jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yukio Ando | Hiroki Kondo | Takayoshi Tsutsumi | Kenji Ishikawa | Makoto Sekine | Masaru Hori
Source: Diamond and Related Materials (article, 2024-10-30)
Field: Materials Science — Materials Chemistry

Analysis of the synergetic effect of process parameters of hydrogenated amorphous carbon deposition in plasma-enhanced chemical vapor deposition using machine learning The synergetic effects of process parameters on hydrogenated amorphous carbon (a-C:H) films properties were quantitatively analyzed in plasma-enhanced chemical vapor deposition. Predictive models, created from experimental datasets and machine learning, indicate a synergetic effect between the H 2 ratio and ion energy impacting on the substrate. At a H 2 ratio of 75 %, etch rates of 200 nm/min were predicted, regardless of radio frequency (RF) bias. These rates decreased to 120–140 nm/min at lower H 2 ratios, primarily depending on RF bias. Quadrupole mass spectrometry and Raman scattering were used to investigate the underlying mechanisms. High H 2 ratios led to a greater presence of low-mass hydrogen-rich molecules in the plasma. The C 2 H 3 radical intensity at a 75 % H 2 ratio was 15 times greater than that at 0 % at an RF bias of 50 W, and similar trends were observed for other low-mass neutrals. The synergetic effects of H 2 ratio and RF bias decreased the film's hydrogen content. Machine learning and those diagnostics revealed that ion bombardment induces dehydrogenation of a-C:H, influenced by hydrogen-rich species. This study demonstrates that machine learning can uncover complex plasma processes and optimize material synthesis. • Synergetic effects of H 2 ratio and ion energy in PECVD were clarified. • High etch-resistant hydrogenated amorphous carbon was acquired. • Basically, gas composition ratio was significant to make high-resistant film. • Substrate bias had larger effect on etch resistance at lower H 2 gas ratio region. • a-C:H film was dehydrogenated by sputtering of impinging ions. Chemical vapor deposition ; Plasma-enhanced chemical vapor deposition ; Deposition (geology) ; Chemical engineering ; Materials science ; Carbon film ; Amorphous carbon ; Carbon fibers ; Plasma ; Plasma processing ; Amorphous solid ; Analytical Chemistry (journal) ; Nanotechnology ; Thin film ; Chemistry ; Composite material ; Environmental chemistry ; Organic chemistry ; Physics ; Engineering ; Geology Diamond and Carbon-based Materials Research ; Ion-surface interactions and analysis ; Semiconductor materials and devices
Authors: Sagar Ghosh | Sheng Zhang | Cheng Chen | Gia-Wei Chern
Source: Physical Review Materials (article, 2024-12-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Kinetics of orbital ordering in cooperative Jahn-Teller models: Machine-learning enabled large-scale simulations We present a scalable machine-learning (ML) force-field model for the adiabatic dynamics of cooperative Jahn-Teller (JT) systems. Large-scale dynamical simulations of the JT model also shed light on the orbital ordering dynamics in colossal magnetoresistance manganites. The JT effect in these materials describes the distortion of local oxygen octahedra driven by a coupling to the orbital degrees of freedom of ${e}_{g}$ electrons. An effective electron-mediated interaction between the local JT modes leads to a structural transition and the emergence of long-range orbital order at low temperatures. Assuming the principle of locality, a deep-learning neural-network model is developed to accurately and efficiently predict the electron-induced forces that drive the dynamical evolution of JT phonons. A group-theoretical method is utilized to develop a descriptor that incorporates the combined orbital and lattice symmetry into the ML model. Large-scale Langevin dynamics simulations, enabled by the ML force-field models, are performed to investigate the coarsening dynamics of the composite JT distortion and orbital order after a thermal quench. The late-stage coarsening of orbital domains exhibits pronounced freezing behaviors, which are likely related to the unusual morphology of the domain structures. Our paper highlights a promising avenue for multiscale dynamical modeling of correlated electron systems. Materials science ; Condensed matter physics ; Statistical physics ; Electron ; Adiabatic process ; Physics ; Thermodynamics ; Quantum mechanics Magnetic and transport properties of perovskites and related materials ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Kehan Wang | Longkun Xu | Wei Shao | Haishun Jin | Qiang Wang | Ming Ma
Source: The Journal of Physical Chemistry Letters (article, 2024-01-04)
Field: Materials Science — Materials Chemistry

A Multiple-Fidelity Method for Accurate Simulation of MoS<sub>2</sub> Properties Using JAX-ReaxFF and Neural Network Potentials Reactive force field (ReaxFF) is a commonly used force field for modeling chemical reactions at the atomic level. Recently, JAX-ReaxFF, combined with automatic differentiation, has been used to efficiently parametrize ReaxFF. However, its analytical formula may lead to inaccurate predictions. While neural network-based potentials (NNPs) trained on density functional theory-labeled data offer a more accurate method, it requires a large amount of training data to be trained from scratch. To overcome these issues, we present a multiple-fidelity method that combines JAX-ReaxFF and NNP and apply the method on MoS<sub>2</sub>, a promising two-dimensional semiconductor for flexible electronics. By incorporating implicit prior physical information, ReaxFF can serve as a cost-effective way to generate pretraining data, facilitating more accurate simulations of MoS<sub>2</sub>. Moreover, in the Mo-S-H system, the pretraining strategy can reduce root-mean-square errors of energy by 20%. This approach can be extended to a wide variety of material systems, accelerating their computational research. ReaxFF ; Fidelity ; Force field (fiction) ; Computer science ; Artificial neural network ; Molecular dynamics ; Chemistry ; Artificial intelligence ; Computational chemistry Machine Learning in Materials Science ; 2D Materials and Applications ; Fuel Cells and Related Materials
Authors: Chiheb Ben Mahmoud | Louise A. M. Rosset | Jonathan R. Yates | Volker L. Deringer
Source: arXiv (Cornell University) (preprint, 2024-12-19)
Field: Chemistry — Spectroscopy

Graph-neural-network predictions of solid-state NMR parameters from spherical tensor decomposition Nuclear magnetic resonance (NMR) is a powerful spectroscopic technique that is sensitive to the local atomic structure of matter. Computational predictions of NMR parameters can help to interpret experimental data and validate structural models, and machine learning (ML) has emerged as an efficient route to making such predictions. Here, we systematically study graph-neural-network approaches to representing and learning tensor quantities for solid-state NMR -- specifically, the anisotropic magnetic shielding and the electric field gradient. We assess how the numerical accuracy of different ML models translates into prediction quality for experimentally relevant NMR properties: chemical shifts, quadrupolar coupling constants, tensor orientations, and even static 1D spectra. We apply these ML models to a structurally diverse dataset of amorphous SiO$_2$ configurations, spanning a wide range of density and local order, to larger configurations beyond the reach of traditional first-principles methods, and to the dynamics of the $α\unicode{x2013}β$ inversion in cristobalite. Our work marks a step toward streamlining ML-driven NMR predictions for both static and dynamic behavior of complex materials, and toward bridging the gap between first-principles modeling and real-world experimental data. Tensor decomposition ; Decomposition ; Tensor (intrinsic definition) ; Artificial neural network ; Graph ; Solid-state ; Mathematics ; Computer science ; Materials science ; Chemistry ; Combinatorics ; Pure mathematics ; Artificial intelligence ; Physical chemistry ; Organic chemistry Advanced NMR Techniques and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Sadman Sadeed Omee | Nihang Fu | Rongzhi Dong | Ming Hu | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2024-01-16)
Field: Materials Science — Materials Chemistry

Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study In real-world material research, machine learning (ML) models are usually expected to predict and discover novel exceptional materials that deviate from the known materials. It is thus a pressing question to provide an objective evaluation of ML model performances in property prediction of out-of-distribution (OOD) materials that are different from the training set distribution. Traditional performance evaluation of materials property prediction models through random splitting of the dataset frequently results in artificially high performance assessments due to the inherent redundancy of typical material datasets. Here we present a comprehensive benchmark study of structure-based graph neural networks (GNNs) for extrapolative OOD materials property prediction. We formulate five different categories of OOD ML problems for three benchmark datasets from the MatBench study. Our extensive experiments show that current state-of-the-art GNN algorithms significantly underperform for the OOD property prediction tasks on average compared to their baselines in the MatBench study, demonstrating a crucial generalization gap in realistic material prediction tasks. We further examine the latent physical spaces of these GNN models and identify the sources of CGCNN, ALIGNN, and DeeperGATGNN's significantly more robust OOD performance than those of the current best models in the MatBench study (coGN and coNGN), and provide insights to improve their performance. Benchmark (surveying) ; Property (philosophy) ; Computer science ; Machine learning ; Generalization ; Redundancy (engineering) ; Artificial intelligence ; Set (abstract data type) ; Data mining ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Neural Network Applications
Authors: Arjun Neyyathala | Edvin Fako | Sandip De | Daria Gashnikova | Florian Maurer | Jan‐Dierk Grunwaldt | Stephan A. Schunk | Schirin Hanf
Source: ChemRxiv (preprint, 2024-05-03)
Field: Chemistry — Organic Chemistry

From poison to promotor –unique Rh4 structural motifs in supported rhodium sulphides as hydroformylation catalysts The hydroformylation of alkenes is a cornerstone transformation for the chemical industry, central for both functionalizing and extending the carbon backbone of an alkene. In our study, we explored silica-supported crystalline rhodium sulfide nanoparticles as heterogeneous catalysts in hydroformylation reactions, and found that RhxSy systems (x=17, y=15 or x=2, y=3 with 1 wt.% Rh on SiO2) greatly outperform metallic Rh nanoparticles. These systems proved to be exceptionally competitive when benchmarked against other cutting-edge catalysts in terms of activity, with Rh17S15/SiO2 being the superior catalyst candidate. By employing local environment descriptors, unsupervised machine learning and density functional theory, we have examined structure-performance relationships. Our results highlight that the presence of S in close proximity to the catalytic site unlocks the tunability of the surface catalytic properties. This allows for the substrate affinity to be modulated, in particular for Rh17S15, with adsorption energies rivaling those of pristine Rh and improved spatial resolution. Hydroformylation ; Rhodium ; Catalysis ; Chemistry ; Materials science ; Organic chemistry Organometallic Complex Synthesis and Catalysis ; Catalysis and Hydrodesulfurization Studies ; Nanomaterials for catalytic reactions
Authors: Sunil Kumar Sahu | Anil Shrivastav | N. Kumar Swamy | Vikas Dubey | Dharma K. Halwar | M. Tanooj Kumar | M. C. Rao
Source: Journal of Applied Spectroscopy (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Prediction of the Excitation Wavelength of Phosphors Phosphor ; Excitation ; Excitation wavelength ; Wavelength ; Materials science ; Computer science ; Optoelectronics ; Physics Luminescence Properties of Advanced Materials ; Radiation Detection and Scintillator Technologies ; Terahertz technology and applications
Authors: Yoyo Hinuma
Source: Science and Technology of Advanced Materials Methods (article, 2024-09-27)
Field: Materials Science — Materials Chemistry

Hashing to detect identical ordering of species in a shared sublattice Finding very low energy configurations of species on a shared sublattice is unavoidable in some problems governed by ground states of the sublattice, such as voltage curve estimation of battery cathode materials. Calculations of partial occupancy systems, most notably in first-principles calculations, require the atom species to be explicitly specified at partial occupancy sites. Brute-force calculations over all relevant configurations are becoming a realistic approach with the recent development in easily accessible universal neural network potential calculations, and detecting and removing duplicate configurations of species is critical to reduce computational cost. A digest calculation procedure inspired by hashing in computer cryptography is proposed that projects the local environments of atoms in a crystal into a scalar number ‘digest’. The digest can tolerate numerical precision errors to some extent, and configurations with the same local environments are identified by simply sorting the digest numbers and detecting crystals with very close digests. Hash function ; Computer science ; Condensed matter physics ; Geography ; Physics ; Computer security Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: Tamio Oguchi
Source: Science and Technology of Advanced Materials Methods (article, 2024-05-16)
Field: Materials Science — Materials Chemistry

Crystal structure map for materials classification and modeling For classifying and modeling properties of crystalline materials in terms of structure, a three-step workflow with (1) generation of structure feature vectors, (2) evaluation of distances among the feature vectors as a measure of similarity in structure, and (3) mapping of each structure in a low-dimensional space with principal components using dimension reduction is proposed. The obtained distance and resulting principal components are useful for classifying similar crystal structures for a given set of materials systems and for constructing descriptors in machine-learning analyses of properties. The eigenvector of the principal components indicates which part of the original structure feature vector is contained as important information. Examples are demonstrated for classification and property modeling of Al[Formula: see text]O[Formula: see text] polymorphs including amorphous structures and of the alloy configurations of Si-doped LaFe[Formula: see text] compounds. Crystal structure ; Materials science ; Computer science ; Artificial intelligence ; Crystallography ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Chayanon Atthapak | Annop Ektarawong | Teerachote Pakornchote | Björn Alling | Thiti Bovornratanaraks
Source: Advanced Theory and Simulations (article, 2024-04-25)
Field: Materials Science — Materials Chemistry

Thermodynamic Stability Prediction of Triple Transition‐Metal (Ti−Mo−V)3C2${\rm (Ti-Mo-V)}_3{\rm C}_2$ MXenes via Cluster Correlation‐Based Machine Learning Abstract The representation of atomic configurations through cluster correlations, along with the cluster expansion approach, has long been used to predict formation energies and determine the thermodynamic stability of alloys. In this work, a comparison is conducted between the traditional cluster expansion method based on density functional theory and other potential machine learning models, including decision tree‐based ensembles and multi‐layer perceptron regression, to explore the alloying behavior of different elements in multi‐component alloys. Specifically, these models are applied to investigate the thermodynamic stability of triple transition‐metal MXenes, a multi‐component alloy in the largest family of 2D materials that are gaining attention for several outstanding properties. The findings reveal the triple transition‐metal ground‐state configurations in this system and demonstrate how the configuration of transition metal atoms (Ti, Mo, and V atoms) influences the formation energy of this alloy. Moreover, the performance of machine learning algorithms in predicting formation energies and identifying ground‐state structures is thoroughly discussed from various aspects. MXenes ; Cluster expansion ; Cluster (spacecraft) ; Perceptron ; Stability (learning theory) ; Materials science ; Chemical stability ; Density functional theory ; Transition metal ; Component (thermodynamics) ; Thermodynamics ; Machine learning ; Ground state ; Artificial intelligence ; Artificial neural network ; Chemistry ; Computer science ; Physics ; Atomic physics ; Computational chemistry ; Nanotechnology MXene and MAX Phase Materials ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Khoukha Khoussa | Larbi BOUBCHIR | Patrick Lévêque
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Organic Photovoltaics: Predicting Power Conversion Efficiency from the Molecular Chemical Structure of (Donor/Acceptor) Pairs Acceptor ; Organic solar cell ; Photovoltaics ; Energy conversion efficiency ; Chemistry ; Materials science ; Organic chemistry ; Optoelectronics ; Engineering ; Photovoltaic system ; Electrical engineering ; Physics ; Polymer Machine Learning in Materials Science ; Conducting polymers and applications ; Organic Electronics and Photovoltaics
Authors: Chiwen Feng | Yanwei Liang | Jiaying Sun | Renhai Wang | Huaijun Sun | Huafeng Dong
Source: Physical Chemistry Chemical Physics (article, 2024-12-13)
Field: Materials Science — Materials Chemistry

Predicting miscibility in binary compounds: a machine learning and genetic algorithm study The combination of data science and materials informatics has significantly propelled the advancement of multi-component compound synthesis research. This study employs atomic-level data to predict miscibility in binary compounds using machine learning, demonstrating the feasibility of such predictions. We have integrated experimental data from the Materials Project (MP) database and the Inorganic Crystal Structure Database (ICSD), covering 2346 binary systems. We applied a random forest classification model to train the constructed dataset and analyze the key factors affecting the miscibility of binary systems and their significance while predicting binary systems with high synthetic potential. By employing advanced genetic algorithms on the Co-Eu system, we discovered three novel thermodynamically stable phases, CoEu<sub>8</sub>, Co<sub>3</sub>Eu<sub>2</sub>, and CoEu. This research offers valuable theoretical insights to guide experimental synthesis endeavors in binary and complex material systems. Miscibility ; Binary number ; Component (thermodynamics) ; Informatics ; Materials informatics ; Genetic algorithm ; Materials science ; Computer science ; Algorithm ; Thermodynamics ; Machine learning ; Engineering ; Mathematics ; Health informatics ; Physics ; Polymer Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Asif Momin
Source: Indian Journal of Science and Technology (article, 2024-11-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Review of Electrical Steels with their Properties and Recent Trends for Improvement Objectives: This review study provides a thorough analysis of the magnetic behavior and characteristics of electrical steels, including established and novel materials (3%wt Silicon). Methods: We investigate how composition, microstructure, and processing methods affect the magnetic performance of these steels as well as the basic principles guiding their behavior. Thermomechanical processing can be observed for its impact on the material’s microstructure and magnetic properties. This review discusses testing over samples for conventional processes for different % of cold worked or strain hardening and annealing at higher temperatures i.e., 1200°C. For uncertainties regarding the impact of impurities or inclusions on their magnetic properties, the study was carried out to know the effect of cerium addition on the material’s magnetic properties of the material. Findings: It was observed that rolling temperature has significant effect over magnetic properties of the material. A notable improvement in the magnetic permeability of the material and its critical performance metric for soft magnetic materials is indicated by the increase in “Gamma max”. Similarly, the effects of compressive stresses, doping effect, grain size, annealing, and other manufacturing processes were studied, and observed their impact on magnetic properties. This review discusses the literature on future scope with the latest trends. This helps clarify the trade-offs associated with maximizing electrical steels for certain uses. Novelty: This study presents a novel approach towards electrical steel and its magnetic properties by incorporating different alloying elements and optimizing rolling &amp; annealing parameters. A key innovation is the application of machine learning and neural network to predict and optimize material behavior, core loss and provide methods to improve magnetic properties of electrical steels. Significance: Electrical steels are crucial in transformers and electric motors, enhancing efficiency by minimizing energy losses and improving magnetic performance. This can be achieved by changing manufacturing parameters and operating conditions with the help of latest trends. Keywords: Electrical steel, Magnetic properties, Machine learning, Composition, Microstructure, Core loss Computer science ; Materials science Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Electric Motor Design and Analysis
Authors: L. Wang | Ying-Bin Cheng | Jian Zhou
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Molecular Dynamics Study on Phonon Coherent Transport in Iii-V Semiconductor Superlattices Phonon ; Superlattice ; Semiconductor ; Condensed matter physics ; Molecular dynamics ; Physics ; Dynamics (music) ; Materials science ; Statistical physics ; Quantum mechanics Thermal properties of materials ; Semiconductor materials and interfaces ; Semiconductor Quantum Structures and Devices
Authors: Khadijah Mohammedsaleh Katubi | Syed Shoaib Ahmad Shah | Sumaira Naeem | Tayyaba Najam | Z.A. Alrowaili | M.S. Al-Buriahi
Source: Chemical Physics Letters (article, 2024-06-27)
Field: Chemistry — Physical and Theoretical Chemistry

A systematic approach to generate polymer library: A search for polymers with high dipole moment Polymer ; Dipole ; Moment (physics) ; Materials science ; Polymer science ; Chemical physics ; Physics ; Chemistry ; Organic chemistry ; Composite material ; Quantum mechanics Various Chemistry Research Topics ; Carbon Nanotubes in Composites ; Nanofabrication and Lithography Techniques