jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yan Liu
Source:  (article, 2022-12-09)
Field: Materials Science — Materials Chemistry

Performance Analysis of Low Temperature Solid Oxide Fuel Cell Based on Artificial Intelligence Technology In order to solve the problems of poor output performance and large output oscillation of traditional low-temperature solid oxide fuel cells, artificial intelligence technology was introduced in this paper to analyze the performance of low-temperature solid oxide fuel cells. Firstly, the steady-state control system of the battery was constructed, and the three-dimensional structure design and electrical performance optimization of the battery were realized. Then, the electrode potential induction analysis model was constructed to analyze the carbon / metal oxide electrode materials with stable mechanical and electrochemical properties. Thirdly, combined with the three-phase regulation of the battery electrode, the microstructure area in the fuel cell electric field is controlled. Finally, according to the fuel cell output voltage, the fuel cell ion mass conservation model is constructed. Artificial intelligence is used to obtain the optimal solution of fuel cell voltage output, so as to complete the analysis of fuel cell steady-state performance. The simulation results show that this method can control the output of the low-temperature solid oxide fuel cell well and reduce the output oscillation of the cell, which has a certain theoretical reference significance for the performance of the cell. Solid oxide fuel cell ; Battery (electricity) ; Oxide ; Voltage ; Materials science ; Electrode ; Computer science ; Automotive engineering ; Anode ; Electrical engineering ; Engineering ; Power (physics) ; Chemistry ; Thermodynamics Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Catalytic Processes in Materials Science
Authors: Ziheng Lu | Wenlei Shi | Lixin Sun | Haiguang Liu | Tie‐Yan Liu
Source: arXiv (Cornell University) (preprint, 2022-05-10)
Field: Materials Science — Materials Chemistry

Driving atomic structures of molecules, crystals, and complex systems with local similarity kernels Accessing structures of molecules, crystals, and complex interfaces with atomic level details is vital to the understanding and engineering of materials, chemical reactions, and biochemical processes. Currently, determination of accurate atomic positions heavily relies on advanced experimental techniques that are difficult to access or quantum chemical calculations that are computationally intensive. We describe an efficient data-driven LOcal SImilarity Kernel Optimization (LOSIKO) approach to obtain atomic structures by matching embedded local atomic environments with that in databases followed by maximizing their similarity measures. We show that LOSIKO solely leverages on geometric data and can incorporate quantum chemical databases constructed under different approximations. By including known stable entries, chemically informed atomic structures of organic molecules, inorganic solids, defects, and complex interfaces can be obtained, with similar accuracy compared to the state-of-the-art quantum chemical approaches. In addition, we show that by carefully curating the databases, it is possible to obtain structures with bias towards target material features for inverse design. Similarity (geometry) ; Quantum chemical ; Matching (statistics) ; Kernel (algebra) ; Molecule ; Computer science ; Inverse ; Quantum ; Physics ; Mathematics ; Quantum mechanics ; Artificial intelligence ; Geometry ; Image (mathematics) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Nevin Taşaltın | Selcan Karakuş | Barbaros Akkurt
Source: Journal of the Turkish Chemical Society Section A Chemistry (article, 2022-10-03)
Field: Materials Science — Materials Chemistry

Facile Preparation of Carbon Nanopillar Array Carbon-based nanostructures have attracted extensive interest in obtaining advanced sensing electronic devices in environmental and biological monitoring applications as an alternative to conventional materials. Herein, the facile preparation, control of the growth, and artificial intelligence–based morphological information of the carbon nanopillar array in the Anodized Aluminum Oxide (AAO) template were investigated. A facile approach for controlling the growth of the nanostructure was proposed as a two-step anodization technique for AAO and Plasma Enhanced Chemical Vapor Deposition (PECVD) for carbon nanopillar array. It involved the competitive carbon deposition between the carbon nanopillars electrodeposited on the AAO template and at the bottom of the pores of AAO under vacuum conditions. The morphology and structure of the prepared carbon nanopillars were reported in detail. Hexagonally straight AAO nanotubes were approximately 65 nm in diameter and 360 nm in length, with 90 nm interpore distances. The AAO nanotube density is approximately 1.75x1014 cm-2. Carbon nanopillars with a width of ~60 nm were used to create a low-dimensional nanostructure. This controllable preparation leads to the facile and impressive preparation of a free-standing carbon nanopillar array, especially for various chemical sensor applications. Nanopillar ; Nanotechnology ; Nanostructure ; Materials science ; Carbon nanotube ; Carbon fibers ; Chemical vapor deposition ; Anodizing ; Template ; Nanolithography ; Chemical engineering ; Fabrication ; Aluminium ; Composite material ; Composite number Anodic Oxide Films and Nanostructures ; Nanowire Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Ram G. Goel
Source: International Journal of Science and Research (IJSR) (article, 2022-11-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhancing Electronic Performance with a Gallium Oxide Semiconductor In order to facilitate future communications, the digitalization of society, and the development of applications for artificial intelligence, robust electrical components are absolutely necessary. They should have low energy consumption while achieving ever increasing power densities, which would result in increased operational efficacy. Their footprints should be as tiny as feasible. The purpose of the research is to define the frequency and power limitations for the Gallium Oxide semiconductor, to describe the kind of doping that is suited for Gallium Oxide semiconductors, and to offer a review of defect characterisation using optical and electrical spectroscopic techniques. Because there is no minority carrier storage effect in SBD and its switching loss is quite low, it is beginning to replace p-n junction diodes as a component in power electronic systems as wide bandgap (WBG) semiconductor material technology advances. This is due to the fact that SBD devices based on WBG semiconductors have a lower switching loss. In a-Ga2O3 MOSFETs, sluggish trapping and detrapping as well as the associated threshold voltage variations are regularly seen. This makes it anything but a simple problem to solve. A mapping of the interface states as well as its optimization is essential for each potentially promising dielectric material. Additionally, typical stability studies, such as Positive Bias Temperature Instability, must also be performed (PBTI). Gallium ; Semiconductor ; Gallium oxide ; Materials science ; Optoelectronics ; Oxide ; Nanotechnology ; Metallurgy Ga2O3 and related materials
Authors: Kexin Chen | Guangyong Chen | Junyou Li | Yuansheng Huang | Pheng‐Ann Heng
Source: arXiv (Cornell University) (preprint, 2022-08-22)
Field: Materials Science — Materials Chemistry

MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails Artificial intelligence has deeply revolutionized the field of medicinal chemistry with many impressive applications, but the success of these applications requires a massive amount of training samples with high-quality annotations, which seriously limits the wide usage of data-driven methods. In this paper, we focus on the reaction yield prediction problem, which assists chemists in selecting high-yield reactions in a new chemical space only with a few experimental trials. To attack this challenge, we first put forth MetaRF, an attention-based differentiable random forest model specially designed for the few-shot yield prediction, where the attention weight of a random forest is automatically optimized by the meta-learning framework and can be quickly adapted to predict the performance of new reagents while given a few additional samples. To improve the few-shot learning performance, we further introduce a dimension-reduction based sampling method to determine valuable samples to be experimentally tested and then learned. Our methodology is evaluated on three different datasets and acquires satisfactory performance on few-shot prediction. In high-throughput experimentation (HTE) datasets, the average yield of our methodology's top 10 high-yield reactions is relatively close to the results of ideal yield selection. Random forest ; Yield (engineering) ; Computer science ; Machine learning ; Artificial intelligence ; Dimension (graph theory) ; Differentiable function ; Selection (genetic algorithm) ; Sampling (signal processing) ; Chemical space ; Quality (philosophy) ; Mathematics ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Qiang Wei | Yue Liu | Chaoqun Xia
Source: Chinese Journal of Space Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Progress of Materials Science in Space Technology in China (2020–2022) In this paper, the main research work and related reports of materials science research in China’s space technology field during 2020–2022 are summarized. This paper covers Materials Sciences in Space Environment, Materials Sciences for Space Environment, Materials Behavior in Space Environment and Space experimental hardware for material investigation. With the rapid development of China’s space industry, more scientists will be involved in materials science, space technology and earth science researches. In the future, a series of disciplines such as space science, machinery, artificial intelligence, digital twin and big data will be further integrated with materials science, and space materials will also usher in new development opportunities. China ; Space (punctuation) ; Space Science ; Computer science ; Astrobiology ; Physics ; Astronomy ; Geography Titanium Alloys Microstructure and Properties ; Bone Tissue Engineering Materials ; Advanced materials and composites
Authors: Fabio Le Piane | Matteo Baldoni | Mauro Gáspari | Francesco Mercuri
Source: arXiv (Cornell University) (preprint, 2022-01-18)
Field: Chemistry — Physical and Theoretical Chemistry

Molecular and Materials Basic Ontology: development and first steps Advanced materials and their applications have become a key field of research, and it looks like this trend is not going to change soon. For that reason, the need for systematic and efficient methods for organizing knowledge in the field and conduct computational or experimental investigations is stronger than ever. In this work, we present a basic implementation of MAMBO - an ontology for molecular materials and their applications in real-life scenarios. The development of MAMBO has been guided by the needs of the research community involved in the development of novel materials with functional properties, with particular attention to the nanoscale. MAMBO aims at extending the current work in the field, while retaining a modular nature in order to allow straightforward extension of concepts and relations to neighboring domains. Our work is expected to enable the systematic integration of computational and experimental data in specific domains of interest (nanomaterials, molecular materials, organic an polymeric materials, supramolecular and bio-organic systems, etc.). Moreover, MAMBO is developed with a strong focus on the applications of data-driven frameworks for the design of novel materials with tailored characteristics. Computer science ; Modular design ; Ontology ; Field (mathematics) ; Nanotechnology ; Data science ; Systems engineering ; Materials science ; Engineering ; Epistemology History and advancements in chemistry ; Machine Learning in Materials Science ; Chemistry and Chemical Engineering
Authors: Dinesh V. Vidhani | Rosemary Ubeda | Thalia Sautie | Diana Vidhani | Manoharan Mariappan
Source: Research Square (preprint, 2022-11-22)
Field: Chemistry — Organic Chemistry

Zwitterionic Bergman-Triggered Cascade Polymerization: A Plateau Reaction Leading to Metal-Graphene Nanoribbon Semiconductors Using Boron-Metal Couple Abstract With a rapid growth in artificial intelligence, designing high-speed and low power semiconducting material is of utmost importance. This investigation provides a theoretical basis to access covalently-bonded transition metal-graphene nanoribbon (TM-GNR) hybrid semiconductors whose DFT-computed bandgaps were much narrower than the commonly used pentacene. A systematic optimization of substrates containing remotely-placed boronyl groups and the transition metals produced the zwitterions via ionic Bergman cyclization ( i-BC ) and unlocked the polymerization of metal-substituted polyenynes. Aside from i-BC , the subsequent steps showed plateau-TS involving structureless transition regions. A multivariate analysis revealed a strong dependence of activation energy and the cyclization mode on the electronic nature of boron and Au(I). Consequently, three regions corresponding to radical Bergman ( r-BC ), ionic Bergman ( i-BC ), and ionic Schreiner-Pascal ( i-SP ) cyclizations were identified. The boundaries between these regions corresponded to the mechanistic shift induced by three-center-three-electron (3c-3e) hydrogen bond, three-center-four-electron (3c-4e) hydrogen bond, and vacant p-orbital on boron. The ideal combination for cascade polymerization was observed near the boundary between i-BC and i-SP . Ionic bonding ; Graphene ; Materials science ; Boron ; Semiconductor ; Transition metal ; Covalent bond ; Chemical physics ; Chemistry ; Polymer chemistry ; Nanotechnology ; Catalysis ; Organic chemistry ; Optoelectronics Synthesis and Properties of Aromatic Compounds ; Molecular Junctions and Nanostructures ; Fullerene Chemistry and Applications
Authors: Dr. Parul Mishra
Source: International Journal of Pharmacognosy and Life Science (article, 2022-01-01)
Field: Materials Science — Biomaterials

Nanochemistry in medicinal chemistry in targeted drug delivery of nanoparticles to tumer sites Nanochemistry has emerged as a transformative discipline in medicinal chemistry, particularly in the development of targeted drug delivery systems for cancer therapy. Conventional chemotherapy suffers from poor specificity, systemic toxicity, and limited bioavailability. Nanoparticle-based drug delivery systems offer a promising alternative by enabling controlled, site-specific delivery of therapeutic agents. This study presents a comprehensive analysis of nanochemical principles underlying nanoparticle design, tumor-targeting mechanisms, and interactions with the tumor microenvironment. Key nanoparticle systems including polymeric, lipid-based, metallic, and dendrimeric carriers are evaluated for their physicochemical properties and therapeutic performance. Mechanisms such as passive targeting via the enhanced permeability and retention (EPR) effect, active targeting through ligand-receptor interactions, and stimuli-responsive drug release are critically discussed. Furthermore, emerging strategies such as immuno-nanomedicine, theranostics, and biomimetic nanocarriers are examined. Despite significant advancements, challenges related to biological barriers, toxicity, scalability, and clinical translation persist. This paper highlights current progress and outlines future directions toward personalized nanomedicine and AI-assisted nanoparticle design. Nanocarriers ; Nanomedicine ; Drug delivery ; Nanochemistry ; Nanocapsules ; Targeted drug delivery ; Drug carrier ; Drug Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; RNA Interference and Gene Delivery
Authors: Kai Feng | Jia‐Wei Zhang | Sichen Qin | Xuan Meng | Cheng Li | Wei Gao
Source: 2022 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP) (article, 2022-10-30)
Field: Materials Science — Materials Chemistry

Charge Evacuation Performance of the Insulating Polymers with Various Surface Roughness Polyimide (PI) plays an important role in 5G communications, artificial intelligence, and aerospace, which has excellent dielectric characteristics, high mechanical strength and stable chemical property. However, the performance of polyimide is restricted by surface discharge and flashover induced by the surface charge accumulation of the insulating polymers. The roughness of polymer’s surface can strongly affect the process of the charge transport. Thus, the impact of surface roughness on surface charge characteristics of polyimide was investigated after corona discharge in this work. The results indicated that increasing surface roughness could hinder the accumulation and decay of charges, which could provide an important reference for the development of polyimide in various fields. Surface roughness ; Charge (physics) ; Polymer ; Surface finish ; Materials science ; Engineering physics ; Composite material ; Environmental science ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Advanced Sensor and Energy Harvesting Materials
Authors: Takeshi Aoyagi
Source: NIPPON GOMU KYOKAISHI (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Data Platform for the Design of Functional Polymeric Materials The overview of “AIST Materials Gate Data Platform” (DPF), which have been developed in the national project “Ultra-High-Throughput Design and Prototyping Technology for Ultra-Advanced Materials Development Project (U2M project)”, is introduced. The U2M project developed five DPFs for different targeted materials, and the detail of DPF for functional polymeric materials are introduced in this article. The DPF contains the data of structures and properties of polymeric materials obtained by the computational simulation and experimental observation. Especially, the higher-order structures such as phase separated structure of polymer blends and dispersion of fillers in polymer matrices, and corresponding properties such as elastic constant and thermal conductivity are focused in the DPF. In April 2022, after the completion of the U2M project, the materials design platform will be operational, and the consortium will be organized to accelerate data-driven materials design. In addition to the introduction of DPFs, we will introduce the plans for these activities to implement the fruits of the project in industry. Systems engineering ; Process engineering ; Materials science ; Computer science ; Nanotechnology ; Engineering Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Injection Molding Process and Properties
Authors: Wei, Sandy, '22
Source: Scholarly and Creative Works from DePauw University (DePauw University) (article, 2022-05-01)
Field: Chemistry — Physical and Theoretical Chemistry

NMR Matcher NMR Matcher is an artificial intelligence analysis program using the Python language. The program translates 3D chemical small molecules into numerical expressions that can be understood by a computer. The program was developed based on the fact that in chemical reactions the product structures are often different from the predicted ones, and using NMR Matcher one can quickly evaluate whether the predicted structures and the product profiles agree. In the process of using NMR Macher, the user will input the structure of the predicted molecule and the NMR spectrum of the product. The spectrum input can contain both HNMR and CNMR 1D spectra. The program will compare the predicted NMR data with the input product NMR pattern based on the structure of the input molecule, and determine whether the reaction proceeds as predicted based on the similarity to the predicted product pattern. The purpose of NMR Matcher is to help undergraduate students better understand NMR patterns, improve experimental efficiency, and reduce human error. In conclusion, the NMR Matcher is a useful tool for undergraduate students in chemistry research and will help them to improve their research experience. Python (programming language) ; Nuclear magnetic resonance spectroscopy ; Carbon-13 NMR ; Product (mathematics) ; Proton NMR ; Chemical shift ; Fluorine-19 NMR ; Process (computing) Various Chemistry Research Topics ; Machine Learning in Materials Science ; Molecular spectroscopy and chirality
Authors: Srinivasan V | Aravindhan D | Jeeva T | Sambasivam J | Keerthana Salas E
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-02-19)
Field: Materials Science — Materials Chemistry

AI Based Water Treatment using Graphene Nano-Material Increasing population and industrial growth, combined with climate change, led to global water problems. The graphene-based membrane materials that are used for water purification using Artificial Intelligene(AI). Graphene-based membrane materials are believed to be the advanced materials for the water treatment process because of their atomic thickness and tunable functionalities. The effect of membrane fabrication techniques and operating conditions on the separation and membrane fouling mechanisms are discussed in depth. To extend the applications of graphene membranes, more attention should be paid to the water permeability and membrane selectivity in long-term operation at conditions similar to those in the field for performance analysis using AI. While literature reviews have mainly concerned the ability of graphene materials to adsorb water contaminants, there is very limited updated information by artificial intelligence, related to its sieving properties. Moreover, the literature lacks a comparative discussion of graphene-based membranes with other hybrid membrane materials. Thus, a profound understanding of graphene-based membranes is much needed to unlock the potential for graphene materials using Artificial Intelligence(AI). Graphene ; Membrane ; Water treatment ; Fouling ; Membrane technology ; Population ; Adsorption ; Synthetic membrane Graphene research and applications ; Membrane Separation Technologies ; Graphene and Nanomaterials Applications
Authors: Takeru Torigoe | Position
Source: Angewandte Chemie International Edition (article, 2022-04-14)
Field: Chemistry — Physical and Theoretical Chemistry

Takeru Torigoe My first experiment was recrystallization of B 2 (pin) 2 for catalytic borylation, and one of my current research interests is still to explore catalytic reactions using B 2 (pin) 2 .Ichose my current career path because chemistry is not only fun, but Ia malso interested in education.Theg reatest scientific advance of the next decade will be significant development of artificial intelligence to accelerate scientific research. Chose ; Path (computing) ; Group (periodic table) ; Mathematics education ; Psychology ; Chemistry ; Computer science ; Political science ; Organic chemistry Various Chemistry Research Topics