jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M Karplus | W Miller | E Neher | H Schwarz | J Simons | G Somorjai | R Zare | B Albinsson | H Kondoh | J Zhang | C Adamo | M Devries | C Diaz | S George | D Ghosh | K Holmberg | G Meijer | G Patwari | M Rodgers | S Sampath | M Suhm | A Troisi | Olga Mass | Devan Watt | Lance Patten | Ryan Pensack | Jeunghoon Lee | Daniel Turner | Bernard Yurke | William Knowlton | Sha-Sha Liu | Xiao-Mei Tang
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Contents list Permissions Request permissions Contents list Phys. Chem. Chem. Phys., 2023, 25, 27897 DOI: 10.1039/D3CP90215C This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Acknowledgement ; Attribution ; Chromatin structure remodeling (RSC) complex ; Computer science ; Library science ; Chemistry ; Computer security ; Psychology Chemical and Physical Properties of Materials ; Advanced Physical and Chemical Molecular Interactions ; Machine Learning in Materials Science
Authors: Jinyu Zhou | Wenxiang Wang | Shanwen Wang | Jia Wang | Yan Gu | Xianfeng Jin | Birong Tian | Yuhua Wang | Jiachi Zhang
Source: Research Square (preprint, 2023-06-21)
Field: Materials Science — Materials Chemistry

A stress-charged non-Newtonian-fluid persistent luminescent material Abstract It presents a BaSi 2 O 2 N 2 :Eu 2+ /PVA persistent luminescence (PersL) gel material that traps can be charged by stress instead of irradiation. It can emit green PersL without preirradiation at room temperature, and even after initial PersL disappears, stored PersL traces can be stimulated again at high temperature. The gel material is demonstrated to be a typical non-Newtonian fluid showing shear thinning characteristics and variable mechanical behaviors. It is viscous at low frequencies, but exhibits elastic behaviors at high frequencies. Typically, the gel material can be made into fixed shapes or pulled into strips. Also, it can be hard enough to bore holes on apples, elastic enough to bounce and even melt. Furthermore, the gel material shows good stability, self-healing, water resistance and biocompatibility. Some typical intelligent light-emitting devices based on the BaSi 2 O 2 N 2 :Eu 2+ /PVA gel material are designed to show potential applications in safety signage, emergency lighting, anticounterfeiting and tooth occlusion analysis. Stress (linguistics) ; Non-Newtonian fluid ; Newtonian fluid ; Mechanics ; Materials science ; Physics ; Philosophy Luminescence Properties of Advanced Materials ; Luminescence and Fluorescent Materials ; Lanthanide and Transition Metal Complexes
Authors: Ana Sanz-Matìas | Fabrice Roncoroni | Siddharth Sundararaman | David Prendergast
Source: arXiv (Cornell University) (preprint, 2023-03-14)
Field: Chemistry — Electrochemistry

Ca-dimers, solvent layering, and dominant electrochemically active species in Ca(BH$_4$)$_2$ in THF Divalent ions, such as Mg, Ca, and Zn, are being considered as competitive, safe, and earth-abundant alternatives to Li-ion electrochemistry. However, the challenge remains to match electrode and electrolyte materials that stably cycle with these new formulations, based primarily on controlling interfacial phenomena. We explore the formation of electroactive species in the electrolyte Ca(BH$_4$)$_2$ in THF through molecular dynamics simulation. Free-energy analysis indicates that this electrolyte has a majority population of neutral Ca dimers and monomers, albeit with diverse molecular conformations as revealed by unsupervised learning techniques, but with an order of magnitude lower concentration of possibly electroactive charged species, such as the monocation, CaBH$_4^+$ , which we show is produced via disproportionation of neutral Ca(BH$_4$)$_2$ complexes. Dense layering of THF molecules within 1 nm of the electrode surface (modeled here using graphite) hinders the approach of reducible species to within 0.6 nm and instead enhances the local concetration of species in a narrow intermediate-density layer from 0.7-0.9 nm. A dramatic increase in the monocation population in this intermediate layer is induced at negative bias, supplied by local dimer disproportionation. We see no evidence to support any functional role of fully-solvated Ca$^{2+}$ in the electrochemical activity of this electrolyte. The consequences for performance and alternative formulations are discussed in light of this molecular-scale insight. Electrolyte ; Disproportionation ; Chemistry ; Electrochemistry ; Divalent ; Dimer ; Molecule ; Population ; Ion ; Supporting electrolyte ; Standard electrode potential ; Electrode ; Inorganic chemistry ; Chemical physics ; Physical chemistry ; Organic chemistry ; Catalysis Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Advancements in Battery Materials
Authors: Dmitry Zankov | Timur Madzhidov | Pavel Polishchuk | Pavel Sidorov | Alexandre Varnek
Source: ChemRxiv (preprint, 2023-03-13)
Field: Materials Science — Materials Chemistry

Multi-Instance Learning Approach to the Modeling of Enantioselectivity of Conformationally Flexible Organic Catalysts The computational design of chiral organic catalysts for asymmetric synthesis is a promising technol-ogy, which may significantly reduce the material and human resources required for the preparation of enantiopure compounds. Herein, for the modelling of catalysts enantioselectivity, we propose to use the Multi-Instance Learning (MIL) approach accounting for multiple catalyst conformers and requir-ing neither conformers selection nor their spatial alignment. A catalyst was represented by an ensem-ble of conformers, each encoded by 3D pmapper descriptors. A catalyzed chemical transformation was converted into a single molecular graph - ondensed Graph of Reaction (CGR) - encoded by 2D fragment descriptors. A whole chemical reaction was finally encoded by concatenated 3D catalyst and 2D transformation descriptors. The performance of the proposed method was demonstrated in the modelling of enantioselectivity of homogeneous and phase-transfer reactions and compared with some state-of-the-art approaches Conformational isomerism ; Enantiopure drug ; Catalysis ; Homogeneous ; Graph ; Chemistry ; Transformation (genetics) ; Molecular descriptor ; Computer science ; Quantitative structure–activity relationship ; Combinatorial chemistry ; Computational chemistry ; Molecule ; Stereochemistry ; Enantioselective synthesis ; Theoretical computer science ; Mathematics ; Organic chemistry ; Combinatorics Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Molecular spectroscopy and chirality
Authors: Ronaldo Giro | Brenda de Souza Ferrari | Matteo Manica | Teodoro Laino | M. Steiner
Source: Research Square (preprint, 2023-11-29)
Field: Materials Science — Materials Chemistry

Predicting polymerization reactions via transfer learning using chemical language models Retrosynthetic analysis ; Polymerization ; Polymer ; Computer science ; Transformer ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Alexander Soshnikov | R. K. Lindsey | Anant D. Kulkarni | Nir Goldman
Source: arXiv (Cornell University) (preprint, 2023-10-23)
Field: Materials Science — Materials Chemistry

A Reactive Molecular Dynamics Model for Uranium/Hydrogen Containing Systems Uranium-based materials are valuable assets in the energy, medical, and military industries. However, understanding their sensitivity to hydrogen embrittlement is particularly challenging due to the toxicity of uranium and computationally expensive nature of the quantum-based methods generally required to study such processes. In this regard, we have developed a Chebyshev Interaction Model for Efficient Simulation (ChIMES) model that can be employed to compute energies and forces of U and UH3 bulk structures with vacancies and hydrogen interstitials with similar accuracy to Density Functional Theory (DFT) while yielding linear scaling and orders of magnitude improvement in computational efficiency. We show that that the bulk structural parameters, uranium and hydrogen vacancy formation energies, and diffusion barriers predicted by the ChIMES potential are in strong agreement with the reference DFT data. We then use ChIMES to conduct molecular dynamics simulations of the temperature-dependent diffusion of a hydrogen interstitial and determine the corresponding diffusion activation energy. Our model has particular significance in studies of actinides and other high-Z materials, where there is a strong need for computationally efficient methods to bridge length and time scales between experiments and quantum theory. Density functional theory ; Molecular dynamics ; Hydrogen ; Diffusion ; Uranium ; Materials science ; Scaling ; Vacancy defect ; Statistical physics ; Chemical physics ; Chemistry ; Physics ; Computational chemistry ; Thermodynamics ; Condensed matter physics ; Quantum mechanics ; Mathematics ; Metallurgy Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: Christopher Seibel | Markus Uehlein | Tobias Held | Pavel N. Terekhin | Sebastian T. Weber | B. Rethfeld
Source: arXiv (Cornell University) (preprint, 2023-07-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Time-resolved spectral densities of non-thermal electrons in gold Noble-metal nanoparticles for photocatalysis have become a major research object in recent years due to their plasmon-enhanced strong light-matter interaction. The dynamics of the hot electrons in the noble metal are crucial for the efficiency of the photocatalysis and for the selective control of reactions. In this work, we present a kinetic description of the non-equilibrium electron distribution created by photoexcitation, based on full energy-resolved Boltzmann collision integrals for the laser excitation as well as for the electron-electron thermalization. The laser-induced electronic non-equilibrium and the inherently included secondary electron generation govern the dynamics of non-thermal electrons. Applying our method to gold, we show a significant dependence of hot electron dynamics on kinetic energy. Specifically, the timescales of the relaxation as well as the qualitative behavior are depending on the evaluated energy window. During the thermalization processes there are cases of increasing electron density as well as of decreasing electron density. Studying the influence of excitation parameters, we find that the photon energy and the fluence of the exciting laser can be tuned to influence not only the initial excitation but also the subsequent characteristics of the time-resolved electronic spectral density dynamics. The electronic thermalization including secondary electron generation leads to time-dependent spectral densities which differ from their specific final equilibrium values for picoseconds after irradiation ended. Thermalisation ; Atomic physics ; Plasmon ; Electron ; Photoexcitation ; Excitation ; Kinetic energy ; Molecular physics ; Materials science ; Physics ; Excited state ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; nanoparticles nucleation surface interactions ; Advanced Chemical Physics Studies
Authors: Maxwell C. Venetos | Masha Elkin | Connor P. Delaney | John F. Hartwig | Kristin A. Persson
Source: ChemRxiv (preprint, 2023-09-25)
Field: Chemistry — Spectroscopy

Deconvolution and Analysis of 1H NMR Spectra of Crude Reaction Mixtures NMR spectroscopy is an important analytical technique in synthetic organic chemistry, but its integration into high-throughput experimentation workflows has been limited by the necessity to manually analyze NMR spectra of new chemical entities. Current efforts to automate the analysis of NMR spectra rely on comparisons to databases of reported spectra for known compounds, and, therefore, are incompatible with the exploration of new chemical space. By reframing the NMR spectrum of a reaction mixture as a joint probability distribution, we have used Hamiltonian Monte Carlo Markov Chain (HMCMC) and density functional theory (DFT) to fit predicted NMR spectra to those of crude reaction mixtures. This approach enables the deconvolution and analysis of spectra of containing mixtures of compounds, without relying on reported spectra. The utility of our approach to analyze crude reaction mixtures is demonstrated with experimental spectra of reactions that generate a mixture of isomers, such as Wittig olefination and C--H functionalization reactions. The correct identification of compounds in a reaction mixture and their relative concentrations is achieved with mean absolute error as low as 1%. NMR spectra database ; Chemistry ; Deconvolution ; Spectral line ; Nuclear magnetic resonance spectroscopy ; Chemical shift ; Analytical Chemistry (journal) ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Computer science ; Algorithm ; Physics Molecular spectroscopy and chirality ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Jingzhou Wang | Huachao Xie | Yuanqing Wang | Runhai Ouyang
Source: arXiv (Cornell University) (preprint, 2023-01-17)
Field: Materials Science — Materials Chemistry

Distilling Accurate Descriptors from Multi-Source Experimental Data for Discovering Highly Active Perovskite OER Catalysts Perovskite oxides are promising catalysts for oxygen evolution reaction (OER), yet the huge chemical space remains largely unexplored due to the lack of effective approaches. Here, we report the distilling of accurate descriptors from multi-source experimental data for accelerated catalysts discovery by using the new method SCMT-SISSO that overcomes the challenge of data inconsistency between different sources. While many previous descriptors for the catalytic activity were proposed based on respective small datasets, we obtained the new 2D descriptor (d_B, n_B) based on 13 experimental datasets collected from different publications and the SCMT-SISSO. Great universality and predictive accuracy, and the bulk-surface correspondence, of this descriptor have been demonstrated. With this descriptor, hundreds of unreported candidate perovskites with activity greater than the benchmark catalyst Ba0.5Sr0.5Co0.8Fe0.2O3 were identified from a large chemical space. Our experimental validations on five candidates confirmed the three highly active new perovskite catalysts SrCo0.6Ni0.4O3, Rb0.1Sr0.9Co0.7Fe0.3O3, and Cs0.1Sr0.9Co0.4Fe0.6O3. Catalysis ; Perovskite (structure) ; Benchmark (surveying) ; Computer science ; Materials science ; Chemical space ; Universality (dynamical systems) ; Space (punctuation) ; Chemistry ; Physics ; Geology ; Crystallography ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Machine Learning and ELM
Authors: Brenda S. Ferrari | Matteo Manica | Ronaldo Giro | Teodoro Laino | M. Steiner
Source: arXiv (Cornell University) (preprint, 2023-10-17)
Field: Materials Science — Materials Chemistry

Predicting polymerization reactions via transfer learning using chemical language models Polymers are candidate materials for a wide range of sustainability applications such as carbon capture and energy storage. However, computational polymer discovery lacks automated analysis of reaction pathways and stability assessment through retro-synthesis. Here, we report the first extension of transformer-based language models to polymerization reactions for both forward and retrosynthesis tasks. To that end, we have curated a polymerization dataset for vinyl polymers covering reactions and retrosynthesis for representative homo-polymers and co-polymers. Overall, we obtain a forward model Top-4 accuracy of 80% and a backward model Top-4 accuracy of 60%. We further analyze the model performance with representative polymerization and retro-synthesis examples and evaluate its prediction quality from a materials science perspective. Retrosynthetic analysis ; Polymerization ; Polymer ; Computer science ; Transformer ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Fatemeh Mollaamin | Majid Monajjemi
Source: Applied Chemical Engineering (article, 2023-12-14)
Field: Materials Science — Materials Chemistry

Carbenes trapping on Aluminum-Magnesium surface implanted with Silicon, Germanium, Tin: Promising of semiconductors by molecular modeling approach Al-Mg surface doped silicon, germanium and tin is theoretically studied using first-principles density functional theory (DFT) at the CAM-B3LYP/EPR-III, LANL2DZ,6-31+G(d,p) level of theory to explore the chemical adsorption and corrosion inhibition of organic carbenes through coating process. The fluctuation of NQR is estimated the inhibiting role of pyridine and its derivatives (picoline, 3-picoline,4-picoline,2,4-lutidine) for (Si, Ge, Sn)-doped Al-Mg alloy nanosheet due to concerning nitrogen in the benzene ring of related heterocyclic compounds becoming close to the monolayer nanosurface of Al-Mg-X (X = Si, Ge, Sn) nanoalloys. The NMR spectroscopy remarks that (Si, Ge, Sn)-doped Al-Mg alloy nanosheet has maximum band wavelengths approximately between 10 ppm–2000 ppm accompanying the sharpest peaks for inhibitors → Al-Mg-X which are between 10 ppm–100 ppm. IR spectroscopy has exhibited that (Si, Ge, Sn)-doped Al-Mg alloy nanosheet with the fluctuation in the frequency of intra-atomic interaction leads us to the most influence in the vicinage atoms generated due to inter-atomic interaction. The maximum IR spectrum for complexes of [inhibitor → Al-Mg-X (X = Si, Ge, Sn)] is observed in the frequency range between 500 cm−1–3500 cm−1. This work exhibits that proper monitoring of the coating mechanism by Langmuir adsorption can illustrate inhibiting the aluminum nanoalloys corrosion through an investigation of their structural and thermodynamic properties. This work investigates the characteristics, band structure, and projected density of state (PDOS) of Al-Mg nanoalloy doped with Si, Ge, Sn elements for increasing the corrosion inhibition of the surface through adsorption of organic molecules of carbenes in the surface coatings process. This article can be helpful in a range of applications which uses Al-Mg alloy for the study of energy storage and adsorption of air pollution or water contamination. Many different approaches such as surface coatings, alloying and doping can be adopted to protect the surface. Density functional theory ; Materials science ; Germanium ; Tin ; Nanosheet ; Alloy ; Silicon ; Adsorption ; Amorphous solid ; Doping ; Monolayer ; Physical chemistry ; Analytical Chemistry (journal) ; Crystallography ; Computational chemistry ; Chemistry ; Nanotechnology ; Metallurgy ; Organic chemistry Boron and Carbon Nanomaterials Research ; Molecular Junctions and Nanostructures ; Semiconductor materials and devices
Authors: Subrahmanyam Bandaru | Agnieszka Jastrzębska | Magdalena Birowska
Source: arXiv (Cornell University) (preprint, 2023-04-14)
Field: Materials Science — Materials Chemistry

Recent progress in thermoelectric MXene-based structures versus other 2D materials Thermoelectricity is a next-generation solution for efficient waste heat management. Although various thermoelectric materials exist, there is still a lot of scope for advancement, especially in room temperature applications. Recently, two-dimensional (2D) materials, including MXenes, showed promise as thermoelectric materials. On the other hand, MXenes generally exhibit metallic behavior that can hinder thermoelectric performance. Nevertheless, the variety of MXene's chemical composition and surface functionalization facilitate the research path based on energy band engineering, carrier concentration, and mobility. Multiple strategies to enhance the thermoelectric properties of layered MXenes materials, including structural modifications, defects, band gap engineering, etc. have been comprehensively demonstrated. In addition, advanced structural engineering such as nanostructuring MXenes with materials of different dimensions, creating van der Waals heterostructures, alloying, and utilizing MXenes as nanoinclusions or nanocomposites is presented. The thermoelectric efficiency of MXenes over the landscape of other 2D and conventional thermoelectric materials has been systematically compared. Meanwhile, a future approach has been proposed to enhance the thermoelectric properties of novel members of the flatland, MBenes exhibiting an incredible diversity of structures and crystal symmetries. Finally, potential applications in thermoelectrics and future prospects of MXenes are discussed. This article provides a timely and unique review of MXenes advantages and limitations that have never been so well understood and established. This creates a comfort zone for rational tailoring of their structure-property-performance relationship, which is crucial concerning the thermoelectric performance, widely covered in this review. MXenes ; Thermoelectric effect ; Thermoelectric materials ; Materials science ; Nanotechnology ; Engineering physics ; Engineering ; Physics MXene and MAX Phase Materials ; Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications
Authors: Dakota Folmsbee | David Ryan Koes | Geoffrey Hutchison
Source: ChemRxiv (preprint, 2023-11-07)
Field: Chemistry — Spectroscopy

Systematic Comparison of Experimental Crystallographic Geometries and Gas-Phase Computed Conformers for Torsion Preferences We have performed exhaustive torsion sampling on more than 3 million compounds using the GFN2 method and performed a comparison of experimental crystallographic and gas-phase conformers. Many conformer sampling methods derive torsional angle distributions from experimental crystallographic data, limiting the torsion preferences to molecules that must be stable, synthetically accessible, and able to be crystallized. In this work, we evaluate the differences in torsional preferences of experimental crystallographic geometries and gas-phase computed conformers from a broad selection of compounds to determine whether torsional angle distributions obtained from semi-empirical methods are suitable for conformer sampling. We find that differences in torsion preferences can be mostly attributed to a lack of available experimental crystallographic data, with some deviations derived from gas-phase geometry differences. GFN2 demonstrates the ability to provide accurate and reliable torsional preferences that can provide a basis for new methods free from limitations of experimental data collection. We provide Gaussian-based fits and sampling distributions suitable for torsion sampling, and propose an alternative to ETKDG using quantum-torsion derived distance geometry methods (QTDG). Conformational isomerism ; Torsion (gastropod) ; Gaussian ; Dihedral angle ; Limiting ; Gas phase ; Umbrella sampling ; Molecule ; Statistical physics ; Materials science ; Crystallography ; Mathematics ; Geometry ; Computational chemistry ; Thermodynamics ; Chemistry ; Physics ; Molecular dynamics ; Quantum mechanics Molecular spectroscopy and chirality ; Crystallography and molecular interactions ; Computational Drug Discovery Methods