jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shih‐Chieh Hsu
Source:  (article, 2023-03-16)
Field: Chemistry — Analytical Chemistry

Research on the detection of dithiocarbamate pesticides by using surface-enhanced Raman spectroscopy (Conference Presentation) In this study, the SERS technique was used to detect Thiram, Ferbam and Ziram in Dithiocarbamate by electrochemical cyclic voltammetry. The SERS substrate was prepared first. It was found that the best SERS effect was obtained when using 1 M sulfuric acid as the electrolyte and scanning for six times. The growth of nanoparticles is very uniform, and the SERS substrate using this condition can detect 5 ppm of Thiram, 10 ppm of Ferbam, and 5 ppm of Ziram, respectively. Since it can be seen that the Raman spectra of the Dithiocarbamate pesticides are similar, in this study, with the assistance of artificial intelligence, the Raman spectra combined with artificial intelligence and machine learning algorithms were analyzed, and the prediction accuracy reached 96.7% respectively. With a prediction accuracy of 95%, it proves the feasibility of combining SERS substrates with artificial intelligence, and develops a fast, accurate and inexpensive pesticide detection system. Dithiocarbamate ; Thiram ; Raman spectroscopy ; Surface-enhanced Raman spectroscopy ; Substrate (aquarium) ; Sulfuric acid ; Pesticide ; Electrolyte ; Materials science ; Chemistry ; Nanotechnology ; Analytical Chemistry (journal) ; Chromatography ; Inorganic chemistry ; Electrode ; Organic chemistry ; Raman scattering ; Optics Spectroscopy and Chemometric Analyses ; Melamine detection and toxicity ; Water Quality Monitoring and Analysis
Authors: Masato Kato | Shinya Nakamichi | Shun Hirooka | Masashi Watanabe | Tatsutoshi Murakami | Katsunori Ishii
Source: Transactions of the Atomic Energy Society of Japan (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Sintering Density Prediction Model for MOX Fuel Pellet Uranium and plutonium mixed oxide (MOX) pellets used as fast reactor fuels have been produced from several raw materials by mechanical blending through various processes, such as ball milling, additive blending, granulation, pressing, and sintering. It is essential to control the pellet density, which is one of the important fuel specifications, but it is difficult to understand the relationships among many parameters in the production of MOX pellets. The database for the production of MOX pellets was prepared from production results in Japan, and input data of eighteen types were chosen from the production process to form a data set. A machine learning model for predicting the sintered density of MOX pellets was derived using a gradient boosting regressor and could represent the sintered density of MOX pellets with R2 = 0.996 as a parameter that affects production conditions, such as the type of raw material used and sintering temperature. Pellets ; MOX fuel ; Pellet ; Sintering ; Materials science ; Raw material ; Granulation ; Plutonium ; Metallurgy ; Nuclear engineering ; Process engineering ; Composite material ; Uranium ; Radiochemistry ; Engineering ; Chemistry Nuclear Materials and Properties ; Iron and Steelmaking Processes ; Nuclear reactor physics and engineering
Authors: Suhani Thapliyal | Tejaswini Singh | Ankita Nainwal | Garima Sharma
Source:  (article, 2023-10-16)
Field: Chemistry — Analytical Chemistry

Multiclass Classification of Cereals Using Artificial Neural Network A diverse variety of finer features of objects may now be detected, processed, analyzed, and displayed by machine learning and imaging systems from their digital photographs in real-time thanks to recent advancements in hardware and software. The food business must keep up with population expansion by raising output and raising product standards. Cereals, which are at the bottom of the food industry's pyramid and are produced for both human and animal use, are directly or indirectly tied to the rise in food production. The cornerstone of human civilization and an integral part of daily living are cereal grains. Food, energy, and raw materials for industry have all become insatiable human needs in recent years, and agricultural production has grown significantly as a result. Also, cereal grains are an essential component of human diets and significant raw materials for both domestic and international trade. Automatic segregation of cereal grains becomes significant with time and advent of new technologies. Manual efforts not only lead to errors but are also less cost efficient. This study shows the potential of machine learning techniques for cereal recognition using computer vision, and it also discusses some problems that need to be fixed or looked into further in order to hasten the use of learning algorithms. Artificial neural network ; Artificial intelligence ; Computer science ; Multiclass classification ; Machine learning ; Pattern recognition (psychology) ; Support vector machine Spectroscopy and Chemometric Analyses
Authors: Rafat Mohammadi | Behrad Karimi | John Kieffer | Daniel Hashemi
Source: arXiv (Cornell University) (preprint, 2023-11-17)
Field: Materials Science — Materials Chemistry

A molecular dynamics simulation study of thermal conductivity of plumbene We investigate the thermal conductivity of plumbene using molecular dynamics simulations, overcoming existing limitations by optimizing the parameters of Tersoff and Stillinger-Weber potentials via artificial neural networks. Our findings indicate that at room temperature, the thermal conductivity of a 1050 A* 300 A plumbene sheet is approximately 8 W/m.K, significantly lower (23%) than that of bulk lead. Our analysis elucidates that thermal conductivity is enhanced by increased sample length, while it is reduced by temperature. Moreover, plumbene samples with zigzag edges display superior thermal conductivity compared to those with armchair edges. In addition, the thermal conductivity of plumbene exhibits an increase at low tensile strains, whereas it decreases as the strains become larger. This investigation provides crucial insights into the thermal conductivity behavior of plumbene under varying conditions. Thermal conductivity ; Zigzag ; Molecular dynamics ; Materials science ; Conductivity ; Thermal ; Composite material ; Thermodynamics ; Chemistry ; Computational chemistry ; Physics ; Physical chemistry ; Mathematics ; Geometry Thermal properties of materials ; Material Dynamics and Properties ; Earthquake Detection and Analysis
Authors: Lara Koehler | Pierre Ronceray | Martin Lenz
Source: arXiv (Cornell University) (preprint, 2023-12-11)
Field: Materials Science — Materials Chemistry

How do particles with complex interactions self-assemble? In living cells, proteins self-assemble into large functional structures based on specific interactions between molecularly complex patches. Due to this complexity, protein self-assembly results from a competition between a large number of distinct interaction energies, of the order of one per pair of patches. Current self-assembly models however typically ignore this aspect, and the principles by which it determines the large-scale structure of protein assemblies are largely unknown. Here, we use Monte-Carlo simulations and machine learning to start to unravel these principles. We observe that despite widespread geometrical frustration, aggregates of particles with complex interactions fall within only a few categories that often display high degrees of spatial order, including crystals, fibers, and oligomers. We then successfully identify the most relevant aspect of the interaction complexity in predicting these outcomes, namely the particles' ability to form periodic structures. Our results provide a first extensive characterization of the rich design space associated with identical particles with complex interactions, and could inspire engineered self-assembling nanoobjects as well as help understand the emergence of robust functional protein structures. Self-assembly ; Frustration ; Monte Carlo method ; Nanotechnology ; Scale (ratio) ; Computer science ; Statistical physics ; Biological system ; Physics ; Materials science ; Biology ; Mathematics Pickering emulsions and particle stabilization ; Supramolecular Self-Assembly in Materials ; Bacteriophages and microbial interactions
Authors: William Z. Van Benschoten | Laura Weiler | Gabriel J. Smith | Songhang Man | Taylor DeMello | James J. Shepherd
Source: arXiv (Cornell University) (preprint, 2023-05-11)
Field: Materials Science — Materials Chemistry

Electronic specific heat capacities and entropies from density matrix quantum Monte Carlo using Gaussian process regression to find gradients of noisy data We present a machine learning approach to calculating electronic specific heat capacities for a variety of benchmark molecular systems. Our models are based on data from density matrix quantum Monte Carlo, which is a stochastic method that can calculate the electronic energy at finite temperature. As these energies typically have noise, numerical derivatives of the energy can be challenging to find reliably. In order to circumvent this problem, we use Gaussian process regression to model the energy and use analytical derivatives to produce the specific heat capacity. From there, we also calculate the entropy by numerical integration. We compare our results to cubic splines and finite differences in a variety of molecules whose Hamiltonians can be diagonalized exactly with full configuration interaction. We finally apply this method to look at larger molecules where exact diagonalization is not possible and make comparisons with more approximate ways to calculate the specific heat capacity and entropy. Statistical physics ; Monte Carlo method ; Quantum Monte Carlo ; Gaussian ; Entropy (arrow of time) ; Heat capacity ; Gaussian process ; Applied mathematics ; Kriging ; Density matrix ; Mathematics ; Quantum ; Mathematical optimization ; Computer science ; Physics ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Gaussian Processes and Bayesian Inference
Authors: Malin Luking | David van der Spoel | Johan Elf | Gareth A. Tribello
Source: arXiv (Cornell University) (preprint, 2023-02-15)
Field: Chemistry — Spectroscopy

Can molecular dynamics be used to simulate biomolecular recognition? There are many problems in biochemistry that are difficult to study experimentally. Simulation methods are appealing due to direct availability of atomic coordinates as a function of time. However, direct molecular simulations are challenged by the size of systems and the time scales needed to describe relevant motions. In theory, enhanced sampling algorithms can help to overcome some of the limitations of molecular simulations. Here, we discuss a problem in biochemistry that offers a significant challenge for enhanced sampling methods and that could, therefore, serve as a benchmark for comparing approaches that use machine learning to find suitable collective variables. More in particular, we study the transitions LacI undergoes upon moving between being non-specifically and specifically bound to DNA. It is found that many degrees of freedom change during this transition and that the transition does not occur reversibly in simulations if only a subset of these degrees of freedom are biased. We also explain why this problem is so important to biologists and the transformative impact that a simulation of it would have on the understanding of DNA regulation. Molecular dynamics ; Degrees of freedom (physics and chemistry) ; Benchmark (surveying) ; Computer science ; Transformative learning ; Function (biology) ; Sampling (signal processing) ; Transition (genetics) ; Statistical physics ; GRASP ; Dynamics (music) ; Biological system ; Chemistry ; Physics ; Computational chemistry ; Biology Mass Spectrometry Techniques and Applications ; RNA and protein synthesis mechanisms ; Protein Structure and Dynamics
Authors: Ryo Fukasawa | Toru Asahi | Takuya Taniguchi
Source: ChemRxiv (preprint, 2023-07-13)
Field: Materials Science — Materials Chemistry

Effectiveness and limitation of the performance prediction of perovskite solar cells by process informatics Perovskite solar cells have garnered significant interest owing to their low fabrication costs and comparatively high power conversion efficiency (PCE). The performance of these cells is influenced not solely by material composition but also by experimental processes, rendering PCE prediction a challenging endeavor. It is also crucial to quantitatively assess the impact of process conditions on performance. In this work, we developed machine learning regression incorporating process information derived from an open-access perovskite database. Our analysis showed that the split of process information influenced the prediction accuracy and clarified the relative contribution of each process condition. The limitation of performance prediction was also prone to data degeneracy. The insights gained from this work may facilitate the data-driven design of innovative perovskite solar cells. Rendering (computer graphics) ; Process (computing) ; Informatics ; Perovskite (structure) ; Energy conversion efficiency ; Degeneracy (biology) ; Computer science ; Materials informatics ; Fabrication ; Artificial intelligence ; Materials science ; Data mining ; Machine learning ; Health informatics ; Engineering ; Optoelectronics Machine Learning in Materials Science ; Machine Learning and ELM ; Chalcogenide Semiconductor Thin Films
Authors: Daniel N. Blaschke | Mashroor S. Nitol
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

LEAD - LEArning Damage LEAD is a suite of scripts to enable rapid estimation of model parameters for damage models for ductile materials using machine learning. In its current version, the only supported damage model is TEPLA, though others are planned. The data we train on are comprised of free surface velocity data as well as porosity data. Some details about our method can be found in the preprint LA-UR-23-20468 (arxiv.org/abs/2301.07790) Lead (geology) ; Environmental science ; Geology ; Paleontology High voltage insulation and dielectric phenomena ; Real-time simulation and control systems
Authors: Igor D. Borges | Antônio S. N. Aguiar | Leonardo R. Almeida | Gerardo Aguirre | Hamilton B. Napolitano
Source: Acta Crystallographica Section A Foundations and Advances (article, 2023-07-07)
Field: Chemistry — Organic Chemistry

Structural insights into dibrominated 2,3-benzaldehyde compounds with potential as antioxidant agents in biodiesel Biodiesel is a promising renewable fuel that has the potential to reduce negative aspects associated with fossil fuels, such as pollution, greenhouse gas emissions and resource depletion.However, biodiesel is more prone to oxidation, limiting its shelf life and effectiveness as a fuel.To address this issue, three variations of dibrominated 2,3-dimethoxybenzaldehyde (DMB) 1,2 have been developed to reduce the rate of oxidation and preserve the physical-chemical properties of biodiesel.This study explores 4,5-bromo-2,3-dimethoxybenzaldehyde (4,5DMB), 4,6-bromo-2,3-dimethoxybenzaldehyde (4,6DMB) and 5,6-bromo-2,3dimethoxybenzaldehyde (5,6DMB) potential as additive to improve the oxidative stability of biodiesel 3 .The research involves the synthesis, crystallization, and comprehensive structural characterization of DMB compounds.The 4,5DMB (C 9 H 8 Br 2 O 3 -I2/a; R 1 = 0.0475; Goof = 1.049), the 4,6DMB (C 9 H 8 Br 2 O 3 -P2 1 /c; R 1 = 0.0318; Goof = 1.036) and 5,6DMB (C 9 H 8 Br 2 O 3 -P2 1 /c; R 1 = 0.0391; Goof = 1.023).Theoretical calculations by the density functional theory (DFT), at M06-2X/6-311++G(d,p) level of theory were conducted to explore the dibrominated compound electronic properties, as well as the supramolecular arrangements that may contribute to their antioxidant potential.To identify the dibrominated compound reactive sites were used the Fukui functions 4 .Finally, machine learning models were implemented using pySiRC to estimate kinetic parameters of the oxidation process.The -OH radical reaction rate constant from an aqueous model was used to demonstrate that the dibrominated 2,3benzaldehyde compounds effectively reduce the rate of oxidation and improve the stability of biodiesel. Benzaldehyde ; Biodiesel ; Antioxidant ; Organic chemistry ; Chemistry ; Materials science ; Catalysis Free Radicals and Antioxidants
Authors: Xiaoyu Yang | Zhichun Bi | Chenghui Yin | Hui Huang | Yongxin Li
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

A Novel Hybrid Sensor Array Based On The Polyphenol Oxidase And Its Nanozymes Combined With The Machine Learning Based Dual Output Model to Identify Tea Polyphenols and Chinese Teas Polyphenol ; Dual (grammatical number) ; Polyphenol oxidase ; Chemistry ; Computer science ; Biological system ; Artificial intelligence ; Machine learning ; Biology ; Biochemistry ; Antioxidant ; Enzyme ; Art Advanced Nanomaterials in Catalysis ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Lorenzo Gigli | Alexander Goscinski | Michele Ceriotti | Gareth A. Tribello
Source: arXiv (Cornell University) (preprint, 2023-10-19)
Field: Materials Science — Materials Chemistry

Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of Density Functional Theory (DFT). However, running large simulation cells with DFT is computationally demanding, while simulations of small cells are often plagued with non-physical effects that are a consequence of the system's finite size. To avoid these finite-size effects one is thus often forced to use empirical models that describe the physics of the material in terms of effective interaction terms, that are fitted using the results from DFT. In this study we use a machine-learning (ML) potential trained on DFT, in combination with accelerated sampling techniques, to converge the thermodynamic properties of Barium Titanate (BTO) with first-principles accuracy and a full atomistic description. Our results indicate that the predicted Curie temperature depends strongly on the choice of DFT functional and system size, because of emergent long-range directional correlations in the local dipole fluctuations. Our findings demonstrate how the combination of ML models and traditional bottom-up modeling allow one to investigate emergent phenomena with the accuracy of first-principles calculations over the large size and time scales afforded by empirical models. Density functional theory ; Barium titanate ; Dipole ; Ferroelectricity ; Statistical physics ; Phase transition ; Curie temperature ; Sampling (signal processing) ; Materials science ; Computer science ; Physics ; Condensed matter physics ; Quantum mechanics ; Optics Machine Learning in Materials Science ; Theoretical and Computational Physics ; Neural Networks and Applications
Authors: Xiaoyu Yang | Zhichun Bi | Chenghui Yin | Hui Huang | Yongxin Li
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

A Novel Hybrid Sensor Array Based on the Polyphenol Oxidase and its Nanozymes Combined with the Machine Learning Based Dual Output Model to Identify Tea Polyphenols and Chinese Teas Polyphenol ; Dual (grammatical number) ; Chemistry ; Polyphenol oxidase ; Biological system ; Computer science ; Biochemistry ; Biology ; Antioxidant ; Enzyme ; Art Advanced Nanomaterials in Catalysis ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Pablo S. Cornaglia | Matías Núñez | D. J. García
Source: arXiv (Cornell University) (preprint, 2023-07-29)
Field: Materials Science — Materials Chemistry

Unveiling Exotic Magnetic Phases in Fibonacci Quasicrystalline Stacking of Ferromagnetic Layers through Machine Learning In this study, we conduct a comprehensive theoretical analysis of a Fibonacci quasicrystalline stacking of ferromagnetic layers, potentially realizable using van der Waals magnetic materials. We construct a model of this magnetic heterostructure, which includes up to second neighbor interlayer magnetic interactions, that displays a complex relationship between geometric frustration and magnetic order in this quasicrystalline system. To navigate the parameter space and identify distinct magnetic phases, we employ a machine learning approach, which proves to be a powerful tool in revealing the complex magnetic behavior of this system. We offer a thorough description of the magnetic phase diagram as a function of the model parameters. Notably, we discover among other collinear and non-collinear phases, a unique ferromagnetic alternating helical phase. In this non-collinear quasiperiodic ferromagnetic configuration the magnetization decreases logarithmically with the stack height. Fibonacci number ; Condensed matter physics ; Ferromagnetism ; Stacking ; Magnetization ; Phase diagram ; Quasicrystal ; Quasiperiodic function ; Magnetic domain ; Superlattice ; Materials science ; Physics ; Phase (matter) ; Magnetic field ; Mathematics ; Nuclear magnetic resonance ; Quantum mechanics Quasicrystal Structures and Properties ; Theoretical and Computational Physics
Authors: Nianze Tao | Hiromi Morimoto | Stefano Leoni
Source: arXiv (Cornell University) (preprint, 2023-08-21)
Field: Materials Science — Materials Chemistry

Comprehensive Molecular Representation from Equivariant Transformer The tradeoff between precision and performance in molecular simulations can nowadays be addressed by machine-learned force fields (MLFF), which combine \textit{ab initio} accuracy with force field numerical efficiency. Different from conventional force fields however, incorporating relevant electronic degrees of freedom into MLFFs becomes important. Here, we implement an equivariant transformer that embeds molecular net charge and spin state without additional neural network parameters. The model trained on a singlet/triplet non-correlated \ce{CH2} dataset can identify different spin states and shows state-of-the-art extrapolation capability. Therein, self-attention sensibly captures non-local effects, which, as we show, can be finely tuned over the network hyper-parameters. We indeed found that Softmax activation functions utilised in the self-attention mechanism of graph networks outperformed ReLU-like functions in prediction accuracy. Increasing the attention temperature from $τ= \sqrt{d}$ to $\sqrt{2d}$ further improved the extrapolation capability, indicating a weighty role of nonlocality. Additionally, a weight initialisation method was purposed that sensibly accelerated the training process. Softmax function ; Extrapolation ; Transformer ; Equivariant map ; Activation function ; Computer science ; Superposition principle ; Singlet state ; Artificial neural network ; Algorithm ; Artificial intelligence ; Physics ; Theoretical computer science ; Mathematics ; Voltage ; Quantum mechanics ; Pure mathematics ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics