jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Julia V. Burykina | Daniil A. Boiko | Valentina V. Ilyushenkova | Dmitry B. Eremin | Valentine P. Ananikov
Source: Doklady Physical Chemistry (article, 2020-05-01)
Field: Chemistry — Spectroscopy

Comprehensive Mass Spectrometric Mapping of Chemical Compounds for the Development of Algorithms for Machine Learning and Artificial Intelligence The influence of the accuracy of mass measurements on the number of possible structural compositions and the computation time of computer-aided interpretation of mass spectrometric data has been evaluated. Experimental measurements have been performed for two model objects in the range of small and medium masses using high, ultrahigh, and extreme high resolution electrospray ionization mass spectrometers. The number of possible solutions have been examined and prospects of using machine learning in combination with mass spectrometry for predicting new data on reaction mechanisms and searching for hidden relationships in the chemical space have been demonstrated. It has been shown that there are two types of relationships between the molecular formula and the mass determination error depending on the ion mass: a nonlinear curve is observed for small molecules and a linear relationship is observed for large molecules. Mass spectrometry ; Algorithm ; Computation ; Range (aeronautics) ; Electrospray ionization ; Resolution (logic) ; Chemical space ; Molecule ; Interpretation (philosophy) ; Chemistry ; Ionization ; Analytical Chemistry (journal) ; Biological system ; Artificial intelligence ; Computer science ; Ion ; Materials science ; Chromatography ; Drug discovery ; Organic chemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Liangrui Pan | Pronthep Pipitsunthonsan | Mitchai Chongcheawchamnan
Source:  (article, 2020-06-01)
Field: Chemistry — Analytical Chemistry

Classification of Hazardous Chemicals with Raman Spectrum by Convolution Neural Network Dangerous chemicals have always been the hidden danger of social security, how to accurately identify chemicals is very important. In this experiment, the Raman scattering instrument will provide us with the Raman spectrum signal of about 190 chemical substances, each of which has its own characteristics. However, the traditional methods of identifying and classifying chemicals are not only inefficient, but also lack of security. This study proved the feasibility of using neural network to classify chemical substances. For one-dimensional signal, the experiment mainly uses the semi-supervised learning method to establish the 1D-DCNN model and simulate the real noise environment. One-dimensional signal is used as input and then the model is trained to get the model. The experimental results show that the accuracy of toxic and toxic, flammable, corrosive, environment hazard, health hazard, safe, expansive, harmful classification is 99% ± 1%. This shows that the 1D-DCNN model has strong anti-interference and robustness for signals in noise environments. This rapid classification method will provide reference value for the identification of chemical substances. Robustness (evolution) ; Flammable liquid ; Computer science ; Convolutional neural network ; Artificial intelligence ; Hazard ; Noise (video) ; SIGNAL (programming language) ; Identification (biology) ; Artificial neural network ; Environmental noise ; Hazardous waste ; Pattern recognition (psychology) ; Machine learning ; Data mining ; Engineering ; Acoustics ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Jacob Kerner | Horst A. von Recum
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-11-11)
Field: Materials Science — Materials Chemistry

Predicting Drug Interactions to Unassociated Biomedical Implants Using Machine Learning Techniques and Model Polymers Abstract Affinity based drug delivery mechanisms increase efficacy and minimalize off target effects when compared to non-specific methods due to the localization of drugs within target areas. While this is beneficial for targeted delivery, introduction of foreign polymeric medical devices into the body provide a potential area of localization due to high affinity between administered drugs and polymers. Previous attempts at creating models to predict affinity between small molecule drugs and polymers require a specific model be trained for each individual polymer failing to incorporate input features of both the polymer (host) and small molecule drug (guest). Within, we propose a universal model built using a neural network and quantitative structure activity relationships to predict the binding energy between guest and host molecules using input features. The trained model returned a correlation value, R 2 , of 0.9806 and 0.9958 between predicted and experimental binding affinity for the training and validation sets, respectively. This correlates to a mean absolute error of 0.951 kJ/mol and 0.771 kJ/mol for the training and validation sets, respectively. While limited to the current polymers used to train the model, the dataset can be expanded, and models retrained for further applications. Polymer ; Molecular descriptor ; Small molecule ; Drug ; Computer science ; Molecule ; Quantitative structure–activity relationship ; Biological system ; Training set ; Drug delivery ; Mean absolute error ; Artificial neural network ; Drug discovery ; Materials science ; Machine learning ; Artificial intelligence ; Combinatorial chemistry ; Computational biology ; Nanotechnology ; Chemistry ; Mean squared error ; Pharmacology ; Mathematics ; Biology ; Biochemistry ; Statistics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Zhilei Wang | Toshio Ogawa | Yoshitaka Adachi
Source: Advanced Theory and Simulations (article, 2020-01-21)
Field: Materials Science — Materials Chemistry

Machine‐Learning‐Based Image Similarity Analysis for Use in Materials Characterization Abstract Materials with similar microstructural features exhibit similar properties, a fact which often provides useful insights for a detailed understanding of the materials. An analysis of material similarity in terms of microstructural images is proposed for predicting some properties of interest. This similarity analysis is inspired by the application of medical image retrieval to guide diagnostic decisions. Some relevant analyzing techniques including machine‐learning algorithms of zero‐normalized cross‐correlation, mutual information, maximum likelihood estimation, principal component analysis, and self‐organizing map are applied in this work. These techniques are systematically employed to identify the variances of the query images based on the metrics of image natural properties (such as brightness which is measured in pixel) or metallurgical features contained in the microstructural images. It is shown that the employed methods exhibit consistent similarity evaluation results. The proposed similarity analysis of microstructural images is expected to provide a new avenue for understanding the materials paradigm. Similarity (geometry) ; Computer science ; Principal component analysis ; Artificial intelligence ; Pattern recognition (psychology) ; Image (mathematics) ; Brightness ; Pixel ; Data mining ; Characterization (materials science) ; Machine learning ; Materials science Machine Learning in Materials Science ; Mineral Processing and Grinding ; Image Retrieval and Classification Techniques
Authors: Xiting Sun | Hongfu Yuan | Chunfeng Song | Xiaoyu Li | Aiqin Hu
Source: Analytical Methods (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

A novel deep learning-based chemical image identification method of infrared spectroscopy using external perturbation Perturbation-induced infrared spectroscopy combined with a deep learning-based chemical image identification method enables rapid and non-destructive identification of different samples with a quite similar composition and morphological complexity. Infrared spectroscopy ; Infrared ; Perturbation (astronomy) ; Spectroscopy ; Identification (biology) ; Biological system ; Artificial intelligence ; Chemistry ; Computer science ; Pattern recognition (psychology) ; Analytical Chemistry (journal) ; Materials science ; Physics ; Optics ; Chromatography ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: K. Brajesh | Vishnu Sresht | Qingyi Yang | Rayomond J. Unwalla | Meihua Tu | Alan M. Mathiowetz | Gregory A. Bakken
Source: ChemRxiv (preprint, 2020-12-24)
Field: Materials Science — Materials Chemistry

TorsionNet: A Deep Neural Network to Rapidly Predict Small Molecule Torsion Energy Profiles with the Accuracy of Quantum Mechanics TorsionNet: A Deep Neural Network to Rapidly Predict Small Molecule Torsion Energy Profiles with the Accuracy of Quantum Mechanics Brajesh K. Rai*,1, Vishnu Sresht1, Qingyi Yang2, Ray Unwalla2, Meihua Tu2, Alan M. Mathiowetz2, and Gregory A. Bakken3 1Simulation and Modeling Sciences and 2Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States 3Digital, Pfizer, Eastern Point Road, Groton, Connecticut 06340, United States ABSTRACT Fast and accurate assessment of small molecule dihedral energetics is crucial for molecular design and optimization in medicinal chemistry. Yet, accurate prediction of torsion energy profiles remains a challenging task as current molecular mechanics methods are limited by insufficient coverage of druglike chemical space and accurate quantum mechanical (QM) methods are too expensive. To address this limitation, we introduce TorsionNet, a deep neural network (DNN) model specifically developed to predict small molecule torsion energy profiles with QM-level accuracy. We applied active learning to identify nearly 50k fragments (with elements H, C, N, O, F, S, and Cl) that maximized the coverage of our corporate library and leveraged massively parallel cloud computing resources to perform DFT torsion scan of these fragments, generating a training dataset of 1.2 million DFT energies. By training TorsionNet on this dataset, we obtain a model that can rapidly predict the torsion energy profile of typical druglike fragments with DFT-level accuracy. Importantly, our method also provides a direct estimate of the uncertainty in the predicted profiles without any additional calculations. In this report, we show that TorsionNet can reliably identify the preferred dihedral geometries observed in crystal structures. We also present practical applications of TorsionNet that demonstrate how consideration of DNN-based strain energy leads to substantial improvement in existing lead discovery and design workflows. A benchmark dataset (TorsionNet500) comprising 500 chemically diverse fragments with DFT torsion profiles (12k DFT-optimized geometries and energies) has been created and is made freely available. Dihedral angle ; Computer science ; Torsion (gastropod) ; Artificial neural network ; Quantum ; Artificial intelligence ; Statistical physics ; Molecule ; Physics ; Data mining ; Computational science ; Quantum mechanics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Leonardo J. Duarte | Roy E. Bruns
Source: The Journal of Physical Chemistry A (article, 2020-04-06)
Field: Materials Science — Materials Chemistry

QTAIM Atomic Charge and Polarization Parameters and Their Machine-Learning Transference among Boron-Halide Molecules Atomic charges are invariant for out-of-plane distortions, making their molecular vibrations enticing for electronic structure studies. Of planar molecules, the boron trihalides contain some of the most polar bonds known to chemistry, although their out-of-plane bending intensities are very small contrary to expectations from atomic charge models. Here, the out-of-plane infrared intensities of the BX<sub>(2)</sub>X<sub>(3)</sub>X<sub>(4)</sub> (X<sub>(2)</sub>, X<sub>(3)</sub>, X<sub>(4)</sub> = H, F, Cl, Br) molecules are investigated using quantum theory of atoms in molecules atomic charges and atomic dipoles within the formulism of the charge, charge transfer, dipolar polarization model at the QCISD/aug-cc-pVTZ quantum level. Dipole moments induced by equilibrium charge displacement of atoms perpendicular to the molecular plane are almost completely cancelled by their electronic density polarizations. The calculated boron trihalide intensities are small for molecules with such polar bonds ranging from 0.6 to 106.1 km mol<sup>-1</sup>. Even though the Cl atomic charge of -0.72 e in BCl<sub>3</sub> is more negative than the hydrogen values of -0.67 e in BH<sub>3</sub>, the hydride out-of-plane intensity of 82.0 km mol<sup>-1</sup> is an order of magnitude larger than that of the trichloride, 6.3 km mol<sup>-1</sup>. Owing to their diverse electronic structures, transference of atomic charges and dipole parameters among the boron trihalides is extremely challenging and does not result in accurate intensity values. For this reason, a machine-learning decision-tree algorithm was used to perform the transference procedure. Decision trees were optimized using quantum-level intensity values. Atomic charge and dipole parameters were estimated for a set of 12 test set molecules. These parameters provided intensity estimates with a root-mean-square error of 2.1 km mol<sup>-1</sup> compared with QCISD/aug-cc-pVTZ reference values. Trihalide ; Dipole ; Chemistry ; Atomic physics ; Molecule ; Boron ; Bond dipole moment ; Molecular physics ; Halide ; Electric dipole moment ; Physics ; Inorganic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Chemical Physics Studies
Authors: Dario Caramelli | Jarosław M. Granda | Dario Cambié | S. Hessam M. Mehr | Alon Henson | Leroy Cronin
Source: ChemRxiv (preprint, 2020-09-08)
Field: Materials Science — Materials Chemistry

An Artificial Intelligence that Discovers Unpredictable Chemical Reactions We present an artificial intelligence, built to autonomously explore chemical reactions in the laboratory using deep learning. The reactions are performed automatically, analysed online, and the data is processed using a convolutional neural network (CNN) trained on a small reaction dataset to assess the reactivity of reaction mixtures. The network can be used to predict the reactivity of an unknown dataset, meaning that the system is able to abstract the reactivity assignment regardless the identity of the starting materials. The system was set up with 15 inputs that were combined in 1018 reactions, the analysis of which lead to the discovery of a ‘multi-step, single-substrate’ cascade reaction and a new mode of reactivity for methylene isocyanides. p-Toluenesulfonylmethyl isocyanide (TosMIC) in presence of an activator reacts consuming six equivalents of itself to yield a trimeric product in high (unoptimized) yield (47%) with formation of five new C-C bonds involving sp-sp2 and sp-sp3 carbon centres. A cheminformatics analysis reveals that this transformation is both highly unpredictable and able to generate an increase in complexity like a one-pot multicomponent reaction. Reactivity (psychology) ; Computer science ; Cheminformatics ; Chemistry ; Artificial intelligence ; Combinatorial chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Opeoluwa Owoyele | Pinaki Pal
Source: Preprints.org (preprint, 2020-12-11)
Field: Chemistry — Organic Chemistry

A Neural Ordinary Differential Equations Approach for Chemical Kinetics Solvers The main bottleneck when performing computational fluid dynamics (CFD) simulations of combustion systems is the computation and integration of the highly non-linear and stiff chemical source terms. In recent times, machine learning has emerged as a promising tool to accelerate combustion chemistry, involving the use of regression models to predict the chemical source terms as functions of the thermochemical state of the system. However, combustion is a highly nonlinear phenomenon, and this often leads to divergence from the true solution when the neural network representation of chemical kinetics is integrated in time. This is because these approaches minimize the error during training without guaranteeing successful integration with ordinary differential equation (ODE) solvers. In this work, a novel neural ODE approach to combustion modeling is developed to address this issue. The source terms predicted by the neural network are integrated during training, and by backpropagating errors through the ODE solver, the neural network weights are adjusted accordingly to minimize the difference between the predicted and actual ODE solutions. It is shown that even when the dimensionality of the thermochemical manifold is trimmed to remove redundant species, the proposed approach accurately captures the correct physical behavior and reproduces the results obtained using the full chemical kinetic mechanism. Ode ; Ordinary differential equation ; Artificial neural network ; Computer science ; Bottleneck ; Curse of dimensionality ; Solver ; Combustion ; Nonlinear system ; Applied mathematics ; Differential equation ; Representation (politics) ; Mathematical optimization ; Machine learning ; Mathematics ; Chemistry ; Physics ; Mathematical analysis Chemical Thermodynamics and Molecular Structure ; Combustion and flame dynamics ; Advanced Combustion Engine Technologies
Authors: Shusen Liu | Bhavya Kailkhura | Jize Zhang | Anna M. Hiszpanski | Emily Robertson | Donald Loveland | T. Yong-Jin Han
Source: arXiv (Cornell University) (preprint, 2020-07-16)
Field: Materials Science — Materials Chemistry

Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges. However, despite the effectiveness in building predictive models, fundamental challenges exist in extracting actionable knowledge from deep neural networks due to their opaque nature. In this work, we propose techniques for exploring the behavior of deep learning models by injecting domain-specific actionable attributes as tunable "knobs" in the analysis pipeline. By incorporating the domain knowledge in a generative modeling framework, we are not only able to better understand the behavior of these black-box models, but also provide scientists with actionable insights that can potentially lead to fundamental discoveries. Deep learning ; Data science ; Computer science ; Pipeline (software) ; Domain (mathematical analysis) ; Scientific discovery ; Artificial intelligence ; Black box ; Domain knowledge ; Generative grammar ; Deep neural networks ; Machine learning ; Cognitive science Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Topic Modeling
Authors: N. E. Babushkina | A. A. Lyapin | Anastasia Kovaleva
Source: E3S Web of Conferences (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Analysis of neural network results based on experimental data during indentation The article is devoted to the development of machine learning methods for classes of technical problems, including determining the properties of materials. According to the authors, the neural network approximation algorithm is able to take into account the behavior of materials in various experimental conditions. The article provides illustrative examples of how a neural network with a single hidden layer can approximate a function of several variables with a given accuracy. As part of the study, a number of experimental measurements were made. The structure of the neural network and its main components are described. Artificial neural network ; Computer science ; Experimental data ; Function (biology) ; Artificial intelligence ; Indentation ; Algorithm ; Machine learning ; Pattern recognition (psychology) ; Mathematics ; Statistics Structural mechanics and materials ; Engineering Diagnostics and Reliability ; Surface Treatment and Coatings
Authors: Christopher Zhou | William M. Grumbles | Thomas R. Cundari
Source: ChemRxiv (preprint, 2020-07-14)
Field: Materials Science — Materials Chemistry

Using Machine Learning to Predict the pKa of C–H Bonds. Relevance to Catalytic Methane Functionalization Six machine learning models (random forest, neural network, support vector machine, k-nearest neighbors, Bayesian ridge regression, least squares linear regression) were trained on a dataset of 3d transition metal-methyl and -methane complexes to predict p&lt;i&gt;K&lt;sub&gt;a&lt;/sub&gt;&lt;/i&gt;(C–H), a property demonstrated to be important in catalytic activity and selectivity. Results illustrate that the machine learning models are quite promising, with RMSE metrics ranging from 4.6 to 8.8 p&lt;i&gt;K&lt;sub&gt;a&lt;/sub&gt;&lt;/i&gt; units, despite the relatively modest amount of data available to train on. Importantly, the machine learning models agreed that (a) conjugate base properties were more impactful than those of the corresponding conjugate acid, and (b) the energy of the highest occupied molecular orbital conjugate base was the most significant input feature in the prediction of p&lt;i&gt;K&lt;sub&gt;a&lt;/sub&gt;&lt;/i&gt;(C–H). Furthermore, results from additional testing conducted using an external dataset of Sc-methyl complexes demonstrated the robustness of all models, with RMSE metrics ranging from 1.5 to 6.6 p&lt;i&gt;K&lt;sub&gt;a&lt;/sub&gt;&lt;/i&gt; units. In all, this research demonstrates the potential of machine learning models in organometallic catalyst development. Machine learning ; Artificial intelligence ; Chemistry ; Catalysis ; Artificial neural network ; Linear regression ; Conjugate ; Statistical learning ; Mathematics ; Algorithm ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods