jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Arunraj Chidambaram
Source: Infoscience (Ecole Polytechnique Fédérale de Lausanne) (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Accelerated Discovery of Metal-Organic Frameworks (MOFs) For Energy Related Applications Metal-Organic Frameworks (MOFs) are an emerging class of materials that consist of metal ions which are linked with organic ligands via coordination bonds to form extended networks. MOF structures consists predominantly of open voids which makes them amongst the most porous materials synthesized to date. The aim of this thesis is to contribute to the development of a platform for a systematic study of the influence of synthetic conditions on MOFs and thereby, assess how this can affect the surface area of MOFs. To achieve this aim, a platform that utilises high-throughput robotic synthesis guided by genetic algorithm and machine learning was devised. This led to a rational synthesis of a MOF with the highest surface area reported to date. MOFs are considered as promising adsorbents for carbon capture from flue gasses emitted from fossil fuel fired power plants. Whilst there are many MOFs that are optimised for separation of CO2 from N2, their separation capability is detrimentally affected when they are subjected to realistic flue gas conditions that contains H2O vapour. This is due to the competition between CO2 and H2O for the same adsorption sites. To address this, through close collaboration with computational scientists, we discovered a new class of materials for CO2/H2O separations. Metal-organic framework ; Energy (signal processing) ; Chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Chuyu Wei
Source: eScholarship (California Digital Library) (article, 2020-01-01)
Field: Chemistry — Spectroscopy

Tomographic Laser Absorption Imaging of Combustion Gases in the Mid-wave Infrared This dissertation describes advancements in mid-infrared laser absorption tomography for spatio-temporal measurements of thermochemistry in reacting flows relevant to combustion systems. Tunable laser absorption spectroscopy is combined with tomographic reconstruction techniques to resolve small diameter ( &amp;lt; 1 cm) non-uniform flow fields with steep spatial gradients, leveraging emerging mid-wave infrared photonics. Multiple novel measurement methods, hardware configurations, and image processing techniques were investigated. Initially, a mid-infrared laser absorption tomography sensing method was developed for quantitative measurement of CO and CO2 concentrations and temperature distributions in turbulent premixed jet flames using a translation-stage-mounted optical system. This sensing approach was used to examine effects of varying fuel structure on carbon oxidation over a range of Reynolds number regimes. It was found that spatial and temporal resolution is limited in this method due to the finite laser beam size (~ 1 mm) and the slow mechanical translation of the optical system. To address these limitations, a novel laser absorption imaging (LAI) technique, that expands a single laser beam and replaces the detector with a high-speed infrared camera, was introduced to achieve enhanced spatial and temporal resolution for thermo-chemical imaging. As a demonstration of this new technique, distributions of combustion species were imaged in both axisymmetric and non-axisymmetric flow fields using linear tomography algorithms. For non-axisymetric flows, the limited view tomography problem often results in a blurring effect and artifacts in the reconstructed flow-field. In an effort to address these issues, state-of-the-art deep learning neural networks were developed and applied to solve the limited angle inversion. Initial results suggest that deep neural networks have potential to more accurately predict flame structures with fewer projection angles than linear tomography. This work provides a foundation for a new approach to quantitative time-resolved 3D thermo-chemical imaging in high-temperature reacting flows. Optics ; Tomographic reconstruction ; Laser ; Absorption (acoustics) ; Combustion ; Image resolution ; Tomography ; Temporal resolution ; Detector ; Infrared ; Materials science ; Optical tomography ; Physics ; Chemistry Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Combustion and flame dynamics
Authors: Marc Stieffenhofer | Michael Wand | Tristan Bereau
Source: Machine Learning Science and Technology (preprint, 2020-09-09)
Field: Materials Science — Materials Chemistry

Adversarial reverse mapping of equilibrated condensed-phase molecular structures Abstract A tight and consistent link between resolutions is crucial to further expand the impact of multiscale modeling for complex materials. We herein tackle the generation of condensed molecular structures as a refinement—backmapping—of a coarse-grained (CG) structure. Traditional schemes start from a rough coarse-to-fine mapping and perform further energy minimization and molecular dynamics simulations to equilibrate the system. In this study we introduce DeepBackmap: A deep neural network based approach to directly predict equilibrated molecular structures for condensed-phase systems. We use generative adversarial networks to learn the Boltzmann distribution from training data and realize reverse mapping by using the CG structure as a conditional input. We apply our method to a challenging condensed-phase polymeric system. We observe that the model trained in a melt has remarkable transferability to the crystalline phase. The combination of data-driven and physics-based aspects of our architecture help reach temperature transferability with only limited training data. Transferability ; Computer science ; Molecular dynamics ; Phase (matter) ; Artificial neural network ; Statistical physics ; Energy minimization ; Generative grammar ; Representation (politics) ; Artificial intelligence ; Algorithm ; Physics ; Machine learning ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Phase Equilibria and Thermodynamics
Authors: Giovanni Drera | Chahan M. Kropf | L. Sangaletti
Source: Machine Learning Science and Technology (preprint, 2020-02-25)
Field: Materials Science — Surfaces, Coatings and Films

Deep neural network for x-ray photoelectron spectroscopy data analysis Abstract In this work, we characterize the performance of a deep convolutional neural network designed to detect and quantify chemical elements in experimental x-ray photoelectron spectroscopy data. Given the lack of a reliable database in literature, in order to train the neural network we computed a large (&lt;100 k) dataset of synthetic spectra, based on randomly generated materials covered with a layer of adventitious carbon. The trained net performs as well as standard methods on a test set of ≈500 well characterized experimental x-ray photoelectron spectra. Fine details about the net layout, the choice of the loss function and the quality assessment strategies are presented and discussed. Given the synthetic nature of the training set, this approach could be applied to the automatization of any photoelectron spectroscopy system, without the need of experimental reference spectra and with a low computational effort. X-ray photoelectron spectroscopy ; Artificial neural network ; Convolutional neural network ; Spectral line ; Computer science ; Set (abstract data type) ; Data set ; Training set ; Function (biology) ; Artificial intelligence ; Pattern recognition (psychology) ; Algorithm ; Biological system ; Data mining ; Physics ; Nuclear magnetic resonance Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Soki ITO | Hiroyuki Harada
Source: Transactions of the JSME (in Japanese) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Modeling and sensorless tension control of SMA actuator using GRU This paper proposes a method for sensorless tension control of shape memory alloy(SMA) actuators. Sensorless control has advantages in cost and size. Gated recurrent unit(GRU) was adopted to model complex characteristics of the SMA actuators. GRU is one of the machine learning technologies, which is evolved from the recurrent neural network. Modeling of SMA actuators are not easy due to their characteristics such as nonlinearity, hysteretic behavior, effects of temperature and stress. They are often responsible for control difficulties such as inaccuracy and instability. In this study, GRU is used to estimate the relation among the applied voltage(duty ratio), electrical-resistance, tension, displacement and room temperature. Two SMA models were created by GRU: the sensing model and the inverse model. Both models were trained with the original dataset obtained experimentally. A control framework for the sensorless tension control including the two SMA models was proposed. The sensing model which estimates the tension and displacement from the applied voltage, electrical-resistance and room temperature substituted for sensors. The inverse model which estimates the applied voltage canceled or reduced the effects of the hysteresis and nonlinearity as the feedforward controller. The results of experiments with the proposed control framework show that the actual tensions closely followed the target tensions without sensors. SMA* ; Actuator ; Control theory (sociology) ; Shape-memory alloy ; Hysteresis ; Feed forward ; Displacement (psychology) ; Voltage ; Nonlinear system ; Tension (geology) ; Controller (irrigation) ; Computer science ; Engineering ; Control engineering ; Control (management) ; Materials science ; Artificial intelligence ; Physics ; Electrical engineering Shape Memory Alloy Transformations
Authors: Tanvi P. Gujarati | Tyler Y. Takeshita | Andreas Hintennach | Eunseok Lee
Source: arXiv (Cornell University) (preprint, 2020-04-02)
Field: Materials Science — Materials Chemistry

A Heuristic Quantum-Classical Algorithm for Modeling Substitutionally\n Disordered Binary Crystalline Materials Improving the efficiency and accuracy of energy calculations has been of\nsignificant and continued interest in the area of materials informatics, a\nfield that applies machine learning techniques to computational materials data.\nHere, we present a heuristic quantum-classical algorithm to efficiently model\nand predict the energies of substitutionally disordered binary crystalline\nmaterials. Specifically, a quantum circuit that scales linearly in the number\nof lattice sites is designed and trained to predict the energies of quantum\nchemical simulations in an exponentially-scaling feature space. This circuit is\ntrained by classical supervised-learning using data obtained from\nclassically-computed quantum chemical simulations. As a part of the training\nprocess, we introduce a sub-routine that is able to detect and rectify\nanomalies in the input data. The algorithm is demonstrated on the complex\nlayer-structured of Li-cobaltate system, a widely-used Li-ion battery cathode\nmaterial component. Our results shows that the proposed quantum circuit model\npresents a suitable choice for modelling the energies obtained from such\nquantum mechanical systems. Furthermore, analysis of the anomalous data\nprovides important insights into the thermodynamic properties of the systems\nstudied.\n Quantum ; Scaling ; Heuristic ; Algorithm ; Binary number ; Statistical physics ; Computer science ; Quantum machine learning ; Lattice (music) ; Quantum algorithm ; Computational science ; Physics ; Quantum mechanics ; Mathematics ; Artificial intelligence ; Geometry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Topic Modeling
Authors: Hiroki Sugisawa | Tomonori Ida | Roman V. Krems
Source: arXiv (Cornell University) (preprint, 2020-01-20)
Field: Materials Science — Materials Chemistry

Interpolation and extrapolation in a 51-dimensional configuration space: system-agnostic construction of high-dimensional PES Machine learning is becoming an increasingly powerful tool for physics research. High-dimensional physics problems are generally modelled by neural networks (NNs). However, NNs require large data and are problem-specific. An alternative approach is probabilistic modelling based on Gaussian processes (GPs), which are system-agnostic and can be fully automated. However, GPs suffer from the numerical complexity and have been limited to low-dimensional physics problems. Here, we illustrate that it is possible to build an accurate GP model of a 51-dimensional physical system based on $5000$ inputs, using the fragmentation of the input space and entropy-based enhancement of the model complexity. We demonstrate the potency of these algorithms by constructing the global potential energy surface (PES) for a molecular system with $19$ atoms. We illustrate that GP models thus constructed have generalization power, allowing us to extrapolate the PES from low energies ($ 20,000$ cm$^{-1}$). This opens the prospect for building machine-learning models that can be used to search for new physics, such as phase transitions, in high-dimensional physics problems with unknown property landscapes by extrapolation. Extrapolation ; Gaussian process ; Phase space ; Interpolation (computer graphics) ; Computer science ; Probabilistic logic ; Algorithm ; Statistical physics ; Gaussian ; Theoretical computer science ; Artificial intelligence ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Protein Structure and Dynamics
Authors: Akash Ravi | P. Prakash | N Kailashnath
Source: arXiv (Cornell University) (preprint, 2020-04-23)
Field: Materials Science — Materials Chemistry

Prediction of Reduced Glass Transition Temperature using Machine Learning The advent of computational material sciences has paved the way for data-driven approaches for modeling and fabrication of materials. The prediction of properties like the glass-forming ability (GFA) by using the variation in alloy composition remains to be a challenging problem in the field of material sciences. It also results in significant financial concerns for the manufacturing industry. Despite the existence of various empirical guides for the prediction of GFA, a comprehensive prediction model is still highly desirable. This work focuses on studying some of the popular machine learning algorithms for the prediction of the reduced glass transition temperature (Trg) of material compositions. From the experimentation, we conclude that the ensemble model performs better for predicting Trg. This result can prove instrumental in the branch of material sciences by helping us to develop materials having remarkable properties. Glass transition ; Transition (genetics) ; Materials science ; Artificial intelligence ; Thermodynamics ; Machine learning ; Computer science ; Composite material ; Physics ; Chemistry ; Polymer Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics ; Injection Molding Process and Properties
Authors: Chao Liang | Hailin Jiang | Shaopeng Lin | Huashan Li | Biao Wang
Source: Advanced Intelligent Systems (article, 2020-11-16)
Field: Materials Science — Materials Chemistry

Intelligent Generation of Evolutionary Series in a Time‐Variant Physical System via Series Pattern Recognition Intelligent generation of time‐variant control series remains the critical challenge for acquiring the desired system evolution, due to the difficulties in perceiving temporal correlation and conducting appropriate feedback propagation. A machine learning (ML) algorithm named time‐series generative adversarial network (TSGAN) is developed to overcome the difficulties, by incorporating a long short‐term memory (LSTM) kernel for recognizing multirange temporal patterns beyond the Markovian approximation and an adversarial training mechanism for efficient optimization. A variety of time series are examined by temperature‐control experiments, and the results demonstrate an exceptional accuracy (&gt;95%, 35% higher than prevalent ML methods) as well as strong transferability and stability of the TSGAN algorithm. The dependence of generation performance on underlying statistical mechanisms associated with different ML algorithms, including the deep neural network (DNN), hidden Markov model (HMM), LSTM, and TSGAN, is elucidated by analyzing the generation quality of characteristic temporal patterns. The capability of generating arbitrarily complex response series opens an opportunity for inverse design of time‐variant functionals as strenuously pursued in material science and modern technology. Computer science ; Series (stratigraphy) ; Artificial intelligence ; Machine learning ; Kernel (algebra) ; Hidden Markov model ; Time series ; Stability (learning theory) ; Artificial neural network ; Recurrent neural network ; Pattern recognition (psychology) ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Tamara Tultabayeva
Source: International Journal of Advanced Trends in Computer Science and Engineering (article, 2020-08-25)
Field: Chemistry — Analytical Chemistry

Analyzing Soft Cheeses With Vegetable Additives With Neural Network A wide range of dairy-based products with additives of plant origin is due to the variety of forms of raw materials and methods of processing it. New soft cheeses with vegetable additives have been developed. Physical and chemical parameters of plant raw materials were studied, and it was found that the wheat germ contains (%) -28.42 protein, 6.7 -fat, carbohydrates -19.98. The formula of vegetable additives for the production of soft cheeses has been developed, the ratio of vegetable raw materials and wheat germ is 70:30, while the mass fraction of protein in the additive is 10.6%. Mathematically, neural networks can be considered as a class of statistical modeling methods, which, in turn, can be divided into three classes: probability density estimation, classification and regression. The problem of estimating the probability density by the method of mixing Gaussian distributions is solved with the help of inverse distribution networks and the generalized d-rule. Food science ; Artificial neural network ; Computer science ; Chemistry ; Artificial intelligence Spectroscopy and Chemometric Analyses
Authors: Subramanian K. R. S. Sankaranarayanan
Source: ECS Meeting Abstracts (article, 2020-11-23)
Field: Materials Science — Materials Chemistry

Machine Learning the Metastable Phase Diagram of Carbon We integrate first-principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the metastable phases of a given elemental composition and construct "metastable" phase diagrams for materials far-from-equilibrium. Using carbon, a prototypical system with a vast number of metastable phases without parent in equilibrium, we demonstrate automatic metastable phase diagram construction to map hundreds of metastable states ranging from near equilibrium to those far-from-equilibrium (500 meV/atom). Moreover, we incorporate the free energy calculations into a neural-network-based learning of the equations of state that allows for efficient construction of metastable phase diagrams. We use the metastable phase diagram and identify domains of relative stability and synthesizability of metastable materials. High temperature high pressure experiments using a diamond anvil cell on graphite sample coupled with high-resolution transmission electron microscopy (HRTEM) confirm our metastable phase predictions. Our introduced approach is general and broadly applicable to single and multi-component systems. Metastability ; Phase diagram ; Phase (matter) ; Materials science ; Graphite ; Chemical physics ; Thermodynamics ; Statistical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Phase Equilibria and Thermodynamics
Authors: Colin A. Grambow | Kyle L. Swanson | Kevin Yang | Lior Hirschfeld | Wengong Jin
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-03-17)
Field: Chemistry — Spectroscopy

chemprop-reaction: Release version 1.1.0 Message Passing Neural Networks for Molecule and Reaction Property Prediction Computer science Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Laura Connolly | Amoon Jamzad | Martin Kaufmann | Rachel Rubino | Alireza Sedghi | Tamás Ungi | Mark Asselin | Scott S.-H. Yam | John F. Rudan | Christopher J.B. Nicol | Gábor Fichtinger | Parvin Mousavi
Source:  (article, 2020-03-16)
Field: Chemistry — Spectroscopy

Classification of tumor signatures from electrosurgical vapors using mass spectrometry and machine learning: a feasibility study PURPOSE: The iKnife is a new surgical tool designed to aid in tumor resection procedures by providing enriched chemical feedback about the tumor resection cavity from electrosurgical vapors. We build and compare machine learning classifiers that are capable of distinguishing primary cancer from surrounding tissue at different stages of tumor progression. In developing our classification framework, we implement feature reduction and recognition tools that will assist in the translation of xenograft studies to clinical application and compare these tools to standard linear methods that have been previously demonstrated. METHODS: Two cohorts (n=6 each) of 12 week old female immunocompromised (<i>Rag2−/−;Il2rg−/−</i>) mice were injected with the same human breast adenocarcinoma (MDA-MB-231) cell line. At 4 and 6 weeks after cell injection, mice in each cohort were respectively euthanized, followed by iKnife burns performed on tumors and tissues prior to sample collection for future studies. A feature reduction technique that uses a neural network is compared to traditional linear analysis. For each method, we fit a classifier to distinguish primary cancer from surrounding tissue. RESULTS: Both classifiers can distinguish primary cancer from metastasis and surrounding tissue. The classifier that uses a neural network achieves an accuracy of 96.8% and the classifier without the neural network achieves an accuracy of 96%. CONCLUSIONS: The performance of these classifiers indicate that this device has the potential to offer real-time, intraoperative classification of tissue. This technology may be used to assist in intraoperative margin detection and inform surgical decisions to offer a better standard of care for cancer patients. Artificial intelligence ; Computer science ; Classifier (UML) ; Artificial neural network ; Machine learning ; Support vector machine ; Pattern recognition (psychology) Mass Spectrometry Techniques and Applications ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies