jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yidi Liu | Qi Yang | Junjie Cheng | Long Zhang | Sanzhong Luo | Jin‐Pei Cheng
Source: ChemPhysChem (article, 2023-05-03)
Field: Chemistry — Organic Chemistry

Prediction of Nucleophilicity and Electrophilicity Based on a Machine‐Learning Approach Nucleophilicity and electrophilicity dictate the reactivity of polar organic reactions. In the past decades, Mayr et al. established a quantitative scale for nucleophilicity (N) and electrophilicity (E), which proved to be a useful tool for the rationalization of chemical reactivity. In this study, a holistic prediction model was developed through a machine-learning approach. rSPOC, an ensemble molecular representation with structural, physicochemical and solvent features, was developed for this purpose. With 1115 nucleophiles, 285 electrophiles, and 22 solvents, the dataset is currently the largest one for reactivity prediction. The rSPOC model trained with the Extra Trees algorithm showed high accuracy in predicting Mayr's N and E parameters with R<sup>2</sup> of 0.92 and 0.93, MAE of 1.45 and 1.45, respectively. Furthermore, the practical applications of the model, for instance, nucleophilicity prediction of NADH, NADPH and a series of enamines showed potential in predicting molecules with unknown reactivity within seconds. An online prediction platform (http://isyn.luoszgroup.com/) was constructed based on the current model, which is available free to the scientific community. Nucleophile ; Electrophile ; Reactivity (psychology) ; Chemistry ; Computational chemistry ; Machine learning ; Artificial intelligence ; Representation (politics) ; Biological system ; Combinatorial chemistry ; Computer science ; Biochemical engineering ; Organic chemistry ; Engineering Chemical Reaction Mechanisms ; Organic Chemistry Cycloaddition Reactions ; Chemistry and Chemical Engineering
Authors: Yash Raj Singh | Darshil B. Shah | Mangesh Kulkarni | Shreyanshu R. Patel | Dilip Maheshwari | Jignesh S. Shah | Shreeraj Shah
Source: Analytical Methods (review, 2023-01-01)
Field: Chemistry — Spectroscopy

Current trends in chromatographic prediction using artificial intelligence and machine learning Artificial intelligence (AI) and machine learning (ML) gained tremendous growth and are rapidly becoming popular in various fields of prediction due to their potential abilities, accuracy, and speed. Machine learning algorithms employ historical data to analyze or predict information using patterns or trends. AI and ML were most employed in chromatographic predictions and particularly attractive options for liquid chromatography method development, as they can help achieve desired results faster, more accurately, and more efficiently. This review aims at exploring various AI and ML models employed in the determination of chromatographic characteristics. This review also aims to provide deep insight into reported artificial neural network (ANN) associated techniques which maintained better accuracy and significant possibilities for chromatographic characteristics prediction in liquid chromatography over classical linear models and also emphasizes the integration of a fuzzy system with an ANN, as this integrated study provides more efficient and accurate methods in chromatographic prediction than other linear models. This study also focuses on the retention prediction of a target molecule employing QSRR methodology combined with an ANN, highlighting a more effective technique than the QSRR alone. This approach showed the benefits of combining AI or ML algorithms with the QSRR to obtain more accurate retention predictions, emphasizing the potential of artificial intelligence and machine learning for overcoming adversities in analytical chemistry. Artificial intelligence ; Artificial neural network ; Machine learning ; Computer science Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Analytical Methods in Pharmaceuticals
Authors: Jie Xiao | Guodong Fang | Xiangyu Jin | Bing Wang | Songhe Meng
Source: Chemical Engineering Journal (article, 2023-07-28)
Field: Chemistry — Spectroscopy

A comprehensive study of pyrolysis characteristics of silicone-modified phenolic aerogel matrix Nanocomposites: Kinetic Analysis, ReaxFF MD Simulations, and ANN prediction Nanocomposite ; ReaxFF ; Materials science ; Chemical engineering ; Pyrolysis ; Methanol ; Molecular dynamics ; Aerogel ; Thermal decomposition ; Activation energy ; Chemistry ; Composite material ; Organic chemistry ; Computational chemistry Aerogels and thermal insulation ; Catalytic Processes in Materials Science ; Flame retardant materials and properties
Authors: Natsuki Saito | Anna Nawachi | Yuta Kondo | Jeesoo Choi | Hiroyuki Morimoto | Takashi Ohshima
Source: Bulletin of the Chemical Society of Japan (article, 2023-04-18)
Field: Chemistry — Inorganic Chemistry

Functional Group Evaluation Kit for Digitalization of Information on the Functional Group Compatibility and Chemoselectivity of Organic Reactions Abstract Digitalization of information on organic reactions is essential for developing next-generation organic syntheses with artificial intelligence and machine-learning (ML) methods. In this regard, reliable information on functional group compatibility and chemoselectivity is critical for understanding the applicability of the reactions. Herein, we report the digitalization of organic reactions using a functional group evaluation (FGE) kit that allows for accurate and rapid assessment of information on the functional group compatibility and chemoselectivity of given organic reactions. Our 26 FGE compounds were used to evaluate 4 types of organic reactions (i.e., cross-coupling, proline-catalyzed aldol, condensation, and ketimine synthesis reactions). As a result, we obtained valuable information for developing a new model of retrosynthetic analysis tools and a deeper understanding of these reactions. Chemoselectivity ; Chemistry ; Functional group ; Compatibility (geochemistry) ; Retrosynthetic analysis ; Organic reaction ; Combinatorial chemistry ; Organic chemistry ; Catalysis ; Total synthesis ; Chemical engineering ; Engineering Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Catalytic C–H Functionalization Methods
Authors: Taija L. Fischer | Margarethe Bödecker | Sophie M. Schweer | Jennifer Dupont | Valéria Lepère | Anne Zehnacker | Martin A. Suhm | Benjamin Schröder | Tobias Henkes | Diego M. Andrada | Roman M. Balabin | Haobam Kisan Singh | Himangshu Pratim Bhattacharyya | Manabendra Sarma | Silvan Käser | Kai Töpfer | Luis Itza Vazquez-Salazar | Eric D. Boittier | Markus Meuwly | Giacomo Mandelli | Cecilia Lanzi | Riccardo Conte | Michele Ceotto | Fabian Dietrich | Vicente Cisternas | Ramachandran Gnanasekaran | Michael Hippler | Mahmoud Jarraya | M. Hochlaf | Narasimhan Viswanathan | Thomas Nevolianis | Gabriel Rath | Wassja A. Kopp | Kai Leonhard | Ricardo A. Mata
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Chemistry — Spectroscopy

The first HyDRA challenge for computational vibrational spectroscopy Vibrational spectroscopy in supersonic jet expansions is a powerful tool to assess molecular aggregates in close to ideal conditions for the benchmarking of quantum chemical approaches. The low temperatures achieved as well as the absence of environment effects allow for a direct comparison between computed and experimental spectra. This provides potential benchmarking data which can be revisited to hone different computational techniques, and it allows for the critical analysis of procedures under the setting of a blind challenge. In the latter case, the final result is unknown to modellers, providing an unbiased testing opportunity for quantum chemical models. In this work, we present the spectroscopic and computational results for the first HyDRA blind challenge. The latter deals with the prediction of water donor stretching vibrations in monohydrates of organic molecules. This edition features a test set of 10 systems. Experimental water donor OH vibrational wavenumbers for the vacuum-isolated monohydrates of formaldehyde, tetrahydrofuran, pyridine, tetrahydrothiophene, trifluoroethanol, methyl lactate, dimethylimidazolidinone, cyclooctanone, trifluoroacetophenone and 1-phenylcyclohexane-<i>cis</i>-1,2-diol are provided. The results of the challenge show promising predictive properties in both purely quantum mechanical approaches as well as regression and other machine learning strategies. Lernaean Hydra ; Quantum chemical ; Joint (building) ; Gas phase ; Spectroscopy ; Chemical physics ; Quantum ; Computer science ; Statistical physics ; Physics ; Chemistry ; Physical chemistry ; Molecule ; Biology ; Engineering ; Quantum mechanics Molecular Spectroscopy and Structure ; Atmospheric Ozone and Climate ; Spectroscopy and Laser Applications
Authors: Yufeng Mao | Xiaokang Yao | Ze Yu | Zhongfu An | Huili Ma
Source: Angewandte Chemie International Edition (article, 2023-12-23)
Field: Materials Science — Materials Chemistry

Ground‐State Orbital Descriptors for Accelerated Development of Organic Room‐Temperature Phosphorescent Materials Organic materials with room-temperature phosphorescence (RTP) are in high demand for optoelectronics and bioelectronics. Developing RTP materials highly relies on expert experience and costly excited-state calculations. It is a challenge to find a tool for effectively screening RTP materials. Herein we first establish ground-state orbital descriptors (π<sub>FMOs</sub> ) derived from the π-electron component of the frontier molecular orbitals to characterize the RTP lifetime (τ<sub>p</sub> ), achieving a balance in screening efficiency and accuracy. Using the π<sub>FMOs</sub> , a data-driven machine learning model gains a high accuracy in classifying long τ<sub>p</sub> , filtering out 836 candidates with long-lived RTP from a virtual library of 19,295 molecules. With the aid of the excited-state calculations, 287 compounds are predicted with high RTP efficiency. Impressively, experiments further confirm the reliability of this workflow, opening a novel avenue for designing high-performance RTP materials for potential applications. Phosphorescence ; Excited state ; Workflow ; Materials science ; Molecular orbital ; Atomic orbital ; Nanotechnology ; Bioelectronics ; Computer science ; Chemistry ; Molecule ; Electron ; Fluorescence ; Atomic physics ; Database ; Physics ; Organic chemistry ; Biosensor Luminescence and Fluorescent Materials ; Organoboron and organosilicon chemistry ; Molecular Sensors and Ion Detection
Authors: Hongjian Tang | Lunbo Duan | Jianwen Jiang
Source: Langmuir (review, 2023-11-03)
Field: Chemistry — Inorganic Chemistry

Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective Metal-organic frameworks (MOFs) have attracted tremendous interest because of their tunable structures, functionalities, and physiochemical properties. The nearly infinite combinations of metal nodes and organic linkers have led to the synthesis of over 100,000 experimental MOFs and the construction of millions of hypothetical counterparts. It is intractable to identify the best candidates in the immense chemical space of MOFs for applications via conventional trial-to-error experiments or brute-force simulations. Over the past several years, machine learning (ML) has substantially transformed the way of MOF discovery, design, and synthesis. Driven by the abundant data from experiments or simulations, ML can not only efficiently and accurately predict MOF properties but also quantitatively derive structure-property relationships for rational design and screening. In this Perspective, we summarize recent achievements in leveraging ML for MOFs from the aspects of data acquisition, featurization, model training, and applications. Then, current challenges and new opportunities are discussed for the future exploration of ML to accelerate the development of new MOFs in this vibrant field. Chemical space ; Computer science ; Metal-organic framework ; Perspective (graphical) ; Nanotechnology ; Property (philosophy) ; Field (mathematics) ; Space (punctuation) ; Artificial intelligence ; Biochemical engineering ; Machine learning ; Data science ; Materials science ; Engineering ; Chemistry ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: V. M. Rahul | V. Balaji | S. Narendranath
Source: Results in Engineering (article, 2023-05-02)
Field: Materials Science — Materials Chemistry

Optimization of wire-EDM process parameters for Ni–Ti-Hf shape memory alloy through particle swarm optimization and CNN-based SEM-image classification Shape memory alloys (SMAs) have the unique ability to regain their shape under specified conditions, making them extremely useful for a wide range of applications. However, non-traditional machining techniques could be more effective for high-temperature SMAs as they provide better shape recovery properties than conventional methods, necessitating this study's unconventional wire electric discharge machining procedure. The surface morphology of Ni–Ti-Hf-based alloys was examined using scanning electron microscopy. A convolutional neural network model was used to categorize SEM images based on the material removal rate, utilizing the pixel intensity histogram of the processed images. The study discovered that the material remelted as the discharge energy increased, leaving lumps and globules on the surface. The size of debris lumps, pores, and globules increased with increasing Ra values. Moreover, widening the inter-electrode gap by expanding the servo-voltage facilitated efficiently removing debris from the machined site. The TOPSIS method for particle swarm optimization was employed to find the optimal solutions, yielding TON = (124.239, 116.228), TOFF = (54.532, 40.781), Servo voltage = (36.216, 43.766), and Wire-Feed = (2.157, 8). These results were validated through experimentation, with minimal error percentages of 2.01%, 2.046% (MRR), and 3.86%, 1.611% (Ra) for both sets of input parameters, respectively. • Ni–Ti-Hf SMAs were analyzed using a CNN model for SEM image classification. • Increasing the T ON leads to the increased formation of lumps and globules. • MRR and Ra were optimized using the Particle swarm optimization algorithm. • The regression model in PSO yielded two optimal solutions for MRR and Ra values. • The 1-Dimensional CNN model had an accuracy of 92.67%. Particle swarm optimization ; Shape-memory alloy ; Materials science ; Process (computing) ; Alloy ; Process optimization ; Particle (ecology) ; Image (mathematics) ; Mechanical engineering ; Computer science ; Artificial intelligence ; Metallurgy ; Composite material ; Algorithm ; Engineering ; Chemical engineering ; Geology Shape Memory Alloy Transformations ; Advanced Machining and Optimization Techniques ; Welding Techniques and Residual Stresses
Authors: Samaneh Rashtbari | Gholamreza Dehghan | Arezu Marefat | Simin Khataee | Alireza Khataee
Source: Ultrasonics Sonochemistry (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Proficient sonophotocatalytic degradation of organic pollutants using Co3O4/TiO2 nanocomposite immobilized on zeolite: Optimization, and artificial neural network modeling The health of all living organisms is greatly influenced by the quality of the water. Therefore, developing cost-effective, eco-friendly, and easily accessible methods is desperately needed to meet the high global demand for clean water. Recently, nanozyme-based dye degradation methods have been promising for the remediation of water pollution. In this work, peroxidase-mimic Co<sub>3</sub>O<sub>4</sub>/TiO<sub>2</sub> nanocomposite was synthesized and characterized for its size, morphology, and crystalline structure. Colorimetric assay results showed that the peroxidase-like activity of the Co<sub>3</sub>O<sub>4</sub>/TiO<sub>2</sub> nanocomposite was considerably enhanced compared to the pure Co<sub>3</sub>O<sub>4</sub> NPs and TiO<sub>2</sub> NPs. Besides excellent enzyme-mimic activity, the higher sonophotocatalytic dye degradation capability of the nanocomposite after immobilization on zeolite (Co<sub>3</sub>O<sub>4</sub>/TiO<sub>2</sub>@Ze) was also demonstrated. Under optimal conditions (pH = 5.0, 25 °C), 0.1 g/L of catalyst was able to degrade 100 % of methylene blue (MB) with 600 μM in the presence of 30 μM H<sub>2</sub>O<sub>2</sub> within 12 min. GC/MS analysis and toxicity studies revealed less toxic metabolite production after treatment of MB with sonophotocatalytic Co<sub>3</sub>O<sub>4</sub>/TiO<sub>2</sub>@Ze. Modeling of MB degradation using artificial neural networks (ANN) with a 5:6:1 topology was successfully performed, and the results confirmed the fitness of theoretical and experimental outputs according to the calculated correlation coefficient values. The prepared nanocomposite could thus be used as a promising and highly effective catalyst for the removal of organic dyes from polluted water. Nanocomposite ; Degradation (telecommunications) ; Catalysis ; Chemical engineering ; Zeolite ; Pollutant ; Environmental remediation ; Methylene blue ; Chemistry ; Environmental pollution ; Materials science ; Nuclear chemistry ; Photocatalysis ; Nanotechnology ; Organic chemistry ; Contamination ; Computer science ; Environmental science Advanced Nanomaterials in Catalysis ; Nanoparticles: synthesis and applications ; Nanoplatforms for cancer theranostics
Authors: Xiaohua Li | Ziheng Huang | Shanshan Shao | Yixi Cai
Source: Fuel (article, 2023-08-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning prediction of physical properties and nitrogen content of porous carbon from agricultural wastes: Effects of activation and doping process Nitrogen ; Materials science ; Porosity ; Carbon fibers ; Chemical engineering ; Raw material ; Doping ; Saturation (graph theory) ; Composite material ; Chemistry ; Organic chemistry ; Mathematics ; Composite number Supercapacitor Materials and Fabrication ; Adsorption and biosorption for pollutant removal ; Catalysis and Hydrodesulfurization Studies
Authors: Baptiste Py | Adeleke Maradesa | Francesco Ciucci
Source: Electrochimica Acta (article, 2023-12-29)
Field: Chemistry — Electrochemistry

From theory to practice: Unlocking the distribution of capacitive times in electrochemical impedance spectroscopy Dielectric spectroscopy ; Electrical impedance ; Parametric statistics ; Capacitive sensing ; Deconvolution ; Computer science ; Discrete cosine transform ; Electrode ; Electronic engineering ; Materials science ; Artificial intelligence ; Algorithm ; Electrochemistry ; Electrical engineering ; Mathematics ; Engineering ; Physics Electrochemical Analysis and Applications ; Fuel Cells and Related Materials ; Conducting polymers and applications
Authors: Tianyuan Liu | Jie Chen | Lili Kong | Xia Li | Xiaofan Chen
Source: Food Bioscience (article, 2023-12-13)
Field: Chemistry — Analytical Chemistry

Utilization of a portable Raman spectrometer combined with a PCA-SVM model for starch type differentiation Starch ; Principal component analysis ; Raman spectroscopy ; Support vector machine ; Artificial intelligence ; Spectrometer ; Pattern recognition (psychology) ; Chemistry ; Biological system ; Analytical Chemistry (journal) ; Corn starch ; Computer science ; Chromatography ; Materials science ; Food science ; Optics ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Samuel Rischke | Lisa Hahnefeld | Bo Burla | Frank Behrens | Robert Gurke | Timothy J. Garrett
Source: Journal of Mass Spectrometry and Advances in the Clinical Lab (review, 2023-02-17)
Field: Chemistry — Spectroscopy

Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers. Workflow ; Computer science ; Scope (computer science) ; Biomarker discovery ; Data science ; Quality (philosophy) ; Set (abstract data type) ; Data quality ; Data mining ; Reliability (semiconductor) ; Proteomics ; Engineering ; Database Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Clinical Laboratory Practices and Quality Control
Authors: Shunsaku Yasumura | Kenichiro Saita | Takumi Miyakage | Ken Nagai | Kenichi Kon | Takashi Toyao | Zen Maeno | Tetsuya Taketsugu | Ken‐ichi Shimizu
Source: Nature Communications (article, 2023-07-03)
Field: Materials Science — Materials Chemistry

Designing main-group catalysts for low-temperature methane combustion by ozone The catalytic combustion of methane at a low temperature is becoming increasingly key to controlling unburned CH<sub>4</sub> emissions from natural gas vehicles and power plants, although the low activity of benchmark platinum-group-metal catalysts hinders its broad application. Based on automated reaction route mapping, we explore main-group elements catalysts containing Si and Al for low-temperature CH<sub>4</sub> combustion with ozone. Computational screening of the active site predicts that strong Brønsted acid sites are promising for methane combustion. We experimentally demonstrate that catalysts containing strong Bronsted acid sites exhibit improved CH<sub>4</sub> conversion at 250 °C, correlating with the theoretical predictions. The main-group catalyst (proton-type beta zeolite) delivered a reaction rate that is 442 times higher than that of a benchmark catalyst (5 wt% Pd-loaded Al<sub>2</sub>O<sub>3</sub>) at 190 °C and exhibits higher tolerance to steam and SO<sub>2</sub>. Our strategy demonstrates the rational design of earth-abundant catalysts based on automated reaction route mapping. Methane ; Ozone ; Combustion ; Catalysis ; Group (periodic table) ; Environmental science ; Chemical engineering ; Chemistry ; Environmental chemistry ; Materials science ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies