jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jordan Peper | John H. Kalivas
Source: Applied Spectroscopy (article, 2024-06-05)
Field: Chemistry — Analytical Chemistry

Local Modeling by Adapting Source Calibration Models to Analyte Shifted Target Domain Samples Without Reference Values Spectral multivariate calibration aims to derive models characterizing mathematical relationships between sample analyte amounts and corresponding spectral responses. These models are effective at predicting target domain sample analyte amounts when target samples are within the analyte and spectral calibration source domain. Models fail when target samples shift (analyte amounts and/or spectra) from the original calibration domain model. A total recalibration solution requires acquisition of new sample reference values and spectra. However, obtaining enough reference values to distinguish the target domain may be challenging or expensive. A simpler approach adapts the original model to the target domain using target sample spectra without analyte reference values (unlabeled). Analytical chemists have developed several machine learning algorithms using unlabeled regression domain adaptation processes. Unfortunately, prediction accuracy declines for these methods depending on how much the target domain analyte distribution has shifted from the calibration distribution, and regression transfer learning methods are instead needed. Regression domain adaptation and transfer learning are often referred to as model updating in analytical chemistry, but regression domain adaptation only applies to spectral shifts. The regression transfer learning method presented in this paper named null augmentation regression constant analyte (NARCA) leverages unlabeled repeat spectra of a single target sample to update an original calibration model to the shifted target domain sample. With sample repeat spectra, the analyte amount can be assumed constant or nearly constant for NARCA and because models are formed for one sample, NARCA operates as a local modeling method. The performance of NARCA as a regression transfer learning method is evaluated using five near-infrared data sets. Analyte ; Calibration ; Linear regression ; Chemistry ; Sample (material) ; Biological system ; Regression analysis ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Statistics ; Machine learning ; Mathematics ; Chromatography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: Jun-Li Cao | Ting Song | Zhen-Shuai Lei | Hai-Jun Peng | Zi‐Jiang Liu | Xiao Wei Sun
Source: Journal of Molecular Liquids (article, 2024-09-28)
Field: Chemistry — Physical and Theoretical Chemistry

Effect of temperature on the symmetrization of AlOOH hydrogen bonds Symmetrization ; Hydrogen bond ; Chemistry ; Hydrogen ; Computational chemistry ; Thermodynamics ; Organic chemistry ; Mathematics ; Physics ; Molecule ; Combinatorics Crystallography and molecular interactions ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Peng Xu | Xing Hu | Shaojie Zhou | Yali Mao | Guoliang Ma | Yunliang Ma | Tianjian Liu | Chao Yuan
Source: ACS Applied Electronic Materials (article, 2024-10-25)
Field: Materials Science — Materials Chemistry

Wafer-Level Simultaneous Film Thickness and Thermal Conductivity Characterizations for Bonded Si-on-Si Wafer bonding and thinning processes in three-dimensional Si–Si bonding technology may cause the nonuniform thickness of the Si thin film. This in turn will lead to nonuniform thermal conductivity (TC) as phonon boundary scattering dominates TC at the submicron scale. The simultaneous scanning thickness and TC of the Si thin film are challenging. In this paper, we experimentally investigate the transient thermoreflectance (TTR) technique’s capability and effectiveness of simultaneously measuring the TC and thickness of the Si thin film for bonded Si–Si samples. Uniform samples, combined with scanning electron microscopy and phonon-based thermal conductivity models, were used to verify the technique’s capability. A small deviation (∼10%) was found between TTR and SEM measurements for a film thickness less than 1 μm. From the Monte Carlo error analysis, we obtained a fitting error of ∼3% for the film thickness and ∼10–25% for TC. The fitting uncertainty was then elaborated through sensitivity analysis. We also developed a wafer-level high-throughput thickness scanning system, achieved by a high-precision automatic scanning stage combined with the deep learning-based fast-fitting algorithm. We performed the scanning measurements on the sample with a nonuniform thickness, observing local inhomogeneity, which is difficult to detect with the general single-point method. The scanning area is 16 mm × 6 mm and the spatial resolution is ∼60 μm. The TTR technique enables a platform to characterize the structure and thermal properties of advanced Si–Si bonded materials. Wafer ; Materials science ; Thermal conductivity ; Composite material ; Conductivity ; Thermal ; Optoelectronics ; Chemistry Thermal properties of materials ; 3D IC and TSV technologies ; Semiconductor materials and interfaces
Authors: B.M. Girish | Golluri Ricky Rakshith | Atanu Kumar Paul | Vinoth Kumar Raja | Gourhari Chakraborty
Source: Polymer Bulletin (article, 2024-05-26)
Field: Materials Science — Biomaterials

Hydrolytic and soil degradation of cellulosic material (paper): optimization of parameters using ANN and RSM Cellulosic ethanol ; Hydrolysis ; Degradation (telecommunications) ; Hydrolytic degradation ; Pulp and paper industry ; Cellulose ; Materials science ; Chemical engineering ; Polymer science ; Chemistry ; Organic chemistry ; Computer science ; Engineering Advanced Cellulose Research Studies ; Recycling and Waste Management Techniques
Authors: Wonjun Shin | Seongwoo Jang | Yunhyoung Hwang | Jihoon Han
Source: Engineering With Computers (article, 2024-10-26)
Field: Materials Science — Materials Chemistry

Data-driven prediction of the mechanical behavior of nanocrystalline graphene using a deep convolutional neural network with PCA Convolutional neural network ; Nanocrystalline material ; Artificial neural network ; Artificial intelligence ; Deep learning ; Materials science ; Pattern recognition (psychology) ; Computer science ; Machine learning ; Nanotechnology Graphene research and applications ; Machine Learning in Materials Science ; Carbon Nanotubes in Composites
Authors: Juan I. Rodríguez | Ulises A. Vergara-Beltrán
Source: The Journal of Physical Chemistry A (article, 2024-09-30)
Field: Materials Science — Materials Chemistry

Physics-Inspired Evolutionary Machine Learning Method: From the Schrödinger Equation to an Orbital-Free-DFT Kinetic Energy Functional We introduce a machine learning (ML)-supervised model function (which is in fact a functional rather than a regular function) that is inspired by the variational principle of physics. This ML hypothesis evolutionary method, termed ML-Ω, allows us to go from data to differential equation(s) underlying the physical (chemical, engineering, etc.) phenomena from which the data are derived from. The fundamental equations of physics can be derived from this ML-Ω evolutionary method when the proper training data is used. By training the ML-Ω model function with only three hydrogen-like atom energies, the method can find Schrödinger's exact functional and, from it, Schrödinger's fundamental equation. Then, in the field of density functional theory (DFT), when the model function is trained with the energies from the known Thomas-Fermi (TF) formula <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>E</mml:mi><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>0.7687</mml:mn><mml:msup><mml:mi>Z</mml:mi><mml:mrow><mml:mn>7</mml:mn><mml:mo>/</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math>, it correctly finds the exact TF functional. Finally, the method is applied to find a local orbital-free (OF) functional expression of the independent electron kinetic energy functional <i>T</i><sub>s</sub> based on the γ<i>TF</i>λ<i>vW</i> model. By considering the theoretical energies of only five atoms (He, Be, Ne, Mg, and Ar) as the training set, the evolutionary ML-Ω method finds an ML-Ω-OF-DFT local <i>T</i><sub>s</sub> functional (γ<i>TF</i>λ<i>vW</i>(0.964,1/4)) that outperforms all the OF-DFT functionals of a representative group. Moreover, our ML-Ω-OF functional overcomes the difficulty of LDA's and some local generalized gradient approximation (GGA)-DFT's functionals to describe the stretched bond region at the correct spin configuration of diatomic molecules. Nonsmooth and nonclosed form functionals can be considered in the ML-Ω model function and still be effectively trained. Although our evolutionary ML-Ω model function can work without an explicit prior-form functional, by using the techniques of symbolic regression, in this work, we exploit prior-form functional expressions to make the training process simpler and faster. The ML-Ω method can be considered at the intersection of ML and the natural sciences. Kinetic energy ; Physics ; Energy (signal processing) ; Schrödinger equation ; Statistical physics ; Computer science ; Theoretical physics ; Classical mechanics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Mahesh K. Sit | Subhasish Das | Kousik Samanta
Source: The Journal of Physical Chemistry A (article, 2024-09-16)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Mixed Quantum-Classical Dynamics without Explicit Nonadiabatic Coupling: Application to the Photodissociation of Peroxynitric Acid We have devised a hybrid quantum-classical scheme utilizing machine-learned potential energy surfaces (PES), which circumvents the need for explicit computation of nonadiabatic coupling elements. The quantities necessary to account for the nonadiabatic effects are directly obtained from the PESs. The simulation of dynamics is based on the fewest-switches surface-hopping method. We applied this scheme to model the photodissociation of both N-O and O-O bonds in a conformer of peroxynitric acid (HO<sub>2</sub>NO<sub>2</sub>). Adiabatic PES data for the six lowest states of this molecule were computed at the CASSCF level for various nuclear configurations. These served as the training data for the machine-learning models for the PESs. The dynamics simulation was initiated on the lowest optically bright singlet excited state (S<sub>4</sub>) and propagated along the two Jacobi coordinates <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mover><mml:mi>J</mml:mi><mml:mo>→</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mover><mml:mi>J</mml:mi><mml:mo>→</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> while accounting for the nonadiabatic effects through transitions between PESs. Our analysis revealed that there is a very high chance of dissociation of the N-O bond leading to the HO<sub>2</sub> and NO<sub>2</sub> fragments. Surface hopping ; Photodissociation ; Coupling (piping) ; Potential energy surface ; Quantum ; Potential energy ; Computation ; Quantum chemistry ; Molecular dynamics ; Surface (topology) ; Quantum dynamics ; Scheme (mathematics) ; Vibronic coupling ; Chemistry ; Computational chemistry ; Physics ; Quantum mechanics ; Statistical physics ; Computer science ; Materials science ; Mathematics ; Photochemistry ; Molecule ; Algorithm ; Mathematical analysis ; Geometry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Chengjun Yang
Source: MedScien (article, 2024-02-19)
Field: Chemistry — Electrochemistry

Nanoparticle-based electrochemical sensing Electrochemical sensing has emerged as a potent technique for detecting and quantifying various analytes due to its inherent sensitivity, simplicity, and potential for miniaturization. Leveraging their distinctive physicochemical properties, nanoparticles have sparked a revolution in electrochemical sensing, enhancing sensing platforms’ sensitivity, selectivity, and stability. This paper presents the background and significance of nanoparticle-based electrochemical sensing, along with the diverse applications of nanoparticles in science and engineering. The fundamental principles of nanoparticles in electrochemical sensing are explored, highlighting their unique advantages in electrocatalysis, charge transfer facilitation, and signal amplification. The applications of various types of nanoparticles in electrochemical sensing are discussed, elucidating the role of nanomaterials in designing advanced conduction strategies such as impedance spectroscopy, cyclic voltammetry, and amperometry.Furthermore, this review delves into the applications of nanoparticle-based electrochemical sensors across domains, including gas detection, environmental monitoring, and the field of biosensors. The practicality of these sensors in real-world scenarios is showcased through case studies, emphasizing their rapid response, high specificity, and potential for multi-analyte detection. The challenges and prospects of this field are also examined. Addressing nanoparticle stability, reproducibility, and scalability issues is crucial for the successful commercial translation of these sensing platforms. In conclusion, this review underscores the transformative impact of nanoparticles on the landscape of electrochemical sensing. Their multifunctionality, coupled with evolving conduction methodologies, promises to develop ultra-sensitive and reliable sensing platforms that contribute to advancements across various application domains. With the continuous evolution of nanoparticle synthesis techniques, electrochemical sensing is poised to achieve new frontiers of sensitivity and specificity. Nanotechnology ; Nanoparticle ; Materials science ; Nanomaterials ; Biosensor ; Computer science ; Analyte ; Chemistry Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors
Authors: Rushil Shah | Abhijit Saha | Zahraa Najah | Akula Prakash | Uma M. Reddy | Amandeep Nagpal | V Haripriya
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Role of Quantum Dots and Nanostructures in Photovoltaic Energy Conversion Nanostructures and quantum dots have substantial effects on enhancing photovoltaic energy conversion efficiency, as evidenced in this comprehensive study. Materials that are nanostructured and nanosized particles are commonly used to address the urgent issues related to energy conversion. The use of nanostructured substances to address issues with energy and natural resources has garnered a lot of interest lately. Directional nanostructures in particular show promise for the conversion, collection, and storage of energy. Due to their unique properties, such as electrical conductivity, mechanical energy, and photoluminescence, quantum dots made from carbon (CQDs) and graphene quantum dots (GQDs) have been integrated into hybrid photovoltaic-thermoelectric systems (PV-TE). It evaluates the effects of nanostructures on solar energy technologies, in particular how they can improve power conversion and light absorption in solar cells. Optical light detectors, which transform photonic energy into signals that are electrical, are among the many optoelectronic uses of CQDs that have drawn attention because they are essential components of contemporary imaging and communication systems, such as visible light cameras, machine vision, medical X-ray and near-infrared image processing, and visible light detection devices. Besides supercapacitors, the study investigates how nanostructures could play a crucial role in contributing to addressing the global energy crisis sustainably, by working as photocatalysts for hydrogen synthesis and supercapacitors. Quantum dot ; Photovoltaic system ; Nanostructure ; Energy transformation ; Materials science ; Optoelectronics ; Nanotechnology ; Engineering physics ; Quantum ; Physics ; Electrical engineering ; Engineering ; Quantum mechanics Quantum Dots Synthesis And Properties ; ZnO doping and properties ; Carbon and Quantum Dots Applications
Authors: Ken‐ichi Nomura | Ankit Mishra | Tian Sang | Rajiv K. Kalia | Aiichiro Nakano | Priya Vashishta
Source: The Journal of Physical Chemistry Letters (article, 2024-05-09)
Field: Materials Science — Materials Chemistry

Molecular Autonomous Pathfinder Using Deep Reinforcement Learning Diffusion in solids is a slow process that dictates rate-limiting processes in key chemical reactions. Unlike crystalline solids that offer well-defined diffusion pathways, the lack of similar structural motifs in amorphous or glassy materials poses great challenges in bridging the slow diffusion process and material failures. To tackle this problem, we propose an AI-guided long-term atomistic simulation approach: molecular autonomous pathfinder (MAP) framework based on deep reinforcement learning (DRL), where the RL agent is trained to uncover energy efficient diffusion pathways. We employ a Deep Q-Network architecture with distributed prioritized replay buffer, enabling fully online agent training with accelerated experience sampling by an ensemble of asynchronous agents. After training, the agents provide atomistic configurations of diffusion pathways with their energy profile. We use a piecewise nudged elastic band to refine the energy profile of the obtained pathway and the corresponding diffusion time on the basis of transition-state theory. With the MAP framework, we demonstrate atomistic diffusion mechanisms in amorphous silica with time scales comparable to experiments. Computer science ; Reinforcement learning ; Pathfinder ; Asynchronous communication ; Bridging (networking) ; Diffusion ; Amorphous solid ; Deep learning ; Artificial intelligence ; Distributed computing ; Chemistry ; Physics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced Memory and Neural Computing
Authors: François Cornet | Bardi Benediktsson | Bjarke Hastrup | Mikkel N. Schmidt | Arghya Bhowmik
Source: ChemRxiv (preprint, 2024-03-28)
Field: Chemistry — Organic Chemistry

OM-DIFF: INVERSE-DESIGN OF ORGANOMETALLIC CATALYSTS WITH GUIDED EQUIVARIANT DENOISING DIFFUSION Organometallic complexes are ubiquitous in homogeneous catalysis and other technological applications. Optimization of such complexes for specific applications is challenging due to the large variety of possible metal-ligand combinations and ligand-ligand interactions. Here we present OM-Diff, an inverse design framework based on a diffusion generative model for in-silico design of such complexes from scratch. Given the importance of the spatial structure of a catalyst, the model directly operates on all-atom (including hydrogen) representations in 3D space. To handle the symmetries inherent to that data representation, OM-Diff combines an equivariant diffusion model and an equivariant property predictor to drive sampling at inference time. The model can conditionally generate novel ligands beyond those in the training dataset. We demonstrate the potential of the proposed approach by designing catalysts for a family of cross-coupling reactions, and validating a selection of novel proposed compounds with DFT calculations. Inverse ; Equivariant map ; Diffusion ; Catalysis ; Mathematics ; Chemistry ; Materials science ; Pure mathematics ; Physics ; Thermodynamics ; Organic chemistry ; Geometry Microwave-Assisted Synthesis and Applications ; Inorganic and Organometallic Chemistry ; Epoxy Resin Curing Processes
Authors: Prashant R. Bhendarkar | Hitesh M. Rakhade | Arpit B. Mendhe | Neha S. Barse | Deepti Chaudhary | Avinash C. Mendhe
Source: Prabha Materials Science Letters (review, 2024-12-27)
Field: Materials Science — Materials Chemistry

Advances in Metal Chalcogenides and Metal Oxides Supercapacitors: A Comprehensive Review of Fundamental Mechanisms and Recent Progress The rising worldwide demand for energy storage devices has driven significant advancements in studies regarding supercapacitor (SC), particularly in the context of renewable green energy systems and electronics. SCs have come up as a critical technology, providing ultra-fast charging, long lifespan and high-power density when compared to conventional batteries. These attributes make SCs ideal for applications that needs surging energy delivery, such as electric vehicles and grid energy storage systems. Lately wide range of research has been focused on enhancing the energy density through the development of advanced evolved materials and device architectures. This review article discusses recent advancements in SC, particularly in electrode materials, such as transition metal oxides (e.g., RuO2, MnO2, V2O5) and metal chalcogenides (e.g., MoS2), which exhibit high surface area, great electrical conductivity, and mechanical stability. These materials, alongside carbon-based materials like graphene and conducting polymers, have demonstrated significant improvements in charge storage capacity and energy transfer efficiency. Hybrid materials containing metal oxides with carbon-based contents have shown great promise in enhancing both energy and power densities. The review also discusses recent trends in electrolytes, including ionic liquids, aqueous solutions, and solid-state electrolytes, which have played significant role in improving SC performance by widening the voltage window and enhancing stability. Despite these technological advancements, challenges such as cost-effective material production and scalability remain barriers to widespread commercialization. The customization of SCs into hybrid energy storage systems alongside batteries and fuel cells presents a promising avenue for future development. This review article pins down the importance of ongoing research to overcome these challenges while fully realizing the potential of SCs in the evolving energy landscape. Nanotechnology ; Supercapacitor ; Metal ; Materials science ; Metallurgy ; Chemistry ; Electrochemistry ; Electrode MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Advancements in Battery Materials
Authors: Abdul Jaleel Laghari | Umair Aftab | Aneela Tahira | Muhammad Yameen Solangi | Ahmed Ali Hulio | Ghulam Mustafa Thebo | Muhammad Ishaque Abro | Muhammad Ali Bhatti | Susheel Kumar | Elmuez A. Dawi | Ayman Nafady | Antonia Infantes‐Molina | Mélanie Emo | Brigitte Vigolo | Zafar Hussain Ibupoto
Source: Journal of Cluster Science (article, 2024-05-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Novel Approach for Efficient Water Oxidation and Supercapacitor Applications Based on Morphologically Transformed, Surface Rich Oxygen Vacancies of Co3o4 Nanostructures Co-Synthesized with Potato Starch Peel Extract Tafel equation ; Supercapacitor ; Materials science ; Overpotential ; Chemical engineering ; Nanostructure ; Oxygen evolution ; Cobalt oxide ; Electrode ; Capacitance ; Specific surface area ; Nanotechnology ; Oxide ; Electrochemistry ; Catalysis ; Chemistry ; Metallurgy ; Organic chemistry Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Advanced battery technologies research