jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexis M. Fenton | Fikile R. Brushett
Source:  (preprint, 2021-07-06)
Field: Chemistry — Electrochemistry

Using Voltammetry Augmented with Physics-Based Modeling and Bayesian Hypothesis Testing to Estimate Electrolyte Composition Voltammetry is a foundational electrochemical technique that can qualitatively and quantitatively probe electroactive species in electrolytes and as such has been used in numerous fields of study. Recently, automation has been introduced into voltammetric analyses to extend their capabilities (e.g., Bayesian parameter estimation, compound identification &lt;i&gt;via&lt;/i&gt; machine learning); however, opportunities exist to enable more versatile methods across a wider range of electrolyte and experimental conditions. Here, we present a protocol that uses experimental voltammetry, physics-driven models, binary hypothesis testing, and Bayesian inference to enable robust labeling of electroactive species in multicomponent electrolytes across multiple techniques. We first describe the development of this protocol, and we subsequently validate the methodology in a case study involving five &lt;i&gt;N&lt;/i&gt;-functionalized phenothiazine derivatives. In this analysis, the protocol correctly labeled an electrolyte containing 10H-phenothiazine and 10-methylphenothiazine from both cyclic voltammograms and cyclic square wave voltammograms, demonstrating its ability to identify electroactive constituents of a multicomponent solution. Finally, we identify areas of further improvement (e.g., achieving greater detection accuracy) and future applications to potentially enhance &lt;i&gt;in situ&lt;/i&gt; or &lt;i&gt;operando&lt;/i&gt; diagnostic workflows. Electrolyte ; Phenothiazine ; Voltammetry ; Protocol (science) ; Cyclic voltammetry ; Bayesian probability ; Electrochemistry ; Computer science ; Chemistry ; Biological system ; Materials science ; Electrode ; Artificial intelligence ; Physical chemistry Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Conducting polymers and applications
Authors: Zhiyou Ouyang | B. Sun | Wei Tang | Tao Han | Kexin Zhang
Source:  (article, 2021-12-03)
Field: Materials Science — Materials Chemistry

Attention based Bi-LSTM for Power Line Partial Discharge Fault Detection Partial discharge is one of the faults in power distribution networks, especially in overhead lines with covered conductor of the medium voltage distribution networks, which may damage equipment and stop it’s functioning entirely. However, it is more difficult to detect partial discharge faults because of the different physical and chemical reactions and the same characterization caused by partial discharge faults, and the noise interference of the environment itself. By deploying distributed high-frequency sampling sensors to collecting three-phase voltage signal and using machine learning algorithms to detect the presence of partial discharge fault, can not only not interfere with the normal work of transmission lines to realize on-line detection of partial discharge fault, also can pass the upgrade in fault detection fault detection algorithm which can improve accuracy, therefore, it has become one of the main methods of partial discharge fault detection. In this paper, an attention mechanism based bidirectional long short term memory (Attention-LSTM) fault detection model is proposed, which can take full advantage of the bidirectional long neural network (Bi-LSTM) and attention mechanism, hence, no feature engineering expert is needed to solve the power line partial discharge fault detection issues. Experimental results show that the proposed model outperforms the existing methods in all selected performance metrics. Partial discharge ; Computer science ; Line (geometry) ; Fault detection and isolation ; Fault (geology) ; Power (physics) ; Artificial intelligence ; Real-time computing ; Electrical engineering ; Voltage ; Engineering ; Geology ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Elevator Systems and Control
Authors: Sanjar Adilov
Source:  (preprint, 2021-09-16)
Field: Materials Science — Materials Chemistry

Generative Pre-Training from Molecules SMILES is a line notation for entering and representing molecules. Being inherently a language construct, it allows estimating molecular data in a self-supervised fashion by employing machine learning methods for natural language processing (NLP). The recent success of attention-based neural networks in NLP has made large-corpora transformer pretraining a de facto standard for learning representations and transferring knowledge to downstream tasks. In this work, we attempt to adapt transformer capabilities to a large SMILES corpus by constructing a GPT-2-like language model. We experimentally show that a pretrained causal transformer captures general knowledge that can be successfully transferred to such downstream tasks as focused molecule generation and single-/multi-output molecular-property prediction. For each task, we freeze model parameters and attach trainable lightweight networks between attention blocks—adapters—as alternative to fine-tuning. With a relatively modest setup, our transformer outperforms the recently proposed ChemBERTa transformer and approaches state-of-the-art MoleculeNet and Chemprop results. Overall, transformers pretrained on SMILES corpora are promising alternatives that do not require handcrafted feature engineering, make few assumptions about structure of data, and scale well with the pretraining data size. Transformer ; Computer science ; Notation ; Artificial intelligence ; Generative grammar ; Natural language processing ; Language model ; Machine learning ; Artificial neural network ; Feature learning ; Voltage ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Timothée Fages | Franck Jolibois | Romuald Poteau
Source: Theoretical Chemistry Accounts (article, 2021-07-01)
Field: Materials Science — Materials Chemistry

Recognition of the three-dimensional structure of small metal nanoparticles by a supervised artificial neural network Artificial neural network ; Artificial intelligence ; Computer science ; Nanoparticle ; Test set ; Discretization ; Pattern recognition (psychology) ; Machine learning ; Biological system ; Materials science ; Nanotechnology ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Oliver T. Unke | Stefan Chmiela | Huziel E. Sauceda | Michael Gastegger | Igor Poltavsky | Kristof T. Schütt | Alexandre Tkatchenko | Klaus‐Robert Müller
Source: Chemical Reviews (preprint, 2021-03-11)
Field: Materials Science — Materials Chemistry

Machine Learning Force Fields In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy of <i>ab initio</i> methods and the efficiency of classical FFs. The key idea is to learn the statistical relation between chemical structure and potential energy without relying on a preconceived notion of fixed chemical bonds or knowledge about the relevant interactions. Such universal ML approximations are in principle only limited by the quality and quantity of the reference data used to train them. This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them. The core concepts underlying ML-FFs are described in detail, and a step-by-step guide for constructing and testing them from scratch is given. The text concludes with a discussion of the challenges that remain to be overcome by the next generation of ML-FFs. Computer science ; Scratch ; Key (lock) ; Quality (philosophy) ; Artificial intelligence ; Ab initio ; Machine learning ; Force field (fiction) ; Relation (database) ; Theoretical computer science ; Data mining ; Chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Sunil Verma | R. N. Behera
Source: RASAYAN Journal of Chemistry (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

SOLUBILITY OF ORGANIC COMPOUNDS IN SUPERCRITICAL CARBON DIOXIDE: A QSPR STUDY The quantitative-structure-property-relation (QSPR) model has been used to predict the solubility of a wide range of compounds (with a broader chemical diversity) in supercritical carbon dioxide. The dataset contains 26 molecules (with 324 data points) covering food, textile, pharmaceuticals and petrochemical industries. The concept of Artificial Neural Network with open source software and in-house programs have been used to create the model avoiding the use of commercial and expensive software. The model is found to be robust and performs better compared to many earlier models developed. Quantitative structure–activity relationship ; Supercritical carbon dioxide ; Solubility ; Supercritical fluid ; Artificial neural network ; Petrochemical ; Carbon dioxide ; Biochemical engineering ; Software ; Process engineering ; Computer science ; Textile ; Organic chemistry ; Chemistry ; Materials science ; Artificial intelligence ; Machine learning ; Engineering Zeolite Catalysis and Synthesis ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure
Authors: Lihua Shen | Muhammad Arif Khan | Xianyong Wu | Jian Cai | Tian Lu | Tai Ning | Zhanmin Liu | Wencong Lu | Daixin Ye | Hongbin Zhao | Jiujun Zhang
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Fe−N−C Single-Atom Nanozymes Based Sensor Array for Dual Signal Selective Determination of Antioxidants Dual (grammatical number) ; Chemistry ; SIGNAL (programming language) ; Atom (system on chip) ; Combinatorial chemistry ; Nanotechnology ; Materials science ; Computer science ; Embedded system Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Christoph Dösinger | Tobias Spitaler | Alexander Reichmann | Daniel Scheiber | Lorenz Romaner
Source: BHM Berg- und Hüttenmännische Monatshefte (article, 2021-12-21)
Field: Materials Science — Materials Chemistry

Applications of Data Driven Methods in Computational Materials Design Abstract In today’s digitized world, large amounts of data are becoming available at rates never seen before. This holds true also for materials science where high-throughput simulations and experiments continuously produce new data. Data driven methods are required which can make best use of the information stored in large data repositories. In the present article, two of such data driven methods are presented. First, we apply machine learning to generalize and extend the results obtained from computationally intense density functional theory (DFT) simulations. We show how grain boundary segregation energies can be trained with gradient boosting regression and extended to many more positions in the grain boundary for a complete description. The second method relies on Bayesian inference, which can be used to calibrate models to give data and quantification of the model uncertainty. The method is applied to calibrate parameters in thermodynamic models of the Gibbs energy of Ti-W alloys. The uncertainty of the model parameters is quantified and propagated to the phase boundaries of the Ti-W system. Computer science ; Bayesian inference ; Inference ; Boosting (machine learning) ; Bayesian probability ; Boundary (topology) ; Experimental data ; Gradient boosting ; Algorithm ; Machine learning ; Data mining ; Artificial intelligence ; Mathematics ; Random forest ; Statistics Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Pìotr Bregestovski | Daria Ponomareva
Source: Journal of Evolutionary Biochemistry and Physiology (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Photochromic Modulation of Cys-loopLigand-gated Ion Channels Advances in molecular and cellular biology, as well as the development of chemical synthesis and modern technologies, enriched the contemporary experimental research with new directions in which light plays a key role as a tool for modulating biological functions. One of them is photopharmacology, a field that uses chemically synthesized light-controlled compounds that can modulate the functions of proteins. When illuminated at specific wavelengths, these synthetic photoswitches are capable of modulating functions of receptors, ion channels and enzymes. This review briefly describes compounds that modulate the functions of ionotropic Cys-loop receptors for acetylcholine, GABA, and glycine. The nicotinic acetylcholine receptor (nAChR) is the first receptor-operated channel for which a way of modulation using light-dependent molecules has been discovered. In the 1970s–80s, blockers and activators of nAChR were created, consisting of azobenzene (light-controlled switch) and agonists. In the current millennium, new compounds have been created to provide light-controlled modulation of nAChR activity. These new photochromes are selective to muscle and neuronal nAChR, and are promising to study the physiological role of nAChRs in the nervous system. An extensive library of photochromic compounds is available for light-controlling of GABA receptor function. Some of them modulate the activity via interaction with the agonist site, the others are light-controlled blockers of chloride-selective ion channels. Recently, the first two photochromic modulators of glycine receptor activity have also been developed. These achievements demonstrate that photopharmacology opens up unique possibilities for remote control of physiological functions, as well as for studying the processes of inhibition and excitation in neural networks and models of neuronal pathologies. Ionotropic effect ; Ion channel ; Chemistry ; Glycine receptor ; Biophysics ; Receptor ; Azobenzene ; Ligand-gated ion channel ; Transient receptor potential channel ; Nicotinic agonist ; Neuroscience ; Glycine ; Biochemistry ; Biology ; Glutamate receptor ; Molecule ; Amino acid Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; bioluminescence and chemiluminescence research
Authors: Jiaxu Cui | Bo Yang | Bingyi Sun | Jiming Liu
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2021-05-18)
Field: Materials Science — Materials Chemistry

Cost-aware Graph Generation: A Deep Bayesian Optimization Approach Graph-structured data is ubiquitous throughout the natural and social sciences, ranging from complex drug molecules to artificial neural networks. Evaluating their functional properties, e.g., drug effectiveness and prediction accuracy, is usually costly in terms of time, money, energy, or environment, becoming a bottleneck for the graph generation task. In this work, from the perspective of saving cost, we propose a novel Cost-Aware Graph Generation (CAGG) framework to generate graphs with optimal properties at as low cost as possible. By introducing a robust Bayesian graph neural network as the surrogate model and a goal-oriented training scheme for the generation model, the CAGG can approach the real expensive evaluation function and generate search space close to the optimal property, to avoid unnecessary evaluations. Intensive experiments conducted on two challenging real-world applications, including molecular discovery and neural architecture search, demonstrate its effectiveness and applicability. The results show that it can generate the optimal graphs and reduce the evaluation costs significantly compared to the state-of-the-art. Computer science ; Bottleneck ; Artificial intelligence ; Bayesian optimization ; Graph ; Machine learning ; Bayesian probability ; Artificial neural network ; Theoretical computer science ; Mathematical optimization ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: R. Arturo Rubio | Mikko Hokka
Source: EPJ Web of Conferences (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Numerical modeling of the dynamic strain aging in steels at high strain rates and high temperatures Carbon steels can be heat treated to produce different microstructural variations and mechanical properties. At high temperatures the material plasticity and strength can be influenced by diffusional effects like the Portevin-Le Chatelier effect, leading to a commonly observed increased strength at elevated temperatures. The diffusional effects are influenced by the chemical composition, but also the heat treatment history that affects the local composition and especially the concentrations of free solute atoms. In this work, a numerical approach was implemented to reproduce the thermomechanical behaviour of two different microstructural variants of steel grade C45. The experimental data used to calibrate the model includes information of the plastic behaviour of material subjected to dynamic compression loading at a wide range of temperatures. Special emphasis was focused to describe the effects of the dynamic strain aging (DSA) on the flow stress. A strategy based on machine learning was implemented to obtain a model that reproduces the strengthening of the material due to diffusional effects. Cubic Support Vector Machine models were trained for both microstructure variants of the steel and different surfaces were obtained to describe the topology of the flow stress as function of temperature and strain rate. The model predictions were compared to the behaviour described by the Johnson-Cook model to estimate the influence of the DSA effect on the strength of the material at high strain rates and temperatures. Furthermore, the model quantifies how the microstructure affects the strength of the material and the strength of the DSA-hardening. Materials science ; Microstructure ; Plasticity ; Dynamic strain aging ; Flow stress ; Strain rate ; Hardening (computing) ; Strain hardening exponent ; Work hardening ; Work (physics) ; Composite material ; Thermodynamics ; Physics High-Velocity Impact and Material Behavior ; Microstructure and Mechanical Properties of Steels ; Advanced Surface Polishing Techniques
Authors: Fabio Urbina | Kushal Batra | Kevin J Luebke | Jason C. White | Daniel Matsiev | Lori L. Olson | Jeremiah P. Malerich | Maggie A. Z. Hupcey | Peter B. Madrid | Sean Ekins
Source: ChemRxiv (preprint, 2021-08-19)
Field: Chemistry — Analytical Chemistry

UV-adVISor: Attention-Based Recurrent Neural Networks to Predict UV-Vis Spectra Ultraviolet-visible (UV-Vis) absorption spectra are routinely collected as part of high-performance liquid chromatography (HPLC) analysis systems and can be used to identify chemical reaction products by comparison to reference spectra. Here, we present UV-adVISor as a new computational tool for predicting UV-Vis spectra from a molecule’s structure alone. UV-Vis prediction was approached as a sequence-to-sequence problem. We utilized Long-Short Term Memory and attention-based neural networks with Extended Connectivity Fingerprint diameter 6 or molecule SMILES to generate predictive models for UV-spectra. We have produced two spectrum datasets (Dataset I, N = 949 and Dataset II, N = 2222) using different compound collections and spectrum acquisition methods to train, validate, and test our models. We evaluated the prediction accuracy of the complete spectra by the correspondence of wavelengths of absorbance maxima and with a series of statistical measures (the best test set median model parameters are in parentheses for Model II), including RMSE (0.064), R2 (0.71), and dynamic time warping (DTW, 0.194) of the entire spectrum curve. Scrambling molecule structures with experimental spectra during training resulted in a degraded R2, confirming the utility of the approaches for prediction. UV-adVISor is able to provide fast and accurate predictions for libraries of compounds. Spectral line ; Artificial neural network ; Ultraviolet ; Dynamic time warping ; Absorbance ; Pattern recognition (psychology) ; Biological system ; Artificial intelligence ; Test set ; Computer science ; Chemistry ; Physics ; Optics ; Chromatography Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Patrick Wefing | Florian Conradi | Johannes Rämisch | Peter Neubauer | Jan Schneider
Source:  (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms Free amino nitrogen (FAN) concentrations in beer mash can be determined with machine learning algorithms from near-infrared (NIR) spectra. NIR spectroscopy is an alternative to a classical chemical analysis and allows for the application of inline process quality control. This study investigates the capabilities of different machine learning techniques such as Ordinary Least Squares (OLS) regression, Decision Tree Regressor (DTR), Bayesian Ridge Regression (BRR), Ridge Regression (RR), K-nearest neighbours (KNN) regression as well as Support Vector Regression (SVR) to predict the FAN content in beer mash from NIR spectra. Various pre-processing strategies such as principal component analysis (PCA) and data standardization were used to process NIR data that were used to train the machine learning algorithms. Algorithm training was conducted with NIR data obtained from 16 beer mashes with varying FAN concentrations. The trained models were then validated with 4 beer mashes that were not used for model training. Machine learning algorithms based on linear regression showed the highest prediction accuracy on unpre-processed data. BRR reached a root mean square error of calibration (RMSEC) of 2.58 mg/L (R2 = 0.96) and a prediction accuracy (RMSEP) of 2.81 mg/L (R2 = 0.96). The FAN concentration range of the investigated samples was between approx. 180 and 220 mg/L. Machine learning based NIR spectra analysis is an alternative to classical chemical FAN level determination methods and can also be used as inline sensor system. Machine learning ; Artificial intelligence ; Support vector machine ; Principal component analysis ; Calibration ; Partial least squares regression ; Linear regression ; Regression ; Mean squared error ; Algorithm ; Mathematics ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Spectroscopy Techniques in Biomedical and Chemical Research