jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hieu Huynh | Khanh Le | Linh Vu | Trang T. Nguyen | Matthew Holcomb | Stefano Forli | Hung Phan
Source: Journal of Computational Chemistry (article, 2024-03-18)
Field: Chemistry — Physical and Theoretical Chemistry

Synergy of machine learning and density functional theory calculations for predicting experimental Lewis base affinity and Lewis polybase binding atoms Investigation of Lewis acid-base interactions has been conducted by ab initio calculations and machine learning (ML) models. This study aims to resolve two critical tasks that have not been quantitatively investigated. First, ML models developed from density functional theory (DFT) calculations predict experimental BF<sub>3</sub> affinity with Pearson correlation coefficients around 0.9 and mean absolute errors around 10 kJ mol<sup>-1</sup>. The ML models are trained by DFT-calculated BF<sub>3</sub> affinity of more than 3000 adducts, with input features readily obtained by rdkit. Second, the ML models have the capability of predicting the relative strength of Lewis base binding atoms in Lewis polybases, which is either an extremely challenging task to conduct experimentally or a computationally expensive task for ab initio methods. The study demonstrates and solidifies the potential of combining DFT calculations and ML models to predict experimental properties, especially those that are scarce and impractical to empirically acquire. Lewis acids and bases ; Density functional theory ; Computational chemistry ; Base (topology) ; Chemistry ; Mathematics ; Organic chemistry ; Catalysis Crystallography and molecular interactions ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Vinod Kumar
Source: International Journal of Advanced Chemistry Research (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Mechanistic studies of transition metal-catalyzed cross-coupling reactions Transition metal-catalyzed cross-coupling reactions have become indispensable tools in modern organic synthesis, enabling the efficient formation of Carbon-Carbon (C-C) and carbon-heteroatom (C-X) bonds. This research provides a comprehensive mechanistic analysis of three pivotal cross-coupling reactions: the palladium-catalyzed Suzuki-Miyaura coupling, the nickel-catalyzed Kumada coupling, and the iron-catalyzed Stille coupling. These reactions are critically evaluated in terms of their catalytic cycles, including key steps such as oxidative addition, transmetalation, and reductive elimination. The mechanistic insights discussed are supported by recent computational advances, particularly in density functional theory (DFT), which have provided significant predictive power in optimizing reaction conditions and designing novel catalysts. By integrating experimental and computational studies, this work contributes to a deeper understanding of the catalytic mechanisms involved in transition metal-catalyzed cross-coupling reactions, offering valuable insights for the development of more effective and sustainable synthetic methodologies. This study underscores the continued evolution of cross-coupling reactions as a cornerstone of organic synthesis. Catalysis ; Transition metal ; Coupling reaction ; Coupling (piping) ; Chemical physics ; Materials science ; Chemistry ; Computational chemistry ; Organic chemistry ; Metallurgy Asymmetric Hydrogenation and Catalysis ; Catalytic Cross-Coupling Reactions
Authors: V Shreyas | Jose Siguenza | Karan Bania | Bharath Ramsundar
Source: arXiv (Cornell University) (preprint, 2024-08-12)
Field: Chemistry — Physical and Theoretical Chemistry

Open-Source Molecular Processing Pipeline for Generating Molecules Generative models for molecules have shown considerable promise for use in computational chemistry, but remain difficult to use for non-experts. For this reason, we introduce open-source infrastructure for easily building generative molecular models into the widely used DeepChem [Ramsundar et al., 2019] library with the aim of creating a robust and reusable molecular generation pipeline. In particular, we add high quality PyTorch [Paszke et al., 2019] implementations of the Molecular Generative Adversarial Networks (MolGAN) [Cao and Kipf, 2022] and Normalizing Flows [Papamakarios et al., 2021]. Our implementations show strong performance comparable with past work [Kuznetsov and Polykovskiy, 2021, Cao and Kipf, 2022]. Pipeline (software) ; Open source ; Computer science ; Chemistry ; Programming language ; Software Various Chemistry Research Topics
Authors: Qian Xu | Qiang Ma
Source: Processes (article, 2024-09-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Editorial on the Special Issue “Progresses in Electrochemical Energy Conversion and Storage—Materials, Structures and Simulation”—Towards Better Electrochemical Energy Conversion and Storage Technologies Energy is at the heart of the sustainable development agenda, with an emphasis on efficient conversion and storage [...] Electrochemical energy storage ; Electrochemical energy conversion ; Energy storage ; Electrochemistry ; Energy transformation ; Process engineering ; Materials science ; Computer science ; Nanotechnology ; Engineering ; Supercapacitor ; Chemistry ; Physics ; Electrode ; Power (physics) ; Thermodynamics Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Advanced Battery Materials and Technologies
Authors: К. A. Andrianova | Лилия Миниахмедовна Амирова
Source: Журнал прикладной химии (article, 2024-02-15)
Field: Materials Science — Biomaterials

Функционально-градиентные материалы: получение, свойства, применение (обзор) Psychology Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; TiO2 Photocatalysis and Solar Cells
Authors: Md Mobarak Hossain Polash | M. B. Stone | Songxue Chi | Daryoosh Vashaee
Source:  (preprint, 2024-04-19)
Field: Materials Science — Materials Chemistry

Designing Spin-Crossover Systems to Enhance Thermopower and Thermoelertic Figure-of-Merit in Paramagnetic Materials Thermoelectric materials, capable of converting temperature gradients into electrical power, have been traditionally limited by a trade-off between thermopower and electrical conductivity. This study introduces a novel, broadly applicable approach that enhances both the spin-driven thermopower and the thermoelectric figure-of-merit (zT) without compromising electrical conductivity, using temperature-driven spin crossover. Our approach, supported by both theoretical and experimental evidence, is demonstrated through a case study of chromium doped-manganese telluride, but is not confined to this material and can be extended to other magnetic materials. By introducing dopants to create a high crystal field and exploiting the entropy changes associated with temperature-driven spin crossover, we achieved a significant increase in thermopower, by approximately 136 μV/K, representing more than a 200% enhancement at elevated temperatures within the paramagnetic domain. Our exploration of the bipolar semiconducting nature of these materials reveals that suppressing bipolar magnon/paramagnon-drag thermopower is key to understanding and utilizing spin crossover-driven thermopower. These findings, validated by inelastic neutron scattering, X-ray photoemission spectroscopy, thermal transport, and energy conversion measurements, shed light on crucial material design parameters. We provide a comprehensive framework that analyzes the interplay between spin entropy, hopping transport, and magnon/paramagnon lifetimes, paving the way for the development of high-performance spin-driven thermoelectric materials. Seebeck coefficient ; Condensed matter physics ; Thermoelectric effect ; Figure of merit ; Materials science ; Thermoelectric materials ; Magnon ; Ferromagnetism ; Physics ; Optoelectronics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Quantum and electron transport phenomena ; Magnetic and transport properties of perovskites and related materials
Authors: Feitong Song | Ji Feng
Source: arXiv (Cornell University) (preprint, 2024-06-22)
Field: Materials Science — Materials Chemistry

NeuralSCF: Neural network self-consistent fields for density functional theory Kohn-Sham density functional theory (KS-DFT) has found widespread application in accurate electronic structure calculations. However, it can be computationally demanding especially for large-scale simulations, motivating recent efforts toward its machine-learning (ML) acceleration. We propose a neural network self-consistent fields (NeuralSCF) framework that establishes the Kohn-Sham density map as a deep learning objective, which encodes the mechanics of the Kohn-Sham equations. Modeling this map with an SE(3)-equivariant graph transformer, NeuralSCF emulates the Kohn-Sham self-consistent iterations to obtain electron densities, from which other properties can be derived. NeuralSCF achieves state-of-the-art accuracy in electron density prediction and derived properties, featuring exceptional zero-shot generalization to a remarkable range of out-of-distribution systems. NeuralSCF reveals that learning from KS-DFT's intrinsic mechanics significantly enhances the model's accuracy and transferability, offering a promising stepping stone for accelerating electronic structure calculations through mechanics learning. Artificial neural network ; Functional connectivity ; Computer science ; Statistical physics ; Psychology ; Artificial intelligence ; Neuroscience ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Muhammad İmran Yousuf | Gurdev Singh | Bireswar Sinha
Source: Agriculture Association of Textile Chemical and Critical Reviews (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Nanoparticles in Agriculture and Medicine A Dual-Action Approach for Sustainable Farming and Health Agriculture ; Sustainable agriculture ; Action (physics) ; Integrated farming ; Dual (grammatical number) ; Business ; Medicine ; Geography ; Art Nanoparticles: synthesis and applications
Authors: Dicho Stratiev | Dobromir Yordanov | Aijun Guo
Source: Processes (article, 2024-12-16)
Field: Chemistry — Analytical Chemistry

Special Issue: “Petroleum Characterization and Bioprocesses: Numerical and Experimental Investigation” Petroleum is a valuable mineral source rich in hydrocarbons, which, after refining, can be used either as components in the production of internal combustion engine fuels, or in feeds for the production of chemicals [...] Petroleum ; Combustion ; Refining (metallurgy) ; Biochemical engineering ; Petroleum engineering ; Environmental science ; Production (economics) ; Waste management ; Characterization (materials science) ; Petroleum product ; Process engineering ; Chemistry ; Engineering ; Materials science ; Organic chemistry ; Nanotechnology ; Economics Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Process Optimization and Integration
Authors: C M Sulaikha | Aditya Somasundaram
Source: ShodhKosh Journal of Visual and Performing Arts (article, 2024-06-30)
Field: Chemistry — Analytical Chemistry

CURRENT CHALLENGES, AND FUTURE OPPORTUNITIES FOR FERMENTED TEA LEAF SEGMENTATION, CLASSIFICATION, AND OPTIMIZATION Fermented tea leaves emerged as a significant agricultural commodity on the global scene. This type of product experiences segmentation, classification, and optimization due to the different textures, different stages of fermentation, and environmental influences. The article reviews the progresses and limitations made by automatic systems in the realm of image-based analysis of fermented tea leaves, machine learning algorithms, and optimization methods. The challenges of high segmentation accuracy in heterogeneous samples, robust classification among diverse tea varieties, and scaling of optimization strategies for quality enhancement are some key challenges. Apart from hybrid optimization algorithms designed to interpret the gap, future areas of opportunities that utilize deep learning and multimodal fusion. Highlights from different hyperspectral imaging approaches and AI-driven models providing quick solutions with high accuracy and cost-effectiveness. Segmentation ; Biotechnology ; Artificial intelligence ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; GABA and Rice Research
Authors: Graciele M. Arvelos | Mariví Fernández-Serra | Alexandre Reily Rocha | Luana S. Pedroza
Source: arXiv (Cornell University) (preprint, 2024-10-31)
Field: Chemistry — Electrochemistry

Probing Water-Electrified Electrode interfaces: Insights from Au and Pd The water/electrode interface under an applied bias potential is a challenging out-of-equilibrium phenomenon, which is difficult to accurately model at the atomic scale. In this study, we employ a combined approach of Density Functional Theory (DFT) and non-equilibrium Green's function (NEGF) methods to analyze the influence of an external bias on the properties of water adsorbed on Au(111) and Pd(111) metallic electrodes. Our results demonstrate that while both Au and Pd-electrodes induce qualitatively similar structural responses in adsorbed water molecules, the quantitative differences are substantial, driven by the distinct nature of water-metal bonding. Our findings underscore the necessity of quantum-mechanical modeling for accurately describing electrochemical interfaces. Electrode ; Materials science ; Nanotechnology ; Environmental science ; Chemistry ; Optoelectronics ; Physical chemistry Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Analytical Chemistry and Sensors
Authors: Qian Xin | Gang Quan | Te‐Huan Liu | Ronggui Yang
Source: arXiv (Cornell University) (preprint, 2024-11-12)
Field: Materials Science — Materials Chemistry

API Phonons: Python Interfaces for Phonon Transport Modeling API Phonons is a Python software package to predict the transport dynamics of heat-carrying phonons. Using the powerful syntax of Python, this package provides modules and functions interfacing between different packages for atomistic simulations, lattice dynamics, and phonon-phonon interaction calculations including LAMMPS, Quippy, Phonopy, and ShengBTE. API Phonons enabled complex phonon calculations, including (1) extracting harmonic and anharmonic force constants from arbitrary interatomic potentials, which can be used as inputs for solving Boltzmann transport equations; (2) predicting thermal conductivity using Kubo's linear response theory, which captures both quasiparticle transport and inter-band coherent transport; and (3) modeling of ultrafast pump-probe thermal responses using a Green's function approach based on mode-resolved phonon properties for studying ballistic, hydrodynamic, and diffusive transport dynamics. The package provides a flexible, easy-to-use, and extensive platform for modeling phonon transport physics through Python programming. Phonon ; Python (programming language) ; Physics ; Computer science ; Condensed matter physics ; Programming language Thermal properties of materials
Authors: Ahmad Zainul Ihsan | Said Fathalla | Stefan Sandfeld
Source: Neural Computing and Applications (article, 2024-12-14)
Field: Materials Science — Materials Chemistry

Modeling dislocation dynamics data using semantic web technologies The research in Materials Science and Engineering focuses on the design, synthesis, properties, and performance of materials. An important class of materials that is widely investigated are crystalline materials, including metals and semiconductors. Crystalline material typically contains a specific type of defect called "dislocation". This defect significantly affects various material properties, including bending strength, fracture toughness, and ductility. Researchers have devoted a significant effort in recent years to understanding dislocation behaviour through experimental characterization techniques and simulations, e.g., dislocation dynamics simulations. This paper presents how data from dislocation dynamics simulations can be modelled using semantic web technologies through annotating data with ontologies. We extend the dislocation ontology by adding missing concepts and aligning it with two other domain-related ontologies (i.e., the Elementary Multi-perspective Material Ontology and the Materials Design Ontology), allowing for efficiently representing the dislocation simulation data. Moreover, we present a real-world use case for representing the discrete dislocation dynamics data as a knowledge graph (DisLocKG) which can depict the relationship between them. We also developed a SPARQL endpoint that brings extensive flexibility for querying DisLocKG. Computer science ; Ontology ; Dislocation ; Semantic Web ; Materials science ; Information retrieval Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Advanced Materials Characterization Techniques
Source: International Journal of Corrosion and Scale Inhibition (article, 2024-06-27)
Field: Materials Science — General Materials Science

Surface modification of aluminum alloy AMg6 by ethanol solutions of alkyl phosphates The paper considers the possibility of obtaining superhydrophobic (SHP) protective films from ethanol solutions of phosphoric acid esters (monododecyl phosphate and didodecyl phosphate) on the surface of aluminum alloy AMg6 after its pre-etching with 10% NaOH solution at t = 60C in order to obtain polymodal roughness.It is shown that the alloy treatment in alkyl phosphate solutions leads to superhydrophobization of the surface.The stability of obtained coatings in alkylphosphate solutions is compared with similar alloy surface treatment in dodecylphosphonic and dodecanoic acid solutions.The protective ability of the coatings obtained was evaluated by polarization measurements and corrosion tests in a temperaturehumidity chamber.It was established that the best anti-corrosion properties are shown by films formed from solution containing 10 mmolL -1 of the monododecyl-and didodecylphosphate mixture. Alkyl ; Corrosion ; Alloy ; Materials science ; Ethanol ; Aluminium ; Inorganic chemistry ; Metallurgy ; Chemistry ; Organic chemistry Material Properties and Applications
Authors: Natalia E. Powers‐Riggs | Benedikt O. Birgisson | Sumana L. Raj | Elisa Biasin | Philipp Lenzen | Diana Bregenholt Zederkof | Morten Haubro | Dagrún K. V. Tveiten | Robert J. Hartsock | Tim B. van Driel | Kristjan Kunnus | Matthieu Chollet | Joseph S. Robinson | S. Nelson | Ruaridh Forbes | Kristoffer Haldrup | Kasper S. Pedersen | Gianluca Levi | Asmus Ougaard Dohn | Hannes Jónsson | Klaus B. Møller | Adi Natan | M. Nielsen | Kelly J. Gaffney
Source: ChemRxiv (preprint, 2024-03-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Characterization of deformational isomerization potential and interconversion dynamics with ultrafast x-ray solution scattering Dimeric complexes composed of d8 square planar metal centers and rigid bridg- ing ligands provide model systems to understand the interplay between attractive dispersion forces and steric strain, in order to assist the development reliable meth- ods to model metal dimer complexes more broadly. [Ir2(dimen)4]2+ (dimen = para- diisocyanomenthane) presents a unique case study for such phenomena, as distortions of the optimal structure of a ligand with limited conformational flexibility counteracts the attractive dispersive forces from the metal and ligand to yield a complex with two ground state deformational isomers. Here, we use ultrafast X-ray solution scattering (XSS) and optical transient absorption spectroscopy (OTAS) to reveal the nature of the equilibrium distribution and the exchange rate between the deformational isomers. The two ground state isomers have spectrally distinct electronic excitations that enable the selective excitation of one isomer or the other using a femtosecond duration pulse of visible light. We then track the dynamics of the non-equilibrium depletion of the electronic ground state population – often termed the ground state hole – with ultra- fast XSS and OTAS, revealing a restoration of the ground state equilibrium in 2.3 ps. This combined experimental and theoretical study provides a critical test of various density functional approximations in the description of bridged d8-d8 metal complexes. The results show that density functional theory calculations can reproduce the primary experimental observations if dispersion interactions are added and a hybrid functional, which includes exact exchange, is used. Density functional theory ; Ground state ; Isomerization ; Chemistry ; Chemical physics ; Dispersion (optics) ; Steric effects ; Population ; Molecular physics ; Molecular dynamics ; Computational chemistry ; Crystallography ; Atomic physics ; Physics ; Stereochemistry ; Optics ; Organic chemistry Magnetism in coordination complexes ; X-ray Spectroscopy and Fluorescence Analysis ; Advanced Chemical Physics Studies
Authors: Nurit Manukovsky | Noy Vaisleib | Michal Arbel Haddad | Amir Goldbourt
Source: arXiv (Cornell University) (preprint, 2024-08-15)
Field: Chemistry — Spectroscopy

Prediction of NMR Parameters and geometry in 133Cs-containing compounds using Density Functional Theory The need to immobilize low-level nuclear waste, in particular 137Cs-bearing waste, has led to a growing interest in geopolymer-based waste matrices, in addition to optimization attempts of cement matrix compositions for this specific application. Although the overall phase composition and structure of these matrices are well characterized, the binding sites of Cs in these materials have not been clearly identified. Recent studies have suggested that combining the sensitivity of solid-state Nuclear Magnetic Resonance (SSNMR) to the local atomic structure with other structural techniques provides insights into the mode of Cs binding and release. Density Functional Theory (DFT) can provide the connection between spectroscopic parameters and geometric properties. However, the reliability of DFT results strongly relies on the choice of a suitable exchange-correlation functional, which for 133Cs, the NMR surrogate for such studies, is not well-established. In this work we benchmark various functionals against their performance in predicting the geometry of various simple Cs compounds, their NMR quadrupolar coupling constants, and their chemical shift values, while prioritizing the ability to incorporate dispersion interactions and maintaining low computational cost. We examined Cs salts, Cs oxides, perovskites, caged materials, a borate glass and a cesium fluoroscandate. While no single functional performs equally well for all parameters, the results show rev-vdW-DF2 and PBEsol+D3 to be leading candidates for these systems, in particular with respect to geometry and chemical shifts, which are of high importance for Cs-immobilization matrices. Density functional theory ; Geometry ; Materials science ; Mathematics ; Computational chemistry ; Chemistry Advanced NMR Techniques and Applications