jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yang Zhong | Binhua Zhang | Hongyu Yu | Xin-Gao Gong | Hongjun Xiang
Source: arXiv (Cornell University) (preprint, 2023-06-02)
Field: Materials Science — Materials Chemistry

Accelerating the electronic-structure calculation of magnetic systems by equivariant neural networks Complex spin-spin interactions in magnets can often lead to magnetic superlattices with complex local magnetic arrangements, and many of the magnetic superlattices have been found to possess non-trivial topological electronic properties. Due to the huge size and complex magnetic moment arrangement of the magnetic superlattices, it is a great challenge to perform a direct DFT calculation on them. In this work, an equivariant deep learning framework is designed to accelerate the electronic calculation of magnetic systems by exploiting both the equivariant constraints of the magnetic Hamiltonian matrix and the physical rules of spin-spin interactions. This framework can bypass the costly self-consistent iterations and build a direct mapping from a magnetic configuration to the ab initio Hamiltonian matrix. After training on the magnets with random magnetic configurations, our model achieved high accuracy on the test structures outside the training set, such as spin spiral and non-collinear antiferromagnetic configurations. The trained model is also used to predict the energy bands of a skyrmion configuration of NiBrI containing thousands of atoms, showing the high efficiency of our model on large magnetic superlattices. Superlattice ; Antiferromagnetism ; Hamiltonian (control theory) ; Magnetic moment ; Condensed matter physics ; Magnetic structure ; Magnet ; Physics ; Spin (aerodynamics) ; Skyrmion ; Topology (electrical circuits) ; Magnetic field ; Magnetization ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Magnetic properties of thin films ; Physics of Superconductivity and Magnetism
Authors: Mingxing Zhang
Source: Highlights in Science Engineering and Technology (article, 2023-11-29)
Field: Chemistry — Electrochemistry

Detection of Heavy Metal Ions Based on Gold Nanoparticles Gold nanoparticles, abbreviated as AuNPs, have been widely used in catalysis, optics, electronics, and biotechnology due to their unique performance brought by large specific surface area. In recent years, with the improvement of health awareness, the use of gold nanoparticles to detect heavy metal ions has become a research hotspot. The detection is based on colorimetric techniques, which means the metal ions are detected through the color change in solutions. This color change is visible or could be distinguished by ultraviolet-vissible spectrometers. Numerous studies have been published on gold nanoparticles, especially for its synthesis methods. This work introduced the chemical, physical, and biological preparation methods of gold nanoparticles. Understanding mechanisms for detecting heavy metal ions using AuNPs is also very important. Aggregating mechanism, anti-aggregation mechanism, and etching mechanism were also introduced. In addition, applications of using AuNPs to detect heavy metal ions were also introduced. This work will help promote the further development of this technology. Colloidal gold ; Nanotechnology ; Metal ions in aqueous solution ; Nanoparticle ; Materials science ; Metal ; Metallurgy Electrochemical Analysis and Applications ; Nanoparticles: synthesis and applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Hieu Huynh | Khanh Le | Linh Vu | Trang T. Nguyen | Matthew Holcomb | Stefano Forli | Hung Phan
Source: ChemRxiv (preprint, 2023-11-22)
Field: Materials Science — Materials 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 BF3 affinity with Pearson correlation coefficients around 0.9 and mean absolute errors around 10 kJ mol-1. The ML models are trained by DFT-calculated BF3 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 ; Ab initio ; Computational chemistry ; Chemistry ; Base (topology) ; Mathematics ; Organic chemistry ; Mathematical analysis Machine Learning in Materials Science ; Crystallography and molecular interactions ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Jia Qu | Shaohong You
Source: E3S Web of Conferences (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Review on the Application and Preparation of Ozone Catalysts This paper reviews the recent advances in the application of ozone catalysts and their preparation. The main categories of ozone catalysts are introduced, and the properties of the more widely used metal oxide catalysts, supported catalysts, carbonaceous material catalysts and novel catalysts are summarized. The supported catalysts and carbonaceous material catalysts have better catalytic effect compared with metal oxide catalysts. The four most commonly used catalyst preparation methods are reviewed, and the prospects for catalyst development based on the current research status are presented. Catalysis ; Industrial catalysts ; Oxide ; Ozone ; Catalyst support ; Materials science ; Metal ; Chemical engineering ; Chemistry ; Organic chemistry ; Metallurgy ; Engineering Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques ; Nanomaterials for catalytic reactions
Authors: Kaushal Kumar | Mithun Kori | Himanshu Pandey | Satyesh Raj Anand | Neha Mishra | S. P. Shrivastava
Source: Mediterranean Journal of Chemistry (article, 2023-03-21)
Field: Chemistry — Organic Chemistry

Study of In Silico on Schiff Base Ligand Against Mycobacterium Tuberculosis Here, we have synthesized the hetero-atoms containing; 3, 4, 6–Triazabicyclo [6, 3, 1] dodeca–1 (12), 2, 6, 8, 10–pentene–5–thione (TBD) macrocyclic Schiff base ligand for the application in antituberculosis (anti-TB). This TBD ligand moiety has high donor ability due to the presence of three nitrogen donor atoms, which are also the reason for the interaction between the ligand and protein molecule. The TBD Schiff base ligand is characterized by various spectroscopic techniques such as; Fourier-Transform Infrared (FT-IR), Proton Nuclear Magnetic Resonance (1HNMR), and Ultraviolet-Visible (UV-Vis) and Electron Spray Ionization (ESI) Mass spectroscopy, to understand the bond stretching, the electronic environment of protons, electronic transitions (p–p* and &lt;em&gt;n&lt;/em&gt;–p*), and M/Z values, respectively. The computational study was carried out to calculate the molecular docking score using AutoDock Vina software against the glutamine protein enzyme (PDB ID-3ZXR). The molecular docking score was –6.3 kcal mol&lt;sup&gt;-1&lt;/sup&gt; for the TBD Schiff base ligand, whereas –4.6 kcal mol&lt;sup&gt;-1&lt;/sup&gt; is reported for the standard drug (Pyrazinamide). The product formation yield of TBD Schiff base ligand is found to be ~78 % during synthesis. Chemistry ; Schiff base ; Ligand (biochemistry) ; AutoDock ; Moiety ; Stereochemistry ; Crystallography ; In silico Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Computational Drug Discovery Methods
Authors: Mojgan Najafi | Ramin Gholamhosseiny
Source: Colloid & Nanoscience Journal (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

Effect of composition, electrolyte pH, annealing temperature and electrodeposition waveform on the magnetic properties of Co-Cu nanowire alloys Arrays of Co0.2Cux, with 0<x<0.1, nanowires (NWs) have been synthesized. CoCu NWs have been grown into the AAO templates by ac electrodeposition. The anodic aluminum oxide template (AAO) were synthesized by a two-step anodization process of pure Al foils in sulfuric acid. The effect of small addition of Cu to Co NWs by varying the concentrations of Cu+2 ions in the electrolyte, as well as the effect of annealing on the magnetic properties of NWs was investigated. Eventually, the effect of changing waveforms applied to nanowire deposition was studied. The results showed that the coercivity of cobalt NWs decreased by adding copper impurity as a non-magnetic material from 1337 Oe to 660 Oe. By annealing of the alloy NWs up to 450 °C, the coercivity increases from 1140 Oe value for pure Co NWs up to 1541 Oe for CoCu NWs due to the removal of structural stresses of the nanowire. Nanowire ; Coercivity ; Materials science ; Annealing (glass) ; Electrolyte ; Anodizing ; Alloy ; Impurity ; Chemical engineering ; Cobalt ; Aluminium ; Anodic Aluminum Oxide ; Copper ; Metallurgy ; Analytical Chemistry (journal) ; Nanotechnology ; Electrode ; Chemistry ; Condensed matter physics ; Physical chemistry Anodic Oxide Films and Nanostructures ; Electrodeposition and Electroless Coatings ; Magnetic properties of thin films
Authors: Jun Zhang | Xiaohan Lin | E Weinan | Yi Qin Gao
Source: arXiv (Cornell University) (preprint, 2023-05-02)
Field: Materials Science — Materials Chemistry

Invertible Coarse Graining with Physics-Informed Generative Artificial Intelligence Multiscale molecular modeling is widely applied in scientific research of molecular properties over large time and length scales. Two specific challenges are commonly present in multiscale modeling, provided that information between the coarse and fine representations of molecules needs to be properly exchanged: One is to construct coarse grained models by passing information from the fine to coarse levels; the other is to restore finer molecular details given coarse grained configurations. Although these two problems are commonly addressed independently, in this work, we present a theory connecting them, and develop a methodology called Cycle Coarse Graining (CCG) to solve both problems in a unified manner. In CCG, reconstruction can be achieved via a tractable deep generative model, allowing retrieval of fine details from coarse-grained simulations. The reconstruction in turn delivers better coarse-grained models which are informed of the fine-grained physics, and enables calculation of the free energies in a rare-event-free manner. CCG thus provides a systematic way for multiscale molecular modeling, where the finer details of coarse-grained simulations can be efficiently retrieved, and the coarse-grained models can be improved consistently. Granularity ; Computer science ; Invertible matrix ; Construct (python library) ; Multiscale modeling ; Process (computing) ; Molecular dynamics ; Algorithm ; Theoretical computer science ; Mathematics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced NMR Techniques and Applications
Authors: Nicholas J. Terrill | A. Bombardi | Francesco Carlà | Gianfelice Cinque | Matthew J. Derry | Adam Milsom | Giuliano Siligardi | Tim Snow | Paul D. Topham | Xiangbing Zeng | Thomas Zinn
Source: Synchrotron Radiation News (article, 2023-03-04)
Field: Materials Science — Materials Chemistry

Polymer and Soft Matter Research at Diamond Light Source Polymer and soft matter research have played an integral part in the development of Diamond Light Source ever since the facility took its first users in 2007. Early experiments explored highly swollen cubic lipid scaffolds [Citation1] using pressure [Citation2] to elicit phase transitions and liquid-­crystal engineering [Citation3]. The facility now comprises 33 active synchrotron instruments, together with 13 electron microscopes, and other offline ­facilities. Diamond has an active polymer and soft matter science program exploring new phase space as well as many in <i>operando </i>studies. Later in the article, we will describe the opportunities available to this research community from the planned machine upgrade, which includes a higher-energy, lower divergence ring with better coherence [Citation4]. Soft matter ; Diamond ; Materials science ; Polymer ; Light source ; Optics ; Nanotechnology ; Optoelectronics ; Physics ; Composite material ; Engineering ; Chemical engineering Diamond and Carbon-based Materials Research ; Catalysis and Oxidation Reactions ; Nonlinear Optical Materials Studies
Authors: Danny Reidenbach | Aditi S. Krishnapriyan
Source: arXiv (Cornell University) (preprint, 2023-06-26)
Field: Materials Science — Materials Chemistry

CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation Molecular conformer generation (MCG) is an important task in cheminformatics and drug discovery. The ability to efficiently generate low-energy 3D structures can avoid expensive quantum mechanical simulations, leading to accelerated virtual screenings and enhanced structural exploration. Several generative models have been developed for MCG, but many struggle to consistently produce high-quality conformers. To address these issues, we introduce CoarsenConf, which coarse-grains molecular graphs based on torsional angles and integrates them into an SE(3)-equivariant hierarchical variational autoencoder. Through equivariant coarse-graining, we aggregate the fine-grained atomic coordinates of subgraphs connected via rotatable bonds, creating a variable-length coarse-grained latent representation. Our model uses a novel aggregated attention mechanism to restore fine-grained coordinates from the coarse-grained latent representation, enabling efficient generation of accurate conformers. Furthermore, we evaluate the chemical and biochemical quality of our generated conformers on multiple downstream applications, including property prediction and oracle-based protein docking. Overall, CoarsenConf generates more accurate conformer ensembles compared to prior generative models. Conformational isomerism ; Computer science ; Equivariant map ; Cheminformatics ; Representation (politics) ; Autoencoder ; Theoretical computer science ; Biological system ; Algorithm ; Computational chemistry ; Artificial intelligence ; Chemistry ; Mathematics ; Physics ; Deep learning ; Pure mathematics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Protein Structure and Dynamics
Authors: Yunzhu Chen | Sun Hongjuan | Tongjiang Peng | Tongxi Gao | Wenjin Ding | Tao Hui | Lei Jiang
Source: Molecules (article, 2023-01-13)
Field: Materials Science — Biomaterials

The Expanded Vermiculite Was Quickly Prepared by the Catalytic Action of Manganese Dioxide on Hydrogen Peroxide and Its Adsorption Properties to Cd The structure and activity of vermiculite can be maintained by expanding vermiculite (Vrm) with hydrogen peroxide. However, it is time-consuming. In past studies, little attention has been paid to the catalytic properties of manganese dioxide on hydrogen peroxide to improve the swelling efficiency of vermiculite. In this experiment, this catalytic effect was utilized to swell Vrm in a short time. The samples were then used to adsorb Cd from the solution. Through a series of characterization tests. The results showed that the exothermic rate was 1960.42-2089.164 J/min and the total exothermic heat was 39,208.4-41,783.28 J when expanding 10 gVrm, which could have a good expansion effect. The expansion was completed in about 40 min. Compared with Vrm, the adsorption of Cd is enhanced by about 30%. It is consistent with the proposed secondary kinetic adsorption model. This study provides a new perspective and theoretical guidance for improving the efficiency of Vrm stripping by hydrogen peroxide. A kind of expanded Vrm with better Cd adsorption efficiency was also prepared. Hydrogen peroxide ; Vermiculite ; Adsorption ; Chemistry ; Manganese ; Exothermic reaction ; Catalysis ; Inorganic chemistry ; Materials science ; Organic chemistry ; Composite material Clay minerals and soil interactions ; Adsorption and biosorption for pollutant removal ; Iron oxide chemistry and applications
Authors: Leslie Glasser | Joel F. Liebman
Source: Research Square (preprint, 2023-12-20)
Field: Materials Science — Materials Chemistry

Thermochemistry of Monocharged Cation Substitutions in Ionic Solids Abstract There is considerable interest in the synthesis of new materials with properties adapted to particular technological purposes such as for electronics, catalysis, energy storage, and biomaterials. A rather straightforward synthetic procedure is substitution of principal ions in the crystal for one another and some general principles relating to structure have been developed which guide the material designer as to which substitutions are likely to lead to stable materials. These are briefly reported. We here consider the thermochemical consequences, rather than the structural implications, of unit-charged cation substitutions, namely of NH 4 + , Li + , Na + , K + , Rb + , Cs + , Tl + , Ag + and Cu + , among a large group of ionic solids. It is observed that the formation enthalpies hardly differ among these materials while their absolute entropies, heat capacities and formula unit volumes follow similar linear trends but with some relative displacements. Thermochemistry ; Ionic bonding ; Standard enthalpy of formation ; Ion ; Chemistry ; Formula unit ; Ionic crystal ; Substitution (logic) ; Standard enthalpy change of formation ; Crystal structure ; Unit (ring theory) ; Thermodynamics ; Nanotechnology ; Materials science ; Physical chemistry ; Crystallography ; Organic chemistry ; Physics ; Mathematics ; Computer science Thermal and Kinetic Analysis ; Ionic liquids properties and applications ; Chemical Thermodynamics and Molecular Structure
Authors: N. Merkhatuly | Amantai N. Iskanderov | Saltanat B. Abeuova | Ablaykhan N. Iskanderov
Source: Eurasian Journal of Chemistry (article, 2023-04-29)
Field: Chemistry — Organic Chemistry

Synthesis of Push-Pull Azulene-Based Compounds Nowadays, non-benzenoid aromatic hydrocarbons are widely used as synthons for the production of new organic semiconductors with interesting photophysical characteristics. For instance, a non-benzoid azulene hydrocarbon with a polar structure, a narrow energy gap between the highest free and lowest occupied molecular orbital and the ability to form stable ions can be intended as a structural moiety for the synthesis of new conjugated compounds with important optical and electronic properties. The article discusses the synthesis and investigation of the optical properties of new push-pull azulene-based compounds. It shows that Friedel-Crafts acylation is used as the key reaction for the synthesis of methyl- and phenyl-dicyanovinylated azulenes. The interaction proceeds regioselectively at the C1 and C3 positions of the five-membered azulene ring. It is identified that the synthesis of push-pull dicyanovinylated azulenes by Knoevenagel condensation of azulenyl ketones with malononitrile proceeds easily in Py and DMSO. The UF-vis spectra of the obtained azulene push-pull compounds demonstrated strong electron absorption in the visible zone with λmax at 410, 430, 434 and 452 nm associated with transport of charge between the donor azulene ring and the acceptor dicyanovinyl group. The scheme of resonant structures shows the mechanism of intramolecular donor-acceptor interaction. The structure of the synthesized push-pull dicyanovinylated nonazulenes was elucidated using modern physicochemical and spectroscopic research methods. Azulene ; Combinatorial chemistry ; Chemistry ; Computer science ; Stereochemistry ; Organic chemistry Synthesis and Properties of Aromatic Compounds ; Luminescence and Fluorescent Materials ; Photochromic and Fluorescence Chemistry
Authors: Yangyang Chen | Zixu Wang | Xiucai Ye
Source: ChemRxiv (preprint, 2023-09-05)
Field: Materials Science — Materials Chemistry

DiffSeqMol: A Non-Autoregressive Diffusion-Based Approach for Molecular Sequence Generation and Optimization The application of deep generative models for molecular discovery has witnessed a significant surge in recent years. Currently, the field of molecular generation and molecular optimization is predominantly governed by autoregressive models regardless of how molecular data is represented. However, an emerging paradigm in the generation domain is diffusion models, which treat data non-autoregressively and has achieved significant breakthroughs in areas such as image generation. The potential and capability of diffusion models in molecular generation and optimization tasks remain largely unexplored. In order to investigate the potential applicability of diffusion models in the domain of molecular exploration, we proposed DiffSeqMol, a molecular sequence generation model, underpinned by diffusion process. DiffSeqMol distinguishes itself from traditional autoregressive methods by its capacity to draw samples from random noise and direct generating the entire molecule. Through experiment evaluations, we demonstrated that DiffSeqMol can achieve, even surpass, the performance of established state-of-the-art models on unconditional generation tasks and molecular optimization tasks. Autoregressive model ; Computer science ; Generative grammar ; Diffusion ; Domain (mathematical analysis) ; Sequence (biology) ; Generative model ; Artificial intelligence ; Mathematics ; Chemistry ; Econometrics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics