jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Natalia Kireeva | А. Yu. Tsivadze
Source: Journal of Alloys and Compounds (article, 2024-04-08)
Field: Materials Science — Materials Chemistry

Oxide ceramics of A2M3O12 family with negative and close-to-zero thermal expansion coefficients: Machine learning-based modeling of functional characteristics Thermal expansion ; Negative thermal expansion ; Orthorhombic crystal system ; Thermodynamics ; Ceramic ; Phase transition ; Materials science ; Anharmonicity ; Chemistry ; Thermal stability ; Atmospheric temperature range ; Condensed matter physics ; Composite material ; Physics ; Crystal structure ; Crystallography Thermal Expansion and Ionic Conductivity ; Microwave Dielectric Ceramics Synthesis ; Magnesium Oxide Properties and Applications
Authors: Samuel P. Gleason | Jakob C. Dahl | Mahmoud Elzouka | Xingzhi Wang | Dana O. Byrne | Hannah Cho | Mumtaz Gababa | Ravi Prasher | Sean Lubner | Emory M. Chan | A. Paul Alivisatos
Source: ACS Nano (article, 2024-12-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Automated Gold Nanorod Spectral Morphology Analysis Pipeline The development of a colloidal synthesis procedure to produce nanomaterials with high shape and size purity is often a time-consuming, iterative process. This is often due to quantitative uncertainties in the required reaction conditions and the time, resources, and expertise intensive characterization methods required for quantitative determination of nanomaterial size and shape. Absorption spectroscopy is often the easiest method for colloidal nanomaterial characterization. However, due to the lack of a reliable method to extract nanoparticle shapes from absorption spectroscopy, it is generally treated as a more qualitative measure for metal nanoparticles. This work demonstrates a gold nanorod (AuNR) spectral morphology analysis tool, called AuNR-SMA, which is a fast and accurate method to extract quantitative structural information from colloidal AuNR absorption spectra. To demonstrate the practical utility of this model, we apply it to three distinct applications. First, we demonstrate this model's utility as an automated analysis tool in a high-throughput AuNR synthesis procedure by generating quantitative size information from optical spectra. Second, we use the predictions generated by this model to train a machine learning model to predict the resulting AuNR size distributions under specified reaction conditions. Third, we apply this model to spectra extracted from the literature where no size distributions are reported and impute unreported quantitative information on AuNR synthesis. This approach can potentially be extended to any other nanocrystal system where absorption spectra are size dependent, and accurate numerical simulation of absorption spectra is possible. In addition, this pipeline could be integrated into automated synthesis apparatuses to provide interpretable data from simple measurements, help explore the synthesis science of nanoparticles in a rational manner, or facilitate closed-loop workflows. Nanorod ; Pipeline (software) ; Nanomaterials ; Characterization (materials science) ; Absorption (acoustics) ; Materials science ; Nanotechnology ; Colloidal gold ; Absorption spectroscopy ; Computer science ; Raman spectroscopy ; Nanoparticle ; Biological system ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Nanoparticles: synthesis and applications
Authors: Arulmurugan Senthilnathan | Vishnu Saseendran | Pınar Acar | Namiko Yamamoto | Veera Sundararaghavan
Source: Acta Materialia (article, 2024-07-31)
Field: Materials Science — Materials Chemistry

Comparison and validation of stochastic microstructure characterization and reconstruction: Machine learning vs. deep learning methodologies Materials science ; Characterization (materials science) ; Microstructure ; Artificial intelligence ; Machine learning ; Metallurgy ; Nanotechnology ; Computer science Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced X-ray and CT Imaging
Authors: Elena Gazzarrini | Rose K. Cersonsky | Marnik Bercx | Carl S. Adorf | Nicola Marzari
Source: npj Computational Materials (article, 2024-04-12)
Field: Materials Science — Materials Chemistry

The rule of four: anomalous distributions in the stoichiometries of inorganic compounds Why are materials with specific characteristics more abundant than others? This is a fundamental question in materials science and one that is traditionally difficult to tackle, given the vastness of compositional and configurational space. We highlight here the anomalous abundance of inorganic compounds whose primitive unit cell contains a number of atoms that is a multiple of four. This occurrence-named here the <i>rule of four</i>-has to our knowledge not previously been reported or studied. Here, we first highlight the rule's existence, especially notable when restricting oneself to experimentally known compounds, and explore its possible relationship with established descriptors of crystal structures, from symmetries to energies. We then investigate this relative abundance by looking at structural descriptors, both of global (packing configurations) and local (the smooth overlap of atomic positions) nature. Contrary to intuition, the overabundance does not correlate with low-energy or high-symmetry structures; in fact, structures which obey the <i>rule of four</i> are characterized by low symmetries and loosely packed arrangements maximizing the free volume. We are able to correlate this abundance with local structural symmetries, and visualize the results using a hybrid supervised-unsupervised machine learning method. Homogeneous space ; Intuition ; Theoretical physics ; Stoichiometry ; Relative species abundance ; Statistical physics ; Space (punctuation) ; Physics ; Abundance (ecology) ; Chemistry ; Computer science ; Mathematics ; Geometry ; Biology ; Epistemology ; Physical chemistry ; Ecology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Kateřina Plachká | Veronika Pilařová | Taťána Gazárková | František Švec | Jean‐Christophe Garrigues | Lucie Nováková
Source: Analytical Chemistry (article, 2024-07-29)
Field: Chemistry — Spectroscopy

Advancing Fundamental Understanding of Retention Interactions in Supercritical Fluid Chromatography Using Artificial Neural Networks: Polar Stationary Phases with –OH Moieties The retention behavior in supercritical fluid chromatography and its stability over time are still unsatisfactorily explained phenomena despite many important contributions in recent years, especially focusing on linear solvation energy relationship modeling. We studied polar stationary phases with predominant -OH functionalities, i.e., silica, hybrid silica, and diol columns, and their retention behavior over time. We correlated molecular descriptors of analytes with their retention using three organic modifiers of the CO<sub>2</sub>-based mobile phase. The differences in retention behavior caused by using additives, namely, 10 mmol/L NH<sub>3</sub> and 2% H<sub>2</sub>O in methanol, were described in correlation to analyte properties and compared with the CO<sub>2</sub>/methanol mobile phase. The structure of >100 molecules included in this study was optimized by semiempirical AM1 quantum mechanical calculations and subsequently described by 226 molecular descriptors including topological, constitutional, hybrid, electronic, and geometric descriptors. An artificial neural networks simulator with deep learning toolbox was trained on this extensive set of experimental data and subsequently used to determine key molecular descriptors affecting the retention by the highest extent. After comprehensive statistical analysis of the experimental data collected during one year of column use, the retention on different stationary phases was fundamentally described. The changes in the retention behavior during one year of column use were described and their explanation with a proposed interpretation of changes on the stationary phase surface was suggested. The effect of the regeneration procedure on the retention was also evaluated. This fundamental understanding of interactions responsible for retention in SFC can be used for the evidence-based selection of stationary phases suitable for the separation of particular analytes based on their specific physicochemical properties. Chemistry ; Solvation ; Polar ; Supercritical fluid chromatography ; Supercritical fluid ; Chromatography ; Stationary phase ; Chemical polarity ; Column chromatography ; Organic chemistry ; Molecule Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Advanced Chemical Sensor Technologies
Authors: Anmol Jindal | Tarak Karmakar
Source: The Journal of Physical Chemistry Letters (article, 2024-09-23)
Field: Materials Science — Materials Chemistry

Unveiling the Role of Solvent in Solution Phase Chemical Reactions using Deep Potential-Based Enhanced Sampling Simulations We have used a deep learning-based active learning strategy to develop <i>ab initio</i> level accurate machine-learned (ML) potential for a solution-phase reactive system. Using this ML potential, we carried out enhanced sampling simulations to sample the reaction process efficiently. Multiple transitions between the reactant and product states allowed us to calculate the converged free energy surface for the reaction. As a prototypical example, we have investigated the Menshutkin reaction, a classic bimolecular nucleophilic substitution reaction (S<sub>N</sub>2) in aqueous medium. Our analyses revealed that water stabilizes the ionic product state by enhanced solvation, facilitating the reaction and making it more spontaneous. Our approach expands the scope of studying the chemical reaction under realistic conditions, such as explicit solvents at finite temperatures, closely mimicking experiments. Solvent ; Sampling (signal processing) ; Phase (matter) ; Chemistry ; Computer science ; Organic chemistry ; Computer vision Machine Learning in Materials Science ; Nuclear Physics and Applications ; Computational Drug Discovery Methods
Authors: Menglin Zhu | Joseph Lanier | Jose Flores | Victor da Cruz Pinha Barbosa | Daniel Russell | Becky Haight | Patrick M. Woodward | Fengyuan Yang | Jinwoo Hwang
Source: Scientific Reports (article, 2024-02-20)
Field: Materials Science — Materials Chemistry

Structural degeneracy and formation of crystallographic domains in epitaxial LaFeO3 films revealed by machine-learning assisted 4D-STEM Structural domains and domain walls, inherent in single crystalline perovskite oxides, can significantly influence the properties of the material and therefore must be considered as a vital part of the design of the epitaxial oxide thin films. We employ 4D-STEM combined with machine learning (ML) to comprehensively characterize domain structures at both high spatial resolution and over a significant spatial extent. Using orthorhombic LaFeO<sub>3</sub> as a model system, we explore the application of unsupervised and supervised ML in domain mapping, which demonstrates robustness against experiment uncertainties. The results reveal the consequential formation of multiple domains due to the structural degeneracy when LaFeO<sub>3</sub> film is grown on cubic SrTiO<sub>3</sub>. In situ annealing of the film shows the mechanism of domain coarsening that potentially links to phase transition of LaFeO<sub>3</sub> at high temperatures. Moreover, synthesis of LaFeO<sub>3</sub> on DyScO<sub>3</sub> illustrates that a less symmetric orthorhombic substrate inhibits the formation of domain walls, thereby contributing to the mitigation of structural degeneracy. High fidelity of our approach also highlights the potential for the domain mapping of other complicated materials and thin films. Orthorhombic crystal system ; Materials science ; Degeneracy (biology) ; Epitaxy ; Thin film ; Annealing (glass) ; Domain (mathematical analysis) ; Complex oxide ; Substrate (aquarium) ; Computer science ; Perovskite (structure) ; Chemical physics ; Oxide ; Crystallography ; Nanotechnology ; Crystal structure ; Chemistry ; Bioinformatics ; Mathematics ; Geology ; Biology ; Composite material Electronic and Structural Properties of Oxides ; Multiferroics and related materials ; Magnetic and transport properties of perovskites and related materials
Authors: Christian Brosseau
Source: Macromolecular Theory and Simulations (article, 2024-03-07)
Field: Materials Science — Polymers and Plastics

Modeling the Interface Between Phases in Dense Polymer‐Carbon Black Nanoparticle Composites by Dielectric Spectroscopy: Where Are We Now and What are the Opportunities? Abstract The macroscopic properties of polymer nanocomposites (PNC) rely largely on the interphase between the polymer chains and the filler particles. One significant difficulty to solve this issue is to quantitatively model the structure‐property correlations due to the interfacial region in these complex materials. While dielectric spectroscopy (DS) measurements are routinely used to characterize the effective permittivity of filled polymers, fitting standard effective medium models and mixing equations to these data remains notoriously difficult to interpret. This is due to the absence of explicit reference to internal length scales characterizing the interfaces in the PNC. As an illustrative example, a two‐level homogenization framework is proposed which enables the extraction of useful information on the impact of a thin interphase confined on a nanometer length scale based on broadband DS data. This model leads to new ways of tuning the interphase so as to optimize the material's response to electric field, a situation relevant for electromagnetic shielding. This approach provides guidance on how to observe directly and experimentally the actual properties of the interface between the phases (as opposed to model‐based inference). Aside from its secure physical foundation in the theory of effective medium, a significant advantage of this approach is that a genetic algorithm (GA) technique applied to this physics‐based model enables the uniqueness of the fit parameters to be considered, as the GA method is robust in terms of finding globally optimum solutions, therefore placing confidence in non‐universal values of the percolation exponents. Recent work in physics‐informed machine learning indicates that the effective dielectric properties of PNC with many degrees of freedom due to their complex morphology can be described by considering only a few degrees of freedom describing the interface features between the phases in these composites. Interphase ; Homogenization (climate) ; Materials science ; Statistical physics ; Dielectric ; Carbon black ; Permittivity ; Percolation (cognitive psychology) ; Nanocomposite ; Polymer nanocomposite ; Percolation threshold ; Composite material ; Computer science ; Physics ; Electrical resistivity and conductivity ; Optoelectronics Polymer Nanocomposites and Properties ; Dielectric materials and actuators ; High voltage insulation and dielectric phenomena
Authors: Toshikatsu Tanaka
Source: IEEE Transactions on Dielectrics and Electrical Insulation (article, 2024-10-29)
Field: Materials Science — Materials Chemistry

Progress in Nanodielectrics: Future From the Past Decade Since the early 21st century, nanodielectrics have attracted significant attention. Initially regarded as exotic materials, they have since evolved into widely accessible advanced materials. Research has primarily focused on solid polymer nanocomposites and has recently expanded to include liquid nanocomposites, known as nanofluids. Over the past decade, considerable progress has been made in the field of nanodielectrics. To capture this progress, more than 400 papers published in the IEEE Transactions on Dielectrics and Electrical Insulation and IEEE Electrical Insulation Magazine between 2011 and 2023 have been collected and reviewed. The evaluation of host materials, guest nanoparticles, and their combinations has been carried out to identify those that deliver superior performance. Notable advances in microscopic studies of interphases and particle dispersions are highlighted. Applications in high-voltage (HV) equipment and power electronics devices are also referenced to promote further research in this area. In addition, it is important to recognize that machine learning (ML) plays a critical role in accelerating the research and development of nanodielectrics and their applications. Materials science ; Engineering physics ; Computer science ; Engineering High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Ferroelectric and Piezoelectric Materials
Authors: Tianpu Xiao | Yang Li | Xiantao He | Liangju Wang | Dongxing Zhang | Cui Tao | Kailiang Zhang | Lei Bao | Senjian An | Xiaoshuang Zhang
Source: Journal of Hazardous Materials (article, 2024-11-30)
Field: Chemistry — Analytical Chemistry

A green and efficient method for detecting nicosulfuron residues in field maize using hyperspectral imaging and deep learning Hyperspectral imaging ; Field (mathematics) ; Environmental science ; Artificial intelligence ; Remote sensing ; Agricultural engineering ; Biological system ; Chemistry ; Agronomy ; Computer science ; Biology ; Engineering ; Mathematics ; Geology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Gerhard Jung | Giulio Biroli | Ludovic Berthier
Source: Machine Learning Science and Technology (article, 2024-08-07)
Field: Materials Science — Materials Chemistry

Normalizing flows as an enhanced sampling method for atomistic supercooled liquids Abstract Normalizing flows can transform a simple prior probability distribution into a more complex target distribution. Here, we evaluate the ability and efficiency of generative machine learning methods to sample the Boltzmann distribution of an atomistic model for glass-forming liquids. This is a notoriously difficult task, as it amounts to ergodically exploring the complex free energy landscape of a disordered and frustrated many-body system. We optimize a normalizing flow model to successfully transform high-temperature configurations of a dense liquid into low-temperature ones, near the glass transition. We perform a detailed comparative analysis with established enhanced sampling techniques developed in the physics literature to assess and rank the performance of normalizing flows against state-of-the-art algorithms. We demonstrate that machine learning methods are very promising, showing a large speedup over conventional molecular dynamics. Normalizing flows show performances comparable to parallel tempering and population annealing, while still falling far behind the swap Monte Carlo algorithm. Our study highlights the potential of generative machine learning models in scientific computing for complex systems, but also points to some of its current limitations and the need for further improvement. Supercooling ; Sampling (signal processing) ; Statistical physics ; Materials science ; Computer science ; Thermodynamics ; Physics Material Dynamics and Properties ; Theoretical and Computational Physics ; Phase Equilibria and Thermodynamics
Authors: Andrii Trelin | Anastasiia Skvortsova | Anastasia Olshtrem | Sergii Chertopalov | David Mareš | Ladislav Lapčák | Martin Vondráček | Petr Sajdl | V. Jeřábek | J. Maixner | J. Lančok | Zdeněk Sofer | Jakub Regner | Zdeňka Kolská | Václav Švorčı́k | Oleksiy Lyutakov
Source: The Journal of Physical Chemistry C (article, 2024-04-10)
Field: Materials Science — Materials Chemistry

Surface-Enhanced Raman Spectroscopy and Artificial Neural Networks for Detection of MXene Flakes’ Surface Terminations The properties of MXene flakes, a new class of two-dimensional materials, are strictly determined by their surface termination. The most common termination groups are oxygen-containing (=O or -OH) and fluorine (-F), and their relative ratio is closely related to flake stability and catalytic activity. The surface termination can vary significantly among MXene flakes depending on the preparation route and is commonly determined after flake preparation by using X-ray photoelectron spectroscopy (XPS). In this paper, as an alternative approach, we propose the combination of surface-enhanced Raman spectroscopy (SERS) and artificial neural networks (ANN) for the precise and reliable determination of MXene flakes' (Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub>) surface chemistry. Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub> flakes were independently prepared by three scientific groups and subsequently measured using three different Raman spectrometers, employing resonant excitation wavelengths. Manual analysis of the SERS spectra did not enable accurate determination of the flake surface termination. However, the combined SERS-ANN approach allowed us to determine the surface termination with a high accuracy. The reliability of the method was verified by using a series of independently prepared samples. We also paid special attention to how the results of the SERS-ANN method are affected by the flake stability and differences in the conditions of flake preparation and Raman measurements. This way, we have developed a universal technique that is independent of the above-mentioned parameters, providing the results with accuracy similar to XPS, but enhanced in terms of analysis time and simplicity. Raman spectroscopy ; Surface-enhanced Raman spectroscopy ; Surface (topology) ; Materials science ; Artificial neural network ; Spectroscopy ; Nanotechnology ; Raman scattering ; Computer science ; Artificial intelligence ; Optics ; Physics ; Mathematics ; Geometry MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene and Nanomaterials Applications
Authors: Hasan Can Gülbalkan | Alper Uzun | Seda Keskın
Source: Applied Physics Letters (article, 2024-05-13)
Field: Chemistry — Inorganic Chemistry

Combining computational screening and machine learning to explore MOFs and COFs for methane purification Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) have great potential to be used as porous adsorbents and membranes to achieve high-performance methane purification. Although the continuous increase in the number and diversity of MOFs and COFs is a great opportunity for the discovery of novel adsorbents and membranes with superior performances, evaluating such a vast number of materials in the quickest and most effective manner requires the development of computational approaches. High-throughput computational screening based on molecular simulations has been extensively used to identify the most promising MOFs and COFs for methane purification. However, the enormous and ever-growing material space necessitates more efficient approaches in terms of time and effort. Combining data science with molecular simulations has recently accelerated the discovery of optimal MOF and COF materials for methane purification and revealed the hidden structure–performance relationships. In this perspective, we highlighted the recent developments in combining high-throughput molecular simulations and machine learning to accurately identify the most promising MOF and COF adsorbents and membranes among thousands of candidates for separating methane from other gases including acetylene, carbon dioxide, helium, hydrogen, and nitrogen. After providing a brief overview of the topic, we reviewed the pioneering contributions in the field and discussed the current opportunities and challenges that we need to direct our efforts for the design and discovery of adsorbent and membrane materials. Methane ; Computer science ; Materials science ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Coal Properties and Utilization ; Industrial Gas Emission Control