jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jianing Lu | Cheng Wang | Yingkai Zhang
Source: Journal of Chemical Theory and Computation (article, 2019-05-29)
Field: Materials Science — Materials Chemistry

Predicting Molecular Energy Using Force-Field Optimized Geometries and Atomic Vector Representations Learned from an Improved Deep Tensor Neural Network The use of neural networks to predict molecular properties calculated from high level quantum mechanical calculations has made significant advances in recent years, but most models need input geometries from DFT optimizations which limit their applicability in practice. In this work, we explored how machine learning can be used to predict molecular atomization energies and conformation stability using optimized geometries from Merck Molecular Force Field (MMFF). On the basis of the recently introduced deep tensor neural network (DTNN) approach, we first improved its training efficiency and performed an extensive search of its hyperparameters, and developed a DTNN_7ib model which has a test accuracy of 0.34 kcal/mol mean absolute error (MAE) on QM9 data set. Then using atomic vector representations in the DTNN_7ib model, we employed transfer learning (TL) strategy to train readout layers on the QM9<sub>M</sub> data set, in which QM properties are the same as in QM9 [calculated at the B3LYP/6-31G(2df,p) level] while molecular geometries are corresponding local minima optimized with MMFF94 force field. The developed TL_QM9<sub>M</sub> model can achieve an MAE of 0.79 kcal/mol using MMFF optimized geometries. Furthermore, we demonstrated that the same transfer learning strategy with the same atomic vector representation can be used to develop a machine learning model that can achieve an MAE of 0.51 kcal/mol in molecular energy prediction using MMFF geometries for an eMol9_C<sub>M</sub> conformation data set, which consists of 9959 molecules and 88 234 conformations with energies calculated at the B3LYP/6-31G* level. Our results indicate that DFT-level accuracy of molecular energy prediction can be achieved using force-field optimized geometries and atomic vector representations learned from deep tensor neural network, and integrated molecular modeling and machine learning would be a promising approach to develop more powerful computational tools for molecular conformation analysis. Hyperparameter ; Maxima and minima ; Force field (fiction) ; Artificial neural network ; Tensor (intrinsic definition) ; Computer science ; Field (mathematics) ; Stability (learning theory) ; Test set ; Energy minimization ; Representation (politics) ; Statistical physics ; Physics ; Artificial intelligence ; Computational chemistry ; Chemistry ; Machine learning ; Mathematics ; Geometry ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Stefan Gugler | Jon Paul Janet | Heather J. Kulik
Source: Molecular Systems Design & Engineering (article, 2019-07-04)
Field: Materials Science — Materials Chemistry

Enumeration of <i>de novo</i> inorganic complexes for chemical discovery and machine learning Enumerated, <italic>de novo</italic> transition metal complexes have unique spin state properties and accelerate machine learning model training. Enumeration ; Computer science ; Chemistry ; Combinatorics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Dipendra Jha | Logan Ward | Zijiang Yang | Christopher Wolverton | Ian Foster | Wei‐keng Liao | Alok Choudhary | Ankit Agrawal
Source:  (preprint, 2019-07-25)
Field: Materials Science — Materials Chemistry

IRNet Materials discovery is crucial for making scientific advances in many domains. Collections of data from experiments and first-principle computations have spurred interest in applying machine learning methods to create predictive models capable of mapping from composition and crystal structures to materials properties. Generally, these are regression problems with the input being a 1D vector composed of numerical attributes representing the material composition and/or crystal structure. While neural networks consisting of fully connected layers have been applied to such problems, their performance often suffers from the vanishing gradient problem when network depth is increased. Hence, predictive modeling for such tasks has been mainly limited to traditional machine learning techniques such as Random Forest. In this paper, we study and propose design principles for building deep regression networks composed of fully connected layers with numerical vectors as input. We introduce a novel deep regression network with individual residual learning, IRNet, that places shortcut connections after each layer so that each layer learns the residual mapping between its output and input. We use the problem of learning properties of inorganic materials from numerical attributes derived from material composition and/or crystal structure to compare IRNet's performance against that of other machine learning techniques. Using multiple datasets from the Open Quantum Materials Database (OQMD) and Materials Project for training and evaluation, we show that IRNet provides significantly better prediction performance than the state-of-the-art machine learning approaches currently used by domain scientists. We also show that IRNet's use of individual residual learning leads to better convergence during the training phase than when shortcut connections are between multi-layer stacks while maintaining the same number of parameters. Residual ; Computer science ; Artificial neural network ; Artificial intelligence ; Convergence (economics) ; Machine learning ; Support vector machine ; Deep learning ; Layer (electronics) ; Regression ; Algorithm ; Mathematics ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Mohammad Javad Azarhoosh | Rouein Halladj | Sima Askari | Abbas Aghaeinejad‐Meybodi
Source: Ultrasonics Sonochemistry (article, 2019-06-17)
Field: Chemistry — Inorganic Chemistry

Performance analysis of ultrasound-assisted synthesized nano-hierarchical SAPO-34 catalyst in the methanol-to-lights-olefins process via artificial intelligence methods Catalysis ; Methanol ; Nano- ; Materials science ; Selectivity ; Diethylamine ; Artificial neural network ; Chemical engineering ; Computer science ; Biological system ; Chemistry ; Organic chemistry ; Artificial intelligence ; Composite material Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Yuji Umeda | Hiroyuki Hayashi | Hiroki Moriwake | Isao Tanaka
Source: Japanese Journal of Applied Physics (article, 2019-07-25)
Field: Materials Science — Materials Chemistry

Prediction of dielectric constants using a combination of first principles calculations and machine learning Abstract This study reports the method of exploring new dielectric materials by combining a large set of first principles calculations and machine learning. A database of dielectric constants was constructed using the first principles calculations. Crystal structures of 3382 candidate compounds were obtained from the Materials Project database. Harmonic phonon calculations were made to select the compounds showing no imaginary phonon modes. The dielectric constants were then calculated using the density function perturbation theory resulting in 2504 compounds to be constructed in the database. Machine learning methods were adopted to correct the calculated dielectric constants for the systematic errors found between the calculated and the experimental dielectric constants. A random forest model with 68 feature variables successfully predicted dielectric constants within the 50% error range of the logarithmic of the dielectric constant. The predicted dielectric constants for most of the compounds were in the range 3–100. Dielectric ; Perturbation theory (quantum mechanics) ; Range (aeronautics) ; Phonon ; Logarithm ; Density functional theory ; Materials science ; Condensed matter physics ; Thermodynamics ; Statistical physics ; Chemistry ; Computational physics ; Computational chemistry ; Mathematics ; Quantum mechanics ; Physics ; Mathematical analysis ; Optoelectronics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Ferroelectric and Piezoelectric Materials
Authors: Francesca Grisoni | Daniel Merk | Lukas Friedrich | Gisbert Schneider
Source: ChemMedChem (article, 2019-04-11)
Field: Chemistry — Organic Chemistry

Design of Natural‐Product‐Inspired Multitarget Ligands by Machine Learning A virtual screening protocol based on machine learning models was used to identify mimetics of the natural product (-)-galantamine. This fully automated approach identified eight compounds with bioactivities on at least one of the macromolecular targets of (-)-galantamine, with different polypharmacological profiles. Two of the computer-generated hits possess an expanded spectrum of bioactivity on targets relevant to the treatment of Alzheimer's disease and are suitable for hit-to-lead expansion. These results advocate multitarget drug design by advanced virtual screening protocols based on chemically informed machine learning models. Virtual screening ; Galantamine ; Computer science ; Natural product ; Drug discovery ; Machine learning ; Artificial intelligence ; Product (mathematics) ; Protocol (science) ; Computational biology ; Bioinformatics ; Chemistry ; Medicine ; Disease ; Biology ; Dementia ; Donepezil ; Mathematics Chemical synthesis and alkaloids ; Computational Drug Discovery Methods ; Cholinesterase and Neurodegenerative Diseases
Authors: Ahmed Rady | Joel E. Fischer | Stuart Reeves | Brian Logan | Nicholas J. Watson
Source: Sensors (article, 2019-12-31)
Field: Chemistry — Analytical Chemistry

The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods <b>:</b> Food allergens present a significant health risk to the human population, so their presence must be monitored and controlled within food production environments. This is especially important for powdered food, which can contain nearly all known food allergens. Manufacturing is experiencing the fourth industrial revolution (Industry 4.0), which is the use of digital technologies, such as sensors, Internet of Things (IoT), artificial intelligence, and cloud computing, to improve the productivity, efficiency, and safety of manufacturing processes. This work studied the potential of small low-cost sensors and machine learning to identify different powdered foods which naturally contain allergens. The research utilised a near-infrared (NIR) sensor and measurements were performed on over 50 different powdered food materials. This work focussed on several measurement and data processing parameters, which must be determined when using these sensors. These included sensor light intensity, height between sensor and food sample, and the most suitable spectra pre-processing method. It was found that the K-nearest neighbour and linear discriminant analysis machine learning methods had the highest classification prediction accuracy for identifying samples containing allergens of all methods studied. The height between the sensor and the sample had a greater effect than the sensor light intensity and the classification models performed much better when the sensor was positioned closer to the sample with the highest light intensity. The spectra pre-processing methods, which had the largest positive impact on the classification prediction accuracy, were the standard normal variate (SNV) and multiplicative scattering correction (MSC) methods. It was found that with the optimal combination of sensor height, light intensity, and spectra pre-processing, a classification prediction accuracy of 100% could be achieved, making the technique suitable for use within production environments. Light intensity ; Food processing ; Sample (material) ; Linear discriminant analysis ; Artificial intelligence ; Intensity (physics) ; Computer science ; Population ; Process engineering ; Machine learning ; Pattern recognition (psychology) ; Environmental science ; Food science ; Engineering ; Chemistry ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Dmitry S. Bulgarevich | Susumu Tsukamoto | Tadashi Kasuya | Masahiko Demura | Makoto Watanabe
Source: Science and Technology of Advanced Materials (article, 2019-06-05)
Field: Materials Science — Materials Chemistry

Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools It is demonstrated that optical microscopy images of steel materials could be effectively categorized into classes on preset ferrite/pearlite-, ferrite/pearlite/bainite-, and bainite/martensite-type microstructures with image pre-processing and statistical analysis including the machine learning techniques. Though several popular classifiers were able to get the reasonable class-labeling accuracy, the random forest was virtually the best choice in terms of overall performance and usability. The present categorizing classifier could assist in choosing the appropriate pattern recognition method from our library for various steel microstructures, which we have recently reported. That is, the combination of the categorizing and pattern-recognizing methods provides a total solution for automatic quantification of a wide range of steel microstructures. Bainite ; Pearlite ; Ferrite (magnet) ; Microstructure ; Artificial intelligence ; Martensite ; Materials science ; Image processing ; Pattern recognition (psychology) ; Computer science ; Metallurgy ; Image (mathematics) ; Composite material ; Austenite Machine Learning in Materials Science ; Mineral Processing and Grinding ; Image Processing Techniques and Applications
Authors: Qianrui Liu | Denghui Lu | Mohan Chen
Source: Journal of Physics Condensed Matter (article, 2019-11-18)
Field: Materials Science — Materials Chemistry

Structure and dynamics of warm dense aluminum: a molecular dynamics study with density functional theory and deep potential We perform a systematic study on the structure and dynamics of warm dense aluminum (Al) at temperatures ranging from 0.5 to 5.0 eV with molecular dynamics utilizing both density functional theory (DFT) and the deep potential (DP) method. On one hand, unlike the Thomas-Fermi kinetic energy density functional (KEDF), we find that the orbital-free DFT method with the Wang-Teter non-local KEDF yields properties of warm dense Al that agree well with the Kohn-Sham DFT method, enabling accurate orbital-free DFT simulations of warm dense Al at relatively low temperatures. On the other hand, the DP method constructs a deep neural network that has a high accuracy in reproducing short- and long-ranged properties of warm dense Al when compared to the DFT methods. The DP method is orders of magnitudes faster than DFT and is well-suited for simulating large systems and long trajectories to yield accurate properties of warm dense Al. Our results suggest that the combination of DFT methods and the DP model is a powerful tool for accurately and efficiently simulating warm dense matter. Density functional theory ; Molecular dynamics ; Warm dense matter ; Orbital-free density functional theory ; Hybrid functional ; Kinetic energy ; Statistical physics ; Materials science ; Yield (engineering) ; Physics ; Computational chemistry ; Thermodynamics ; Chemistry ; Quantum mechanics ; Electron Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Semiconductor materials and devices
Authors: Wen‐Sheng Zhao | Kai Fu | Da‐Wei Wang | Meng Li | Gaofeng Wang | Wen‐Yan Yin
Source: Applied Sciences (article, 2019-05-28)
Field: Materials Science — Materials Chemistry

Mini-Review: Modeling and Performance Analysis of Nanocarbon Interconnects As the interconnect delay exceeds the gate delay, the integrated circuit (IC) technology has evolved from a transistor-centric era to an interconnect-centric era. Conventional metallic interconnects face several serious challenges in aspects of performance and reliability. To address these issues, nanocarbon materials, including carbon nanotube (CNT) and graphene, have been proposed as promising candidates for interconnect applications. Considering the rapid development of nanocarbon interconnects, this paper is dedicated to providing a mini-review on our previous work and on related research in this field. Interconnection ; Carbon nanotube ; Materials science ; Reliability (semiconductor) ; Nanotechnology ; Transistor ; Graphene ; Electronic engineering ; Computer science ; Engineering ; Electrical engineering ; Telecommunications ; Physics Carbon Nanotubes in Composites ; Graphene research and applications ; Low-power high-performance VLSI design
Authors: Anna Wójtowicz | Renata Wietecha‐Posłuszny
Source: Applied Physics A (article, 2019-04-09)
Field: Chemistry — Spectroscopy

DESI-MS analysis of human fluids and tissues for forensic applications Desorption electrospray ionization mass spectrometry (DESI-MS) is an ambient ionization technique which application in forensic chemistry appears to be very promising. The technique enables rapid in situ analyses without the need of elaborate sample preparation. Nowadays, many DESI-MS applications in forensics, medical diagnostics and food control have been already reported in the literature. The study summarizes the latest DESI-MS forensic applications in the analysis of biological fluids and tissues, i.e., fingerprints, skin, urine and blood, in which the interest has grown during the recent years. In the publication presented below, the use of blood samples in dried blood spots is also included. The analysis reported covers mainly the determination of medicines and illicit drugs content in biological fluids and tissues, where great DESI-MS potential in screening and drugs identification was recognized. Furthermore, the examples of explosives and condoms components detection are also present. Biological fluids ; Desorption electrospray ionization ; Explosive material ; Forensic toxicology ; Ambient ionization ; Mass spectrometry ; Sample preparation ; Nanotechnology ; Dried blood ; Human blood ; Chemistry ; Chromatography ; Analytical Chemistry (journal) ; Materials science ; Ionization ; Chemical ionization ; Medicine ; Physiology Mass Spectrometry Techniques and Applications ; Forensic Fingerprint Detection Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Mudassir Hasan | M. M. Rashid | Muhammad Mohsin Hossain | Mohammed K. Al Mesfer | Muhammad Arshad | Mohd Danish | Moonyong Lee | Atef El Jery | Narendra Kumar
Source: Polymer Testing (article, 2019-05-21)
Field: Materials Science — Polymers and Plastics

Fabrication of polyaniline/activated carbon composite and its testing for methyl orange removal: Optimization, equilibrium, isotherm and kinetic study Materials science ; Adsorption ; Activated carbon ; Langmuir adsorption model ; Polyaniline ; Monolayer ; Composite number ; Aqueous solution ; Saturation (graph theory) ; Chemical engineering ; Methyl orange ; Nuclear chemistry ; Polymerization ; Composite material ; Organic chemistry ; Polymer ; Chemistry ; Nanotechnology ; Catalysis Conducting polymers and applications ; Adsorption and biosorption for pollutant removal ; Electrochemical sensors and biosensors
Authors: Peter R. Spackman | Li‐Juan Yu | Craig J. Morton | Michael W. Parker | Charles S. Bond | Mark A. Spackman | Dylan Jayatilaka | Sajesh P. Thomas
Source: Angewandte Chemie International Edition (article, 2019-08-06)
Field: Chemistry — Physical and Theoretical Chemistry

Bridging Crystal Engineering and Drug Discovery by Utilizing Intermolecular Interactions and Molecular Shapes in Crystals Most structure-based drug discovery methods utilize crystal structures of receptor proteins. Crystal engineering, on the other hand, utilizes the wealth of chemical information inherent in small-molecule crystal structures in the Cambridge Structural Database (CSD). We show that the interaction surfaces and shapes of molecules in experimentally determined small-molecule crystal structures can serve as effective tools in drug discovery. Our description of the shape and interaction propensities of molecules in their crystal structures can be used to screen them for specific binding compatibility with protein targets, as demonstrated through the high-throughput profiling of around 138 000 small-molecule structures in the CSD and a series of drug-protein crystal structures. Electron-density-based intermolecular boundary surfaces in small-molecule crystal structures and in target-protein pockets are utilized to identify potential ligand molecules from the CSD based on 3D shape and intermolecular interaction matching. Bridging (networking) ; Intermolecular force ; Crystal engineering ; Drug discovery ; Materials science ; Nanotechnology ; Crystal (programming language) ; Crystallography ; Chemical physics ; Crystal structure ; Molecule ; Chemistry ; Computer science ; Organic chemistry ; Supramolecular chemistry Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Computational Drug Discovery Methods
Authors: Loick Pradel Kojom Foko | François Eya’ane Meva | Carole Else Eboumbou Moukoko | Agnes Antoinette Ntoumba | Marie I. Ngaha Njila | Philippe Belle Ebanda Kedi | Lawrence Ayong | Léopold G. Lehman
Source: Malaria Journal (review, 2019-10-03)
Field: Materials Science — Materials Chemistry

A systematic review on anti-malarial drug discovery and antiplasmodial potential of green synthesis mediated metal nanoparticles: overview, challenges and future perspectives This study highlights the high antiplasmodial potential of green-synthesized metal nanoparticles thereby underscoring the possibility to find and develop new anti-malarial drugs based on green synthesis approaches. However, the review also highlights the need for extensive in vitro and in vivo studies to confirm their safety in humans and the elucidation of the mechanism of action. Malaria ; Nanotechnology ; Artemisinin ; Drug ; Drug resistance ; Pharmacology ; Systematic review ; Traditional medicine ; In vivo ; Plasmodium falciparum ; Biotechnology ; Combinatorial chemistry ; Chemistry ; Toxicology ; Biology ; Medicine ; Biochemistry ; MEDLINE ; Materials science ; Microbiology ; Immunology Nanoparticles: synthesis and applications ; Diverse Scientific Research Studies