jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Khalid Hani Abushahla | Halil Arslan
Source: Radiation Physics and Chemistry (article, 2024-12-12)
Field: Materials Science — Materials Chemistry

Prediction of mass attenuation coefficients in mixed alkali and borosilicate glasses using machine learning approaches Borosilicate glass ; Alkali metal ; Attenuation ; Materials science ; Mineralogy ; Chemistry ; Composite material ; Physics ; Optics Radiation Shielding Materials Analysis ; Glass properties and applications ; Advanced X-ray and CT Imaging
Authors: Indranil Saha | Andreas Erlebach | Petr Nachtigall | Christopher J. Heard | Lukáš Grajciar
Source: Catalysis Science & Technology (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Germanium distributions in zeolites derived from neural network potentials This work uses newly developed machine learning potentials to predict how germanium distributes within the zeolite catalysts, depending on both germanium content and the framework topology, aiding the rational zeolite design. Germanium ; Zeolite ; Catalysis ; Work (physics) ; Artificial neural network ; Rational design ; Topology (electrical circuits) ; Computer science ; Materials science ; Chemical engineering ; Chemistry ; Nanotechnology ; Mathematics ; Physics ; Engineering ; Thermodynamics ; Artificial intelligence ; Optoelectronics ; Organic chemistry ; Combinatorics ; Silicon Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; X-ray Diffraction in Crystallography
Authors: Haitham Al Ajmi | Mohammed M. Bait‐Suwailam | Lazhar Khriji | Hassan Al-Lawati
Source: Electronics (article, 2024-02-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction Enhancement of Metasurface Absorber Design Using Adaptive Cascaded Deep Learning (ACDL) Model This paper presents a customized adaptive cascaded deep learning (ACDL) model for the design and performance prediction of metasurface absorbers. A multi-resonant metasurface absorber structure is introduced, with 10 target-driven design parameters. The proposed deep learning model takes advantage of cascading several sub-deep neural network (DNN) layers with forward noise mitigation capabilities. The inherent appearance of sparse data is dealt with in this work by proposing a trained data-adaptive selection technique. On the basis of the findings, the prediction response is quite fast and accurate enough to retrieve the design parameters of the studied metasurface absorber with two patches of 4000- and 7000-sample datasets. The training loss taken from the second DNN of our proposed model showed logarithmic mean squared errors of 0.039 and 0.033 when using Keras and the adaptive method, respectively, with a dataset split of 4000. On the contrary, for a dataset split of 7000, the errors were 0.049 with Keras and 0.045 with the adaptive method. On the other hand, the validation loss was evaluated using the mean square error method, which resulted in a loss of 0.044 with the 4000-sample datasets split with the Keras method, while this was 0.020 with the adaptive method. When extending the dataset to 7000 samples, the validation loss with the Keras splitting method was 0.0073, while it was improved, reaching 0.006, with the proposed adaptive method, and achieved a prediction accuracy of 94%. This proposed deep learning model can be deployed in the design process and synthesis of multi-resonant metasurface absorber structures. The proposed model shows the advantages of making the design process more efficient in sparse dataset handling, being an efficient approach in multi-resonance metasurface data pre-processing, being less time consuming, and being computationally valuable. Computer science ; Mean squared error ; Deep learning ; Artificial neural network ; Logarithm ; Artificial intelligence ; Noise (video) ; Reduction (mathematics) ; Least mean squares filter ; Algorithm ; Pattern recognition (psychology) ; Electronic engineering ; Adaptive filter ; Mathematics ; Engineering ; Image (mathematics) Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Advanced Antenna and Metasurface Technologies
Authors: A.K. Srinithi | A. Bolyachkin | Xin Tang | H. Sepehri‐Amin | Sae Dieb | Akiko T. Saito | Tadakatsu Ohkubo | K. Hono
Source: Scripta Materialia (article, 2024-12-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-driven compositional optimization of La(Fe,Si)13-based magnetocaloric compounds for cryogenic applications Materials science ; Magnetic refrigeration ; Nanotechnology ; Metallurgy ; Magnetization ; Magnetic field Magnetic and transport properties of perovskites and related materials ; Magnetic Properties of Alloys ; Rare-earth and actinide compounds
Authors: V. T. Yerofeyev | Victor Afonin | Marina M. Zotkina | K. S. Stenechkin | Tatyana Tyuryakhina | A. V. Lazarev
Source: Stroitel nye Materialy (article, 2024-03-18)
Field: Materials Science — Materials Chemistry

Analysis of Properties of Polymer Composites with Various Types of Fillers The processes of structure formation of composite building materials (KSM) on different polymer binders are presented. It is shown that one of the most significant components of KSM are fillers, which help to improve their structural and operational characteristics. This work is devoted to the analysis of the results of an experimental study of the properties of epoxy composites with fillers having various elastic-plastic and strength properties. The research was carried out in three stages: at the first stage, studies were conducted aimed at assessing the influence of the nature of the filler on the curing processes of KSM; at the second, the influence of the type of filler and its quantitative content on the strength of composites was established, at the third, compositions were optimized using fillers with different indicators of grain composition and elastic-plastic properties. Powders of glass, dolomite, thermolite, and diatomite were considered as fillers at the first and second stages of the research, and powders of glass, ceramics, and chalk were considered at the second stage. The research at the third stage was carried out using mathematical methods of experiment planning with the construction of a planning matrix for a complete factor experiment and the determination of the values of the response functions relative to the encoded factors. The physico-mechanical properties, degree of curing, and chemical resistance of filled epoxy CCM have been established. On the basis of artificial neural networks, the maximum properties of the studied composites with fillers were determined. An assessment of structural properties based on rank correlation is also proposed. The results of the research can be used to predict the properties of KSM, as well as to clarify the extreme parameters of the properties. The dependences of changes in the properties of polymer composites on the surface characteristics, the dispersion of fillers and the degree of filling were established; preferred fillers for epoxy composites were determined; fillers were determined to assess the effect of elastic surface properties of composites, allowing to improve the strength and deformability of polymer composites; regression models were obtained based on a complete factorial experiment; an assessment of the «structural stability» of the studied composites using Pearson, Kendall, Spearman rank correlation; On the basis of artificial neural networks, the extreme properties of the studied composites with fillers were determined, neural networks. Composite material ; Materials science ; Filler (materials) ; Epoxy ; Curing (chemistry) ; Ceramic ; Polymer ; Composite number ; Dolomite ; Metallurgy Structural mechanics and materials ; Material Properties and Applications ; Environmental and Industrial Safety
Authors: Ganesh Kamath | Alexey Illarionov | Serzhan Sakipov | Leonid Pereyaslavets | Igor V. Kurnikov | Oleg Butin | Е. Н. Воронина | Ilya Ivahnenko | Igor Leontyev | Grzegorz Nawrocki | Mikhail Darkhovskiy | Michael Olevanov | Yevhen K. Cherniavskyi | Christopher Lock | Sean Greenslade | Yi‐Chun Chen | Roger D. Kornberg | Michael Levitt | Boris Fain
Source: The Journal of Physical Chemistry A (article, 2024-01-17)
Field: Materials Science — Materials Chemistry

Combining Force Fields and Neural Networks for an Accurate Representation of Bonded Interactions We present a formalism of a neural network encoding bonded interactions in molecules. This intramolecular encoding is consistent with the models of intermolecular interactions previously designed by this group. Variants of the encoding fed into a corresponding neural network may be used to economically improve the representation of torsional degrees of freedom in any force field. We test the accuracy of the reproduction of the ab initio potential energy surface on a set of conformations of two dipeptides, methyl-capped ALA and ASP, in several scenarios. The encoding, either alone or in conjunction with an analytical potential, improves agreement with ab initio energies that are on par with those of other neural network-based potentials. Using the encoding and neural nets in tandem with an analytical model places the agreements firmly within "chemical accuracy" of ±0.5 kcal/mol. Artificial neural network ; Intramolecular force ; Force field (fiction) ; Ab initio ; Formalism (music) ; Encoding (memory) ; Molecule ; Intermolecular force ; Biological system ; Representation (politics) ; Tandem ; Computer science ; Computational chemistry ; Chemistry ; Chemical physics ; Topology (electrical circuits) ; Physics ; Artificial intelligence ; Materials science ; Mathematics ; Stereochemistry ; Quantum mechanics ; Biology ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Mariya A. Shamraeva | Theodoros Visvikis | Stefanos Zoidis | Ian G. M. Anthony | Sebastiaan Van Nuffel
Source: Journal of the American Society for Mass Spectrometry (article, 2024-10-25)
Field: Chemistry — Analytical Chemistry

The Application of a Random Forest Classifier to ToF-SIMS Imaging Data Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is a potent analytical tool that provides spatially resolved chemical information on surfaces at the microscale. However, the hyperspectral nature of ToF-SIMS datasets can be challenging to analyze and interpret. Both supervised and unsupervised machine learning (ML) approaches are increasingly useful to help analyze ToF-SIMS data. Random Forest (RF) has emerged as a robust and powerful algorithm for processing mass spectrometry data. This machine learning approach offers several advantages, including accommodating nonlinear relationships, robustness to outliers in the data, managing the high-dimensional feature space, and mitigating the risk of overfitting. The application of RF to ToF-SIMS imaging facilitates the classification of complex chemical compositions and the identification of features contributing to these classifications. This tutorial aims to assist nonexperts in either machine learning or ToF-SIMS to apply Random Forest to complex ToF-SIMS datasets. Random forest ; Chemistry ; Classifier (UML) ; Remote sensing ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Geology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Face and Expression Recognition
Authors: Yunqi Cai | Jiangnan Li | Dong Wang
Source: Nature Machine Intelligence (article, 2024-11-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fast and generalizable micromagnetic simulation with deep neural nets Artificial neural network ; Computer science ; Deep neural networks ; Artificial intelligence ; Computer security ; Business Magnetic Properties and Applications ; Magnetic properties of thin films ; Model Reduction and Neural Networks
Authors: Takamitsu Ishiyama | Tomoki Ozawa | Noriyuki Saitoh | Noriko Yoshizawa | Takashi Suemasu | Kaoru Toko
Source: APL Materials (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

High thermoelectric performance in polycrystalline Yb3Ge5 thin films The development of eco-friendly thin-film thermoelectric generators for microenergy harvesting applications is highly desired. Ge-based materials have recently attracted significant research interest because of their superior performance at room temperature as human-compatible thermoelectric constituents. We examined the synthesis of Yb3Ge5 thin films and assessed their thermoelectric properties, considering the exceptional ability of Yb as a heavy metal to scatter phonons. The components of the resulting thin film were strongly dependent on the Yb/Ge deposition ratio and the subsequent annealing temperature. An increase in the Yb content augmented the formation of Yb2O3, whereas an increase in the Ge content facilitated the formation of Ge crystals. These trends were more evident at higher annealing temperatures. By controlling the YbGe composition, a power factor of 1470 µW m−1 K−2 was obtained at an annealing temperature of 700 °C, along with a low thermal conductivity of 3.1 W m−1 K−1. Machine-learning-facilitated data analysis revealed that the high power factor originated from Yb3Ge5. Notably, the power factor reached 1370 µW m−1 K−2 even at a temperature of 500 °C, causing the expansion of heat-resistant plastic films. Thus, we established the impressive potential of Yb3Ge5 as a suitable material for eco-friendly thin-film thermoelectric generators. Materials science ; Thermoelectric effect ; Annealing (glass) ; Thin film ; Thermoelectric materials ; Crystallite ; Seebeck coefficient ; Optoelectronics ; Thermoelectric generator ; Phonon ; Electrical resistivity and conductivity ; Thermal conductivity ; Composite material ; Engineering physics ; Nanotechnology ; Metallurgy ; Condensed matter physics ; Electrical engineering ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Expansion and Ionic Conductivity
Authors: Nicholas Marcella | Stephen Lam | Vyacheslav S. Bryantsev | Santanu Roy | Anatoly I. Frenkel
Source: Physical review. B./Physical review. B (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Neural network based analysis of multimodal bond distributions using extended x-ray absorption fine structure spectra Knowledge of the local coordination environment around atomic species in functional materials is critical for understanding their mechanisms of operation. Heterogeneous mixtures of metal complexes are ubiquitous in catalysts, ionic liquids, molten salts, biological enzymes, and geochemical systems, among many others. Extracting information from ensemble-average measurements about the structural and compositional descriptors of each type of coordination complex comprising the mixture is not generally possible, especially when they possess multimodal bond-length distributions. We developed a method that enables the mapping of an x-ray absorption spectrum on the radial distribution function describing the average environment of the metal ions. The supervised neural network based method utilizes an objective training set, for which the choice of the local structural motifs is completely agnostic to the theoretically expected structure and dynamics of the modeled system. The method was validated using first-principles modeling of structural dynamics of nickel complexation in molten salts, and it applies to a large class of heterogeneous systems, including those studied under in situ and operando conditions. Ionic bonding ; Coordination number ; Artificial neural network ; Absorption (acoustics) ; Extended X-ray absorption fine structure ; Absorption spectroscopy ; Biological system ; Spectral line ; Chemical physics ; Ion ; X-ray absorption spectroscopy ; Molecule ; Chemistry ; Metal ions in aqueous solution ; Computer science ; Materials science ; Artificial intelligence ; Physics ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Srujan Sapkal | Balasubramanian Kandasubramanian | Himanshu Sekhar Panda
Source: Materials Chemistry and Physics (article, 2024-10-04)
Field: Materials Science — Materials Chemistry

Machine learning assisted innovative strategy for experimental validation of functional properties of lead-free ceramics Lead (geology) ; Ceramic ; Materials science ; Nanotechnology ; Biochemical engineering ; Metallurgy ; Engineering ; Geology Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Dario Campisi | A. G. G. M. Tielens | Wilke Dononelli
Source: Monthly Notices of the Royal Astronomical Society (article, 2024-08-13)
Field: Materials Science — General Materials Science

The role of point defect reconstructions and polycyclic aromatic hydrocarbons in silicate dust preservation ABSTRACT Forsterite is a primary constituent of interstellar dust and planetary systems. It is believed to originate from the outflows of oxygen-rich stars and undergo further processing within the interstellar medium through the action of cosmic rays and shocks. Under these harsh conditions, point defects may form, such as MgO Schottky vacancies. These vacancies can then undergo atom reconstruction as part of a chemical process to maintain the system’s crystalline structure. Polycyclic aromatic hydrocarbons (PAHs) are ubiquitously observed interstellar molecules and are thought to form through gas-phase reactions akin to sooting flames. However, their role and impact on dust stability remain unknown. In this study, we employ an atomistic artificial-intelligence-based method, surrogate machine learning trained directly by density functional theory. Specifically, we utilize gofee (global optimization with first-principles energy expressions) to predict possible reconstructions of MgO vacancies on a crystalline forsterite (010) surface as an important component of interstellar dust and planetary systems. We identify nine possible reconstructions involving the formation of unbound Si and O atoms. We investigate their energy stability and find that the reconstruction of Si–O atoms stabilizes the vacancy by about 0.54 eV. Additionally, if PAHs bind with the unbound O and Si atoms of the vacancy, the vacancy is stabilized by approximately 1.76 eV. We demonstrate that PAHs, along with the reconstruction of unbound atoms on the dust surface, affect the stability of the dust, which might open up avenues for diverse chemistry. Silicate ; Physics ; Astrobiology ; Environmental chemistry ; Polycyclic aromatic hydrocarbon ; Astronomy ; Chemistry Engineering and Material Science Research ; Space Exploration and Technology ; Building materials and conservation
Authors: Parastoo Taleghani | Majid Ghassemi | Mahmoud Chizari
Source: Heliyon (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Numerical investigation of micro solid oxide fuel cell performance in combination with artificial intelligence approach The current study presents a multiphysics numerical model for a micro-planar proton-conducting solid oxide fuel cell (H-SOFC). The numerical model considered an anode-supported H-SOFC with direct internal reforming (DIR) of methane. The model solves coupled nonlinear equations, including continuity, momentum, mass transfer, chemical and electrochemical reactions, and energy equations. Furthermore, The numerical model results are used in artificial intelligence (AI) models, the K-nearest neighbour (KNN) and, artificial neural network (ANN), to predict the current density and power density of the H-SOFC. The results show that increasing the air-to-fuel (A/F) ratio decreases the current density and overall cell power. In particular, improvements in power and current density observed in H-SOFC when the A/F ratio is set to 0.5, resulting in a respective increase of 2 % and 7 % compared to the initial state at A/F = 1. With an error rate of less than 1 % and an R-score of around 99 %, the ANN model shows good agreement with the numerical results. Solid oxide fuel cell ; Fuel cells ; Materials science ; Chemical engineering ; Oxide ; Engineering ; Nanotechnology ; Artificial intelligence ; Computer science ; Chemistry ; Metallurgy Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Joseph Musielewicz | Janice Lan | Matt Uyttendaele | John R. Kitchin
Source: The Journal of Physical Chemistry C (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances Graph neural networks (GNNs) have been shown to be astonishingly capable models for molecular property prediction, particularly as surrogates for expensive density functional theory calculations of relaxed energy for novel material discovery. However, one limitation of GNNs in this context is the lack of useful uncertainty prediction methods, as this is critical to the material discovery pipeline. In this work, we show that uncertainty quantification for relaxed energy calculations is more complex than uncertainty quantification for other kinds of molecular property prediction due to the effect that structure optimizations have on the error distribution. We propose that distribution-free techniques are more useful tools for assessing calibration, recalibrating, and developing uncertainty prediction methods for GNNs performing relaxed energy calculations. We also develop a relaxed energy task for evaluating uncertainty methods for equivariant GNNs, based on distribution-free recalibration and using the Open Catalyst Project data set. We benchmark a set of popular uncertainty prediction methods on this task and show that conformal prediction methods, with our novel latent space distance improvements, are the most well-calibrated and efficient approach for relaxed energy calculations. Finally, we demonstrate that our latent space distance method produces results which align with our expectations on an atom clustering example, and on specific equation of state and adsorbate coverage examples from outside the training data set. Estimation ; Artificial neural network ; Graph ; Computer science ; Artificial intelligence ; Space (punctuation) ; Pattern recognition (psychology) ; Mathematics ; Theoretical computer science ; Engineering Machine Learning in Materials Science ; Fault Detection and Control Systems ; Neural Networks and Applications