jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Murat Peksen | Benjamin Steinrücken | H. Spliethoff
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

Recent Developments of the International Future Lab of Hydrogen Economy: Using Artificial Intelligence (AI) Based Machine Learning (ML) in r-SOC Development The International Future Laboratory for Hydrogen Economy at the Technical University of Munich (TUM) has established a research-network model researching in the field of electrically assisted gasification, reversible solid oxide cells, and biocatalytic synthesis of hydrogen. Using experimental and numerical data, the current study elucidates the progress on reversible solid oxide cell technologies. Advanced artificial intelligence (AI)-based machine learning approaches are utilized to evaluate and optimize SOC-relevant processes. The SOC research is giving particular attention to the integrated approach of traditional numerical methods such as CFD and accelerated modern AI methods. The complex thermochemistry of syngas-containing auxiliary components has been assessed using experimentally validated numerical models. The approach is utilized to create data for the successful construction and training of AI-based machine-learning models. Multi-regression studies shed light on the interactions between process variables to improve and prepare SOC-ready fuel which is essential for successful operation. A recently developed ML model is effectively employed to forecast and optimize the reforming procedures with different fuel constellations including oxygen-containing syngas compositions. Consequently, the results contribute to better knowledge and qualitative benefits of the preparation of high-quality, pure syngas, improved fuel utilisation furthering sustainable research and r-SOC operation in a safe, consistent manner. Hence, the availability of valuable information early in the process is achieved. Moreover, the approach reduced prohibitive experiments contributing to the sustainable use of resources. Syngas ; Process (computing) ; Computer science ; Artificial intelligence ; Process engineering ; Fuel cells ; Machine learning ; Hydrogen ; Engineering ; Chemistry Advancements in Solid Oxide Fuel Cells ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Mei Li | Lu Zhang | Lingli Jiang | Zhibo Zhao | Youhua Long | Dongmei Chen | Jun Bin | Chao Kang | Yajuan Liu
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Label-Free Confocal Raman Imaging with Chemometrics for Cellular Investigation of Apple Fruit Ring Rot Infected by &lt;i&gt;Botryosphaeria Dothidea&lt;/i&gt; and Nondestructive Near-Infrared Early Recognition Using Machine Learning Botryosphaeria dothidea ; Chemometrics ; Confocal ; Raman spectroscopy ; Nondestructive testing ; Horticulture ; Materials science ; Chemistry ; Optics ; Biology ; Medicine ; Physics ; Chromatography ; Radiology Spectroscopy and Chemometric Analyses ; Plant Pathogens and Fungal Diseases ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Seunghwa Ryu
Source: JMST Advances (article, 2023-08-31)
Field: Materials Science — Materials Chemistry

K-nanomechanics: advancements and applications of atomistic simulations in the Korean solid mechanics community Nanomechanics ; Bridging (networking) ; Nanotechnology ; Materials science ; Computer science ; Atomic force microscopy Microstructure and mechanical properties ; Composite Material Mechanics ; Force Microscopy Techniques and Applications
Authors: Baoqing Bai | Siyuan Meng | Yanli Guo | Yufei He | Bin Feng | Bo Tao | Jinhua Zhang | Dan Shen | Sanhong Fan | Yukun Yang
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Determination of Amide Herbicides in Food by Salting-Out Vortex- Assisted Dispersive Liquid-Liquid Microextraction Coupled with Gas Chromatography-Tandem Mass Spectrometry Chromatography ; Salting out ; Tandem ; Mass spectrometry ; Chemistry ; Analytical Chemistry (journal) ; Amide ; Gas chromatography ; Materials science ; Organic chemistry Analytical chemistry methods development ; Pesticide Residue Analysis and Safety ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: Pavan Vynatheya | Rosemary A. Mardling | Adrian S. Hamers
Source: arXiv (Cornell University) (preprint, 2023-01-24)
Field: Materials Science — Materials Chemistry

Quadruple-star systems are not always nested triples: a machine learning approach to dynamical stability The dynamical stability of quadruple-star systems has traditionally been treated as a problem involving two `nested' triples which constitute a quadruple. In this novel study, we employed a machine learning algorithm, the multi-layer perceptron (MLP), to directly classify 2+2 and 3+1 quadruples based on their stability (or long-term boundedness). The training data sets for the classification, comprised of $5\times10^5$ quadruples each, were integrated using the highly accurate direct $N$-body code MSTAR. We also carried out a limited parameter space study of zero-inclination systems to directly compare quadruples to triples. We found that both our quadruple MLP models perform better than a `nested' triple MLP approach, which is especially significant for 3+1 quadruples. The classification accuracies for the 2+2 MLP and 3+1 MLP models are 94% and 93% respectively, while the scores for the `nested' triple approach are 88% and 66% respectively. This is a crucial implication for quadruple population synthesis studies. Our MLP models, which are very simple and almost instantaneous to implement, are available on GitHub, along with Python3 scripts to access them. Perceptron ; Stability (learning theory) ; Nested loop join ; Computer science ; Star (game theory) ; Population ; Code (set theory) ; Scripting language ; Algorithm ; Simple (philosophy) ; Parameter space ; Artificial intelligence ; Dynamical systems theory ; Pattern recognition (psychology) ; Mathematics ; Machine learning ; Artificial neural network ; Data mining ; Statistics ; Programming language ; Set (abstract data type) ; Physics Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Advanced Chemical Physics Studies
Authors: Rami Al-Belmpeisi | Vito Tuhtan | Mikkel Bregning Christensen | Rajika L. Kuruwita | Troels Haugbølle
Source: arXiv (Cornell University) (preprint, 2023-07-27)
Field: Chemistry — Spectroscopy

Simulated analogues II: a new methodology for non-parametric matching of models to observations Star formation is a multi-scale problem, and only global simulations that account for the connection from the molecular cloud scale gas flow to the accreting protostar can reflect the observed complexity of protostellar systems. Star-forming regions are characterised by supersonic turbulence and as a result, it is not possible to simultaneously design models that account for the larger environment and in detail reproduce observed stellar systems. Instead, the stellar inventories can be matched statistically, and best matches found that approximate specific observations. Observationally, a combination of single-dish telescopes and interferometers are now able to resolve the nearest protostellar objects on all scales from the protostellar core to the inner 10 AU. We present a new non-parametric methodology which uses high-resolution simulations and post-processing methods to match simulations and observations using deep learning. Our goal is to perform a down-selection from large data sets of synthetic images to a ranked list of best-matching candidates with respect to the observation. This is particularly useful for binary and multiple stellar systems that form in turbulent environments. The objective is to accelerate the rate at which we can do such comparisons, remove biases from hand-picking matches, and contribute to identifying the underlying physical processes that drive the creation and evolution of observed protostellar systems. Protostar ; Physics ; Astrophysics ; Parametric statistics ; Supersonic speed ; Turbulence ; Binary number ; Scale (ratio) ; Matching (statistics) ; Star formation ; Computer science ; Astronomical interferometer ; Statistical physics ; Stars ; Astronomy ; Interferometry ; Mechanics Spectroscopy and Laser Applications ; Astro and Planetary Science ; Astrophysics and Star Formation Studies
Authors: Natalie Maus | Zhiyuan Lin | Maximilian Balandat | Eytan Bakshy
Source: arXiv (Cornell University) (preprint, 2023-11-03)
Field: Materials Science — Materials Chemistry

Joint Composite Latent Space Bayesian Optimization Bayesian Optimization (BO) is a technique for sample-efficient black-box optimization that employs probabilistic models to identify promising input locations for evaluation. When dealing with composite-structured functions, such as f=g o h, evaluating a specific location x yields observations of both the final outcome f(x) = g(h(x)) as well as the intermediate output(s) h(x). Previous research has shown that integrating information from these intermediate outputs can enhance BO performance substantially. However, existing methods struggle if the outputs h(x) are high-dimensional. Many relevant problems fall into this setting, including in the context of generative AI, molecular design, or robotics. To effectively tackle these challenges, we introduce Joint Composite Latent Space Bayesian Optimization (JoCo), a novel framework that jointly trains neural network encoders and probabilistic models to adaptively compress high-dimensional input and output spaces into manageable latent representations. This enables viable BO on these compressed representations, allowing JoCo to outperform other state-of-the-art methods in high-dimensional BO on a wide variety of simulated and real-world problems. Bayesian optimization ; Computer science ; Probabilistic logic ; Artificial intelligence ; Encoder ; Context (archaeology) ; Bayesian probability ; Variety (cybernetics) ; Machine learning ; Joint (building) ; Optimization problem ; Algorithm ; Engineering Machine Learning in Materials Science ; Machine Learning and Data Classification ; Machine Learning and Algorithms
Authors: Mohammad Hossein Keshavarz | Zeinab Shirazi | Mohammad Jafari | Farkhondeh Jannesari
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Organic Chemistry

The Use of the Molecular Structure of Organic Compounds to Assess Their Puf-Air Partition Coefficients Partition coefficient ; Partition (number theory) ; Chemistry ; Environmental chemistry ; Environmental science ; Mathematics ; Organic chemistry ; Combinatorics Chemical Thermodynamics and Molecular Structure ; Thermal and Kinetic Analysis ; Analytical Chemistry and Chromatography
Authors: Bogdan Burlacu | Michael Kommenda | Gabriel Kronberger | Stephan Winkler | Michael Affenzeller
Source: Genetic and evolutionary computation (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data Interatomic potential ; Ab initio ; Computer science ; Symbolic regression ; Electronic structure ; Atomic units ; Statistical physics ; Machine learning ; Genetic programming ; Chemistry ; Computational chemistry ; Physics ; Molecular dynamics ; Quantum mechanics Machine Learning in Materials Science ; RNA and protein synthesis mechanisms ; Electrocatalysts for Energy Conversion
Authors: Young-Jae Choi | Minjae Ghim | Seung-Hoon Jhi
Source: arXiv (Cornell University) (preprint, 2023-09-03)
Field: Materials Science — Materials Chemistry

Study of vacancy ordering and the boson peak in metastable cubic Ge-Sb-Te using machine learning potentials The mechanism of the vacancy ordering in metastable cubic Ge-Sb-Te (c-GST) that underlies the ultrafast phase-change dynamics and prominent thermoelectric properties remains elusive. Achieving a comprehensive understanding of the vacancy-ordering process at an atomic level is challenging because of enormous computational demands required to simulate disordered structures on large temporal and spatial scales. In this study, we investigate the vacancy ordering in c-GST by performing large-scale molecular dynamics simulations using machine learning potentials. The initial c-GST structure with randomly distributed vacancies rearranges to develop a semi-ordered cubic structure with layer-like ordered vacancies after annealing at 700~K for 100~ns. The vacancy ordering significantly affects the lattice dynamical properties of c-GST. In the initial structure with fully disordered vacancies, we observe a boson peak, usually associated with amorphous solids, that consists of localized modes at $\sim$0.575~THz. The boson peak modes are highly localized around specific atomic arrangements of straight vacancy-Te-vacancy trios. As vacancies become ordered, the boson peak disappears and the Debye-Waller thermal \textit{B} factor of Te decreases substantially. This finding indicates that the c-GST undergoes a transition from amorphous-like to crystalline-like solid state by thermal annealing in low-frequency dynamics. Vacancy defect ; Metastability ; Condensed matter physics ; Amorphous solid ; Annealing (glass) ; Materials science ; Lattice (music) ; Scattering ; Molecular dynamics ; Physics ; Crystallography ; Chemistry ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Phase-change materials and chalcogenides ; Machine Learning in Materials Science
Authors: Heng Hu | Junchen Yang | Kang Yan | Tao Tan | Dawei Wu
Source:  (article, 2023-09-03)
Field: Materials Science — Materials Chemistry

Accelerated Search for KNN-Based Ceramics with Large Piezoelectric Constants Using Machine Learning Methods The (K,Na)NbO3(KNN)-based piezoelectric ceramics are one of the most promising lead-free piezoelectric materials to replace toxic lead-based ones for ultrasonic transducer applications owing to their high Curie temperature and excellent piezoelectric properties. However, it is costly to discover multiple doped compositions with enhanced properties based on the traditional trial and error approach. In this study, we proposed an efficient data-driven machine learning(ML) approach to search for KNN-based ceramics with enhanced piezoelectric properties. The designed ML framework efficiently located the potential composition with a high piezoelectric constant d<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">33</inf> for the experiment procedure. The newly synthesized composition achieves an outstanding d<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">33</inf> of ~ 407 pC/N. The results reveal the exceptional efficiency of this approach in accelerating the material design and discovery with tailored properties. Piezoelectricity ; Ceramic ; Curie temperature ; Materials science ; Computer science ; Transducer ; Artificial intelligence ; Physics ; Acoustics ; Composite material ; Condensed matter physics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Microwave Dielectric Ceramics Synthesis
Authors: A. Giraldo | Zowie Werner | Mehdi Rahimi | Woonghee Lee
Source: Research Square (preprint, 2023-05-29)
Field: Materials Science — Materials Chemistry

Breaking Boundaries: TINTO in POKY for Computer Vision-Based NMR Walking Strategies Abstract Nuclear Magnetic Resonance is a crucial technique for studying biological complexes, as it provides precise structural and dynamic information at the atomic level. However, the process of assigning resonances can be time-consuming and challenging, particularly in cases where peaks overlap, or the data quality is poor. In this paper, we present TINTO (Two and three-dimensional Imaging for NMR sTrip Operation via CV/ML), an advanced semiautomatic toolset for NMR resonance assignment. TINTO comprises two separate tools, each tailored for either two-dimensional or three-dimensional imaging. The toolset utilizes a computer-vision approach and a machine learning approach, specifically Structural Similarity Index (SSIM) and Principal Components Analysis (PCA), to perform visual similarity searches of resonances and quickly locate similar strips, and in that way overcome the challenges associated with peak overlap without requiring peak picking. Our tool offers a user-friendly interface and has the potential to enhance the efficiency and accuracy of NMR resonance assignment, particularly in complex cases. This advancement holds promising implications for furthering our understanding of biological systems at the molecular level. TINTO is pre-installed in the POKY suite, which is available at https://poky.clas.ucdenver.edu. Similarity (geometry) ; Computer science ; Process (computing) ; Interface (matter) ; Suite ; Quality (philosophy) ; Artificial intelligence ; Physics ; Image (mathematics) ; Operating system Machine Learning in Materials Science ; Molecular Biology Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Pablo M. Piaggi | Thomas E. Gartner | Roberto Car | Pablo G. Debenedetti
Source: arXiv (Cornell University) (preprint, 2023-02-16)
Field: Materials Science — Materials Chemistry

Melting curves of ice polymorphs in the vicinity of the liquid-liquid critical point The possible existence of a liquid-liquid critical point in deeply supercooled water has been a subject of debate in part due to the challenges associated with providing definitive experimental evidence. Pioneering work by Mishima and Stanley [Nature 392, 164 (1998) and Phys. Rev. Lett. 85, 334 (2000)] sought to shed light on this problem by studying the melting curves of different ice polymorphs and their metastable continuation in the vicinity of the expected location of the liquid-liquid transition and its associated critical point. Based on the continuous or discontinuous changes in slope of the melting curves, Mishima suggested that the liquid-liquid critical point lies between the melting curves of ice III and ice V. Here, we explore this conjecture using molecular dynamics simulations with a purely-predictive machine learning model based on ab initio quantum-mechanical calculations. We study the melting curves of ices III, IV, V, VI, and XIII using this model and find that the melting lines of all the studied ice polymorphs are supercritical and do not intersect the liquid-liquid transition locus. We also find a pronounced, yet continuous, change in slope of the melting lines upon crossing of the locus of maximum compressibility of the liquid. Finally, we analyze critically the literature in light of our findings, and conclude that the scenario in which melting curves are supercritical is favored by the most recent computational and experimental evidence. Thus, although the preponderance of experimental and computational evidence is consistent with the existence of a second critical point in water, the behavior of the melting lines of ice polymorphs does not provide strong evidence in support of this viewpoint, according to our calculations. Supercooling ; Metastability ; Melting curve analysis ; Critical point (mathematics) ; Melting point ; Thermodynamics ; Supercritical fluid ; Compressibility ; Molecular dynamics ; Liquid crystal ; Chemistry ; Materials science ; Physics ; Condensed matter physics ; Computational chemistry ; Geometry ; Mathematics ; Organic chemistry Material Dynamics and Properties ; Phase Equilibria and Thermodynamics ; Theoretical and Computational Physics
Authors: Tong Cheng | Mingjuan Yang | Hongwei Song | Li‐Min Zheng | Rui Zheng | Minghui Yang
Source: Chinese Journal of Chemical Physics (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

Neural network method for constructing intermolecular potential energy surfaces of van der Waals complexes This study proposes a new approach for constructing intermolecular potential energy surfaces (PESs) of van der Waals (vdW) complexes using neural networks. The descriptors utilized in this neural network model are split into two parts: radial parts representing the intermolecular stretching vibrations between monomers and angular parts describing the relative orientation of these molecules. Specifically, the parity-adapted rotational basis functions used in the bound state calculation are taken as the angular descriptors, which ensure the correct symmetry of the PES. The number of orthogonal rotational basis functions is controlled by the maximum value of the angular momentum quantum number. In addition, the symmetry of monomer molecules is achieved by restricting the quantum number of the rotational basis function. The descriptors for five types of van der Waals complexes, including atom-linear, atom-nonlinear, linear-linear, linear-nonlinear and nonlinear-nonlinear molecules complexes, have been derived in this work. The neural network models with these newly developed descriptors were then applied to construct PESs of two van der Waals complexes, Ar-NaCl and N2-OCS. The root-mean-square error values between the fitted and ab initio energies are found to be 0.11 cm−1 and 0.26 cm−1 for Ar-NaCl and N2-OCS, respectively. These results indicate that this method is accurate and effective for constructing high-precision PESs of vdW complexes. van der Waals force ; Intermolecular force ; Van der Waals surface ; Van der Waals strain ; Hamaker constant ; Energy (signal processing) ; Van der Waals radius ; Artificial neural network ; Chemical physics ; Computational chemistry ; DLVO theory ; Chemistry ; Materials science ; Physics ; Computer science ; Physical chemistry ; Quantum mechanics ; Molecule ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Xueji Wang | Ziyi Yang | Fanglin Bao | Tyler Sentz | Zubin Jacob
Source: arXiv (Cornell University) (preprint, 2023-01-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spinning Metasurface Stack for Spectro-polarimetric Thermal Imaging Spectro-polarimetric imaging in the long-wave infrared (LWIR) region plays a crucial role in applications from night vision and machine perception to trace gas sensing and thermography. However, the current generation of spectro-polarimetric LWIR imagers suffer from limitations in size, spectral resolution and field of view (FOV). While meta-optics-based strategies for spectro-polarimetric imaging have been explored in the visible spectrum, their potential for thermal imaging remains largely unexplored. In this work, we introduce a novel approach for spectro-polarimetric decomposition by combining large-area stacked meta-optical devices with advanced computational imaging algorithms. The co-design of a stack of spinning dispersive metasurfaces along with compressed sensing and dictionary learning algorithms allows simultaneous spectral and polarimetric resolution without the need for bulky filter wheels or interferometers. Our spinning-metasurface-based spectro polarimetric stack is compact (&lt; 10 x 10 x 10 cm), robust, and offers a wide field of view (20.5°). We show that the spectral resolving power of our system substantially enhances performance in machine learning tasks such as material classification, a challenge for conventional panchromatic thermal cameras. Our approach represents a significant advance in the field of thermal imaging for a wide range of applications including heat-assisted detection and ranging (HADAR). Polarimetry ; Spinning ; Optics ; Spectral imaging ; Stack (abstract data type) ; Spectral resolution ; Computer science ; Field of view ; Remote sensing ; Physics ; Materials science ; Geology ; Astronomy Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Random lasers and scattering media