jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wuqing Tao | Yongjie Cui | Qidong Zhao | Xinfeng Wu | Yonghou Xiao
Source: Separation and Purification Technology (article, 2024-11-30)
Field: Materials Science — Materials Chemistry

Prediction of the enhanced performance of Cu(I)-modified porous materials towards CO adsorption by using tree-based machine learning models Adsorption ; Porosity ; Tree (set theory) ; Porous medium ; Chemical engineering ; Materials science ; Computer science ; Chemistry ; Engineering ; Mathematics ; Composite material ; Organic chemistry Catalytic Processes in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal
Authors: Kenta Matsutani | Shusuke Kasamatsu | Takeshi Usuki
Source: The Journal of Chemical Physics (article, 2024-11-22)
Field: Materials Science — Ceramics and Composites

Comparison of intermediate-range order in GeO2 glass: Molecular dynamics using machine-learning interatomic potential vs reverse Monte Carlo fitting to experimental data The short-range order and intermediate-range order in GeO2 glass are investigated by molecular dynamics using machine-learning interatomic potential trained on ab initio calculation data and compared with the reverse Monte Carlo fitting of neutron diffraction data. To characterize the structural differences in each model, the total/partial structure factors, coordination number, ring size and shape distributions, and persistent homology analysis were performed. These results show that although the two approaches yield similar two-body correlations, they can lead to three-dimensional models with different short- and intermediate-range ordering. A clear difference was observed especially in the ring distributions; RMC models exhibit a broad distribution in the ring size distribution, while neural network potential molecular dynamics yield much narrower ring distributions. This confirms that the density functional approximation in the ab initio calculations determines the preferred network assembly more strictly than RMC with simple coordination constraints even when using multiple diffraction data. Reverse Monte Carlo ; Molecular dynamics ; Monte Carlo method ; Statistical physics ; Neutron diffraction ; Ab initio ; Diffraction ; Ring (chemistry) ; Range (aeronautics) ; Yield (engineering) ; Materials science ; Molecular physics ; Computational chemistry ; Physics ; Chemistry ; Thermodynamics ; Mathematics ; Optics ; Statistics ; Quantum mechanics Glass properties and applications ; Material Dynamics and Properties ; Phase-change materials and chalcogenides
Authors: Tomoya Shiota | Kenji Ishihara | Tuan Do | Toshio Mori | Wataru Mizukami
Source: arXiv (Cornell University) (preprint, 2024-12-17)
Field: Materials Science — Materials Chemistry

Taming Multi-Domain, -Fidelity Data: Towards Foundation Models for Atomistic Scale Simulations Machine learning interatomic potentials (MLIPs) are changing atomistic simulations in the field of chemistry and materials science. However, constructing a single universal MLIP that can accurately model molecular and crystalline systems remains challenging. A central obstacle is the integration of diverse datasets generated under different computational conditions. We present Total Energy Alignment (TEA), which is an approach that enables the seamless integration of heterogeneous quantum chemical datasets without redundant calculations. Using TEA, we trained MACE-Osaka24, the first open-source MLIP model based on a unified dataset covering molecular and crystalline systems. This universal model displays strong performances across diverse chemical systems, exhibiting similar or improved accuracies in predicting organic reaction barriers compared to those of specialized models, while effectively maintaining state-of-the-art accuracies for inorganic systems. These advancements pave the way for accelerated discoveries in the fields of chemistry and materials science via genuine foundation models for chemistry. Foundation (evidence) ; Scale (ratio) ; Fidelity ; Domain (mathematical analysis) ; Computer science ; Data science ; Statistical physics ; Political science ; Physics ; Mathematics ; Geography ; Cartography Machine Learning in Materials Science ; Scientific Computing and Data Management ; Gas Dynamics and Kinetic Theory
Authors: N. I. Shvetsov-Shilovski | Manfred Lein
Source: Journal of Physics B Atomic Molecular and Optical Physics (article, 2024-02-28)
Field: Chemistry — Spectroscopy

Convolutional neural network for retrieval of the time-dependent bond length in a molecule from photoelectron momentum distributions Abstract We apply deep learning for retrieval of the time-dependent bond length in the dissociating two-dimensional H <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msubsup> <mml:mi/> <mml:mn>2</mml:mn> <mml:mrow> <mml:mo>+</mml:mo> </mml:mrow> </mml:msubsup> </mml:math> molecule using photoelectron momentum distributions. We consider a pump-probe scheme and calculate electron momentum distributions from strong-field ionization by treating the motion of the nuclei classically, semiclassically or quantum mechanically. A convolutional neural network trained on momentum distributions obtained at fixed internuclear distances retrieves the time-varying bond length with an absolute error of 0.2–0.3 a.u. Convolutional neural network ; Momentum (technical analysis) ; Molecule ; Bond ; Computer science ; Physics ; Artificial intelligence ; Quantum mechanics ; Business Mass Spectrometry Techniques and Applications ; Laser-Matter Interactions and Applications ; Advanced Chemical Physics Studies
Authors: Cosimo Ricci | Ágata Gadaleta | Annamaria Gerardino | Angelo Didonna | Giuseppe Ferrara | Francesca Romana Bertani
Source: CABI Agriculture and Bioscience (article, 2024-04-14)
Field: Chemistry — Analytical Chemistry

Portable optical spectroscopy and machine learning techniques for quantification of the biochemical content of raw food materials Abstract Background Accuracy in determining food authenticity, possible contamination, content analysis, and even geographical origin is of considerable scientific and economic value. The aim of this study is to facilitate quantitative evaluation of protein content in the seeds of cereals ( Triticum turgidum var. durum and Tritordeum genotypes) and ripening pomegranate fruits (Wonderful cultivar). Methods Two species of wheat were evaluated in this study: durum wheat, Triticum turgidum var. durum , and Tritordeum ( durum wheat × wild barley ) together with pomegranate fruits of the variety Wonderful. Two different portable Near InfraRed (NIR) spectrometers have been used: a prototype developed in the PhasmaFood project and the commercial SCiO™ molecular sensor. Results Considering the specific samples, the obtained results of the classification models indicate a validation mean absolute error of 0.8% (percentage of total protein content in dry matter) for two species of wheat using Convolutional Neural Network following normalization procedures and 0.32% using Partial Least Square (PLS) analysis applied to Tritordeum samples; visible reflectance spectra have been used to discriminate the two cereal species. A Root Mean Square Error (RMSE) of 1.25 was obtained for the determination of total soluble solids (TSS) over a 2-year period for pomegranate fresh fruits of Wonderful cultivar, which is commonly harvested with TSS values of 16–17. Conclusions The application of portable sensors using NIR spectroscopy can be a valid and rapid alternative to the use of destructive laboratory techniques for the assessment of protein content in intact wheat seeds and ripeness grade (TSS) in intact pomegranates. Ripeness ; Cultivar ; Triticum turgidum ; Dry matter ; Food science ; Mathematics ; Horticulture ; Biology ; Agronomy ; Ripening Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Jared Averitt | Sajedeh Pourianejad | Olubunmi O. Ayodele | Kirby Schmidt | Anthony Trofe | Joseph M. Starobin | Tetyana Ignatova
Source: Carbon (article, 2024-06-21)
Field: Materials Science — Materials Chemistry

Efficient high-throughput method utilizing neural network potentials to calculate interaction energies, validated by clean transfer experiment of CVD graphene with polymer mixtures Graphene ; Materials science ; Polymer ; Metastability ; Chemical physics ; Composite material ; Nanotechnology ; Chemistry ; Organic chemistry Graphene research and applications ; Advanced Sensor and Energy Harvesting Materials ; Radiation Effects in Electronics
Authors: Kunning Tang | Ying Da Wang | Mohamed Regaieg | Georgy Borisochev | Isabelle Jolivet | Ryan T. Armstrong | Peyman Mostaghimi
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Surfaces, Coatings and Films

From Surface to Volume: Deep Learning-Driven 3D Realisation of Super-Large 2D SEM Images for Material Characterisation Realisation ; Volume (thermodynamics) ; Surface (topology) ; Deep learning ; Materials science ; Artificial intelligence ; Computer science ; Geometry ; Mathematics ; Physics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Yuxi Zhu | Bo Xu | C. N. Han | Qiang Ma | Zhenqian Chen
Source: Surfaces and Interfaces (article, 2024-12-07)
Field: Materials Science — Materials Chemistry

Potential correlation between thermal transport and catalytic performance in single metal atom catalysts: A machine-learning interatomic potential and density functional theory study Materials science ; Density functional theory ; Atom (system on chip) ; Catalysis ; Thermal ; Interatomic potential ; Metal ; Chemical physics ; Molecular dynamics ; Thermodynamics ; Computational chemistry ; Metallurgy ; Physics ; Computer science Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Indra Priyadarsini | Vidushi Sharma | Seiji Takeda | Akihiro Kishimoto | Lisa Hamada | Hajime Shinohara
Source: arXiv (Cornell University) (preprint, 2024-06-28)
Field: Materials Science — Materials Chemistry

Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model Development of efficient and high-performing electrolytes is crucial for advancing energy storage technologies, particularly in batteries. Predicting the performance of battery electrolytes rely on complex interactions between the individual constituents. Consequently, a strategy that adeptly captures these relationships and forms a robust representation of the formulation is essential for integrating with machine learning models to predict properties accurately. In this paper, we introduce a novel approach leveraging a transformer-based molecular representation model to effectively and efficiently capture the representation of electrolyte formulations. The performance of the proposed approach is evaluated on two battery property prediction tasks and the results show superior performance compared to the state-of-the-art methods. Transformer ; Electrolyte ; Representation (politics) ; Computer science ; Voltage ; Chemistry ; Engineering ; Electrical engineering ; Electrode Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Various Chemistry Research Topics
Authors: Vipin K. E | Prahallad Padhan
Source: arXiv (Cornell University) (preprint, 2024-08-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unlocking Thermoelectric Potential: A Machine Learning Stacking Approach for Half Heusler Alloys Thermoelectric properties of Half Heusler alloys are predicted by adopting an ensemble modelling approach, specifically the stacking model integrated using Random Forest and XGBoost scheme. Leveraging a diverse dataset encompassing thermal conductivity, the Seebeck coefficient, electrical conductivity, and the figure of merit (ZT), the study demonstrates superior predictive performance of the stacking Model, outperforming individual base models with high R2 values. Key features such as temperature, mean Covalent Radius, and average deviation of the Gibbs energy per atom emerge as critical influencers, highlighting their pivotal roles in optimizing thermoelectric behavior. The unification of Random Forest and XGBoost in the stacking model effectively captures nuanced relationships, offering a holistic understanding of thermoelectric performance in Half Heusler alloys. This work advances predictive modelling in thermoelectricity and provides valuable insights for strategic material design, paving the way for enhanced efficiency and performance in thermoelectric applications. The ensemble modelling framework, coupled with insightful feature selection and meticulous engineering, establishes a robust foundation for future research in pursuing high-performance thermoelectric materials. Stacking ; Thermoelectric effect ; Materials science ; Condensed matter physics ; Engineering physics ; Physics ; Thermodynamics ; Nuclear magnetic resonance Heusler alloys: electronic and magnetic properties ; Advanced Thermoelectric Materials and Devices
Authors: Hyo Jeong Seo | Jun Young Kim | Jun-Young Yang | ChaeWon Mun | Seunghun Lee | Eun Hye Koh | Vo Thi Nhat Linh | Mijeong Kang | Ho Sang Jung
Source: Advanced Sensor Research (article, 2024-06-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

3D Spiky Needle‐Clustered Ag@Au Plasmonic Nanoarchitecture for Highly Sensitive and Machine Learning‐Assisted Detection of Multiple Hazardous Molecules Abstract To develop a field applicable hazardous molecular detection system, highly sensitive and multiplex detection capability is required for practical utilization. Here, a paper‐based 3D spiky needle‐clustered gold grown on silver (Ag@Au) plasmonic nanoarchitecture (3D‐SNCP) is fabricated through whole solution process. The developed substrate is investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X‐ray diffraction (XRD) to find out morphological development mechanism. Also, finite‐domain time difference (FDTD) simulation is conducted for the observation of electromagnetic field (E‐field) distribution. After surface‐enhanced Raman scattering (SERS) characterization, the 3D‐SNCP is utilized for ultra‐sensitive and multiplex hazardous molecular detection, such as bipyridine pesticides including paraquat (PQ), diquat (DQ), and difenzoquat (DIF). Then, each of pesticide molecular Raman signals are trained by a machine learning technique of multinomial logistic regression (MLR), followed by multiplex classificationf of blank, PQ, DQ, DIF, and four mixture types of each pesticide, spiked in real agricultural matrix. The developed 3D‐SNCP substrate combined with the machine learning method successfully verifies the multiple pesticides and it is expected to be applied for various hazardous molecular detection in much complicated matrix environments. Materials science ; Raman scattering ; Nanotechnology ; Plasmon ; Substrate (aquarium) ; Raman spectroscopy ; Analytical Chemistry (journal) ; Optoelectronics ; Optics ; Chemistry ; Chromatography ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Paraquat toxicity studies and treatments
Authors: Seung-Chan Lee | Song-Bae Kim
Source: International Journal of Biological Macromolecules (article, 2024-08-28)
Field: Chemistry — Analytical Chemistry

Synthesis and characterization of Fe(III)-doped beta-cyclodextrin-grafted chitosan cryogel beads for adsorption of diclofenac in aqueous solutions: Adsorption experiments and deep-learning modeling Diclofenac (DCF) is frequently detected in aquatic environments, emphasizing the critical need for its efficient removal globally. Here, we present the synthesis of Fe(III)-doped β-CD-grafted chitosan (Fe/β-CD@CS) cryogel beads designed for adsorbing DCF in aqueous solutions. The beads exhibited an average size of 2.94 ± 0.66 mm and a point of zero charge of 8.03. Adsorption experiments demonstrated that the Langmuir kinetic model provided the most accurate description of the kinetic data, while the Redlich-Peterson isotherm offered the best fit for the equilibrium data. The beads showcased a theoretical maximum adsorption capacity of 712.3 mg/g for DCF, with the adsorption process being identified as exothermic. DCF adsorption on the beads was attributed to hydrogen bonding, metal cation-π interactions, and electrostatic interactions. Reusability tests exhibited that the beads could be regenerated using 0.1 M NaOH. To perform deep learning modeling, adsorption experiments (n = 17), designed utilizing central composite design (CCD), were conducted in duplicate. The CCD framework incorporated input variables such as initial DCF concentration, adsorbent dosage, and solution pH, while the output variable was the DCF removal rate. Utilizing the adsorption data, an artificial neural network (ANN) model was constructed with a topology of 3: 7:10:1, featuring 3 input variables, 7 neurons in the first hidden layer, 10 neurons in the second layer, and 1 output variable. Employing the ANN model data, 3-D response surface plots were generated to elucidate the relationship between input variables and DCF removal rate. Additional adsorption tests were conducted to evaluate the developed ANN model, affirming its reliable predictability for the DCF removal rate. Analysis of the relative importance of the input variables revealed the following order of importance: solution pH (100 %) > adsorbent dosage (75.2 %) > initial DCF concentration (57.7 %). Adsorption ; Aqueous solution ; Langmuir adsorption model ; Chemistry ; Chemical engineering ; Materials science ; Organic chemistry Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Analytical Chemistry and Sensors
Authors: Zhen Xu | Siting Hou | Zhimo Wang | Changjian Xie
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Neural network potentials facilitating accurate complex scaling for molecular resonances: from a model to high dimensional realistic systems Here we propose a neural network based complex scaling (NN-CS) method for computing the complex eigenvalues (<i>E</i><sub>r</sub>-<i>iΓ</i>/2) of molecular resonances, in which the CS of the potential part in the non-Hermitian Hamiltonian is effectively achieved by NNs. Taking a two-dimensional (2D) diabatic model including two states coupled by the conical intersection for example, the NN-CS method is shown to reproduce the eigenvalues of the resonance states quite well. Subsequently, this NN-CS method with a 2D Hamiltonian model is utilized to compute the vibronic resonances in the <sup>1</sup>nσ*-mediated photodissociation of thioanisole based on a new NN diabatic potential energy matrix. The calculated lifetimes of the vibronic resonances are found to be in good agreement with other theoretical results and available experimental data. Finally, the NN-CS method is applied to treat a much more challenging system, namely, the resonances in the six-dimensional (6D) photodissociation continuum of NH<sub>3</sub>, due to its high dimensionalities and all three dissociative coordinates needing to be scaled in the complex scaling of the potential part. Again, the calculated energy positions and widths of the 6D resonances by the NN-CS method agree well with other theoretical results. Our calculations show that the NN-CS method is able to accurately treat the vibronic resonances involving multiple coupled electronic states and resonances in high dimensional realistic systems. Scaling ; Complex system ; Eigenvalues and eigenvectors ; Artificial neural network ; Complex network ; Computer science ; Statistical physics ; Biological system ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Biology Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Shunsaku Yasumura | Taisetsu Kato | Yucheng Qian | Takashi Toyao | Ken‐ichi Shimizu
Source: The Journal of Physical Chemistry C (article, 2024-07-25)
Field: Materials Science — Materials Chemistry

Mechanistic Study on NH<sub>3</sub> Decomposition over Cu-Exchanged CHA Zeolites Using Automated Reaction Route Mapping Combined with Neural Network Potential and Density Functional Theory Calculations The reaction mechanism for the decomposition of NH3 into N2 by Cu-exchanged CHA zeolite (Cu-CHA) was explored by using a combination of neural network potential (NNP) and automated reaction route mapping. According to the revealed reaction route, the formation of N2H4, which thermally decomposes into N2 and H2, is not assisted by both isolated Cu2+ cation and [Cu(OH)]+ species because of their high energy barriers (>300 kJ/mol). In contrast, [Cu(OH)]+ species easily generates [Cu(NH2)]+ species at a much lower energy barrier of 70 kJ/mol. Then, reaction route mapping was performed using two [Cu(NH2)]+ species at Al sites close to each other, resulting in a lower activation barrier than that obtained using only one [Cu(NH2)]+ species to generate N2H4 intermediates (Ea = 119 kJ/mol). In situ X-ray absorption spectroscopy (XAS) experimentally verified that the proximity of the Cu species in zeolite is crucial in suppressing the decomposition of NH3. Density functional theory ; Decomposition ; Artificial neural network ; Computational chemistry ; Chemistry ; Biological system ; Materials science ; Physical chemistry ; Computer science ; Artificial intelligence ; Organic chemistry Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Laraib Kiran | Muhammad Hammad Ijaz | Z.I. Zaki | Mohamed E. Khalifa | Zunaira Shafiq | Zeeshan Zubair | Nimra Sultan | Muhammad Ramzan Saeed Ashraf Janjua
Source: Journal of Solid State Chemistry (article, 2024-12-24)
Field: Materials Science — Materials Chemistry

Data driven design of dyes with high dielectric constant for efficient optoelectronics Dielectric ; High-κ dielectric ; Optoelectronics ; Materials science ; Constant (computer programming) ; Nanotechnology ; Chemistry ; Computer science Machine Learning in Materials Science ; Photochromic and Fluorescence Chemistry ; Photopolymerization techniques and applications