jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: FNU Srinidhi
Source: ChemRxiv (preprint, 2021-02-11)
Field: Chemistry — Spectroscopy

Artificial Neural Network (FFBP-ANN) Based Grey Relational Analysis for Modeling Dyestuff Solubility in Supercritical CO2 with Ethanol as the Co-Solvent The research on dye solubility modeling in supercritical carbon dioxide is gaining prominence over the past few decades. A simple and ubiquitous model that is capable of accurately predicting the solubility in supercritical carbon dioxide would be invaluable for industrial and research applications. In this study, we present such a model for predicting dye solubility in supercritical carbon dioxide with ethanol as the co-solvent for a qualitatively diverse sample of eight dyes. A feed forward back propagation - artificial neural network model based on Levenberg-Marquardt algorithm was constructed with seven input parameters for solubility prediction, the network architecture was optimized to be [7-7-1] with mean absolute error, mean square error, root mean square error and Nash-Sutcliffe coefficient to be 0.026, 0.0016, 0.04 and 0.9588 respectively. Further, Pearson-product moment correlation analysis was performed to assess the relative importance of the parameters considered in the ANN model. A total of twelve prevalent semiempirical equations were also studied to analyze their efficiency in correlating to the solubility of the prepared sample. Mendez-Teja model was found to be relatively efficient with root mean square error and mean absolute error to be 0.094 and 0.0088 respectively. Furthermore, Grey relational analysis was performed and the optimum regime of temperature and pressure were identified with dye solubility as the higher the better performance characteristic. Finally, the dye specific crossover ranges were identified by analysis of isotherms and a strategy for class specific selective dye extraction using supercritical CO2 extraction process is proposed. Solubility ; Supercritical carbon dioxide ; Supercritical fluid ; Mean squared error ; Supercritical fluid extraction ; Approximation error ; Artificial neural network ; Chemistry ; Solvent ; Carbon dioxide ; Backpropagation ; Thermodynamics ; Mathematics ; Computer science ; Algorithm ; Statistics ; Organic chemistry ; Artificial intelligence ; Physics Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Anders Brakestad | Peter Wind | Stig Rune Jensen | Luca Frediani | Kathrin H. Hopmann
Source: ChemRxiv (preprint, 2021-02-01)
Field: Materials Science — Materials Chemistry

Multiwavelets Applied to Metal-Ligand Interactions: Energies Free from Basis Set Errors The following article will be submitted to the Journal of Chemical Physics. It is thus not a peer-reviewed manuscript. After it is hopefully accepted for publication, it will be found (in revised form) at https://aip.scitation.org/journal/jcpTransition metal-catalyzed reactions invariably include steps, where ligands associate or dissociate. In order to obtain reliable energies for such reactions, sufficiently large basis sets need to be employed. In this paper, we have used high-precision Multiwavelet calculations to compute the metal-ligand association energies for 27 transition metal complexes with common ligands such as H2, CO, olefins and solvent molecules. By comparing our Multiwavelet results to a variety of frequently used Gaussian-type basis sets, we show that counterpoise corrections, which are widely employed to correct for basis set superposition errors, often lead to underbinding. Additionally, counterpoise corrections are difficult to employ, when the association step also involves a chemical transformation. Multiwavelets, which can be conveniently applied to all types of reactions, provide a promising alternative for computing electronic interaction energies free from any basis set errors. Counterpoise ; Basis (linear algebra) ; Superposition principle ; Computer science ; Gaussian ; Transformation (genetics) ; Basis set ; Set (abstract data type) ; Algorithm ; Statistical physics ; Dimension (graph theory) ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: В.С. Мясниченко | Павел Матренин | Николай Юрьевич Сдобняков
Source: Physical and Chemical Aspects of the Study of Clusters Nanostructures and Nanomaterials (article, 2021-12-23)
Field: Materials Science — Materials Chemistry

PREDICTION OF BONDING ENERGY BY STRUCTURAL DESCRIPTORS OF METAL NANOALLOYS Обсуждается проблема предсказания энергии связи для тернарных металлических наночастиц и построение моделей обучения на базе структурных дескрипторов. Были построены регрессионные зависимости удельной межатомной энергии связи для тернарной наносистемы Au - Ag - Cu. Использовался ряд из пяти радиальных признаков, зависящих от попарного межатомного расстояния дескрипторов структуры наночастицы. Для более корректной оценки точности была применена кросс-валидация, далее полученные на валидационных частях выборки результаты усреднялись. Полученная модель ограниченно предсказывает значение удельной межатомной энергии связи внутри группы данных для наночастиц одного состава, а для всей выборки средняя по модулю ошибка составляет 14%. При этом модель практически безошибочно определяет состав наночастицы из нескольких вариантов. Наибольшее значение коэффициента детерминации на всей выборке получено с помощью ансамблевого алгоритма случайный лес. Обнаружена отрицательная корреляция между энергией связи наносплава и положением первого пика радиальной функции распределения для атомов меди. The problem of predicting the binding energy for ternary metal nanoparticles and the construction of learning models based on structural descriptors are discussed. Regression dependences of the specific interatomic bond energy were constructed for the ternary Au - Ag - Cu nanosystem. A number of five radial features were used, depending on the pairwise interatomic distance of the nanoparticle structure descriptors. For a more correct assessment of the accuracy, cross-validation was applied, then the results obtained on the validation parts of the sample were averaged. The resulting model limitedly predicts the value of the specific interatomic binding energy within a group of data for nanoparticles of the same composition. For the entire sample the average error in modulus is 14 %. In this case, the model almost accurately determines the composition of a nanoparticle of several variants. The largest value of the coefficient of determination in the entire sample was obtained using an ensemble random forest algorithm. A negative correlation was found between the binding energy of the nanoalloy and the position of the first peak of the radial distribution function for copper atoms. Ternary operation ; Nanoparticle ; Radial distribution function ; Pairwise comparison ; Interatomic potential ; Binding energy ; Position (finance) ; Coordination number ; Materials science ; Chemistry ; Thermodynamics ; Molecular dynamics ; Computational chemistry ; Nanotechnology ; Mathematics ; Computer science ; Physics ; Atomic physics ; Statistics Machine Learning in Materials Science
Authors: Wei Wang | Ben Niu | Wenxuan Jiang | Mengqi Lv | Sa Wang
Source: Research Square (preprint, 2021-09-29)
Field: Chemistry — Electrochemistry

Determining the depth of surface charging layer of single Prussian blue nanoparticles with pseudocapacitive behaviors Prussian blue ; Materials science ; Modulation (music) ; Nanoparticle ; Analytical Chemistry (journal) ; Frequency modulation ; Molecular physics ; Dielectric spectroscopy ; Scattering ; Surface charge ; Diffusion ; Low frequency ; Spectroscopy ; Optoelectronics ; Optics ; Nanotechnology ; Electrochemistry ; Chemistry ; Radio frequency ; Electrode ; Physics ; Acoustics Electrochemical Analysis and Applications ; Conducting polymers and applications ; Electrochemical sensors and biosensors
Authors: Abdul Rauf Shah | Hajira Tahir | Hafiz Muhammad Kifayatullah
Source: Pakistan Journal of Scientific & Industrial Research Series A Physical Sciences (article, 2021-09-29)
Field: Chemistry — Organic Chemistry

Central Composite Design Based Adsorption Study of Malachite Green Dye Using Reduced Graphene Oxide Nano-Adsorbent The adsorption of Malachite Green (MG) dye was well studied and elucidated from the liquid phase using reduced Graphene Oxide (rGO) nano-adsorbent. The desired levels of the factors were determined to be the amount of adsorbent of 0.2 g/L, pH of 8.5, the concentration of the dye as 100 mg/L and the sonication time of 50 min by Central Composite Design (CCD). The removal of the dye was found to be 95% at the optimum levels of the variables. Furthermore, the removal of the dye was higher at the higher values of the amount of adsorbent and pH. Langmuir and Temkin models were observed significant for rGO-MG dye system. The values of Gibb's free energy, the entropy and the enthalpy were found to be -10.502 KJ/mol, 34.314 KJ/mol and 0.147 KJ/mol.K, respectively. The kinetic data were also found well fitted to pseudo second order kinetics model for the said system. Afterwards, the cost of the process was found to be US$0.654/dm3. Therefore, the adsorption process effectively removed the dye from the simulated aqueous phase using rGO nano-adsorbent. Malachite green ; Adsorption ; Graphene ; Enthalpy ; Central composite design ; Aqueous solution ; Oxide ; Chemistry ; Langmuir adsorption model ; Nano- ; Langmuir ; Nuclear chemistry ; Kinetics ; Materials science ; Chemical engineering ; Response surface methodology ; Chromatography ; Organic chemistry ; Nanotechnology ; Composite material ; Thermodynamics Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Guy Jacoby | M. Segal | Tamara Ehm | Inbal Abutbul‐Ionita | Hila Shinar | Salome Azoulay‐Ginsburg | Dganit Danino | Michael M. Kozlov | Roey J. Amir | Roy Beck
Source: ChemRxiv (preprint, 2021-03-18)
Field: Materials Science — Biomaterials

Order from Disorder with Intrinsically Disordered Peptide Amphiphiles Amphiphilic molecules and their self-assembled structures have long been the target of extensive research due to their potential applications in fields ranging from materials design to biomedical and cosmetic applications. Increasing demands for functional complexity have been met with challenges in biochemical engineering, driving researchers to innovate in the design of new amphiphiles. An emerging class of molecules, namely, peptide amphiphiles, combines key advantages and circumvents some of the disadvantages of conventional phospholipids and block-copolymers. Herein, we present new peptide amphiphiles comprised of an intrinsically disordered peptide conjugated to two variants of hydrophobic dendritic domains. These molecules termed intrinsically disordered peptide amphiphiles (IDPA), exhibit a sharp pH-induced micellar phase-transition from low-dispersity spheres to extremely elongated worm-like micelles. We present an experimental characterization of the transition and propose a theoretical model to describe the pH-response. We also present the potential of the shape transition to serve as a mechanism for the design of a cargo hold-and-release application. Such amphiphilic systems demonstrate the power of tailoring the interactions between disordered peptides for various stimuli-responsive biomedical applications. Amphiphile ; Micelle ; Nanotechnology ; Peptide ; Conjugated system ; Amphiphilic molecule ; Materials science ; Self-assembly ; Molecule ; Dispersity ; Copolymer ; Chemistry ; Chemical physics ; Polymer ; Organic chemistry ; Aqueous solution ; Biochemistry ; Polymer chemistry Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Polydiacetylene-based materials and applications
Authors: C. Maheswari | A. Sai Ramya | Rajoo Baskar | Selvakumar Nachimuthu | Rishab Govind Rajaram | Vasanth Malaichamy
Source: Research Square (preprint, 2021-09-03)
Field: Materials Science — Materials Chemistry

Experimental Analysis And Parameter Optimization On The Reduction of NOx From Diesel Engine Using RSM And ANN Model NOx ; Diesel engine ; Reduction (mathematics) ; Automotive engineering ; Diesel fuel ; Response surface methodology ; Environmental science ; Computer science ; Engineering ; Chemistry ; Mathematics ; Machine learning ; Combustion ; Organic chemistry Catalytic Processes in Materials Science ; Vehicle emissions and performance ; Advanced Combustion Engine Technologies
Authors: Adewumi Isaac Popoola | S. Babatunde Akinpelu
Source: European Journal of Applied Physics (article, 2021-07-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Numerical Investigation of the Stability and Spintronic Properties of Selected Quaternary Alloys The use of electronic charge and spins (spintronics) has been proposed for much better data storage. This class of material is believed to have excellent capability for data integrity, low dynamic power consumption and high-density storage that showcases excellent protection against data loss. The spintronic and related properties have been investigated on four newly proposed quaternary alloys (NbRhGeCo, NbRhGeCr, NbRhGeFe and NbRhGeNi) through the first-principles calculation method of the Density Functional Theory (DFT). Specifically, the phonon frequencies, elastic stabilities, and the electronic structure were systematically studied in the full Heusler structure. The results predict that NbRhGeFe and NbRhGeCr are elastically and structurally stable. Both NbRhGeFe and NbRhGeCo are half-metals with ferromagnetic character, but NbRhGeCo is unfortunately elastically unstable. NbRhGeCr and NbRhGeNi are non-magnetic metallic alloys in their spin channels. All the results predict NbRhGeFe to be the only suitable among all the four alloys for spintronic application. Spintronics ; Ferromagnetism ; Materials science ; Density functional theory ; Condensed matter physics ; Spins ; Magnetic storage ; Spin (aerodynamics) ; Physics ; Thermodynamics ; Quantum mechanics Heusler alloys: electronic and magnetic properties ; MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research
Authors: Sergey Artemenko | Victor Mazur
Source: Refrigeration Engineering and Technology (article, 2021-10-15)
Field: Materials Science — Materials Chemistry

EN Машинне навчання для властивостей холодоагентів Міждисциплінарний характер нових цілей, спрямованих на розробку робочих матеріалів для екологічно чистих технологій вимагає більш динамічного використання інформаційних технологій (ІТ) для забезпечення правильних компромісних рішень у конкурентному середовищі. Машинне навчання (ML) — це частина методологій штучного інтелекту (AI), яка використовує алгоритми, які не є прямим рішенням проблеми, а навчаються за допомогою рішень незліченної кількості подібних проблем. Машинне навчання відкрило новий шлях у дослідженні термодинамічної поведінки нових речовин. Різні обчислювальні інструменти були застосовані для вирішення актуальної проблеми - прогнозування фазової поведінки soft речовин під значними екзогенними впливами. Метою цього дослідження є розробка нової точки зору щодо прогнозування термодинамічних властивостей м'яких речовин за допомогою методології, яка передбачає штучні нейронні мережі (ANN) та глобальну фазову діаграму для забезпечення кореляції між структурою та властивостями. В роботі представлено застосування машинного навчання в інженерній термодинаміці для прогнозування азеотропної поведінки бінарних холодоагентів і визначення коефіцієнта продуктивності (COP) для роботи органічного циклу Ренкіна (ORC). За даними про кипіння та критичні точки. Запропоновано новий підхід до прогнозування утворення азеотропного стану в суміші, який розроблено та представлено. Цей підхід використовує синергію нейронних мереж та методології глобальної фазової діаграми для кореляції азеотропних даних для бінарних сумішей на основі лише критичних властивостей та ацентричного коефіцієнта окремих компонентів у сумішах холодоагентів. Це не вимагає інтенсивних розрахунків. Побудова кореляцій ANN між інформаційними атрибутами робочих рідин та критеріями ефективності циклу Ренкіна звужує область компромісів у просторі конкурентних економічних, екологічних та технологічних критеріїв Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Markus Reiher
Source: Israel Journal of Chemistry (preprint, 2021-12-21)
Field: Materials Science — Materials Chemistry

Molecule‐Specific Uncertainty Quantification in Quantum Chemical Studies Abstract Solving the electronic Schrödinger equation for changing nuclear coordinates provides access to the Born‐Oppenheimer potential energy surface. This surface is the key starting point for almost all theoretical studies of chemical processes in electronic ground and excited states (including molecular structure prediction, reaction mechanism elucidation, molecular property calculations, quantum and molecular dynamics). Electronic structure models aim at a sufficiently accurate approximation of this surface. They have therefore become a cornerstone of theoretical and computational chemistry, molecular physics, and materials science. In this work, we elaborate on general features of approximate electronic structure models such as accuracy, efficiency, and general applicability in order to arrive at a perspective for future developments, of which a vanguard has already arrived. Our quintessential proposition is that meaningful quantum mechanical predictions for chemical phenomena require system‐specific uncertainty information for each and every electronic structure calculation, if objective conclusions shall be drawn with confidence. Electronic structure ; Statistical physics ; Quantum ; Schrödinger equation ; Quantum chemistry ; Excited state ; Potential energy surface ; Molecular dynamics ; Property (philosophy) ; Computer science ; Theoretical physics ; Chemistry ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Umit Volkan Ucak | Islambek Ashyrmamatov | Junsu Ko | Juyong Lee
Source: ChemRxiv (preprint, 2021-08-03)
Field: Materials Science — Materials Chemistry

RetroTRAE: retrosynthetic translation of atomic environments with Transformer We present the new retrosynthesis prediction method RetroTRAE using fragment-based tokenization combined with the Transformer architecture. RetroTRAE represents chemical reactions by using the changes of fragment sets of molecules using the atomic environment fragmentation scheme. Atom environments stand as an ideal, chemically meaningful building blocks together producing a high resolution molecular representation. Describing a molecule with a set of atom environments establishes a clear relationship between translated product-reactant pairs due to conservation of atoms in reactions. Our model achieved a top-1 accuracy of 67.1% within the bioactively similar range for USPTO test dataset, outperforming the other state of the art, translation methods. We investigated the effect of different encoding scenarios on predicting the reactant candidates. We also critically assessed the retrieval process that converts a set of fragments into a molecule with respect to coverage, degeneracy and resolution. Our new template-free model for retrosynthetic prediction provides fast and reliable retrosynthetic route planning for substances whose fragmentation patterns are revealed. Retrosynthetic analysis ; Fragmentation (computing) ; Computer science ; Fragment (logic) ; Transformer ; Machine translation ; Artificial intelligence ; Chemistry ; Algorithm ; Programming language ; Engineering ; Stereochemistry ; Voltage Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: David A. Cullen | Michael J. Zachman | Haoran Yu | Debangshu Mukherjee | Shawn Reeves
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

Automated methods for improved characterization of alloy nanoparticle catalysts An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button. Characterization (materials science) ; Alloy ; Materials science ; Nanoparticle ; Catalysis ; Content (measure theory) ; Nanotechnology ; Metallurgy ; Chemistry ; Mathematics ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Sahar Iravani | Tim Conrad
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-02-19)
Field: Chemistry — Spectroscopy

An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data Abstract Analyzing mass spectrometry-based proteomics data with deep learning (DL) approaches poses several challenges due to the high dimensionality, low sample size, and high level of noise. Additionally, DL-based workflows are often hindered to be integrated into medical settings due to the lack of interpretable explanation. We present DLearnMS, a DL biomarker detection framework, to address these challenges on proteomics instances of liquid chromatography-mass spectrometry (LC-MS) - a well-established tool for quantifying complex protein mixtures. Our DLearnMS framework learns the clinical state of LC-MS data instances using convolutional neural networks. Based on the trained neural networks, we show how biomarkers can be identified using layer-wise relevance propagation. This enables detecting discriminating regions of the data and the design of more robust networks. One of the main advantages over other established methods is that no explicit preprocessing step is needed in our DLearnMS framework. Our evaluation shows that DLearnMS outperforms conventional LC-MS biomarker detection approaches in identifying fewer false positive peaks while maintaining a comparable amount of true positives peaks. Code availability The code is available from the following GIT repository: https://github.com/SaharIravani/DlearnMS Preprocessor ; Computer science ; Artificial intelligence ; Proteomics ; Biomarker ; Biomarker discovery ; False positive paradox ; Convolutional neural network ; Workflow ; Code (set theory) ; Deep learning ; Machine learning ; Pattern recognition (psychology) ; Data mining ; Chemistry ; Database Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications