jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mabintou Kalo | Amon Diane Marina N’Tamon | Brahima André Soumahoro | Ali Sanou | Philatryho Atché | Yaya Soro | Barthélemy Attioua
Source: International Research Journal of Pure and Applied Chemistry (article, 2024-11-28)
Field: Chemistry — Spectroscopy

Identification of Compounds in the Ethyl Acetate Fraction of Hyptis suaveolens Leaves by HPLC-ESI- Q-TOF -MS/ MS Analysis Hyptis suaveolens is a plant of Ivory Coast flora. Preliminary studies have shown that the plant's leaves possess multiple medicinal properties, certainly due to the presence of terpenic and phenolic compounds. Yet, the plant's leaves compounds structures have been fewest elucidated. This study aim to determine the structures of a number of known compounds present in the ethyl acetate fraction obtained from the hydroethanol extract of Hyptis suaveolens leaves. Thus, fifteen known compounds structures were identified in ethyl acetate fraction of the plant's leaves by dereplication method. Nine of these compounds, are phenolic compounds and the others are terpenoids. Five of these compounds had not yet been elucidated in the leaves of Hyptis suaveolens. All these bioactive molecules in Hyptis suaveolens leaves makes this plant an alternative for combating many diseases caused by oxidative stress. Chemistry ; Ethyl acetate ; Chromatography ; Fraction (chemistry) Analytical Chemistry and Chromatography
Authors: Usha Devi R. | N. A. Sheela Selvakumari
Source: ShodhKosh Journal of Visual and Performing Arts (article, 2024-06-30)
Field: Chemistry — Analytical Chemistry

ENHANCING TOMATO VARIETY SELECTION AND YIELD PREDICTION MODEL USING ADVANCED MACHINE LEARNING TECHNIQUES One important agricultural practice that greatly contributes to the world's food supply is the growing of tomatoes. However, choosing the right tomato varieties and accurately estimating their yields are difficult undertakings that depend on several variables, such as crop statistics, climate, and geography. Advanced machine learning approaches can be used to improve prediction accuracy, feature selection, and preprocessing to overcome these problems. To optimize tomato variety selection and yield prediction, this work investigates an integrated approach that uses improved preprocessing approaches for outlier and missing information, an advanced feature selection method, and a hybrid algorithm. Variety (cybernetics) ; Selection (genetic algorithm) ; Yield (engineering) ; Machine learning ; Artificial intelligence ; Computer science ; Model selection ; Materials science Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Smart Agriculture and AI
Authors: Hehe Fan | Yi Yang
Source: ChemRxiv (preprint, 2024-12-05)
Field: Materials Science — Materials Chemistry

Microscopic Artificial Intelligence: An Artificial Intelligence Research Roadmap for Molecules, Materials and Proteins The rapid progress in Artificial Intelligence (AI) has led to extraordinary achievements across various domains, significantly impacting every aspect of daily life. This advancement is also revolutionizing research in numerous scientific areas, particularly within bioinformatics, chemistry, pharmaceuticals, and materials science. In this article, we define these interdisciplinary efforts as Microscopic Artificial Intelligence (MicroAI), which focuses on understanding and generating the structures of microscopic particles such as molecules and proteins. However, the specific AI-related challenges and trends in this field remain somewhat unclear. This article briefly explains these challenges and treads from an AI perspective. Artificial intelligence ; Nanotechnology ; Computer science ; Materials science Machine Learning in Materials Science
Authors: Herzain I. Rivera-Arrieta | Lucas Foppa
Source: ChemRxiv (preprint, 2024-11-18)
Field: Materials Science — Materials Chemistry

Rules Describing CO2 Activation on Single-Atom Alloys from DFT-meta-GGA Calculations and Artificial Intelligence Single-atom alloys (SAAs) arise as a promising concept for the design of improved CO2 hydrogenation catalysts. However, from the immense number of possible SAA compositions and structures, only a few might display the properties required to be useful catalysts. Thus, the direct, high-throughput screening of materials is inefficient. Here, we use artificial intelligence to derive rules describing surface sites of SAAs that provide an effective CO2 activation, a crucial initial step to convert the molecule into valuable products. We start by modeling the CO2 interaction with 780 sites of flat and stepped surfaces of SAAs composed by Cu, Zn, and Pd hosts via high-quality DFT-mBEEF calculations. Then, we apply subgroup discovery to determine constraints on key physical parameters, out of 24 offered candidate descriptive parameters, characterizing subgroups (SGs) of surface sites where chemisorbed CO2 displays large elongations of its C-O bonds. The key identified parameters are free-atom properties of the elements constituting the surface sites, such as their electron affinity, electronegativity, and radii of the $d$-orbitals. Additionally, the generalized coordination number is selected as a key geometrical parameter. The SG rules are used to identify promising alloys among more than 1,500 possible single-atom and dual-atom alloys. Some of the promising alloys predicted by the SG rules were explicitly evaluated by additional DFT-mBEEF calculations and confirmed to provide a significant CO2 activation. Atom (system on chip) ; Materials science ; Computational chemistry ; Computer science ; Chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Don N. Futaba
Source: ECS Meeting Abstracts (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

(Invited) Advancing Carbon Nanotube Synthesis: A Data-Driven Strategy to Overcome Trade-Offs Materials and their synthesis play pivotal roles in energy storage and conversion. In particular, carbon nanotubes (CNTs) have exhibited potential due to their unique multifunctionality, positioning them as promising elements in this field. However, despite advance in the application of CNTs, challenges persist in both synthesis and processing. The advent of artificial intelligence (AI) has emerged as a transformative tool, empowering researchers and showing promise to expedite scientific breakthroughs. In this presentation, I will briefly discuss our development of a two-stage CNT synthesis process. In addition, I will discuss the application of AI to augment researchers’ capabilities in synthesis and processing. As examples, I will discuss our effort to apply a data-driven approach to attempt to break through synthetic trade-offs in single wall CNT forest growth. I will also discuss our development of a tool to determine dispersants for CNT processing. Nanotechnology ; Carbon nanotube ; Transformative learning ; Computer science ; Biochemical engineering ; Engineering ; Materials science ; Psychology Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Carbon Nanotubes in Composites
Authors: S Wang | Jessica Hemond | Charles Chen | Rachel Martens
Source:  (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence to Expedite Thermal Index Testing and Accelerate Polymer Regulatory Approvals With the increasing focus on sustainability, industries are rapidly adopting technologies such as 3D printing and making significant investments to achieve their environmental goals. This technological shift is driving a surge in the development of new polymers, including the revival of materials like aliphatic polyketone and the creation of innovative resins such as polyacrylates. These new materials, targeting at specific properties like flame retardancy and heat stabilization, require rigorous regulatory approval to be used in applications with strict safety standards, especially in electrical connectors and components. Validating short-term properties of these resins is relatively quick and straightforward. However, assessing their long-term thermal stability, particularly through tests like UL's Relative Thermal Index (RTI), is a complex and time-consuming process, often taking months. This lengthy testing period poses a significant challenge for new resins entering the market, due to the high time and cost involved. The advancement of artificial intelligence (AI) offers a transformative solution to this problem. This paper explores how AI can be utilized to predict long-term thermal performance accurately, effectively reducing the duration of RTI testing by up to 50% without compromising on reliability. More specifically, An AI-based methodology is proposed to estimate the RTI temperature by incorporating the physics of thermal degradation, where data from shorter testing periods at higher temperatures is used to predict the results of longer testing durations at lower temperatures. Through this innovative approach, AI enables faster regulatory approvals, allowing new polymer developments to reach the market more quickly and efficiently, keeping pace with the dynamic advancements in the field. Index (typography) ; Computer science Machine Learning in Materials Science
Authors: Christopher R. Lages | Vitoria Y Nicoleti | Maicon R Correa | Paulo Mausbach | Felipe C Ramos | Evandro Ares de Araújo | Andrey Z Nascimento | Eduardo X. Miqueles
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-08-23)
Field: Materials Science — Materials Chemistry

Automatic crystal identification for crystallography: <i>a comparison between direct methods and artificial intelligence strategies</i> Abstract Crystallography is an important, and well-established technique for assessing crystalline atomic structure of a wide variety of materials. An auxiliary optical microscope instrument is used to perform the data acquisition at the manaca beamline, a crystallography experimental station from sirius (the 4th generation Brazilian synchrotron). The importance of detecting these crystals in the auxiliary image is to define the best order in which each crystal will be measured by the incident beam since each one of them must be measured only once; improving beamtime and maximizing user experience at the facility. Detecting rough crystal positions from these auxiliary images is a scientific computing task, which could benefit from some open artificial intelligence tools. This manuscript compares a real-time object detection interface (Yolo) with a new, simple and effective strategy obtained from a particular partial differential equation. Identification (biology) ; Artificial intelligence ; Computer science ; Materials science ; Biology X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Enzyme Structure and Function
Authors: Cheng Yang | Thérèse Wild | Yulia Rakova | Stephen Maldonado | Matthew S. Sigman | Corey R. J. Stephenson
Source: ChemRxiv (preprint, 2024-10-28)
Field: Materials Science — Materials Chemistry

Data-driven Workflow for the Development and Discovery of N-oxyl Hydrogen Atom Transfer Catalysts N-oxyl species are promising hydrogen atom transfer (HAT) catalysts to advance C–H bond activation reactions. However, because of the complex structure-activity relationship within the N-oxyl structure, catalyst optimization is a key challenge, particularly for simultaneous improvement across multiple parameters. This paper describes a data-driven approach to op-timize N-oxyl hydrogen atom transfer catalysts. A focused library of 50 N-hydroxy compounds was synthesized and char-acterized by three parameters – oxidation peak potential, HAT reactivity, and stability – to generate a database. Statistical modeling of these activities described by their intrinsic physical organic parameters was used to build predictive models for catalyst discovery and to understand their structure-activity relationships. Virtual screening of 102 synthesizable candidates allowed for rapid identification of several ideal catalyst candidates. These statistical models clearly suggest that N-oxyl sub-structures bearing an adjacent heteroatom as more optimal HAT catalysts compared to the historical focus, phthalimide-N-oxyl, by striking the best balance among all three target experimental properties. Workflow ; Catalysis ; Hydrogen atom ; Chemistry ; Atom (system on chip) ; Hydrogen ; Computer science ; Combinatorial chemistry ; Database ; Organic chemistry ; Operating system ; Group (periodic table) Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Matteo Vettori | Adriano Marchi | Enrico Bellocchio | Alessandro Devo | Davide Belfiori | Francesco Soncini | Francesco Musiari | F. Moroni | A. Pirondi
Source: IOP Conference Series Materials Science and Engineering (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Material Characterization Augmented with Artificial Intelligence. Abstract The present paper investigates the application of artificial intelligence to improve the results from simple, non-instrumented, tensile tests, performed with a desktop-size MaCh3D smart universal testing machine. Non-instrumented tensile tests, performed on any testing machine, are affected by both deterministic and random factors that introduce errors in the test results. Specific features of the MaChh3D tester minimize random factors effects on test results while introducing a larger effect of deterministic factors. Artificial intelligence is identified as a novel approach to correct errors in non-instrumented tensile test, capable of simulating a direct strain measure onto the test, replacing traditional contact or non-contact instrumentations (like strain-gages, extensometer and optical measures) that introduce complexity into test procedure and require time for setup. The resulting AI model implementation is described and its performance evaluated in comparison with instrumented tests, also comparing different training strategies. The developed AI-extensometer (artificial intelligence virtual extensometer), is capable of a precise mechanical properties evaluation, with errors from 0 to 10% depending on the specific parameter. Characterization (materials science) ; Computer science ; Artificial intelligence ; Materials science ; Nanotechnology Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Industrial Vision Systems and Defect Detection
Authors: Jeng-Yuan Tsai | Weiyi Gong | Qimin Yan
Source: arXiv (Cornell University) (preprint, 2024-05-18)
Field: Materials Science — Materials Chemistry

Symmetry-guided data-driven discovery of native quantum defects in two-dimensional materials Drawing on their atomically thin structure, two-dimensional (2D) materials present a groundbreaking avenue for the precision fabrication and systematic manipulation of quantum defects. Through a method grounded in site-symmetry principles, we devise a comprehensive workflow to pinpoint potential native quantum defects across the entire spectrum of known binary 2D materials. Leveraging both symmetry principles and data-driven approaches markedly enhances the identification of spin defects exhibiting triplet ground states. This advancement is pivotal in discovering NV-like quantum defects in 2D materials, which are instrumental in facilitating a set of quantum functionalities. For discerning the multifaceted functionalities of these quantum defect candidates, their magneto-optical properties are comprehensively estimated using high-throughput computations. Our findings underscore that antisite defects in diverse hosts emerge as prospective quantum defects of significance. Crucially, based on our research, we advocate that the 16 antisites present in post-transition metal monochalcogenides (PTMCs) stand out as a prominent 2D-material-based quantum defect platforms, by their precise defect levels, optimal magneto-optical attributes, and the readily accessible nature of their host materials. This work substantially broadens the repertoire of quantum defects within the 2D material landscape, presenting profound implications for the advancement of quantum information science and technologies. Symmetry (geometry) ; Physics ; Quantum ; Theoretical physics ; Quantum mechanics ; Condensed matter physics ; Mathematics ; Geometry Machine Learning in Materials Science
Authors: Pritom J. Bora | Bibhusita Mahanta | Shalabh Gupta | Praveen C. Ramamurthy | Ravi L. Hadimani
Source: Research Square (preprint, 2024-01-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Realization of strong microwave absorption characteristics of Gd 5 Si 2 Ge 2 nanoparticles with materials data-driven discovery Abstract In this study, we utilized a materials data-driven approach to investigate the microwave absorption characteristics of Gd 5 Si 2 Ge 2 nanocomposites. These results suggests that Gd 5 Si 2 Ge 2 nanocomposites possess highly efficient microwave absorption properties. It was observed that varying the loading of the Gd 5 Si 2 Ge 2 nanoparticles in a polymer matrix, such as polydimethylsiloxane (PDMS), resulted in changes in reflection loss (RL). However, it was also found that simply increasing the loading of the Gd 5 Si 2 Ge 2 nanoparticles in PDMS did not improve RL performance. To optimize the RL performance, we employed an electromagnetic data-driven methodology. Obtained results predict a remarkable RL of ≤ -60 dB for a composite containing 60 wt% Gd 5 Si 2 Ge 2 nanoparticles loaded PDMS in the frequency range 8.2–18 GHz. This prediction was supported by experimental data, which showed a minimum RL value of -57 dB with multiple RL≤-10 dB bandwidth. These findings validate the proposed proof of concept of utilizing data-driven methodology to obtain broadband and robust microwave absorption characteristics in Gd 5 Si 2 Ge 2 nanocomposites. Reflection loss ; Materials science ; Microwave ; Nanocomposite ; Polydimethylsiloxane ; Nanoparticle ; Absorption (acoustics) ; Polymer ; Realization (probability) ; Composite number ; Broadband ; Polymer nanocomposite ; Optoelectronics ; Chemical engineering ; Nanotechnology ; Composite material ; Computer science ; Telecommunications Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies
Authors: Zhongxiong Sun | Jinjin Yang | Yifeng Han | Man‐Rong Li
Source: Chemistry of Materials (article, 2024-07-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-Driven High-Throughput Screening and Experimental Realization of Ag<sub>2</sub><i>B</i>(IV)<i>B</i>′(VI)O<sub>6</sub> under Negative Chemical-Pressure Harsh synthetic conditions and high cost seriously hinder the discoveries of metastable materials under extreme conditions. Extending the big-data-driven high-throughput calculation (HTC) to depict the chemically negative pressure (NP) zone enables the creation of efficient and accurate prediction models over the full pressure range. This approach significantly expedites the exploration and discovery of novel multifunctional metastable materials. Here, double perovskites Ag2B(IV)B′(VI)O6 were adopted to illustrate the comprehensive process of big-data-mining, HTC, and experimental realization under chemically NP. High-throughput screening of 32 Ag2BB′O6 compounds, encompassing 9 possible crystal structures and 23 derived magnetic structures, resulting in 1024 potential candidates. Ag2MnTeO6 (AMTO) and Ag2TiTeO6 (ATTO) were selected for the experimental validation. We captured a new polymorph of AMTO (R-3, 3R) at ambient pressure through ion-exchange reaction, a phase theoretically predicted to be stable under NP about −6.6 GPa. Moreover, as predicted by density functional theory calculations, the P-31c (2H) AMTO is an antiferromagnetic semiconductor with magnetic transition at 3 K and direct band gap ∼0.97 eV. This work is expected to guide the exploration of hidden metastable phase by providing a methodological framework and novel conceptual approach for future research. Realization (probability) ; Throughput ; High-throughput screening ; High pressure ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Physics ; Nanotechnology ; Computer science ; Thermodynamics ; Organic chemistry ; Mathematics ; Telecommunications ; Statistics Crystal Structures and Properties ; X-ray Diffraction in Crystallography ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Pavel Martynenko
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Organic Chemistry

Comparison of Quantum Chemical Approach and Mixed Approach Using Ai in Study of Reactions Involving Silanediamides Quantum chemical ; Quantum ; Computational chemistry ; Psychology ; Chemistry ; Theoretical physics ; Quantum mechanics ; Physics ; Molecule Inorganic and Organometallic Chemistry ; Thermal and Kinetic Analysis ; Chemical Reactions and Mechanisms
Authors: Data Study Group Team
Source: Repository KITopen (Karlsruhe Institute of Technology) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Data Study Group Final Report: Johnson Matthey - Data Driven Modelling of Chemical Processes: Towards Flexible Production of Energy Carriers for a Net-Zero Society Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country’s top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Johnson Matthey (JM), an FTSE 250 company, is a global leader in speciality chemicals and sustainable technologies with a 200+ year history of using advanced metals chemistry to tackle the world’s biggest challenges. JM continually seeks to expand on products and services which provide sustainability benefits through the positive impact they have on the environment, resource efficiency and human health. For instance, JM are interested in various new manufacturing processes that help build a sustainable society, such as power-to-X, power-to-fuels, and green hydrogen processes. This challenge mainly pertains to the flexible operation of these processes: we seek to understand how they can be operated with a time-changing feedstock such as electricity. Data Study Group - December 2023 | The Alan Turing Institute Data-driven ; Raw data ; Sustainability ; Turing ; Big data ; Group (periodic table) ; Production (economics) ; Resource (disambiguation) ; Energy (signal processing) Machine Learning in Materials Science ; Big Data and Digital Economy ; CO2 Reduction Techniques and Catalysts
Authors: Hyun Joo | Tae Young Yun | Seung Bo Shim
Source: International Journal of Highway Engineering (article, 2024-12-30)
Field: Materials Science — Materials Chemistry

Research Methods Utilizing Materials Informatics and Molecular Dynamics for the Development of Road-Pavement Materials (II): Material Research Trends Using Artificial Intelligence Technology Informatics ; Materials informatics ; Research development ; Engineering ; Construction engineering ; Computer science ; Systems engineering ; Data science ; Health informatics ; Geology ; Medicine ; Engineering informatics Machine Learning in Materials Science
Authors: Hiyori Takebe | Seijiro Matsubara
Source: Journal of Synthetic Organic Chemistry Japan (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Can digital organic synthesis evolve into AI-driven organic synthesis? Chemistry ; Organic synthesis ; Combinatorial chemistry ; Organic chemistry ; Biochemical engineering ; Catalysis Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods