jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kara A. Randall | Mirina Elizabeth Enderlin | Paraskevi Flouda
Source: MRS Communications (article, 2024-07-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Architectural engineering of nanocomposite electrodes for energy storage Materials science ; Nanocomposite ; Energy storage ; Electrode ; Composite material ; Engineering physics ; Engineering Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Md Hasan-Ur Rahman | Bichar Dip Shrestha Gurung | Bharat K. Jasthi | Etienne Z. Gnimpiéba | Venkataramana Gadhamshetty
Source: Coatings (article, 2024-06-06)
Field: Materials Science — Materials Chemistry

Automated Crack Detection in 2D Hexagonal Boron Nitride Coatings Using Machine Learning Characterizing defects in 2D materials, such as cracks in chemical vapor deposited (CVD)-grown hexagonal boron nitride (hBN), is essential for evaluating material quality and reliability. Traditional characterization methods are often time-consuming and subjective and can be hindered by the limited optical contrast of hBN. To address this, we utilized a YOLOv8n deep learning model for automated crack detection in transferred CVD-grown hBN films, using MATLAB’s Image Labeler and Supervisely for meticulous annotation and training. The model demonstrates promising crack-detection capabilities, accurately identifying cracks of varying sizes and complexities, with loss curve analysis revealing progressive learning. However, a trade-off between precision and recall highlights the need for further refinement, particularly in distinguishing fine cracks from multilayer hBN regions. This study demonstrates the potential of ML-based approaches to streamline 2D material characterization and accelerate their integration into advanced devices. Hexagonal boron nitride ; Boron nitride ; Materials science ; Hexagonal crystal system ; Composite material ; Metallurgy ; Nanotechnology ; Crystallography ; Chemistry Graphene research and applications ; Thermal properties of materials ; 2D Materials and Applications
Authors: Christoph Mahr | Jakob Stahl | Beeke Gerken | Florian F. Krause | Marco Schowalter | Tim Grieb | Lutz Mädler | Andreas Rosenauer
Source: Ultramicroscopy (article, 2024-07-20)
Field: Materials Science — Surfaces, Coatings and Films

Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection Structural and chemical characterization of nanomaterials provides important information for understanding their functional properties. Nanomaterials with characteristic structure sizes in the nanometer range can be characterized by scanning transmission electron microscopy (STEM). In conventional STEM, two-dimensional (2D) projection images of the samples are acquired, information about the third dimension is lost. This drawback can be overcome by STEM tomography, where the three-dimensional (3D) structure is reconstructed from a series of projection images acquired using various projection directions. However, 3D measurements are expensive with respect to acquisition and evaluation time. Furthermore, the method is hardly applicable to beam-sensitive materials, i.e. samples that degrade under the electron beam. For this reason, it is desirable to know whether sufficient information on structural and chemical information can be extracted from 2D-projection measurements. In the present work, a comparison between 3D-reconstruction and 2D-projection characterization of structure and mixing in nanoparticle hetero-aggregates is provided. To this end, convolutional neural networks are trained in 2D and 3D to extract particle positions and material types from the simulated or experimental measurement. Results are used to evaluate structure, particle size distributions, hetero-aggregate compositions and mixing of particles quantitatively and to find an answer to the question, whether an expensive 3D characterization is required for this material system for future characterizations. Projection (relational algebra) ; Characterization (materials science) ; Convolutional neural network ; Scanning transmission electron microscopy ; Biological system ; Electron tomography ; Particle (ecology) ; Materials science ; Mixing (physics) ; Nanomaterials ; Computer science ; Nanotechnology ; Transmission electron microscopy ; Artificial intelligence ; Algorithm ; Physics Electron and X-Ray Spectroscopy Techniques ; Enhanced Oil Recovery Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Dimitri Abrahamsson | Lelouda-Athanasia Koronaiou | Trevor A. Johnson | Junjie Yang | Xiaowen Ji | Dimitra A. Lambropoulou
Source: RSC Advances (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Modeling the relative response factor of small molecules in positive electrospray ionization Technological advancements in liquid chromatography (LC) electrospray ionization (ESI) high-resolution mass spectrometry (HRMS) have made it an increasingly popular analytical technique in non-targeted analysis (NTA) of environmental and biological samples. One critical limitation of current methods in NTA is the lack of available analytical standards for many of the compounds detected in biological and environmental samples. Computational approaches can provide estimates of concentrations by modeling the relative response factor of a compound (RRF) expressed as the peak area of a given peak divided by its concentration. In this paper, we explore the application of molecular dynamics (MD) in the development of a computational workflow for predicting RRF. We obtained measurements of RRF for 48 compounds with LC - quadrupole time-of-flight (QTOF) MS and calculated their RRF. We used the CGenFF force field to generate the topologies and GROMACS to conduct the (MD) simulations. We calculated the Lennard-Jones and Coulomb interactions between the analytes and all other molecules in the ESI droplet, which were then sampled to construct a multilinear regression model for predicting RRF using Monte Carlo simulations. The best performing model showed a coefficient of determination (<i>R</i> <sup>2</sup>) of 0.82 and a mean absolute error (MAE) of 0.13 log units. This performance is comparable to other predictive models including machine learning models. While there is a need for further evaluation of diverse chemical structures, our approach showed promise in predictions of RRF. Electrospray ionization ; Ionization ; Electrospray ; Molecule ; Chemistry ; Extractive electrospray ionization ; Small molecule ; Mass spectrometry ; Analytical Chemistry (journal) ; Computational chemistry ; Chromatography ; Ion ; Organic chemistry ; Protein mass spectrometry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Brianna Thompson | Jesse A. Sherburn | James A. Ross | Yi Zhang
Source: Neural Computing and Applications (article, 2024-01-24)
Field: Materials Science — Materials Chemistry

Estimating kinetic energy reduction for terminal ballistics using a hyperparameter-optimized neural network Hyperparameter ; Computational Science and Engineering ; Reduction (mathematics) ; Artificial neural network ; Kinetic energy ; Terminal (telecommunication) ; Computer science ; Energy (signal processing) ; Artificial intelligence ; Machine learning ; Mathematics ; Statistics ; Physics High-Velocity Impact and Material Behavior ; Energetic Materials and Combustion ; Ion-surface interactions and analysis
Authors: Tibor Nagy | Gergő Róth | Ákos Kuki | Veronika Pardi-Tóth | Dávid Nyul | Zuura Kaldybek Kyzy | Isaac Alexander Iglesias Palacios | Máté Benedek | Lajos Nagy | Miklós Zsuga | Sándor Kéki
Source: Giant (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

The triumvirate of effective and rapid synthesis, analysis, and artificial intelligence to explore the structure-property relationship of copolymers Understanding the structure-property relationship is of paramount importance for tailoring copolymers for specific applications. Poly(N-acryloylmorpholine)-block-poly(N-isopropylacrylamide) (PNAM-b-PNIPAM) diblock copolymers were synthesized by reversible addition–fragmentation chain transfer (RAFT) polymerization with varying Mn and composition, providing the basis for deducing structure-property relationships. The chemical structure of the copolymers was analyzed by mass spectrometry (MS). A novel and efficient mass spectrum processing methodology was developed for the detailed analysis of polymers/copolymers that greatly expands the upper mass limit of the time-of-flight (TOF) analyzers in the linear mode up to 20,000 Da. Our method “makes visible” the mass peaks of the individual copolymer species and their isotopologues providing effective and fast automatized analysis. The self-assembly property of the thermoresponsive PNAM-b-PNIPAM diblocks in aqueous solutions was investigated by dynamic light scattering (DLS) experiments, and quantified by determining the incipient temperature of the phase transition. For rapid evaluation, an artificial neural network (ANN) was created to explore the hidden relationships between the structural information obtained by our novel mass analysis method and the properties as well as to predict the self-assembly behavior of the copolymers. Property (philosophy) ; Copolymer ; Artificial intelligence ; Computer science ; Materials science ; Philosophy ; Epistemology ; Polymer Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Fathi Souaker | Mounir Boukadoum
Source:  (article, 2024-02-27)
Field: Materials Science — Materials Chemistry

Light Siamese Neural Network Architecture for Image Comparison Deep neural networks typically require large training sets and powerful processing hardware, thus making their use difficult in training data or processing resource-limited systems. Siamese neural networks use few-shot learning to address this problem. However, their twins are typically implemented with standard multilayered perceptrons (MLP) or convolutional neural networks (CNN), which still require large training sets and powerful processing hardware. We propose a simplified Siamese model that uses a single Inception module for the twins instead of a full Inception CNN, for 20 times less parameters to learn and an easier implementation. Our validation experiments to assess whether the image of a molecular structure bears similarity with a reference shows 95.6% average accuracy for three different reference molecules, using training sets of just 25 randomly selected pairs of positive and negative examples for the twins. The training took 43 minutes to complete on an Intel I5-based PC station with no GPU acceleration. Computer science ; Convolutional neural network ; Artificial neural network ; Artificial intelligence ; Perceptron ; Pattern recognition (psychology) ; Similarity (geometry) ; Deep learning ; Image processing ; Cellular neural network ; Image (mathematics) ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Saurabh Ghosh | Anthony Tom | Dwaipayan Dasgupta | Ayana Ghosh | Brian D. Wirth
Source: ACS Applied Energy Materials (article, 2024-07-17)
Field: Materials Science — Materials Chemistry

Insights into Prismatic Loop Formation in Irradiated Fe–Cr Alloys from Hypothesis-Driven Active Learning and Causal Analysis Neutron and electron irradiation experimental studies conducted on body-centered cubic Fe and Fe–Cr alloys have established two prismatic dislocation loop populations, which have Burgers vectors of either a/2⟨111⟩ or a⟨100⟩. The loop formation depends on factors such as dose (D), dose rate (Drt), temperature (T), chromium content (Cr%), and other alloying elements. Hence, it is important to understand how irradiation-induced dislocation loops evolve conditional upon the loop characteristics, such as loop density (DD), average loop size d̅, and irradiation parameters (D, Drt, T, and irradiation type), which is still an active area of research. To understand these complex structure–property relationships, machine learning (ML) is employed in a three-step approach. This includes imputing missing data with a k-nearest neighbor, generating functionalized features, and assessing feature importance with random forest classification and regression. Physics-based features are incorporated in a hypothesis-driven active learning scheme to overcome data unavailability challenges. Insights obtained from ML models (i) to categorize dislocation loop types, show the highest correlation with d̅; (ii) Log(DD), obtained through mathematical formulations involving D, Cr%, d̅, and T (e.g., Log(DD) ∼ D + exp(−Cr%) + 1/d̅ and log(DD) ∼ D + exp(−Cr%) + 1/T). Hypothesis-driven active learning is able to predict Log(DD) in which the experimental date is not known. Causal models verify cause–effect relationships for dislocation loop classification and irradiation factors in FeCr alloys. Loop (graph theory) ; Irradiation ; Materials science ; Metallurgy ; Physics ; Mathematics ; Nuclear physics ; Combinatorics Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear reactor physics and engineering
Authors: Izumi Takahara | Fumihiko Uesugi | Koji Kimoto | Kiyou Shibata | Teruyasu Mizoguchi
Source: The Journal of Physical Chemistry C (article, 2024-08-02)
Field: Materials Science — Materials Chemistry

Toward the Atomic-Level Analysis of Ground-State Electronic Structures of Solid Materials via Prediction from Core-Loss Spectra: A Computational Study in Si Local electronic structure in the ground state is essential for understanding the stability and properties of materials. Core-loss spectroscopy using electron or X-ray provides insights into the local electronic structure, but directly observable information is limited to the partial density of state (PDOS) of the conduction band at the excited state. To overcome this limitation, we developed a machine learning (ML) approach by creating a database of Si-K core-loss spectra and corresponding ground-state PDOS for various silicon structures. Using this database, we constructed an ML model capable of predicting the atom-specific ground-state PDOS of the valence and conduction bands from Si-K edges. Our model demonstrated the ability of the ML to extract the complex correlation between ground-state PDOS and Si-K edges. This study provides crucial insights into achieving atomic-level analysis of ground-state electronic structures, paving the way for a deeper understanding of stability and properties of materials. Core (optical fiber) ; Spectral line ; Solid-state ; Materials science ; Electronic structure ; Atomic physics ; Computational physics ; Computational chemistry ; Chemistry ; Physical chemistry ; Physics ; Composite material ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Julia Kacher | Olga S. Sokolova | Mounir Tarek
Source: The Journal of Physical Chemistry B (article, 2024-08-30)
Field: Chemistry — Electrochemistry

A Deep Learning Approach to Uncover Voltage-Gated Ion Channels’ Intermediate States Owing to recent advancements in cryo-electron microscopy, voltage-gated ion channels have gained a greater comprehension of their structural characteristics. However, a significant enigma remains unsolved for a large majority of these channels: their gating mechanism. This mechanism, which encompasses the conformational changes between open and closed states, is pivotal to their proper functioning. Beyond the binary states of open and closed, an ensemble of intermediate states defines the transition path in-between. Due to the lack of experimental data, one might resort to molecular dynamics simulations as an alternative to decipher these states and the transitions between them. However, the high-energy barriers and the colossal time scales involved hinder access to the latter. We present here an application of deep learning as a reliable pipeline for a comprehensive exploration of voltage-gated ion channel conformational rearrangements during gating. We showcase the pipeline performance specifically on the Kv1.2 voltage sensor domain and confront the results with existing data. We demonstrate how our physics-based deep learning approach contributes to the theoretical understanding of these channels and how it might provide further insights into the exploration of channelopathies. Gating ; Pipeline (software) ; Mechanism (biology) ; Computer science ; Ion channel ; Deep learning ; Nanotechnology ; Physics ; Artificial intelligence ; Chemistry ; Neuroscience ; Materials science ; Biology Electrochemical Analysis and Applications ; Ion channel regulation and function ; Advanced Memory and Neural Computing
Authors: Jing Wu | Zhenru Xu | Xunyong Liu | Huaguo Tang | Tongyang Li | Zhuhui Qiao
Source: Ceramics International (article, 2024-02-09)
Field: Materials Science — Ceramics and Composites

Comprehensive study on the mechanical properties of Ti-doped Si3N4 ceramics: Experimental analysis, DFT calculations, and machine learning Materials science ; Tin ; Ceramic ; Fracture toughness ; Sintering ; Flexural strength ; Microstructure ; Doping ; Composite material ; Toughness ; Atom (system on chip) ; Density functional theory ; Metallurgy ; Computational chemistry ; Computer science ; Optoelectronics Advanced ceramic materials synthesis ; Advanced materials and composites ; MXene and MAX Phase Materials
Authors: Zhenxing Hu | Xueyong Ye | Xiaokang Luo | Hao Zhang | Mingguang He | Jiaxing Li | Qian Li
Source: Energies (article, 2024-04-29)
Field: Materials Science — Polymers and Plastics

Prediction Model for Trends in Submarine Cable Burial Depth Variation Considering Dynamic Thermal Resistance Characteristics Fault problems associated with submarine cables caused by variations in their burial depth are becoming increasingly prominent. To address the difficulty of detecting the burial depth of submarine cables and trends in its variation, a prediction model for submarine cable burial depth was proposed which considers the dynamic characteristics of thermal resistance. First, a parallel thermal circuit model of a three-core submarine cable was established, and a formula for calculating the submarine cable’s burial depth was derived based on a formula for calculating the submarine cable’s core temperature. Then, the calculation result was corrected by considering the dynamic characteristics of the thermal resistance of the submarine cable’s structural materials. On this basis, feature vectors associated with the seabed cable burial depth calculation data and time nodes were mined by a convolutional neural network and used as the input parameters of a long short-term memory network for optimization and training, and a prediction model for trends in seabed cable burial depth variation was obtained. Finally, an example analysis was carried out based on the actual electrical parameter data of submarine cables buried by an offshore oil and gas platform. The results showed that the prediction model for trends in variations in the burial depth of submarine cables based on the CNN-LSTM neural network can achieve high prediction accuracy and prediction efficiency. Submarine ; Variation (astronomy) ; Thermal ; Resistance (ecology) ; Marine engineering ; Environmental science ; Geology ; Geotechnical engineering ; Engineering ; Meteorology ; Geography ; Ecology ; Physics Textile materials and evaluations ; Automotive and Human Injury Biomechanics ; Gait Recognition and Analysis
Authors: Hayato Wakai | Atsuto Seko | Hirosato Izuta | Takayuki Nishiyama | Isao Tanaka
Source: Physical review. B./Physical review. B (article, 2024-06-24)
Field: Materials Science — Materials Chemistry

Predictive power of polynomial machine learning potentials for liquid states in 22 elemental systems The polynomial machine learning potentials (MLPs) described by polynomial rotational invariants have been systematically developed for various systems and used in diverse applications in crystalline states. In this study, we systematically investigate the predictive power of the polynomial MLPs for liquid structural properties in 22 elemental systems with diverse chemical bonding properties, including those showing anomalous melting behavior, such as Si, Ge, and Bi. We compare liquid structural properties obtained from molecular dynamics simulations using the density functional theory (DFT) calculation, the polynomial MLPs, and other interatomic potentials in the literature. The current results demonstrate that the polynomial MLPs consistently exhibit high predictive power for liquid structural properties with the same accuracy as that of typical DFT calculations. Polynomial ; Power (physics) ; Mathematics ; Computer science ; Artificial intelligence ; Physics ; Thermodynamics ; Mathematical analysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Qingwei Zhang | Lin Cai | Ningsheng Liao | Yunhua Lu | Junan Zhang | Chao Zhang | Kangli Zeng
Source: Langmuir (article, 2024-03-21)
Field: Materials Science — Materials Chemistry

Work Function Prediction by Graph Neural Networks for Configurationally Hybridized Boron-Doped Graphene Graphene, serving as electrodes, is widely applied in electronic and optoelectronic devices. Work function as one of the fundamental intrinsic characteristics of graphene directly affects the interfacial properties of the electrodes, thereby affecting the performance of the devices. Much work has been done to regulate the work function of graphene to expand its application fields, and doping has been demonstrated as an effective method. However, the numerous types of doped graphene make the investigation of its work function time-consuming and labor-intensive. In order to quickly obtain the relationship between the structure and property, a deep learning method is employed to predict the work function in this study. Specifically, a data set of over 30,000 compositions with the work function on boron-doped graphene at different concentrations and doping positions via density functional theory simulations was established through ab initio calculations. Then, a novel fusion model (GT-Net) combining transformers and graph neural networks (GNNs) was proposed. After that, improved effective GNN-based descriptors were developed. Finally, three different GNN methods were compared, and the results show that the proposed method could accurately predicate the work function with the <i>R</i><sup>2</sup> = 0.975 and RMSE = 0.027. This study not only provides the possibility of designing materials with specific properties at the atomic level but also demonstrates the performance of GNNs on graph-level tasks with the same graph structure and atomic number. Graphene ; Work function ; Doping ; Materials science ; Artificial neural network ; Computer science ; Density functional theory ; Graph ; Biological system ; Nanotechnology ; Artificial intelligence ; Computational chemistry ; Theoretical computer science ; Chemistry ; Optoelectronics Machine Learning in Materials Science ; Graphene research and applications ; Advanced Memory and Neural Computing