jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yan Zeng | Nathan J. Szymanski | Tanjin He | KyuJung Jun | Leighanne C. Gallington | Haoyan Huo | Christopher J. Bartel | Bin Ouyang | Gerbrand Ceder
Source: arXiv (Cornell University) (preprint, 2023-09-11)
Field: Materials Science — Materials Chemistry

Selective formation of metastable polymorphs in solid-state synthesis Metastable polymorphs often result from the interplay between thermodynamics and kinetics. Despite advances in predictive synthesis for solution-based techniques, there remains a lack of methods to design solid-state reactions targeting metastable materials. Here, we introduce a theoretical framework to predict and control polymorph selectivity in solid-state reactions. This framework presents reaction energy as a rarely used handle for polymorph selection, which influences the role of surface energy in promoting the nucleation of metastable phases. Through in situ characterization and density functional theory calculations on two distinct synthesis pathways targeting LiTiOPO4, we demonstrate how precursor selection and its effect on reaction energy can effectively be used to control which polymorph is obtained from solid-state synthesis. A general approach is outlined to quantify the conditions under which metastable polymorphs are experimentally accessible. With comparison to historical data, this approach suggests that using appropriate precursors could enable the synthesis of many novel materials through selective polymorph nucleation. Metastability ; Nucleation ; Materials science ; Solid-state ; Chemical physics ; Characterization (materials science) ; Kinetics ; Density functional theory ; Thermodynamics ; Polymorphism (computer science) ; Nanotechnology ; Chemistry ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Physics Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Advancements in Battery Materials
Authors: Mayssa Abdel Hady
Source: Silicon (article, 2023-09-23)
Field: Materials Science — Materials Chemistry

Recent Advances Ultra-Porous Drug Nano-Carriers: Synthesis and Targeting Approaches Abstract Mesoporous silica has attracted increasing interest due to the pandemic spreading of the viral infection in recent years. These smart materials have many advantages as high loading capacity, high surface area, and unique morphology making them great materials for smart drug carriers. In this review, I summarized the synthesis of Ultra-Porous Drug Nano-Carriers in recent years. Factors affecting (mesoporous nanoparticles) MSN Synthesis as surfactants, Co-surfactants, and solvents were mentioned in the full description and targeting approaches. Types of silica nanoparticles such as Mesoporous SBA-1 silicas, Mesoporous SBA-2 silicas, and hybrid mesoporous materials are also shown in a detailed manner. Future research efforts are also highlighted for AI-based techniques aimed at more accurate tissue engineering prediction and operation optimization in drug carrier-based processes. Mesoporous material ; Materials science ; Nanotechnology ; Mesoporous silica ; Nanoparticle ; Nano- ; Drug carrier ; Porosity ; Nanomaterials ; Drug delivery ; Organic chemistry ; Chemistry ; Catalysis ; Composite material Mesoporous Materials and Catalysis ; Nanoparticle-Based Drug Delivery
Authors: Yuan Liu | An T. Ta | Shubham Pandey | Kyoung Chul Park | Shenyang Hu | Natalia B. Shustova | Simon R. Phillpot
Source: Computational Materials Science (article, 2023-09-29)
Field: Chemistry — Inorganic Chemistry

Binding of uranyl cations to a Zr-based metal-organic framework by density functional theory Uranyl ; Chemistry ; Density functional theory ; Deprotonation ; Binding energy ; Metal-organic framework ; Aqueous solution ; Inorganic chemistry ; Metal ; Adsorption ; Crystallography ; Ion ; Physical chemistry ; Computational chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Radioactive element chemistry and processing ; X-ray Diffraction in Crystallography
Authors: Mousab Ahmed | Hamza Ahmed | Pelumi Adebayo | R.L. Carter | Lotus Khanal
Source: chemistry and materials research (review, 2023-04-01)
Field: Materials Science — Materials Chemistry

Mechanical Properties and Application of Graphite and Graphite-based Nanocomposite : A Review With the recent advancement in nanotechnology, there has been significant progress in the research and development of carbon-based nanoparticles. Graphite, a naturally occurring allotrope of Carbon, exhibits completely different properties in its macromolecular scale than in its larger scale. This review article explores the fundamental properties of graphite nanoparticles (GNP) and its relevance and uses in the industry. The article takes both theory and experimental approach to establish a clear boundary between theoretical and experimental results. Graphite nanoparticles when mixed with a base fluid depending on its use and application, exhibit significant changes in the fluid properties even with the very low concentration. This distinctive phenomenon has applications ranging from direct solar energy absorption to enhancement of thermal conductivity in machineries. However, the use of GNP remains unexplored as further research and development is yet to be done. The article can be a good resource for scholars who want to gain a fundamental understanding of graphite nanoparticles and gain a foundation for the study of other nanoparticles. Keywords: Graphite, Nanoparticle synthesis, Nanofluid, Thermophysical properties, Stability, MD Simulation, Application DOI: 10.7176/CMR/15-1-02 Publication date: April 30 th 2023 Graphite ; Nanoparticle ; Nanotechnology ; Materials science ; Nanofluid ; Nanocomposite ; Carbon fibers ; Thermal conductivity ; Composite material Graphite, nuclear technology, radiation studies
Authors: Qi Han | Xinyang Geng | Stefano Rando | Iku Ohama | Aviral Kumar | Sergey Levine
Source: arXiv (Cornell University) (preprint, 2023-10-16)
Field: Materials Science — Materials Chemistry

Latent Conservative Objective Models for Data-Driven Crystal Structure Prediction In computational chemistry, crystal structure prediction (CSP) is an optimization problem that involves discovering the lowest energy stable crystal structure for a given chemical formula. This problem is challenging as it requires discovering globally optimal designs with the lowest energies on complex manifolds. One approach to tackle this problem involves building simulators based on density functional theory (DFT) followed by running search in simulation, but these simulators are painfully slow. In this paper, we study present and study an alternate, data-driven approach to crystal structure prediction: instead of directly searching for the most stable structures in simulation, we train a surrogate model of the crystal formation energy from a database of existing crystal structures, and then optimize this model with respect to the parameters of the crystal structure. This surrogate model is trained to be conservative so as to prevent exploitation of its errors by the optimizer. To handle optimization in the non-Euclidean space of crystal structures, we first utilize a state-of-the-art graph diffusion auto-encoder (CD-VAE) to convert a crystal structure into a vector-based search space and then optimize a conservative surrogate model of the crystal energy, trained on top of this vector representation. We show that our approach, dubbed LCOMs (latent conservative objective models), performs comparably to the best current approaches in terms of success rate of structure prediction, while also drastically reducing computational cost. Crystal structure prediction ; Computer science ; Algorithm ; Representation (politics) ; Crystal (programming language) ; Crystal structure ; Surrogate model ; Mathematical optimization ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Mathematics ; Chemistry ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: W. J. Pech-Rodríguez | F.J. Rodríguez‐Varela | Carlos Adrián Calles-Arriaga | Eddie N. Armendáriz-Mireles
Source: Revista Mexicana de Ingeniería Química (article, 2023-07-29)
Field: Materials Science — Polymers and Plastics

Practical approach to identify electrochemical parameter in aqueous Potassium ferricyanide by solving the multi-variable Cottrell equation via genetic algorithms Derivation and application of Potassium ferricyanide ; Aqueous solution ; Electrochemistry ; Variable (mathematics) ; Ferricyanide ; Chemistry ; Algorithm ; Biological system ; Potassium ; Genetic algorithm ; Thermodynamics ; Materials science ; Mathematics ; Applied mathematics ; Mathematical optimization ; Inorganic chemistry ; Physics ; Physical chemistry ; Mathematical analysis ; Organic chemistry ; Electrode ; Biology Conducting polymers and applications ; Analytical Chemistry and Sensors ; Electrochemical Analysis and Applications
Authors: Nathan Hu | Amanda Parker
Source: ChemRxiv (preprint, 2023-10-11)
Field: Materials Science — Materials Chemistry

Superior Prediction of Graphene Nanoflake Properties withUnbiased Graph Embedding In order to apply machine learning to study the structure-property relationships of particular nanomaterials domain knowledge is typically required for feature extraction. However, this process may introduce bias if there is a focus on known aspects of structure, impeding the discovery of new science. Here, we develop an approach that uses only atomic Cartesian coordinates to predict the electron affinities, band gap energies, Fermi energies and ionization potentials of simulated graphene nanoflakes from a pubically available data set [1]. The workflow developed represents nanoflakes with graphs that are more representative than the ball-stick atom-bond representation that is intuitive to humans and generates fixed-size embeddings of these graphs using the neural embedding framework graph2vec [2]. Pairing the graph embeddings with a convolutional neural network produced highly accurate predictive models with hold out test set R2 from 0.9 − 0.96 for nanoflakes with a very challenging variation in size from tens to thousands of atoms. These predictions were benchmarked against results for optimised predictive models with geometric domain-driven features [3] exceeded their model accuracy for predictions of Fermi energy, electron affinity and ionisation potential and met their model accuracy for band gap energy. Convolutional neural network ; Embedding ; Computer science ; Graph ; Pairing ; Dimensionality reduction ; Artificial intelligence ; Theoretical computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science
Authors: Nurlaela Rauf
Source: Jurnal Fisika Flux Jurnal Ilmiah Fisika FMIPA Universitas Lambung Mangkurat (article, 2023-05-02)
Field: Materials Science — Materials Chemistry

Recent Progress of ZnO-Based Nanoparticle: Synthesizing Methods of Various Dopant and Applications This review focus on the effect of doping rare earth metals, transition metals, noble metals, poor metals, and non-metals on ZnO nanoparticles. ZnO is a semiconductor material with an average wide energy band gap of 3.2 eV. The doping is used to improve the properties of ZnO which strongly depend on their application. The concentration of doping, the type of doping and the process using sol-gel, hydrothermal and precipitation methods are affected in modifying the ZnO lattice parameters. The transition metal widely used for photocatalysts and sensors. The doped application of ZnO nanoparticles as a semiconductor material has proven advantageous in enabling various photocatalytic, glucose biosensors, VOC detection sensors, antibacterial, biomedical, and optoelectronic spintronic, LED, NLO, and silicon solar cells. This review provided information for scientist in choosing the synthesizing methods of ZnO with desired properties and application in future. Dopant ; Materials science ; Doping ; Nanotechnology ; Semiconductor ; Nanoparticle ; Band gap ; Silicon ; Nanomaterials ; Hydrothermal circulation ; Optoelectronics ; Chemical engineering Advanced Nanomaterials in Catalysis ; ZnO doping and properties
Authors: Hassan Abdel‐Gawad | Reda M. Abdelhameed
Source: Egyptian Journal of Chemistry (article, 2023-06-01)
Field: Chemistry — Inorganic Chemistry

The Chemistry and Applications of Europium-Based Metal-Organic Frameworks (Eu-MOFs) Metal-Organic Frameworks (MOFs) received great interest in different application due to its super adsorption charcheters and high surface area, the most interesting MOFs is Europium based MOFs (Eu-MOFs), it is a class of versatile porous materials, Eu-MOFs have shown enormous potential for different applications such as Hg2+ ions, Fe3+, pesticides, phenols, dyes and other ions, drugs, hypochlorite, selenite & arsenate, miscellanies material, and anticounterfeiting detection. Eu-MOFs were applied in high technology like supercapacitor (SCs), pH and temperature sensing applications. Moreover, Eu-MOFs were applied in biological system dopamine & histadine, 1-hydroxypyrene (1-HP), various types of antibiotic, enzyme detections, and adenosine triphosphate (ATP) detections. Additionally, Eu-MOFs were applied in catalysis for the cyanosilylation and reduction reactions. Eu-MOFs is not only worked in previous application but also worked in gas state for gases and different vapor detections. The present review highlights the recent advances in the field of application, followed by the directions for future research. Europium ; Metal-organic framework ; Chemistry ; Nanotechnology ; Materials science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Lanthanide and Transition Metal Complexes
Authors: Lukas Lätsch | С. А. Гуда | Vladyslav Romankov | Christina Wartmann | Jörg‐M. Neudörfl | Jan Dreiser | Albrecht Berkessel | Alexander A. Guda | Christophe Copéret
Source: ChemRxiv (preprint, 2023-11-14)
Field: Materials Science — Materials Chemistry

Tracking Coordination Environment and Reaction Intermediates in Homo- and Heterogeneous Epoxidation Catalysts via Ti L2,3-edge NEXAFS Ti-based molecules and materials are ubiquitous, and play a major role in both homogeneous and heterogeneous catalytic processes. Understanding the electronic structures of their active sites (oxidation state, local symmetry and ligand environment) is key to developing molecular-level structure-property relationships. In that context, X-ray absorption spectroscopy (XAS) offers a unique combination of element selectivity and sensitivity to local symmetry. Commonly, for early transition metals such as Ti, K-edge XAS is applied for in situ characterization and subsequent structural analysis with high sensitivity towards tetrahedral species. Ti L2,3-edge spectroscopy is in principle complementary and offers specific opportunities to interrogate the electronic structure of five-and six-coordinated species. It is, however, much more rarely implemented, because the use of soft X-rays implies ultra-high vacuum conditions. Furthermore, the interpretation of the data can be challenging. Here, we show how Ti L2,3-edge spectroscopy can help to obtain unique information about both homogeneous and heterogeneous epoxidation catalysts and to develop a molecular-level relationship between spectroscopic signatures and electronic structures. Towards this goal, we first establish a spectral library of molecular Ti reference compounds, comprising various coordination environments with mono- and dimeric Ti species having O, N and Cl-ligands. We next implemented a computational methodology based on multiplet ligand field theory and maximally localized Wannier orbitals benchmarked on our library to understand Ti L2,3-edge spectroscopic signatures. We finally used this approach to track and predict spectra of catalytically relevant intermediates, focusing on Ti-based olefin epoxidation catalysts. X-ray absorption spectroscopy ; XANES ; Electronic structure ; Chemical physics ; Absorption spectroscopy ; Spectroscopy ; Context (archaeology) ; Chemistry ; Oxidation state ; Computational chemistry ; Materials science ; Catalysis ; Crystallography ; Physics ; Organic chemistry Polyoxometalates: Synthesis and Applications ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Tomasz Kosmala | Stefano Agnoli | Gaetano Granozzi
Source: Pure and Applied Chemistry (article, 2023-11-07)
Field: Chemistry — Electrochemistry

Watching atoms at work during reactions Abstract The development of new technologies for the current energy and environmental challenges requires the acquisition of a very fundamental knowledge about the structure and activity of catalytic materials at the nanometric scale. As a consequence, in situ and operando methodologies are blossoming, but only a fraction of them really aims at a local vision that would allow watching atoms at work during reactions. In this short report, we want to outline the merits of a new technique based on scanning tunnelling microscopy (Current-roughness electrochemical scanning tunneling microscopy, cr-EC-STM) which can visualize electrocatalytic reactions down at the single atom level. Results of two case studies in the field of hydrogen evolution reaction (HER) are briefly summarized, witnessing the capability of cr-EC-STM to provide critical information about the structure and catalytic performance of the active sites with atomic resolution. Chemistry ; Scanning tunneling microscope ; Nanotechnology ; Quantum tunnelling ; Catalysis ; Work (physics) ; Atomic units ; Electrochemistry ; Atom (system on chip) ; Chemical physics ; Electrode ; Optoelectronics ; Physical chemistry ; Organic chemistry ; Materials science ; Physics Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Molecular Junctions and Nanostructures
Authors: Alexandre Schoepfer | Rubén Laplaza | Matthew D. Wodrich | Jérôme Waser | Clémence Corminbœuf
Source: ChemRxiv (preprint, 2023-08-08)
Field: Materials Science — Materials Chemistry

Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity Chiral ligands are important components in asymmetric homogeneous catalysis, but their synthesis and screening can be both time-consuming and resource-intensive. Data-driven approaches, in contrast to screening procedures based on intuition, have the potential to reduce the time and resources needed for reaction optimization by more rapidly identifying an ideal catalyst. These approaches, however, are often non-transferable and cannot be applied across different reactions. To overcome this drawback, we introduce a general featurization strategy for bidentate ligands that is coupled with an automated feature selection pipeline and Bayesian ridge regression to perform multivariate linear regression modeling. This approach, which is applicable to any reaction, incorporates electronic, steric, and topological features (rigidity/flexibility, branching, geometry, constitution) and is well-suited for early-stage ligand optimization. Using only a limited number of points per dataset, our workflow capably predicts the enantioselectivity of four metal-catalyzed asymmetric reactions. Uncertainty estimates provided by Bayesian ridge regression permit the use of Bayesian optimization to efficiently explore pools of prospective new ligands. Using this procedure, a new library of 312 chiral bidentate ligands was screened to identify promising ligand candidates for a challenging asymmetric oxy-alkynylation reaction. Computer science ; Bayesian probability ; Chemistry ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Fatima Thamer | Abbas Shwya
Source: Nexo Revista Científica (article, 2023-12-31)
Field: Chemistry — Physical and Theoretical Chemistry

Theoretical study of physisorption of tosylate on AU10, AU15 and AU18 surfaces utilizing DFT approach Charge transfer from organic nanomaterial to the coinage metal surfaces occur as a result to the physisorption physisorption phenomena. Physisorption plays a major role in many applications. A set of properties such as, molecular geometries, contour density maps, electrostatic potentials (ESPs), infrared spectra (IR), electronic states, HOMO and LUMO energies, Energy gaps (Eg), ionization potential (I.P), electron affinity (E.A),dipole moment, polarizabilities , average polarizability , symmetry and density of states by using Gaussian 09 software package algorithms were investigated at LANL2DZ, B3LYP and the ground state energy of (Au10-OTS) , (Au15) , (Au15-OTS), (Au18) and (Au18-OTS) , but the structure of (Au10) has been submitted at LANL2MB,B3LYP basis sets. The research study physisorption phenomena from the organic nanomaterial to sylate (OTS) to the coinage metal surface (gold surfaces), also the research concentrate if charge transfer procedure gets at the surface. Physisorption phenomena and charge transfer procedure that happen at the surface can be known throughout some properties such as the closed energy contour maps, electrostatic potentials, symmetry and density of states. Electronic states and energy gap property have been studied in this research to recognize importance of the geometrical structures in the electronic devices and the different electronic applications in different branch of knowledges. The research had the ionization potential and electron affinity credits to demonstrate if the geometrical structure will behave as a donor or an acceptor because the ionization energy demonstrate ability of the geometrical structure to donate the electrons in an interaction, but the electron affinity describes the capacity of the geometrical structure to accept electrons in an interaction. Physisorption ; Ionization energy ; Chemistry ; Electron affinity (data page) ; Polarizability ; Density functional theory ; HOMO/LUMO ; Binding energy ; Atomic physics ; Chemical physics ; Computational chemistry ; Ionization ; Physical chemistry ; Physics ; Ion ; Organic chemistry ; Molecule Various Chemistry Research Topics ; Machine Learning in Materials Science ; Inorganic and Organometallic Chemistry
Authors: Raghad Abdul Hadi Abdul Qader | Marwa Jassim Mohammad
Source: IAES International Journal of Artificial Intelligence (article, 2023-08-02)
Field: Materials Science — Materials Chemistry

Modelling mechanisms for measurable and detection based on artificial intelligence &lt;p&gt;One of the trendiest areas in the field of materials science is Artificial Intelligence (AI) based physical applications. Typically, more time and resources are needed for traditional experiments and statistical methods. Thus, there is a growing need for applications of AI in the simulation and investigation of novel materials. Usually, there are significant restrictions because there are not any benchmark datasets, sophisticated pre-processing mechanisms, prediction modelling mechanisms, or simulation tools in the literature on materials. This work aims to attempt for examining computational and experimental data-based AI processes. In addition, the state of research into developing new materials and utilizing AI in material modelling tools is implemented. As long as, AI can be used in materials to improve efficiency and prediction accuracy. Also, it is very difficult to determine great learning models, involving data preparation, model architecture, data management, and simulation techniques. Finally, it has been discussed the challenges in realizing AI-based applications in the field of materials science.&lt;/p&gt; Computer science ; Benchmark (surveying) ; Field (mathematics) ; Artificial intelligence ; Machine learning ; Applications of artificial intelligence ; Data mining Machine Learning in Materials Science