jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Annie M. Westerlund | Bente Barge | Lewis Mervin | Samuel Genheden
Source: Molecular Informatics (article, 2023-09-07)
Field: Materials Science — Materials Chemistry

Data‐driven approaches for identifying hyperparameters in multi‐step retrosynthesis The multi-step retrosynthesis problem can be solved by a search algorithm, such as Monte Carlo tree search (MCTS). The performance of multistep retrosynthesis, as measured by a trade-off in search time and route solvability, therefore depends on the hyperparameters of the search algorithm. In this paper, we demonstrated the effect of three MCTS hyperparameters (number of iterations, tree depth, and tree width) on metrics such as Linear integrated speed-accuracy score (LISAS) and Inverse efficiency score which consider both route solvability and search time. This exploration was conducted by employing three data-driven approaches, namely a systematic grid search, Bayesian optimization over an ensemble of molecules to obtain static MCTS hyperparameters, and a machine learning approach to dynamically predict optimal MCTS hyperparameters given an input target molecule. With the obtained results on the internal dataset, we demonstrated that it is possible to identify a hyperparameter set which outperforms the current AiZynthFinder default setting. It appeared optimal across a variety of target input molecules, both on proprietary and public datasets. The settings identified with the in-house dataset reached a solvability of 93 % and median search time of 151 s for the in-house dataset, and a 74 % solvability and 114 s for the ChEMBL dataset. These numbers can be compared to the current default settings which solved 85 % and 73 % during a median time of 110s and 84 s, for in-house and ChEMBL, respectively. Hyperparameter ; Hyperparameter optimization ; Computer science ; Tree (set theory) ; chEMBL ; Set (abstract data type) ; Machine learning ; Artificial intelligence ; Search algorithm ; Monte Carlo tree search ; Search tree ; Data mining ; Algorithm ; Monte Carlo method ; Mathematics ; Support vector machine ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; RNA and protein synthesis mechanisms
Authors: Sujeet Kumar | K. E. K. Vimal | Muhammed Anaz Khan | Yogesh Kumar
Source: International Journal on Interactive Design and Manufacturing (IJIDeM) (article, 2023-07-21)
Field: Materials Science — Metals and Alloys

A hybrid learning approach for modelling the fabrication of super duplex stainless steel thin joints using GTAW process Welding ; Gas tungsten arc welding ; Materials science ; Taguchi methods ; Grey relational analysis ; Mechanical engineering ; Bending ; Duplex (building) ; Composite material ; Arc welding ; Engineering Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Corrosion Behavior and Inhibition
Authors: Wei Gao | Junjie Yin | Ming Liu | Yongliang Zhao | Chaoyang Wang | Junjie Yan
Source: Fuel (article, 2023-11-27)
Field: Materials Science — Materials Chemistry

Enhancement of SCR denitrification control strategy considering fluegas temperature fluctuation: Fundamental principle and performance evaluation NOx ; Selective catalytic reduction ; Environmental science ; Denitrification ; Overshoot (microwave communication) ; Transient (computer programming) ; Coal ; Control theory (sociology) ; Environmental engineering ; Chemistry ; Waste management ; Computer science ; Nitrogen ; Catalysis ; Engineering ; Control (management) ; Telecommunications Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Ammonia Synthesis and Nitrogen Reduction
Authors: Zheyu Zhang | Hao Zhong | Yaoxiang Li | Roger A. Williams | Rundong Peng | Ya Chen | Xiaoli Liu
Source: Cellulose (article, 2023-11-25)
Field: Chemistry — Organic Chemistry

Predicting components of pulpwood feedstock for different physical forms and tree species using NIR spectroscopy and transfer learning Raw material ; Pulp (tooth) ; Pulp and paper industry ; Computer science ; Biological system ; Lignin ; Near-infrared spectroscopy ; Environmental science ; Process engineering ; Pulpwood ; Chemometrics ; Materials science ; Artificial intelligence ; Biochemical engineering ; Machine learning ; Chemistry ; Engineering Wood and Agarwood Research ; Spectroscopy and Chemometric Analyses ; Wood Treatment and Properties
Authors: Andrew Rohskopf | James Goff | Dionysios Sema | Kiarash Gordiz | Ngoc Cuong Nguyen | Asegun Henry | Aidan P. Thompson | Mitchell Wood
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2023-09-18)
Field: Materials Science — Materials Chemistry

Exploring model complexity in machine learned potentials for simulated properties Abstract Machine learning (ML) enables the development of interatomic potentials with the accuracy of first principles methods while retaining the speed and parallel efficiency of empirical potentials. While ML potentials traditionally use atom-centered descriptors as inputs, different models such as linear regression and neural networks map descriptors to atomic energies and forces. This begs the question: what is the improvement in accuracy due to model complexity irrespective of descriptors? We curate three datasets to investigate this question in terms of ab initio energy and force errors: (1) solid and liquid silicon, (2) gallium nitride, and (3) the superionic conductor Li $$_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>10</mml:mn> </mml:msub> </mml:math> Ge(PS $$_{6}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>6</mml:mn> </mml:msub> </mml:math> ) $$_{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>2</mml:mn> </mml:msub> </mml:math> (LGPS). We further investigate how these errors affect simulated properties and verify if the improvement in fitting errors corresponds to measurable improvement in property prediction. By assessing different models, we observe correlations between fitting quantity (e.g. atomic force) error and simulated property error with respect to ab initio values. Graphical abstract Algorithm ; Machine learning ; Computer science ; Artificial intelligence ; Ab initio ; Interatomic potential ; Materials science ; Physics ; Computational chemistry ; Molecular dynamics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; X-ray Diffraction in Crystallography
Authors: Junyoung Heo | Dong-Jun Kim | Hyesung Choi | Sungin Kim | Hoje Chun | Cyril F. Reboul | Cong T. S. Van | Dominika Elmlund | Soonmi Choi | Kihyun Kım | Young‐Gil Park | Hans Elmlund | Byungchan Han | Jungwon Park
Source: Scientific Reports (article, 2023-02-01)
Field: Materials Science — Materials Chemistry

Method for 3D atomic structure determination of multi-element nanoparticles with graphene liquid-cell TEM Determining the 3D atomic structures of multi-element nanoparticles in their native liquid environment is crucial to understanding their physicochemical properties. Graphene liquid cell (GLC) TEM offers a platform to directly investigate nanoparticles in their solution phase. Moreover, exploiting high-resolution TEM images of single rotating nanoparticles in GLCs, 3D atomic structures of nanoparticles are reconstructed by a method called "Brownian one-particle reconstruction". We here introduce a 3D atomic structure determination method for multi-element nanoparticle systems. The method, which is based on low-pass filtration and initial 3D model generation customized for different types of multi-element systems, enables reconstruction of high-resolution 3D Coulomb density maps for ordered and disordered multi-element systems and classification of the heteroatom type. Using high-resolution image datasets obtained from TEM simulations of PbSe, CdSe, and FePt nanoparticles that are structurally relaxed with first-principles calculations in the graphene liquid cell, we show that the types and positions of the constituent atoms are precisely determined with root mean square displacement values less than 24 pm. Our study suggests that it is possible to investigate the 3D atomic structures of synthesized multi-element nanoparticles in liquid phase. Graphene ; Nanoparticle ; Materials science ; Element (criminal law) ; Nanotechnology ; Computer science Quantum Dots Synthesis And Properties ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Authors: Somayeh Faraji | Zhiyu Wang | Paola Lopez-Rivera | Mingjie Liu
Source: Energy Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Advancements in computational approaches for rapid metal site discovery in carbon-based materials for electrocatalysis Computational approaches including quantum simulations, virtual high-throughput screening and machine learning accelerate the advance electrocatalysts discovery with proper accuracy and efficiency. Electrocatalyst ; Virtual screening ; Throughput ; Computer science ; Nanotechnology ; Drug discovery ; Materials science ; Chemistry ; Bioinformatics ; Wireless ; Telecommunications ; Electrochemistry ; Biology Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Nathan Jiscoot | Evgeny A. Uslamin | Evgeny A. Pidko
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Model-based evaluation and data requirements for parallel kinetic experimentation and data-driven reaction identification and optimization Chemfit uses kinetic data to facilitate the discovery of rates within chemical models, the quantification of chemical model accuracy, and the exploration of data quality and quantity requirements needed for reliable kinetic parameter discovery. Identification (biology) ; Kinetic energy ; Computer science ; Experimental data ; Data mining ; Mathematics ; Statistics ; Physics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Matthew S. Johnson | Maciej Gierada | Eric Hermes | David H. Bross | Khachik Sargsyan | Habib N. Najm | Judit Zádor
Source: Journal of Chemical Information and Modeling (article, 2023-08-10)
Field: Materials Science — Materials Chemistry

Pynta─An Automated Workflow for Calculation of Surface and Gas–Surface Kinetics Many important industrial processes rely on heterogeneous catalytic systems. However, given all possible catalysts and conditions of interest, it is impractical to optimize most systems experimentally. Automatically generated microkinetic models can be used to efficiently consider many catalysts and conditions. However, these microkinetic models require accurate estimation of many thermochemical and kinetic parameters. Manually calculating these parameters is tedious and error prone, involving many interconnected computations. We present Pynta, a workflow software for automating the calculation of surface and gas-surface reactions. Pynta takes the reactants, products, and atom maps for the reactions of interest, generates sets of initial guesses for all species and saddle points, runs all optimizations, frequency, and IRC calculations, and computes the associated thermochemistry and rate coefficients. It is able to consider all unique adsorption configurations for both adsorbates and saddle points, allowing it to handle high index surfaces and bidentate species. Pynta implements a new saddle point guess generation method called harmonically forced saddle point searching (HFSP). HFSP defines harmonic potentials based on the optimized adsorbate geometries and which bonds are breaking and forming that allow initial placements to be optimized using the GFN1-xTB semiempirical method to create reliable saddle point guesses. This method is reaction class agnostic and fast, allowing Pynta to consider all possible adsorbate site placements efficiently. We demonstrate Pynta on 11 diverse reactions involving monodenate, bidentate, and gas-phase species, many distinct reaction classes, and both a low and a high index facet of Cu. Our results suggest that it is very important to consider reactions between adsorbates adsorbed in all unique configurations for interadsorbate group transfers and reactions on high index surfaces. Saddle point ; Saddle ; Computer science ; Computation ; Workflow ; Point (geometry) ; Surface (topology) ; Chemistry ; Computational science ; Algorithm ; Mathematical optimization ; Mathematics ; Database ; Geometry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Mahendra D. Shirsat | Tibor Hianik
Source: Preprints.org (preprint, 2023-07-07)
Field: Chemistry — Electrochemistry

Electrochemical Detection of Heavy Metal Ions Based on Nanocomposite Materials Heavy metal ions (HMIs) have acute toxic effects on the health and are dangerous for human ex-istence and the ecosystem. Therefore, its sensitive and selective detection is of great importance. In recent years various nanocomposite materials have been used by researchers for the detection of HMIs by using various modalities of electrochemical techniques. This review article summarizes the recent advances in developing electrochemical sensors based on numerous nanocomposite mate-rials for detecting heavy metal ions. Nanocomposites materials, such as Metal-Organic Framework (MOF), organic conducting polymers (OCP), carbon nanotubes, graphene/reduced graphene oxide, graphitic carbon nitride, metal oxide, chitosan, mxenes, metal nanoparticle-based nanocomposites, etc. have been explored by various researchers to improve the sensing properties of electrochemical sensors. This review emphasized the synthesis and characterization techniques of nanocomposite materials, modalities for HMI detection by electrochemical technique and the electrochemical sensors. Moreover, this review highlights the development of portable biosensors for detecting HMIs in real-world scenarios, such as environmental monitoring, food safety, and clinical diagnosis. This review also demonstrates the importance of electrochemical sensors-based on nanocomposite ma-terials as a reliable, sensitive, and selective tool for detecting HMIs. Nanocomposite ; Materials science ; Graphene ; Nanotechnology ; Graphitic carbon nitride ; Electrochemistry ; Oxide ; Carbon nanotube ; Metal ions in aqueous solution ; Metal ; Chemistry ; Photocatalysis ; Electrode ; Metallurgy Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors
Authors: Mijin Kim | Dana Goerzen | Prakrit V. Jena | Jia Zeng | Rachel A. Meidl | Daniel A. Heller
Source: ChemRxiv (preprint, 2023-07-10)
Field: Materials Science — Materials Chemistry

Human and environmental safety of carbon nanotubes across their life cycle Carbon nanotubes are a large family of carbon-based hollow cylindrical structures with unique physicochemical properties that have motivated research for diverse applications; some have reached commercialization. Recent actions in the European Union that propose to ban this entire class of materials highlight an unmet need to precisely define carbon nanotubes, to better understand their toxicological risk effects on human health and the environment throughout their life cycle, and to communicate science-based policy-driving information comprising their taxonomy, safe sourcing, processing, production, manufacturing, handling, use, transportation, and disposal. In this review, we discuss current information and knowledge gaps regarding these issues. We highlight the significance of life cycle assessments of carbon nanotubes and provide a framework to inform policy decisions. Carbon nanotube ; Commercialization ; European union ; Life-cycle assessment ; Human health ; Nanotechnology ; Business ; Production (economics) ; Risk analysis (engineering) ; Materials science ; Marketing ; Medicine ; Environmental health ; Economics ; International trade Carbon Nanotubes in Composites ; Nanoparticles: synthesis and applications
Authors: Sheheera Irfan | Yasir A. Haleem | Muhammad Imran Irshad | Muhammad Farooq Saleem | Muhammad Arshad Kamran | Muhammad Habib
Source: Optics (article, 2023-05-24)
Field: Materials Science — Materials Chemistry

Tunability of the Optical Properties of Transition-Metal-Based Structural Phase Change Materials Phase transitions are an intriguing yet poorly understood aspect of transition-metal-based materials; these phase transitions can result in changes to the refractive index, absorption coefficient, and other optical properties of the materials. Transition-metal-based materials exist in a variety of crystalline phases and also have metallic, semi-metallic, and semi-conducting characteristics. In this review, we demonstrate that alloyed W- and Mo-based dichalcogenides enable phase transitions in structures, with phase transition temperatures that are tunable across a wide range using various alloy models and modern DFT-based calculations. We also analyze the tuning the optical bandgap of the metal oxide nanoparticles through doping of the transition metal in a manner that is suitable for optical switching and thermal imaging. After the introduction and a brief illustration of the structures and their exceptional properties, we discuss synthetic methodologies and their application as part of important strategies toward the enhanced performance of transition-metal-based dichalcogenides and oxides. In the end, our conclusion highlights the prospects of 2D materials as phase transition materials due to their advantages in terms of scalability and adaptability. Materials science ; Phase transition ; Transition metal ; Phase (matter) ; Characterization (materials science) ; Doping ; Chemical physics ; Alloy ; Nanotechnology ; Band gap ; Oxide ; Optoelectronics ; Condensed matter physics ; Chemistry ; Composite material ; Metallurgy 2D Materials and Applications ; Perovskite Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Piotr Cysewski | Tomasz Jeliński | Maciej Przybyłek
Source: Molecules (article, 2023-06-26)
Field: Chemistry — Spectroscopy

Finding the Right Solvent: A Novel Screening Protocol for Identifying Environmentally Friendly and Cost-Effective Options for Benzenesulfonamide This study investigated the solubility of benzenesulfonamide (BSA) as a model compound using experimental and computational methods. New experimental solubility data were collected in the solvents DMSO, DMF, 4FM, and their binary mixtures with water. The predictive model was constructed based on the best-performing regression models trained on available experimental data, and their hyperparameters were optimized using a newly developed Python code. To evaluate the models, a novel scoring function was formulated, considering not only the accuracy but also the bias-variance tradeoff through a learning curve analysis. An ensemble approach was adopted by selecting the top-performing regression models for test and validation subsets. The obtained model accurately back-calculated the experimental data and was used to predict the solubility of BSA in 2067 potential solvents. The analysis of the entire solvent space focused on the identification of solvents with high solubility, a low environmental impact, and affordability, leading to a refined list of potential candidates that meet all three requirements. The proposed procedure has general applicability and can significantly improve the quality and speed of experimental solvent screening. Solubility ; Solvent ; Computer science ; Hyperparameter ; Regression ; Experimental data ; Python (programming language) ; Regression analysis ; Cross-validation ; Data mining ; Algorithm ; Machine learning ; Chemistry ; Mathematics ; Statistics ; Organic chemistry Analytical Chemistry and Chromatography ; Crystallization and Solubility Studies ; Analytical Methods in Pharmaceuticals
Authors: Ali G. Alkhathami | Waleed Khaled Younis Albahadly | Mohammed Abed Jawad | Montather F. Ramadan | Khulood Majid Alsaraf | Zainab Al-Hawraa Riyad Muedii | Fahad Alsaikhan | Muath Suliman
Source: Frontiers in Materials (article, 2023-09-18)
Field: Chemistry — Inorganic Chemistry

Hydrogel assistant synthesis of new Ti-MOF cross-linked oxidized pectin and chitosan with anti-breast cancer properties Breast cancer is one of the most common diseases of the modern age. Although many methods for its treatment have been reported so far, the report and synthesis of new compounds based on new technologies, especially nanotechnology, is important. One of the laboratory methods for evaluating the anticancer properties of compounds is the in vitro MTT method (3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide). In this study, the in vitro anti-breast cancer activity of the newly synthesized (Titanium Metal-Organic Framework) Ti-MOF cross-linked oxidized pectin and chitosan hydrogel, which uses biopolymers in its synthesis and structure, was investigated. The anticancer activity results showed that the synthetic nanopolymer had cell proliferation and viability of 27% more than the control and (the half maximal inhibitory concentration) IC 50 of 111 μg/mL against breast cancer cells. Before the anticancer evaluation, the structure of the synthesized Ti-MOF cross-linked oxidized pectin, and chitosan hydrogel was confirmed by (X-Ray Diffraction) XRD pattern (Fourier Transform Infrared) FT-IR spectrum (Energy-dispersive X-ray) EDAX spectroscopy, N 2 adsorption/desorption isotherm and (Scanning Electron Microscope) Scanning Electron Microscope images. The results of identification and characterization showed that the synthetic nanopolymer was in the range of nanoparticles. The peaks of the expected functional groups and reactant elements were observed in the FT-IR spectrum and energy-dispersive X-ray spectroscopy of the final product. High physicochemical capabilities such as the uniform morphology, crystallization of particles, and high specific surface area from synthesized Ti-MOF cross-linked oxidized pectin, and chitosan hydrogel were observed. The unique properties of the synthesized Ti-MOF cross-linked oxidized pectin and chitosan hydrogel can be attributed to the appropriate method of its synthesis that was carried out in this study. Chitosan ; Scanning electron microscope ; Fourier transform infrared spectroscopy ; Nuclear chemistry ; Materials science ; Bromide ; Nanoparticle ; Pectin ; Infrared spectroscopy ; Chemistry ; Chemical engineering ; Nanotechnology ; Organic chemistry ; Biochemistry Metal-Organic Frameworks: Synthesis and Applications ; Nanoparticle-Based Drug Delivery ; Advanced Nanomaterials in Catalysis
Authors: I. G. B. N. Makertihartha | Noerma J. Azhari | Grandprix T.M. Kadja
Source: Journal of Engineering and Technological Sciences (review, 2023-05-23)
Field: Chemistry — Inorganic Chemistry

A Review on Zeolite Application for Aromatic Production from Non-Petroleum Carbon-Based Resources The application of zeolite catalyst has been expanded to support on-purpose sustainable technology. This review focused on zeolite application to produce aromatic compounds from non-petroleum carbon-based resources, including methanol, CO2, CO, and biomass. For COx resources, the two main routes for producing aromatics products are discussed, i.e., the olefinic and the oxygenates-mediated route. Moreover, several improvement strategies for enhancing catalytic performance are also discussed, i.e., the addition of metal components, tuning the metal and zeolite structure, and modifying the reaction process. Finally, prospects for future development are formulated. Zeolite ; Biomass (ecology) ; Carbon fibers ; Methanol ; Catalysis ; Petroleum ; Production (economics) ; Waste management ; Oxygenate ; Environmental science ; Materials science ; Chemical engineering ; Chemistry ; Organic chemistry ; Engineering ; Composite material Zeolite Catalysis and Synthesis ; Catalysis for Biomass Conversion ; Catalysis and Hydrodesulfurization Studies