jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mengying Wang | Ying Wang | Jianpeng Zhu | Minglai Yang | Zhenfeng Cao
Source:  (article, 2024-11-22)
Field: Materials Science — Surfaces, Coatings and Films

Research and Development of a Deep Learning-Based Automated Imaging System for FIB-SEM With the rapid advancements in semiconductor and materials science, the demand for high-resolution and high-clarity scanning electron microscope (SEM) images continues to grow. To meet this demand, this study proposes an automated artificial intelligence imaging system for FIB-SEM (Focused Ion Beam-Scanning Electron Microscope). The system aims to achieve efficient identification and automatic capture of specific image locations by integrating artificial intelligence-based image recognition and automation control technologies, utilizing a deep learning algorithm (YOLO). This approach enables precise and intelligent imaging of target features. The system is composed of three primary modules: an artificial intelligence system, an automated imaging system, and a human-machine interface (UI), which collectively enable intelligent control and imaging of the FIB-SEM movable stage. Through optimized AI algorithms and automated imaging workflows, the system significantly enhances the efficiency and quality of feature image acquisition. Computer science ; Artificial intelligence Electron and X-Ray Spectroscopy Techniques
Authors: Nanaji Uppe
Source: i-manager’s Journal on Future Engineering and Technology (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Nature's pharmacy: A deep learning approach for identification of medicinal plants The discovery and use of medicinal plants are essential for both conventional and modern health systems. This study introduces a unique deep learning technique using EfficientNetB3 models for the detection and extraction of medicinal plants. For the chemical models, the version is trained on specialized datasets, including various plant species, to ensure classification accuracy. Through deep learning, this proposed technique provides a reliable and efficient solution for identifying medicinal plants based on specific characteristics. The EfficientNetB3 model demonstrates better overall performance in classification tasks, even with limited computing resources. The application of deep learning in plant chemical identification holds promise in fields such as medicine, ethnobotany, and conservation biology, enabling researchers, health professionals, and enthusiasts to quickly catalog medicinal plants and gain insights into their healing properties. In particular, the EfficientNetB3 model facilitates the efficient identification and classification of medicinal plants, thereby advancing plant research and improving health practices. Pharmacy ; Identification (biology) ; Traditional medicine ; Medicine ; Biology ; Botany ; Nursing Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Ekaterina I. Marchenko | Maria G. Khrenova | Vadim Korolev | Eugene A. Goodilin | Alexey B. Tarasov
Source: arXiv (Cornell University) (preprint, 2024-11-17)
Field: Materials Science — Materials Chemistry

Topological representation of layered hybrid lead halides for machine-learning using universal clusters Layered hybrid halide compounds offer promising functional properties, particularly tunable band gaps, conductivity, light harvesting thus making them prospective for applications in photovoltaics and optoelectronics. This study exemplifies an approach of predicting band gaps using machine learning models enhanced by invariant topological representations of these materials using the atom-specific persistent homology method in order to facilitate the discovery and design of new hybrid halide materials with tailored electronic properties. Halide ; Representation (politics) ; Lead (geology) ; Computer science ; Topology (electrical circuits) ; Artificial intelligence ; Chemistry ; Mathematics ; Geology ; Political science ; Combinatorics Machine Learning in Materials Science ; Supramolecular Self-Assembly in Materials
Authors: Poorva Agrawal | Amit Ganatra | Shrishti Prasad | Pooja Kumari | Rupali Atul Mahajan | Pradnya Mehta
Source:  (article, 2024-11-13)
Field: Materials Science — Materials Chemistry

Supervise Machine Learning Interface to Predict Physical Properties of Binary Solutions The progress in biological and chemical research provides valuable physiological and chemical functionalities using the basic physical properties of life systems. The major among them is Density, Surface tension, Viscosity etc. However, it is not possible to evaluate the precise and accurate values of these properties for the desired concentration. The evolution of supervised machine learning algorithm can provide the predicted values of physical properties, provided the accurate and highly precise experimental values are available. Present study is an attempt to evolve the Supervise machine learning trained network to predict the physical properties of binary solution with selected variables and constrains. An attempt is also made to design the generalized algorithm and method to evaluate any property of binary solution. Computer science ; Binary number ; Interface (matter) ; Machine learning ; Artificial intelligence ; Human–computer interaction ; Parallel computing ; Mathematics ; Arithmetic Machine Learning in Materials Science
Authors: Stephan Lany
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2024-09-06)
Field: Materials Science — Materials Chemistry

Computational Approaches for Clean Energy Materials Currently, 80% of the global final energy consumption occurs in form of fuels and only 20% as electricity. On the other hand, renewable energy additions come almost exclusively in the form of electricity (dominantly photovoltaics and wind). Thus, a successful energy transition will require enormous growth in renewables, sufficient to convert excess electricity into fuels, as well as the development of non-electricity based solar fuel technologies. As much as photovoltaic capacities have grown over the past 20 years, it is far from clear that current technologies and materials are up to the task to grow from here by yet another factor 100 until 2050. Therefore, sustained research efforts on emerging inorganic semiconductors for solar electricity and fuels are essential for facing the double challenge of climate change and energy security. Computational materials science can make important contributions, guiding and supporting research activities through both materials search and discovery and through detailed studies that help to develop a mechanistic understanding of materials performance and bottlenecks. This presentation will highlight three recent computational projects with relevance for photovoltaics and solar fuels (1) Defect graph neural networks (dGNN) for materials discovery in solar thermochemical hydrogen (STCH) [1]. The dGNN approach facilitates broad and fast materials screening for defect properties. (2) Modeling highly off-stoichiometric systems by evaluating the free energy of defect interaction [2]. This approach allows quantitative prediction of H2 production in complex STCH oxides. (3) First-principles atomic structure prediction for interfaces [3]. This work showed how an atomically thin CdCl2 interlayer phase enables in principle ideal electron transport across the incommensurate SnO2/CdTe interface. [1] M.D. Witman, A. Goyal, T. Ogitsu, A.H. McDaniel, S. Lany, Nat. Comput. Sci. (2023). https://doi.org/10.1038/s43588-023-00495-2. [2] A. Goyal, M.D. Sanders, R.P. O'Hayre, S. Lany, PRX Energy 3, 013008 (2024). https://doi.org/10.1103/PRXEnergy.3.013008. [3] A. Sharan, M. Nardone, D. Krasikov, N. Singh, S. Lany, Appl. Phys. Rev. 9, 041411 (2022). https://doi.org/10.1063/5.0104008. Clean energy ; Energy (signal processing) ; Computer science ; Environmental science ; Mathematics ; Environmental protection ; Statistics Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Chemical and Physical Properties of Materials
Authors: Shifat Islam | Mahbub Alam
Source:  (article, 2024-12-18)
Field: Materials Science — Materials Chemistry

Tight Binding Hamiltonian Parameter Extraction and I-V Characteristics of 13 AGNR using Machine Learning Method The Hamiltonian of a unit cell, coupling matrices and the device can be used to find electronic properties like the band structure of a material and the I-V characteristics of the device. As new materials are emerging all the time, it is essential to know the electronic properties of these materials with the help of a unit cell or device Hamiltonian. In this paper we have used a machine learning method to generate the Hamiltonian matrix and the coupling matrices of the unit cell of 13 Armchair Graphene Nanoribbon (AGNR) from the energy band data found from Density Functional Theory. This machine learning generated Hamiltonian of 13-AGNR has been used to find the I-V characteristics of a Nano-FET device and this I-V characteristics has been compared with the already existing results. Computer science ; Extraction (chemistry) ; Hamiltonian (control theory) ; Artificial intelligence ; Mathematical optimization ; Mathematics ; Chemistry ; Chromatography Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design ; Graphene research and applications
Authors: Takahiro Gotow | Tsutomu Sonoda | Tokio Takahashi | Hisashi Yamada | Toshihide Ide | Reiko Azumi | Mitsuaki Shimizu | Yosuke Tsunooka | Shota Seki | Kentaro Kutsukake | Toru Ujihara
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Modeling and Analysis of Undoped Gan Using a Horizontal Laminar Flow Mocvd Reactor Metalorganic vapour phase epitaxy ; Laminar flow ; Materials science ; Flow (mathematics) ; Optoelectronics ; Mechanics ; Nanotechnology ; Physics ; Epitaxy Fusion materials and technologies ; Nuclear reactor physics and engineering ; Magnetic confinement fusion research
Authors: S. Stepanović | Lysi Ekmekciu | Bandar Alghanem | Gérard Hopfgartner
Source: Journal of Mass Spectrometry (article, 2024-05-15)
Field: Chemistry — Spectroscopy

Insights into modifiers effects in differential mobility spectrometry: A data science approach for metabolomics and peptidomics Utilizing a data-driven approach, this study investigates modifier effects on compensation voltage in differential mobility spectrometry-mass spectrometry (DMS-MS) for metabolites and peptides. Our analysis uncovers specific factors causing signal suppression in small molecules and pinpoints both signal suppression mechanisms and the analytes involved. In peptides, machine learning models discern a relationship between molecular weight, topological polar surface area, peptide charge, and proton transfer-induced signal suppression. The models exhibit robust performance, offering valuable insights for the application of DMS to metabolites and tryptic peptides analysis by DMS-MS. Chemistry ; Metabolomics ; Mass spectrometry ; Analyte ; Polar surface area ; Peptide ; Differential (mechanical device) ; SIGNAL (programming language) ; Small molecule ; Chromatography ; Molecule ; Biochemistry ; Organic chemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Chadawan Khamdang | Mengen Wang
Source: arXiv (Cornell University) (preprint, 2024-11-12)
Field: Materials Science — Materials Chemistry

Defect formation in CsSnI$_3$ from Density Functional Theory and Machine Learning Sn-based perovskites as low-toxic materials are actively studied for optoelectronic applications. However, their performance is limited by $p$-type self-doping, which can be suppressed by substitutional doping on the cation sites. In this study, we combine density functional theory (DFT) calculations with machine learning (ML) to develop a predictive model and identify the key descriptors affecting formation energy and charge transition levels of the substitutional dopants in CsSnI$_{3}$. Our DFT calculations create a dataset of formation energies and charge transition levels and show that Y, Sc, Al, Nb, Ba, and Sr are effective dopants that pin the fermi level higher in the band gap, suppressing the $p$-type self-doping. We explore ML algorithms and propose training the random forest regression model to predict the defect formation properties. This work shows the predictive capability of combining DFT with machine learning and provides insights into the important features that determine the defect formation energetics. Density functional theory ; Computer science ; Artificial intelligence ; Cognitive science ; Psychology ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials
Authors: Ye Wang | Linlin Zhong
Source:  (article, 2024-06-16)
Field: Materials Science — Materials Chemistry

Calculation of cross sections based on graph neural networks with limited data Cross section describes the scattering process of microscopic particles, which is one of the key parameters in plasma simulation. Due to the prohibitively high cost of experiments, theoretical calculations have become the primary approach to obtaining cross sections. Physical models used for cross section calculation basically require information from quantum chemistry calculations. However, the computational cost of quantum chemistry calculations increases exponentially with the number of constituent atoms, making the calculation of cross sections for large molecules extremely challenging. Therefore, we propose to leverage graph neural network (GNN) to predict cross sections of large molecules by learning from the known data of small molecules. The framework can be divided into two parts. First, we use GNN as a feature extractor to represent the information from molecule structures, whose nodes can be considered as atoms and edges as bonds. Second, we apply dense neural network (DNN) as a regressor to predict cross sections based on features extracted by GNN. Furthermore, to reduce the requirements for cross section data, we incorporate meta learning into the framework by meta training on QM9 dataset. In this work, we choose ionization cross section as an example to verify the ability of the proposed framework and the results demonstrate broad prospects for further application in other types of cross sections. Computer science ; Graph ; Artificial neural network ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Engineering Diagnostics and Reliability ; Mineral Processing and Grinding
Authors: Xiaoying Zhuang | Han Hu | S.S. Nanthakumar
Source:  (article, 2024-04-25)
Field: Materials Science — Materials Chemistry

Computational Multi-scale Modelling and Inverse Design of Flexoelectric Metamaterials In this work, we present machine learning-assisted flexoelectric materials characterization and apply topological optimization for flexoelectric structures across atomistic to continuum scales. New formulations for nonlinear topological optimization of flexoelectric structures, accounting for nonlocal stress and large deformation processes, will be presented. A robust machine-learning interatomic potentials-based tool is developed for designing advanced energy harvesters in micro scale and interesting phenomena of optimized structures in macro scale will be presented. Metamaterial ; Nonlinear system ; Inverse ; Characterization (materials science) ; Scale (ratio) ; Flexoelectricity ; Computer science ; Materials science ; Macro ; Work (physics) ; Topology (electrical circuits) ; Nanotechnology ; Mechanical engineering ; Physics ; Engineering ; Mathematics ; Geometry ; Piezoelectricity ; Optoelectronics Nonlocal and gradient elasticity in micro/nano structures ; Model Reduction and Neural Networks ; Advanced Materials and Mechanics
Authors: Tianlang Chen | Shengjie Luo | Di He | Shuxin Zheng | Tie‐Yan Liu | Liwei Wang
Source: arXiv (Cornell University) (preprint, 2024-06-24)
Field: Materials Science — Materials Chemistry

GeoMFormer: A General Architecture for Geometric Molecular Representation Learning Molecular modeling, a central topic in quantum mechanics, aims to accurately calculate the properties and simulate the behaviors of molecular systems. The molecular model is governed by physical laws, which impose geometric constraints such as invariance and equivariance to coordinate rotation and translation. While numerous deep learning approaches have been developed to learn molecular representations under these constraints, most of them are built upon heuristic and costly modules. We argue that there is a strong need for a general and flexible framework for learning both invariant and equivariant features. In this work, we introduce a novel Transformer-based molecular model called GeoMFormer to achieve this goal. Using the standard Transformer modules, two separate streams are developed to maintain and learn invariant and equivariant representations. Carefully designed cross-attention modules bridge the two streams, allowing information fusion and enhancing geometric modeling in each stream. As a general and flexible architecture, we show that many previous architectures can be viewed as special instantiations of GeoMFormer. Extensive experiments are conducted to demonstrate the power of GeoMFormer. All empirical results show that GeoMFormer achieves strong performance on both invariant and equivariant tasks of different types and scales. Code and models will be made publicly available at https://github.com/c-tl/GeoMFormer. Representation (politics) ; Architecture ; Computer science ; Artificial intelligence ; Algebra over a field ; Mathematics ; Pure mathematics ; Political science ; Art ; Visual arts Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: S. S. Kawar | Mr. S. K. Kokate | Mr.V. S. Kalyamwar | Mr. V. S. Pawar
Source:  (review, 2024-11-14)
Field: Chemistry — Organic Chemistry

EMERGING TRENDS IN THE SYNTHESIS AND CHARACTERIZATION OF NANOSTRUCTURED MATERIALS: A CRITICAL REVIEW This critical review delves into the recent advancements in the synthesis and characterization of nanostructured materials, a field that has seen significant growth due to its potential applications across various scientific and technological domains. Nanostructured materials, defined by their nanoscale structural features, exhibit unique properties like high surface area-to-volume ratio and altered electronic characteristics. The paper begins with a historical overview, tracing the evolution from the foundational ideas of Richard Feynman to contemporary innovations. It highlights the applications of nanostructured materials in industries such as electronics, energy, medicine, and environmental remediation, emphasizing their role in developing advanced devices and sustainable solutions. The review also focuses on the state-of-the-art characterization techniques, emphasizing their importance in understanding and tailoring material properties for specific applications. Recent trends like 2D materials, nanocomposites, functional nanoparticles, and nanophotonics are examined, along with the impact of emerging technologies like machine learning and computational materials science on material design and discovery. The integration of nanostructured materials with biotechnology and electronics is discussed, showcasing their transformative potential. Challenges such as scalability, reproducibility, cost-effectiveness, and environmental impact are addressed, suggesting future research directions and interdisciplinary collaboration. The paper concludes by summarizing the implications of these emerging trends and their potential to revolutionize various industries and scientific research. Characterization (materials science) ; Materials science ; Nanotechnology Nanomaterials for catalytic reactions
Authors: Ekaterina I. Marchenko | Vadim Korolev | E. A. Kobeleva | Nikolai A. Belich | Natalia N. Udalova | N. N. Eremin | Eugene A. Goodilin | Alexey B. Tarasov
Source: arXiv (Cornell University) (preprint, 2024-04-26)
Field: Chemistry — Inorganic Chemistry

Machine Learning Recognition of hybrid lead halide perovskites and perovskite-related structures out of X-ray diffraction patterns Identification of crystal structures is a crucial stage in the exploration of novel functional materials. This procedure is usually time-consuming and can be false-positive or false-negative. This necessitates a significant level of expert proficiency in the field of crystallography and, especially, requires deep experience in perovskite - related structures of hybrid perovskites. Our work is devoted to the machine learning classification of structure types of hybrid lead halides based on available X-ray diffraction data. Here, we proposed a simple approach to quickly identify of dimensionality of inorganic substructures, types of lead halide polyhedra connectivity and structure types using common powder XRD data and ML - decision tree classification model. The average accuracy of our ML algorithm in predicting the dimensionality of inorganic substructure, type of connection of lead halide and inorganic substructure topology by theoretically calculated XRD pattern among 14 most common structure types reaches 0.86+-0.05, 0.827+-0.028 and 0.71+-0.05, respectively. The validation of our decision tree classification ML model on experimental XRD data shows the accuracies of 1.0 and 0.82 for the dimension and structure type prediction. Thus, our approach can significantly simplify and accelerate the interpretation of highly complicated XRD data for hybrid lead halides. Perovskite (structure) ; Lead (geology) ; Halide ; Diffraction ; X-ray ; Materials science ; Crystallography ; Optics ; Chemistry ; Physics ; Geology ; Inorganic chemistry Pigment Synthesis and Properties ; Perovskite Materials and Applications ; Machine Learning in Materials Science
Authors: Mehmet Ali Sarsıl | Dila Dede | Adildabay Seçilmis | Gizem Çatalkaya | Ata Shaker | Esra Çapanoğlu | Onur Ergen
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Instant Olive Oil Quality Detection and Rapid Food Evaluation: A User-Friendly and Attractive Artificial Intelligence Solution Instant ; Olive oil ; Environmentally friendly ; Computer science ; Quality (philosophy) ; Artificial intelligence ; Edible oil ; User Friendly ; Agricultural engineering ; Food science ; Engineering ; Chemistry ; Biology ; Ecology ; Operating system ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Jae‐Hoon Kim | Jaekyung Lim | Tae‐Yeon Kim | Yunhyoung Nam | Do‐Nyun Kim
Source:  (article, 2024-02-23)
Field: Materials Science — Surfaces, Coatings and Films

Layout to SEM: hotspot prediction via SEM image synthesis Since the rise of transistors and integrated circuits, the semiconductor industry has seen rapid advancements, leading to today's microchips containing hundreds of millions of transistors. A pressing challenge in this industry is the emergence of defects, termed "hotspots," during the manufacturing process, affecting chip performance and reliability. In this study, we introduce a deep learning model that predicts hotspots during the design stage. To predict hotspot, our proposed framework generates Scanning Electron Microscopy (SEM) images from layout by combining segmentation and image-translation network. This model outperformed existing baseline models when tested on real industrial datasets, promising to refine the semiconductor design workflow. Hotspot (geology) ; Computer science ; Artificial intelligence ; Computer vision ; Geology Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Integrated Circuits and Semiconductor Failure Analysis