jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Martin Ševčík | Jan Škvařil | Elena Tomas Aparicio
Source: DiVA (Mälardalen University College) (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Applications of hyperspectral imaging and machine learning methods for real-time classification of waste stream components Near-infrared (NIR) hyperspectral imaging (HSI) was applied together with machine learning methods to enable classification of typical municipal solid waste (MSW) components such as paper, biomass, food residues, plastics, textile and incombustibles. Classification models were developed using partial least square discriminant analysis (PLS-DA), support vector machine (SVM), and radial-basis neural network (RBNN). The overall accuracy of SVM model calculated from classification sensitivity was 85% in prediction pixel by pixel for external sample set. The model outperformed other models in identifying incombustible material but it had higher computational time requirements. The accuracy of RBNN model reached 85% in prediction while being approx. 10 times faster. Minimum computational time was required by PLS-DA model reaching lower accuracy of 81% in prediction. The result indicate that developed models can be successfully used for real-time MSW component classification. NIR hyperspectral imaging coupled with machine learning methods has a large potential to be used for on-line material identification at waste sorting facilities or for pre-sorting at waste-to-energy powerplants. Hyperspectral imaging ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Romit Maulik | Rajeev S. Assary | Prasanna Balaprakash
Source: arXiv (Cornell University) (preprint, 2019-09-18)
Field: Materials Science — Materials Chemistry

Site-specific graph neural network for predicting protonation energy of\n oxygenate molecules Bio-oil molecule assessment is essential for the sustainable development of\nchemicals and transportation fuels. These oxygenated molecules have adequate\ncarbon, hydrogen, and oxygen atoms that can be used for developing new\nvalue-added molecules (chemicals or transportation fuels). One motivation for\nour study stems from the fact that a liquid phase upgrading using mineral acid\nis a cost-effective chemical transformation. In this chemical upgrading\nprocess, adding a proton (positively charged atomic hydrogen) to an oxygen atom\nis a central step. The protonation energies of oxygen atoms in a molecule\ndetermine the thermodynamic feasibility of the reaction and likely chemical\nreaction pathway. A quantum chemical model based on coupled cluster theory is\nused to compute accurate thermochemical properties such as the protonation\nenergies of oxygen atoms and the feasibility of protonation-based chemical\ntransformations. However, this method is too computationally expensive to\nexplore a large space of chemical transformations. We develop a graph neural\nnetwork approach for predicting protonation energies of oxygen atoms of\nhundreds of bioxygenate molecules to predict the feasibility of aqueous acidic\nreactions. Our approach relies on an iterative local nonlinear embedding that\ngradually leads to global influence of distant atoms and a output layer that\npredicts the protonation energy. Our approach is geared to site-specific\npredictions for individual oxygen atoms of a molecule in comparison with\ncommonly used graph convolutional networks that focus on a singular molecular\nproperty prediction. We demonstrate that our approach is effective in learning\nthe location and magnitudes of protonation energies of oxygenated molecules.\n Protonation ; Molecule ; Chemistry ; Chemical physics ; Hydrogen ; Computational chemistry ; Chemical space ; Chemical reaction ; Oxygen ; Graph ; Computer science ; Organic chemistry ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: X. L. Lei | Fred Hohman | Duen Horng Chau | Andrew J. Medford
Source: arXiv (Cornell University) (preprint, 2019-08-20)
Field: Materials Science — Materials Chemistry

ElectroLens: Understanding Atomistic Simulations Through Spatially-resolved Visualization of High-dimensional Features In recent years, machine learning (ML) has gained significant popularity in the field of chemical informatics and electronic structure theory. These techniques often require researchers to engineer abstract "features" that encode chemical concepts into a mathematical form compatible with the input to machine-learning models. However, there is no existing tool to connect these abstract features back to the actual chemical system, making it difficult to diagnose failures and to build intuition about the meaning of the features. We present ElectroLens, a new visualization tool for high-dimensional spatially-resolved features to tackle this problem. The tool visualizes high-dimensional data sets for atomistic and electron environment features by a series of linked 3D views and 2D plots. The tool is able to connect different derived features and their corresponding regions in 3D via interactive selection. It is built to be scalable, and integrate with existing infrastructure. Computer science ; Visualization ; Intuition ; ENCODE ; Scalability ; Popularity ; Data science ; Artificial intelligence ; Human–computer interaction ; Machine learning ; Chemistry ; Cognitive science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Richard Livings | William G. Bachman | Eric Biedermann
Source: Review of Progress in Quantitative Nondestructive Evaluation (article, 2019-12-04)
Field: Chemistry — Analytical Chemistry

Characterizing polymer O-rings nondestructively using resonant ultrasound spectroscopy Polymer O-rings are an essential part of many designs, including mission and safety critical systems. Currently, there are no accurate destructive tests for measuring the polymer properties of O-rings (e.g. durometer) let alone nondestructive methods. As such, it is difficult to identify substandard, nonconforming, or counterfeit O-rings. This work combines resonant ultrasound spectroscopy (RUS) with machine learning and predictive analytics to sort O-rings based on material and durometer (multinomial classification) and to accurately estimate the mass and durometer with an ultrasonic examination that takes less than 10 seconds. Results from a population including eight materials and six durometers are presented and discussed. sort ; Shore durometer ; Polymer ; Population ; Analytics ; Materials science ; Work (physics) ; Spectroscopy ; Computer science ; Acoustics ; Mechanical engineering ; Engineering ; Physics ; Composite material ; Data mining ; Information retrieval Spectroscopy and Chemometric Analyses ; Ultrasonics and Acoustic Wave Propagation ; Water Quality Monitoring and Analysis
Authors: Lucas de Paula Santos Petri | Emanuel Antonio Moutinho | Rondinele Pinheiro Silva | Renato Massoni Capelini | Rogério Salustiano | Guilherme Martinez Figueiredo Ferraz | Estácio Tavares Wanderley Neto
Source:  (article, 2019-11-01)
Field: Materials Science — Materials Chemistry

Partial Discharge Spectrogram Data Augmentation based on Generative Adversarial Networks High voltage insulators are critical elements in power distribution and transmission systems, since their failure may represent losses in power quality and system reliability, leading to faults which bring problems to both consumer and electric utilities. Insulators in operation are subjected to all kinds of pollution which may cause the formation of dry bands when in presence of moisture, which leads to the formation of partial discharges which, in turn, causes degradation and aging of the surface material. Partial discharges origin can be identified by the pattern of their pulses. Machine learning algorithms are one of the most useful and efficient solutions for pattern recognition and classification tasks. In this paper, a methodology is proposed for data augmentation of the training set for an artificial neural network by means of generative adversarial networks. Partial discharge ; Generative grammar ; Reliability (semiconductor) ; Computer science ; Spectrogram ; Artificial neural network ; Adversarial system ; Set (abstract data type) ; Artificial intelligence ; Generative adversarial network ; Reliability engineering ; Power (physics) ; Voltage ; Pattern recognition (psychology) ; Machine learning ; Deep learning ; Engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Image and Signal Denoising Methods
Authors: Joseph Isaac Wong
Source: eScholarship (California Digital Library) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

First-Principles Studies of Surface Energies of Magnetic Full-Heuslers and Machine Learning of Hybrid Perovskites Materials design is a cornerstone of every device. Historically, the materials selection process was characterized by a time consuming, expensive, Edisonian approach. In recent years however, rapid advancements in computational power and materials simulation software has spawned the field of computational materials science. Computational materials science opens a new avenue to materials discovery called high-throughput materials design. This approach allows for rapid prototyping of materials in a large, complex chemical space. In this work, the scope of highthroughput materials design approach is used in the analysis of several topics: magnetic full-heuslers, hybrid perovskites, and grain boundary structures. Using high-throughput density functional theory (DFT), we study the surface energy of 68 magnetic full heuslers to guide the synthesis of magnetic tunnel junctions for applications in memory storage devices. We employ a high-throughput machine learning approach to explore the chemical space of single and double perovskite materials for applications in stable, high-performance solar cells. We also look deeper into hybrid perovskite materials in a literature review of two-dimensional hybrid perovskites, which demonstrate greater stability and tunable band gaps with simple fabrication routes. In addition, their strong binding energies lead to strong light emitting properties, with potential applications in light emitting diode devices. We also examine the configurational entropy of yttria-stabilized zirconia grain boundaries and provide example usage and applications of AIMSGB, an open-source python library for grain boundary structure generation. Grain boundary ; Materials science ; Nanotechnology ; Material Design ; Density functional theory ; Fabrication ; Computer science ; Engineering physics ; Computational science ; Physics Machine Learning in Materials Science ; 2D Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Filippo Cellini | Francesco Lavini | Claire Berger | Walt de Heer | Elisa Riedo
Source: 2D Materials (preprint, 2019-04-23)
Field: Materials Science — Materials Chemistry

Layer dependence of graphene-diamene phase transition in epitaxial and exfoliated few-layer graphene using machine learning Abstract The study of the nanomechanics of graphene—and other 2D materials—has led to the discovery of exciting new properties in 2D crystals, such as their remarkable in-plane stiffness and out of plane flexibility, as well as their unique frictional and wear properties at the nanoscale. Recently, nanomechanics of graphene has generated renovated interest for new findings on the pressure-induced chemical transformation of a few-layer thick epitaxial graphene into a new ultra-hard carbon phase, named diamene. In this work, by means of a machine learning technique, we provide a fast and efficient tool for identification of graphene domains (areas with a defined number of layers) in epitaxial and exfoliated films, by combining data from atomic force microscopy (AFM) topography and friction force microscopy (FFM). Through the analysis of the number of graphene layers and detailed Å-indentation experiments, we demonstrate that the formation of ultra-stiff diamene is exclusively found in 1-layer plus buffer layer epitaxial graphene on silicon carbide (SiC) and that an ultra-stiff phase is not observed in neither thicker epitaxial graphene (2-layer or more) nor exfoliated graphene films of any thickness on silicon oxide (SiO 2 ). Graphene ; Materials science ; Nanomechanics ; Nanotechnology ; Epitaxy ; Layer (electronics) ; Graphene nanoribbons ; Nanoindentation ; Silicon carbide ; Nanoscopic scale ; Phase (matter) ; Microscale chemistry ; Oxide ; Composite material ; Optoelectronics ; Atomic force microscopy ; Chemistry Graphene research and applications ; Force Microscopy Techniques and Applications ; Carbon Nanotubes in Composites
Authors: Jun Zhang | Yao-Kun Lei | Yi Yang
Source: ChemRxiv (preprint, 2019-08-19)
Field: Materials Science — Materials Chemistry

Reinforcement Learning for Multi-Scale Molecular Modeling Molecular simulations are widely applied in the study of chemical and bio-physical systems of interest. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either sticking to the atom level and performing enhanced sampling, or trading details for speed by leveraging coarse-grained models. Although both strategies are promising, either of them, if adopted individually, exhibits severe limitations. In this paper we propose a machine-learning approach to ally these two worlds. In our approach, simulations on different scales are executed simultaneously and benefit mutually from their cross-talks: Accurate coarse-grained (CG) models can be inferred from the fine-grained (FG) simulations; In turn, FG simulations can be boosted by the guidance of CG models. Our method grounds on unsupervised and reinforcement learning, defined by a variational and adaptive training objective, and allows end-to-end and online training of parametric models. Through multiple experiments, we show that our method is efficient and flexible, and performs well on challenging chemical and bio-molecular systems. Computer science ; Reinforcement learning ; Artificial intelligence ; Machine learning ; Parametric statistics ; Scale (ratio) ; Mathematics ; Physics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Software Engineering Research
Authors: Steven W. Cranford
Source: Matter (article, 2019-08-01)
Field: Materials Science — Materials Chemistry

Building Matter Piece by Piece Subatomic particle ; Variety (cybernetics) ; Theme (computing) ; Nanotechnology ; Computer science ; Theoretical physics ; Chemistry ; Physics ; Materials science ; Elementary particle ; Particle physics ; Artificial intelligence MXene and MAX Phase Materials ; Machine Learning in Materials Science ; Nanoporous metals and alloys
Authors: Sarah Alamolhoda | Azfar Hassan | Gerardo Vitale | Nashaat N. Nassar | Pedro Pereira Almao | Mohammad Reza Zakerinasab
Source: Applied Catalysis B: Environmental (article, 2019-11-05)
Field: Materials Science — Materials Chemistry

O-exchange evidenced in Ce-Ni-MFI catalysts during water gas shift reaction: Use of isotopic water (50% H218O - 50% H216O) Water-gas shift reaction ; Catalysis ; Chemistry ; Reaction mechanism ; Inorganic chemistry ; Selectivity ; Oxygen ; Reaction rate ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Jan Hermann | Zeno Schätzle | Frank Noé
Source: arXiv (Cornell University) (article, 2019-09-16)
Field: Materials Science — Materials Chemistry

Deep neural network solution of the electronic Schrödinger equation [New and updated results were published in Nature Chemistry, doi:10.1038/s41557-020-0544-y.] The electronic Schrödinger equation describes fundamental properties of molecules and materials, but can only be solved analytically for the hydrogen atom. The numerically exact full configuration-interaction method is exponentially expensive in the number of electrons. Quantum Monte Carlo is a possible way out: it scales well to large molecules, can be parallelized, and its accuracy has, as yet, only been limited by the flexibility of the used wave function ansatz. Here we propose PauliNet, a deep-learning wave function ansatz that achieves nearly exact solutions of the electronic Schrödinger equation. PauliNet has a multireference Hartree-Fock solution built in as a baseline, incorporates the physics of valid wave functions, and is trained using variational quantum Monte Carlo (VMC). PauliNet outperforms comparable state-of-the-art VMC ansatzes for atoms, diatomic molecules and a strongly-correlated hydrogen chain by a margin and is yet computationally efficient. We anticipate that thanks to the favourable scaling with system size, this method may become a new leading method for highly accurate electronic-strucutre calculations on medium-sized molecular systems. Wave function ; Ansatz ; Cyclobutadiene ; Schrödinger equation ; Diatomic molecule ; Quantum Monte Carlo ; Electronic structure ; Variational Monte Carlo ; Slater determinant ; Quantum mechanics ; Quantum ; Physics ; Electron ; Statistical physics ; Monte Carlo method ; Molecule ; Mathematics ; Atomic orbital Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Martin Ševčík
Source: KTH Publication Database DiVA (KTH Royal Institute of Technology) (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

NEAR-INFRARED SPECTROSCOPY FOR REFUSE DERIVED FUEL : Classification of waste material components using hyperspectral imaging and feasibility study of inorganic chlorine content quantification This degree project focused on examining new possible application of near-infrared (NIR) spectroscopy for quantitative and qualitative characterization of refuse derived fuel (RDF). Particularly, two possible applications were examined as part of the project. Firstly, use of NIR hyperspectral imaging for classification of common materials present in RDF. The classification was studied on artificial mixtures of materials commonly present in municipal solid waste and RDF. Data from hyperspectral camera was used as an input for machine learning models to train them, validate them, and test them. Three classification machine learning models were used in the project; partial least-square discriminant analysis (PLS-DA), support vector machine (SVM), and radial basis neural network (RBNN). Best results for classifying the materials into 11 distinct classes were reached for SVM (accuracy 94%), even though its high computational cost makes it not very suitable for real-time deployment. Second best result was reached for RBNN (91%) and the lowest accuracy was recorded for PLS-DA model (88%). On the other hand, the PLS-DA model was the fastest, being 10 times faster than the RBNN and 100 times faster than the SVM. NIR spectroscopy was concluded as a suitable method for identification of most common materials in RDF mix, except for incombustible materials like glass, metals, or ceramics. The second part of the project uncovered a potential in using NIR spectroscopy for identification of inorganic chlorine content in RDF. Experiments were performed on samples of textile impregnated with a water solution of kitchen salt representing NaCl as inorganic chlorine source. Results showed that contents of 0.2-1 wt.% of salt can be identified in absorbance spectra of the samples. Limitation appeared to be water content of the examined samples, as with too large amount of water in the sample, the influence of salt on NIR absorbance spectrum of water was too small to be recognized. Hyperspectral imaging ; Chlorine ; Spectroscopy ; Environmental science ; Infrared ; Imaging spectroscopy ; Near-infrared spectroscopy ; Remote sensing ; Mineralogy ; Materials science ; Waste management ; Environmental chemistry ; Chemistry ; Geology ; Optics ; Engineering ; Metallurgy Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Justin S. Smith | R.I. Zubatyuk | Benjamin Nebgen | Nicholas Lubbers | Kipton Barros | Adrián E. Roitberg | Olexandr Isayev | Sergei Tretiak
Source: ChemRxiv (preprint, 2019-10-29)
Field: Materials Science — Materials Chemistry

The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules &lt;p&gt;Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models. In chemistry, ML has been used to develop models for predicting molecular properties, for example quantum mechanics (QM) calculated potential energy surfaces and atomic charge models. The ANI-1x and ANI-1ccx ML-based eneral-purpose potentials for organic molecules were developed through active learning; an automated data diversification process. Here, we describe the ANI-1x and ANI-1ccx data sets. To demonstrate data set diversity, we visualize them with a dimensionality reduction scheme, and contrast against existing data sets. The ANI-1x data set contains multiple QM properties from 5M density functional theory calculations, while the ANI-1ccx data set contains 500k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM properties from density functional theory and coupled cluster are provided: energies, atomic forces, multipole moments, atomic charges, and more. We provide this data to the community to aid research and development of ML models for chemistry.&lt;/p&gt; Extrapolation ; Density functional theory ; Coupled cluster ; Multipole expansion ; Data set ; Chemistry ; Computer science ; Quantum chemistry ; Molecule ; Statistical physics ; Physics ; Computational chemistry ; Quantum mechanics ; Mathematics ; Artificial intelligence ; Statistics ; Supramolecular chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: X. L. Lei | Fred Hohman | Duen Horng Chau | Andrew J. Medford
Source:  (article, 2019-10-01)
Field: Materials Science — Materials Chemistry

ElectroLens: Understanding Atomistic Simulations through Spatially-Resolved Visualization of High-Dimensional Features In recent years, machine learning (ML) has gained significant popularity in the field of chemical informatics and electronic structure theory. These techniques often require researchers to engineer abstract "features" that encode chemical concepts into a mathematical form compatible with the input to machine-learning models. However, there is no existing tool to connect these abstract features back to the actual chemical system, making it difficult to diagnose failures and to build intuition about the meaning of the features. We present ElectroLens, a new visualization tool for high-dimensional spatially-resolved features to tackle this problem. The tool visualizes high-dimensional data sets for atomistic and electron environment features by a series of linked 3D views and 2D plots. The tool is able to connect different derived features and their corresponding regions in 3D via interactive selection. It is built to be scalable, and integrate with existing infrastructure. Computer science ; Visualization ; Intuition ; Scalability ; ENCODE ; Data visualization ; Artificial intelligence ; Data science ; Machine learning ; Human–computer interaction ; Cognitive science Machine Learning in Materials Science ; Data Visualization and Analytics ; Complex Network Analysis Techniques