jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ivan S. Novikov | Blazej Grabowski | Fritz Körmann | Alexander V. Shapeev
Source: arXiv (Cornell University) (preprint, 2020-12-23)
Field: Materials Science — Materials Chemistry

Magnetic Moment Tensor Potentials for collinear spin-polarized materials reproduce different magnetic states of bcc Fe We present the magnetic Moment Tensor Potentials (mMTPs), a class of machine-learning interatomic potentials, accurately reproducing both vibrational and magnetic degrees of freedom as provided, e.g., from first-principles calculations. The accuracy is achieved by a two-step minimization scheme that coarse-grains the atomic and the spin space. The performance of the mMTPs is demonstrated for the prototype magnetic system bcc iron, with applications to phonon calculations for different magnetic states, and molecular dynamics simulations with fluctuating magnetic moments. Magnetic moment ; Condensed matter physics ; Tensor (intrinsic definition) ; Electron magnetic dipole moment ; Physics ; Spin (aerodynamics) ; Neutron magnetic moment ; Moment (physics) ; Space (punctuation) ; Nuclear magnetic moment ; Degrees of freedom (physics and chemistry) ; Magnetic field ; Classical mechanics ; Magnetic dipole ; Quantum mechanics ; Mathematics ; Computer science Machine Learning in Materials Science ; Magnetic properties of thin films ; Magnetic Properties and Applications
Authors: Kamal Choudhary | Kevin F. Garrity | Hartman, Steven T. | Ghanshyam Pilania | Francesca Tavazza
Source: arXiv (Cornell University) (preprint, 2020-04-06)
Field: Materials Science — Materials Chemistry

Efficient Computational Design of 2D van der Waals Heterostructures: Band-Alignment, Lattice-Mismatch, Web-app Generation and Machine-learning We develop a computational database, web-apps, and machine-learning (ML) models to accelerate the design and discovery of two-dimensional (2D)-heterostructures. Using density functional theory (DFT) based lattice-parameters and electronic band-energies for 674 non-metallic exfoliable 2D-materials, we generate 226,779 possible heterostructures. We classify these heterostructures into type-I, II and III systems according to Andersons rule, which is based on the relative band-alignments of the non-interacting monolayers. We find that type-II is the most common and the type-III the least common heterostructure type. We subsequently analyze the chemical trends for each heterostructure type in terms of the periodic table of constituent elements. The band alignment data can be also used for identifying photocatalysts and high-work function 2D-metals for contacts. We validate our results by comparing them to experimental data as well as hybrid-functional predictions. Additionally, we carry out DFT calculations of a few selected systems (MoS2/WSe2, MoS2/h-BN, MoSe2/CrI3), to compare the band-alignment description with the predictions from Andersons rule. We develop web-apps to enable users to virtually create combinations of 2D materials and predict their properties. Additionally, we use ML tools to predict band-alignment information for 2D materials. The web-apps, tools and associated data will be distributed through JARVIS-Heterostructure website (https://jarvis.nist.gov/jarvish/). Our analysis, results and the developed web-apps can be applied to the screening and design applications, such as finding novel photocatalysts, photodetectors, and high-work function (WF) 2D-metal contacts. Heterojunction ; Computer science ; Density functional theory ; Electronic band ; Materials science ; Band gap ; Optoelectronics ; Physics ; Quantum mechanics 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Mahmoud Elzouka | Chun Yang | Adrian Albert | Sean Lubner | Ravi Prasher
Source: arXiv (Cornell University) (preprint, 2020-02-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interpretable inverse design of particle spectral emissivity using\n machine learning We examine the optical properties of a system of nano and micro particles of\nvarying size, shape, and material (including metals and dielectrics, and\nsub-wavelength and super-wavelength regimes). Training data is generated by\nnumerically solving Maxwel Equations. We then use a combination of decision\ntree and random forest models to solve both the forward problem (particle\ndesign in, optical properties out) and inverse problem (desired optical\nproperties in, range of particle designs out). We show that on even\ncomparatively sparse datasets these machine learning models solve both the\nforward and inverse problems with excellent accuracy and 4 to 8 orders of\nmagnitude faster than traditional methods. A single trained model is capable of\nhandling the full diversity of our dataset, producing a variety of different\ncandidate particle designs to solve an inverse problem. The interpretability of\nour models confirms that dielectric particles emit and absorb electromagnetic\nradiation volumetrically, while metallic particles interaction with light is\ndominated by surface modes. This work demonstrates the possibility for\napproachable and interpretable machine learning models to be used for rapid\nforward and inverse design of devices that span a broad and diverse parameter\nspace.\n Interpretability ; Inverse ; Range (aeronautics) ; Computer science ; Inverse problem ; Emissivity ; Tree (set theory) ; Dielectric ; Wavelength ; Particle (ecology) ; Algorithm ; Machine learning ; Mathematical optimization ; Mathematics ; Materials science ; Optics ; Physics ; Mathematical analysis ; Optoelectronics ; Geometry Gold and Silver Nanoparticles Synthesis and Applications ; Thermal Radiation and Cooling Technologies ; Plasmonic and Surface Plasmon Research
Authors: Michael McCarthy | John F. Stanton | James H. Thorpe | P. Bryan Changala | Jessie Porterfield | Brandon Carroll | Kin Long Kelvin Lee
Source: Proceedings of the 2020 International Symposium on Molecular Spectroscopy (article, 2020-06-24)
Field: Chemistry — Spectroscopy

EXHAUSTIVE PRODUCT ANALYSIS OF THREE BENZENE DISCHARGES BY MICROWAVE SPECTROSCOPY Using chirped and\ncavity microwave spectroscopies, automated double resonance, new high-speed\nfitting and deep learning algorithms, and large databases of computed\nstructures, the discharge products of benzene alone, or in combination\nwith molecular oxygen or nitrogen, have been exhaustively characterized\nbetween 6.5 and 26 GHz. In total, more than 3300 spectral features\nwere observed; 89% of these, accounting for 97% of the total intensity,\nhave now been assigned to 152 distinct chemical species and 60 of\ntheir variants (i.e., isotopic species and vibrationally excited states).\nRoughly 50 of the products are entirely new or poorly characterized\nat high resolution, including many heavier by mass than the precursor\nbenzene. These findings provide direct evidence for a rich architecture\nof two- and three-dimensional carbon and indicate that benzene growth,\nparticularly the formation of ring–chain molecules, occurs\nfacilely under our experimental conditions. The present analysis also\nillustrates the utility of microwave spectroscopy as a precision tool\nfor complex mixture analysis, irrespective of whether the rotational\nspectrum of a product species is known &lt;i&gt;a priori&lt;/i&gt; or\nnot. From this large quantity of data, for example, it is possible\nto determine with confidence the relative abundances of different\nproduct masses, but more importantly the relative abundances of different\nisomers with the same mass. The complementary nature of this type\nof analysis to traditional mass spectrometry is discussed. Microwave ; Spectroscopy ; Benzene ; Materials science ; Product (mathematics) ; Analytical Chemistry (journal) ; Environmental science ; Computer science ; Physics ; Chemistry ; Environmental chemistry ; Telecommunications ; Mathematics ; Organic chemistry ; Astronomy Molecular Spectroscopy and Structure ; Various Chemistry Research Topics
Authors: Ali Raza | Faaiq Waqar | Árni Sturluson | Cory M. Simon | Xiaoli Z. Fern
Source: arXiv (Cornell University) (preprint, 2020-12-02)
Field: Chemistry — Inorganic Chemistry

Towards explainable message passing networks for predicting carbon dioxide adsorption in metal-organic frameworks Metal-organic framework (MOFs) are nanoporous materials that could be used to capture carbon dioxide from the exhaust gas of fossil fuel power plants to mitigate climate change. In this work, we design and train a message passing neural network (MPNN) to predict simulated CO$_2$ adsorption in MOFs. Towards providing insights into what substructures of the MOFs are important for the prediction, we introduce a soft attention mechanism into the readout function that quantifies the contributions of the node representations towards the graph representations. We investigate different mechanisms for sparse attention to ensure only the most relevant substructures are identified. Metal-organic framework ; Nanoporous ; Fossil fuel ; Computer science ; Adsorption ; Carbon dioxide ; Artificial neural network ; Node (physics) ; Message passing ; Environmental science ; Distributed computing ; Materials science ; Artificial intelligence ; Nanotechnology ; Chemistry ; Engineering ; Waste management Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon Dioxide Capture Technologies
Authors: Roger L. Chang | Julian A. Stanley | Matthew C. Robinson | Joel W. Sher | Zhanwen Li | Yujia A. Chan | Ashton Omdahl | Ruddy Wattiez | Adam Godzik | Sabine Matallana‐Surget
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-03-09)
Field: Materials Science — Materials Chemistry

Unraveling Oxidative Stress Resistance: Molecular Properties Govern Proteome Vulnerability Abstract: Oxidative stress alters cell viability, from microorganism irradiation sensitivity to human aging and neurodegeneration. Deleterious effects of protein carbonylation by reactive oxygen species (ROS) make understanding molecular properties determining ROS-susceptibility essential. The radiation-resistant bacterium Deinococcus radiodurans accumulates less carbonylation than sensitive organisms, making it a key model for deciphering properties governing oxidative stress resistance. We integrated shotgun redox proteomics, structural systems biology, and machine learning to resolve properties determining protein damage by γ-irradiation in Escherichia coli and D. radiodurans at multiple scales. Local accessibility, charge, and lysine enrichment accurately predict ROS-susceptibility. Lysine, methionine, and cysteine usage also contribute to ROS-resistance of the D. radiodurans proteome. Our model predicts proteome maintenance machinery and proteins protecting against ROS are more resistant in D. radiodurans . Our findings substantiate that protein-intrinsic protection impacts oxidative stress resistance, identifying causal molecular properties. One Sentence Summary Proteins differ in intrinsic susceptibility to oxidation, a mode of evolutionary adaptation for stress tolerance in bacteria. Deinococcus radiodurans ; Proteome ; Oxidative stress ; Protein Carbonylation ; Reactive oxygen species ; Biology ; Proteomics ; Oxidative phosphorylation ; Escherichia coli ; Bacteria ; Biochemistry ; Cell biology ; Computational biology ; Chemistry ; Genetics ; Oxidative damage ; Gene Enzyme Structure and Function ; Protein Structure and Dynamics ; Redox biology and oxidative stress
Authors: Ang Guo | Zhiyu Chen | Fang Li | Wenbo Li | Qian Luo
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-07-17)
Field: Chemistry — Spectroscopy

Towards More Reliable Unsupervised Tissue Segmentation Via Integrating Mass Spectrometry Imaging and Hematoxylin-Erosin Stained Histopathological Image Abstract Mass Spectrometry Imaging (MSI) provides a useful tool to divide a tissue section into sub-regions with similar molecular profiles, namely tissue segmentation. However, owing to the lack of ground truth, there is no reliable evaluation approach to assess the validity of unsupervised segmentation outcomes of MSI. We propose a novel solution grounded on a presumption that a segmentation is reliable if it can be reproduced using distinct bio-information extracted from independent sources. Specifically, besides molecular information from MSI data, we also obtain morphological information over a tissue section from its Hematoxylin-Erosin (H&amp;E) stained histopathological image. MSI has high molecular specificity but low spatial resolving power, the H&amp;E image has no molecular specificity but it can capture microscopic details of the tissue with a spatial resolution two magnitudes higher than MSI. The whole H&amp;E image is split into an array of small patches, which correspond to the spatial pixels of MSI. A spectrum of informative morphological features is computed iteratively for each patch and spatial segmentation can be generated by clustering the patches based on their morphological similarities. Adjusted Mutual Information (AMI) score measures the degree of agreement between MSI-based and H&amp;E image-based segmentation outcomes, which is defined by us as an objective and quantitative evaluation metric of segmentation validity. We investigated various candidate morphological features: a combination of Deep Convolution Neural Network (DCNN) features and handcrafted Threshold Adjacency Statistics (TAS) features finally stood out. The most appropriate number of tissue segments was also determined according to AMI score. Moreover, we introduced Co-Clustering algorithm to MSI data to simultaneously group m/z variables and spatial pixels, so potential biomarkers associated to each sub-region were discovered without the need of further analysis. Eventually, by integrating the segmentation outcomes based on MSI and H&amp;E image data, the confidence level of the segment assignment was displayed for each pixel, which offered a much more informative and compelling way to present the segmentation results. Segmentation ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Ground truth ; Mass spectrometry imaging ; Metric (unit) ; Image segmentation ; Cluster analysis ; Pixel ; Scale-space segmentation ; Spatial analysis ; Digital pathology ; Mathematics ; Physics ; Mass spectrometry ; Statistics Mass Spectrometry Techniques and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Metabolomics and Mass Spectrometry Studies
Authors: Emma Lejeune | Bill Zhao
Source: arXiv (Cornell University) (preprint, 2020-10-28)
Field: Materials Science — Materials Chemistry

Exploring the potential of transfer learning for metamodels of heterogeneous material deformation From the nano-scale to the macro-scale, biological tissue is spatially heterogeneous. Even when tissue behavior is well understood, the exact subject specific spatial distribution of material properties is often unknown. And, when developing computational models of biological tissue, it is usually prohibitively computationally expensive to simulate every plausible spatial distribution of material properties for each problem of interest. Therefore, one of the major challenges in developing accurate computational models of biological tissue is capturing the potential effects of this spatial heterogeneity. Recently, machine learning based metamodels have gained popularity as a computationally tractable way to overcome this problem because they can make predictions based on a limited number of direct simulation runs. These metamodels are promising, but they often still require a high number of direct simulations to achieve an acceptable performance. Here we show that transfer learning, a strategy where knowledge gained while solving one problem is transferred to solving a different but related problem, can help overcome this limitation. Critically, transfer learning can be used to leverage both low-fidelity simulation data and simulation data that is the outcome of solving a different but related mechanical problem. In this paper, we extend Mechanical MNIST, our open source benchmark dataset of heterogeneous material undergoing large deformation, to include a selection of low-fidelity simulation results that require 2-4 orders of magnitude less CPU time to run. Then, we show that transferring the knowledge stored in metamodels trained on these low-fidelity simulation results can vastly improve the performance of metamodels used to predict the results of high-fidelity simulations. Computer science ; Leverage (statistics) ; Fidelity ; Transfer of learning ; MNIST database ; Benchmark (surveying) ; High fidelity ; Computation ; Machine learning ; Artificial intelligence ; Algorithm ; Deep learning ; Engineering Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Elasticity and Material Modeling
Authors: Xiufeng Yang | Tanuj Kr Aasawat | Kazuki Yoshizoe
Source: arXiv (Cornell University) (preprint, 2020-06-18)
Field: Materials Science — Materials Chemistry

Practical Large-Scale Distributed Parallel Monte-Carlo Tree Search Applied to Molecular Design. It is common practice to use large computational resources to train neural networks, as is known from many examples, such as reinforcement learning applications. However, while massively parallel computing is often used for training models, it is rarely used for searching solutions for combinatorial optimization problems. In this paper, we propose to apply a hash function based distributed parallel Monte-Carlo Tree Search (MCTS) to a real-world problem of molecular design. By running our massively parallel MCTS combined with a simple RNN on 1024 CPU cores for 10 minutes, we achieved a score on a molecular design problem that significantly outperforms existing work. Whereas existing studies on massively scalable parallel MCTS only compare the number of rollouts, we prove the practicality of the algorithm by comparing the quality of the solutions obtained in practice. This method is generic and is expected to speed up other applications of MCTS. Computer science ; Massively parallel ; Scalability ; Monte Carlo tree search ; Reinforcement learning ; Monte Carlo method ; Parallel computing ; Tree (set theory) ; Hash function ; Scale (ratio) ; Distributed computing ; Theoretical computer science ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Machine Learning and Algorithms ; Computational Drug Discovery Methods
Source: Journal of Xidian University (article, 2020-04-30)
Field: Chemistry — Analytical Chemistry

An Investigation of Wine Quality Testing using Machine Learning Techniques Quality is the most determining factor for any product.Optimal care and best measures are to be taken in assessing the quality of any product.This work deals with determining the quality of wine using intelligence-based learning techniques.In order to estimate the quality of wine, several experiments are performed on wine datasets.The main purpose of our work is to study and discover an efficient machine learning (ML) model that could determine the quality of wine given some Physico-chemical features.This study establishes that selecting important features to evaluate rather than all of them can lead to improved forecasts.According to the results, this approach may provide people who are not wine experts a greater opportunity to choose a fine wine. Quality (philosophy) ; Wine ; Computer science ; Machine learning ; Artificial intelligence ; Chemistry ; Food science Spectroscopy and Chemometric Analyses
Authors: Benjamin Peyton | Connor Briggs | Ruhee D’Cunha | Johannes T. Margraf | T. Daniel Crawford
Source: ChemRxiv (preprint, 2020-04-02)
Field: Materials Science — Materials Chemistry

Machine-Learning Coupled Cluster Properties through a Density Tensor Representation The introduction of machine-learning (ML) algorithms to quantum mechanics enables rapid evaluation of otherwise intractable expressions at the cost of prior training on appropriate benchmarks. Many computational bottlenecks in the evaluation of accurate electronic structure theory could potentially benefit from the application of such models, from reducing the complexity of the underlying wave function parameter space to circumventing the complications of solving the electronic Schrödinger equation entirely. Applications of ML to electronic structure have thus far been focused on learning molecular properties (mainly the energy) from geometric representations. While this line of study has been quite successful, highly accurate models typically require a “big data” approach with thousands of train- ing data points. Herein, we propose a general, systematically improvable scheme for wave function-based ML of arbitrary molecular properties, inspired by the underlying equations that govern the canonical approach to computing the properties. To this end, we combine the established ML machinery of the t-amplitude tensor representation with a new reduced density matrix representation. The resulting model provides quantitative accuracy in both the electronic energy and dipoles of small molecules using only a few dozen training points per system. Representation (politics) ; Computer science ; Tensor (intrinsic definition) ; Wave function ; Function (biology) ; Coupled cluster ; Artificial neural network ; Matrix representation ; Artificial intelligence ; Function representation ; Matrix (chemical analysis) ; Theoretical computer science ; Machine learning ; Mathematics ; Physics ; Quantum mechanics ; Object (grammar) ; Geometry Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Advanced Chemical Physics Studies
Authors: Sameh Shaban
Source: Arab Journal of Nuclear Sciences and Applications (article, 2020-09-28)
Field: Materials Science — Surfaces, Coatings and Films

Developing a New Model for Description of Micro Size Particles in SEM Images for Safeguards Purposes Scanning Electron microscope (SEM) is a fast screening tool for identification of uranium and thorium particles. In the field of micro-particle analysis for safeguards purposes, estimation of uranium size is necessary information especially for scanning of swipes or environmental samples at nuclear facilities. SEM can be used to visualize micro sized particles of any nuclear material. It gives secondary electron images (SEI) at different magnifications power. In this work, the model JEOL 6510 LV instrument that has capabilities to image samples in a range of magnification from X5 to X300,000 was used. The process of imaging was done at optimum conditions; SS=50, WD=10 and appropriate pressure. The system determines the size of any particle in SEI using internal image. Many trials were performed to accomplish a correlation between micro size and pixels to estimate the size of targeted particles. Here, a mathematical model was developed to describe the relation between micro size and pixels depending on an assumption that is called "numbers to zeros" assumption. The mathematical description was manipulated in three different ways; linearly, exponentially and by using artificial neural network (ANN). The model in its three forms was validated using large numbers of SEIs and comparisons with commercial softwares. The fitting correlation coefficient for linear and exponential manipulation was found to be 0.99997 and 1 respectively. While, the performance of the trained data in ANN was 6x10-10. Computer science ; Materials science Electron and X-Ray Spectroscopy Techniques ; Non-Destructive Testing Techniques ; Nuclear Physics and Applications