmethode, jedoch fur einen Bruchteil des Rechenaufwandes. Somit konnen wesentlich langere Simulationen und grosere Systeme behandelt werden, was detaillierte Untersuchungen der Eigenschaften der zugrundeliegenden Referenzmethode erlaubt. Mit Hilfe von NNPs fur gangige Ab-Initio-Modelle beleuchtet die vorliegende Arbeit den Einfluss von van-der-Waals-Wechselwirkungen auf die Eigenschaften von Wasser. Des Weiteren tragt eine Untersuchung von Wasser bei negativen Drucken zur aktuellen Debatte uber den Ursprung der anomalen Eigenschaften bei. Die Dissertation berichtet auserdem von der erstmaligen Entwicklung eines NNPs fur Kupfersulfid, eine Verbindung, die eine komplexe Niedertemperatur-Kristallstruktur und superionisches Verhalten bei hoheren Temperaturen aufweist. Um die benotigten Computersimulationen fur beide System durchfuhren zu konnen, wurde im Zuge des Doktorats ein Programmpaket entwickelt, welches die Erzeugung und Verwendung von NNPs erleichtert. Fusend auf einer Programmbibliothek bietet die Software eine Implementierung der NNP-Methode in einem weitverbreiteten Molekulardynamik-Paket, sowie einen parallelisierten Trainingsalgorithmus basierend auf der Multi-Stream-Variante des Kalman-Filters. Die erzielten Ergebnisse demonstrieren, dass NNPs wertvolle Erkenntnisse uber die Eigenschaften von Ab-Initio-Methoden ermoglichen und sie auch in Zukunft eine wichtige Rolle bei der Suche nach realistischen Modellen auf atomarer Ebene in den Materialwissenschaften, der Physik der kondensierten Materie und der Chemie spielen werden. Chemistry ; Ab initio ; Physics thermodynamics and calorimetric analyses ; Neural Networks and Applications ; Machine Learning in Materials Science
Authors: Pieter Plehiers | Connor W. Coley | Huayi Gao | W.H. Green | Guy Marin | Christian V. Stevens | Kevin M. Van Geem
Source: Ghent University Academic Bibliography (Ghent University) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Machine learning for efficient and continuous retrosynthetic product design : a novel reaction identifier Identifier ; Retrosynthetic analysis ; Computer science ; Product (mathematics) ; Artificial intelligence ; Programming language ; Mathematics ; Chemistry Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Analytical Chemistry and Chromatography
Authors: Pieter Plehiers | Connor W. Coley | Hanyu Gao | William H. Green | Guy Marin | Christian V. Stevens | Kevin M. Van Geem
Source: Ghent University Academic Bibliography (Ghent University) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Machine learning and continuous flow : detection and correction of flow-incompatible reaction conditions Flow (mathematics) ; Computer science ; Artificial intelligence ; Mechanics ; Physics Machine Learning in Materials Science
Authors: T. E. Sharp | Spencer Thomas | Ekin D. Cubuk | Samuel S. Schoenholz | David J. Srolovitz | Andrea J. Liu
Source: Bulletin of the American Physical Society (article, 2018-03-09)
Field: Materials Science — Materials Chemistry

Machine Learning to Analyze the Atomic Energy Landscape in Poly-Crystalline Materials Energy landscape ; Materials science ; Energy (signal processing) ; Computer science ; Engineering physics ; Physics ; Quantum mechanics ; Thermodynamics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Larmuseau, Michiel | Cools-Ceuppens, Maarten | Sluydts, Michael | Verstraelen, Toon | Cottenier, Stefaan
Source: Ghent University Academic Bibliography (Ghent University) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Machine learning and materials science : ab initio screening to microstructure analysis Ab initio ; Microstructure ; Ab initio quantum chemistry methods ; Work (physics) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Authors: Benjamin Nebgen | Nick Lubbers | Andrey Y. Lokhov | Kipton Barros | Sergei Tretiak
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2018-03-08)
Field: Materials Science — Materials Chemistry

Machine Learning Optimal Effective Hamiltonians for Excited State Molecular Systems Left: ANI-1 NN potential of a dihedral rotation compared to reference DFT and other semi-empirical methods.Right: Energy predictions on a large molecular dataset made by HIP-NN. Excited state ; State (computer science) ; Computer science ; Physics ; Statistical physics ; Quantum mechanics ; Algorithm Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Carlos Outeiral Rubiera
Source:  (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Developments in machine learning and bonding indicatorsfor the Quantum Chemical Topology Force Field The Quantum Chemical Topology Force Field (FFLUX) takes a novel approach to molecular simulation. In its modern formulation, classic fixed-form valence potentials are replaced by non-parametric regression models that mimic the total quantum mechanical energy. Likewise the electrostatics are represented by high-order multipolar interactions that converge to the quantum electrostatic potential. Altogether, this approach aims to overcome the obstacles of classical force fields in accurately modelling the energetics of complex chemical phenomena, such as ligand-receptor interactions. This line of research is still in its infancy, and many problems remain unsolved. We discuss some of these hurdles, including the neglect of polarisation, the problems of non-parametric regression when extrapolating beyond the training set, and the curse of dimensionality, which imposes an exponential growth of the models as the system size increases. We then introduce two major contributions to the Quantum Chemical Topology Force Field. Our first contribution sprouts from the need to find an inexpensive exchange indicator, and evolved as a qualitative study of the delocalization index, a magnitude from Quantum Chemical Topology that accounts for the amount of electrons delocalized within two atoms. We showed that this index can account for the covalent interaction between both atoms, and that represents a useful indicator of bond order. The findings for well-known bonding patterns were found to be extensible to uncommon compounds, allowing to explain some previously disputed situations. Our second contribution is the introduction of a novel formulation of kriging that implicitly incorporates permutational symmetry, allowing to reduce computational cost and avoid some nonphysical descriptions. The form of this predictor is reminiscent of the monomial symmetrization employed in the theory of invariant polynomials, and together with a heuristic algorithm allows to greatly reduce the number of training points. We implement this procedure in the DL_POLY 4 molecular dynamics package, and show its capabilities in optimisations and simulations of some small molecules. Topology (electrical circuits) ; Quantum ; Force field (fiction) ; Statistical physics ; Quantum machine learning ; Field (mathematics) ; Computer science ; Physics ; Quantum mechanics ; Mathematics ; Artificial intelligence ; Quantum algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Jack Yang | Sandip De | Joshua E Campbell | Sean Li | Michele Ceriotti | Graeme M. Day
Source: ChemRxiv (preprint, 2018-04-17)
Field: Materials Science — Materials Chemistry

Large–Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction Predictive computational methods have the potential to significantly accelerate the discovery of new materials with targeted properties by guiding the choice of candidate materials for synthesis. Recently, a planar pyrrole azaphenacene molecule (pyrido[2,3-b]pyrido[3`,2`:4,5]-pyrrolo[3,2-g]indole, 1) was synthesized and shown to have promising properties for charge transport, which relate to stacking of molecules in its crystal structure. Building on our methods for evaluating small molecule organic semiconductors using crystal structure prediction, we have screened a set of 27 structural isomers of 1 to assess charge mobility in their predicted crystal structures. Machine--learning techniques are used to identify structural classes across the landscapes of all molecules and we find that, despite differences in the arrangement of hydrogen bond functionality, the predicted crystal structures of the molecules studied here can be classified into a small number of packing types. We analyze the predicted property landscapes of the series of molecules and discuss several metrics that can be used to rank the molecules as promising semiconductors. The results suggest several isomers with superior predicted electron mobilities to 1 and suggest two molecules in particular that represent attractive synthetic targets. Crystal structure prediction ; Molecule ; Stacking ; Organic semiconductor ; Crystal (programming language) ; Chemical physics ; Crystal structure ; Materials science ; Hydrogen bond ; Small molecule ; Pyrrole ; Computational chemistry ; Crystal engineering ; Charge (physics) ; Semiconductor ; Chemistry ; Nanotechnology ; Crystallography ; Computer science ; Physics ; Organic chemistry Machine Learning in Materials Science ; Conducting polymers and applications ; Organic Electronics and Photovoltaics
Authors: Nicholas Walker | Ka-Ming Tam | Mark Jarrell
Source: Bulletin of the American Physical Society (article, 2018-03-06)
Field: Materials Science — Materials Chemistry

Identification of phase transtitions in molecular systems using unsupervised machine learning methods Identification (biology) ; Computer science ; Artificial intelligence ; Phase (matter) ; Unsupervised learning ; Machine learning ; Pattern recognition (psychology) ; Physics ; Biology Machine Learning in Materials Science
Authors: Yehia Amar | Alexei A. Lapkin | Artur M. Schweidtmann | Paul Deutsch
Source: RWTH Publications (RWTH Aachen) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Rational solvent selection in asymmetric hydrogenation using molecular descriptors and machine learning Selection (genetic algorithm) ; Machine learning ; Computer science ; Artificial intelligence ; Chemistry ; Solvent ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Vinusha Reddy Kura
Source: eScholarship (California Digital Library) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Development and Analysis of Plasmonic Nanomaterials for Biosensors Optical bio-nanosensors are powerful alternatives to conventional analytical techniques because they enable direct, real-time and label-free detection of many biological and chemical substances. Surface enhanced Raman scattering (SERS) spectroscopy is a label-free optical biosensing technique that provides target specific information, enables real-time monitoring of continuous flows and multiplex detection of small volatile molecules. For example, the detection of small molecule metabolites has proved to be crucial in study of microbial activity and early detection of diseases. In the first part of this research, I prepare SERS substrates designed to improve the analytical capabilities of SERS. First, synthesis of uniform Au nanopsheres via a kinetically controlled seeded growth method is performed. Second, chemically assembled Au nanoparticle clusters on self-organized templates are characterized as SERS substrates. The enhancement of SERS signals by controlling nanometer gap spacings between plasmonic nanospheres using the length of the chemical cross linker enables detection of various metabolites down to 1 pg.mL-1. In the second part of this work, I discuss the advantages of using deep learning algorithms for quantification of metabolites. Feed-forward neural networks and 1 D- Convolutional neural networks are trained on indole and 2-aminoacetophenone SERS spectral data to get robust quantification of concentrations ranging from 1 pg.mL-1 to 1 mg.mL-1. The models are further assessed for future applications of SERS sensors for multianalyte detection in biological fluids. Biosensor ; Nanosensor ; Plasmon ; Nanotechnology ; Raman scattering ; Nanomaterials ; Materials science ; Nanoparticle ; Raman spectroscopy ; Chemistry ; Optoelectronics Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Masato Sumita | Xiufeng Yang | Shinsuke Ishihara | Ryo Tamura | Koji Tsuda
Source: ChemRxiv (preprint, 2018-04-05)
Field: Materials Science — Materials Chemistry

Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies This work presents a proof-of-concept study in artificial-intelligence-assisted (AI-assisted) chemistry where a machine-learning-based molecule generator is coupled with density functional theory (DFT) calculations, synthesis, and measurement. Although deep-learning-based molecule generators have shown promise, it is unclear to what extent they can be useful in real-world materials development. To assess the reliability of AI-assisted chemistry, we prepared a platform using the ChemTS molecule generator and a DFT simulator, and attempted to generate novel photo-functional molecules whose lowest excited states lie at desired energetic levels. A ten-day run on 12 cores discovered 86potential photo-functional molecules around target lowest excitation levels, designated as 200, 300, 400, 500, and 600 nm. Among the molecules discovered, six were synthesized and five were confirmed to reproduce DFT predictions in ultraviolet visible absorption measurements. This result shows the potential of AI-assisted chemistry to discover ready-to-synthesize novel molecules with modest computational resources.The Android robot is reproduced or modified from work created and shared by Google and used according to terms described in the Creative Commons 3.0 Attribution License. Density functional theory ; Molecule ; Excited state ; Time-dependent density functional theory ; Excitation ; Computer science ; Ultraviolet ; Artificial intelligence ; Nanotechnology ; Chemistry ; Computational chemistry ; Materials science ; Physics ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Various Chemistry Research Topics
Authors: Taro Sugiura | Keiji Okumura
Source: Tetsu-to-Hagane (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Reaction Behavior of Thermal Decomposition of Limestone in the Presence of Carbon –Reactivity Evaluation by Deep Learning– This paper discussed reaction on thermal decomposition of limestone and Boudouard reaction. Reactivity (psychology) ; Thermal decomposition ; Decomposition ; Carbon fibers ; Chemistry ; Chemical engineering ; Materials science ; Organic chemistry ; Engineering ; Composite material ; Composite number ; Medicine Petroleum Processing and Analysis ; Iron and Steelmaking Processes ; Thermal and Kinetic Analysis
Authors: Jens Magnusson
Source:  (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Training Neural Network Potentials for Atomistic Calculations on Carbon Materials : An initial study on diamond structures Machine Learning (ML) and especially implementations of Neural Networks (NNs) is growing in popularity across numerous application areas. One of which is the use of a trained NN as an interatomic ... Artificial neural network ; Training (meteorology) ; Diamond ; Popularity ; Artificial intelligence ; Computer science ; Carbon fibers ; Interatomic potential ; Training set ; Diamond-like carbon ; Machine learning ; Implementation ; Materials science ; Nanotechnology ; Chemistry ; Psychology ; Algorithm ; Metallurgy ; Computational chemistry ; Molecular dynamics ; Physics ; Composite number Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Catalysis and Oxidation Reactions
Authors: Jack Yang | Sandip De | Joshua E Campbell | Sean Li | Michele Ceriotti | Graeme M. Day
Source: ChemRxiv (preprint, 2018-01-01)
Field: Materials Science — Materials Chemistry

Large–Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction Predictive computational methods have the potential to significantly accelerate the discovery of new materials with targeted properties by guiding the choice of candidate materials for synthesis. Recently, a planar pyrrole azaphenacene molecule (pyrido[2,3-b]pyrido[3`,2`:4,5]-pyrrolo[3,2-g]indole, &lt;b&gt;1&lt;/b&gt;) was synthesized and shown to have promising properties for charge transport, which relate to stacking of molecules in its crystal structure. Building on our methods for evaluating small molecule organic semiconductors using crystal structure prediction, we have screened a set of 27 structural isomers of &lt;b&gt;1&lt;/b&gt; to assess charge mobility in their predicted crystal structures. Machine--learning techniques are used to identify structural classes across the landscapes of all molecules and we find that, despite differences in the arrangement of hydrogen bond functionality, the predicted crystal structures of the molecules studied here can be classified into a small number of packing types. We analyze the predicted property landscapes of the series of molecules and discuss several metrics that can be used to rank the molecules as promising semiconductors. The results suggest several isomers with superior predicted electron mobilities to &lt;b&gt;1&lt;/b&gt; and suggest two molecules in particular that represent attractive synthetic targets. Molecule ; Crystal structure prediction ; Stacking ; Crystal (programming language) ; Organic semiconductor ; Crystal structure ; Pyrrole ; Hydrogen bond ; Materials science ; Indole test ; Small molecule ; Crystallography ; Chemistry ; Computational chemistry ; Chemical physics ; Nanotechnology ; Stereochemistry ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Conducting polymers and applications ; Organic Electronics and Photovoltaics
Authors: Angelo Ziletti | Matthias Scheffler | Luca M. Ghiringhelli
Source: Bulletin of the American Physical Society (article, 2018-03-09)
Field: Materials Science — Materials Chemistry

Compact representation of crystal structures using three-dimensional diffraction patterns and deep learning Representation (politics) ; Diffraction ; Materials science ; Crystal (programming language) ; Crystallography ; Computer science ; Artificial intelligence ; Optics ; Physics ; Chemistry ; Political science ; Law X-ray Diffraction in Crystallography
Authors: Reza Ziyaee Sisakht | F. Abbasi Davani | Rouhollah Ghaderi
Source:  (article, 2018-09-01)
Field: Materials Science — Materials Chemistry

The use of artificial neural networks to distinguish naturally occurring radioactive materials from unauthorized radioactive materials using a plastic scintillation detector Scintillation ; Detector ; Scintillation counter ; Radionuclide ; Radiochemistry ; Environmental science ; Optics ; Physics ; Nuclear physics ; Chemistry Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies ; Radiation Detection and Scintillator Technologies
Authors: Jake Yeston
Source: Science (article, 2018-12-13)
Field: Materials Science — Materials Chemistry

Searching for the best conditions Organic Chemistry The vastness of the archival chemistry literature is both a blessing and a curse. The reaction that you're looking for is probably in there, provided you take enough time to search for it. Gao et al. trained a neural network model on 10 million known reactions to speed up this process. Specifically, the model was charged with predicting a catalyst, reagents, solvents, and temperature to achieve a given transformation. When tested, the model's top-10 list of suggestions produced a close match to actual conditions nearly 70% of the time, with a ±20°C error margin in temperature. ACS Cent. Sci. 4 , 1465 (2018). Computer science ; Computational biology ; Biology Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Various Chemistry Research Topics