jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Owen Rettenmaier | Joshua J. Gabriel | Srikanth Patala
Source: Modelling and Simulation in Materials Science and Engineering (article, 2024-09-26)
Field: Materials Science — Materials Chemistry

Modeling martensitic transformation temperatures in Zirconia–Ceria solid solutions using machine learning interatomic potentials Abstract Shape memory ceramics (SMCs), while exhibiting high strength, sizeable recoverable strain, and substantial energy damping, tend to shatter under load and have low reversibility. Recent developments in SMCs have shown significant promise in enhancing the reversibility of the shape memory phase transformation by tuning the lattice parameters and transformation temperatures through alloying. While first-principles methods, such as density functional theory (DFT), can predict the lattice parameters and enthalpy at zero Kelvin, calculating the transformation temperature from free energy at high temperatures is impractical. Empirical potentials can calculate transformation temperatures efficiently for large system sizes but lack compositional transferability. In this work, we develop a model to predict transformation temperatures and lattice parameters for the Zirconia–Ceria solid solutions. We construct a machine learning inter-atomic potential (MLIAP) using an initial dataset of DFT simulations, which is then iteratively expanded using active learning. We utilize reversible scaling to compute the free energy as a function of composition and temperature, from which the transformation temperatures are determined. These transformation temperatures match experimental trends and accurately predict the phase boundary. Finally, we compare other relevant design parameters (e.g. transformation volume change) to demonstrate the applicability of MLIAPs in designing SMCs. Materials science ; Cubic zirconia ; Diffusionless transformation ; Interatomic potential ; Solid solution ; Transformation (genetics) ; Martensite ; Thermodynamics ; Metallurgy ; Molecular dynamics ; Computational chemistry ; Ceramic ; Microstructure Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Indranil Saha | Andreas Erlebach | Petr Nachtigall | Christopher J. Heard | Lukáš Grajciar
Source: ChemRxiv (preprint, 2024-06-19)
Field: Materials Science — Materials Chemistry

Germanium Distributions in Zeolites Derived from Neural Network Potentials. Germanosilicate zeolites have played a pivotal role in the recent surge in synthesis of novel zeolite topologies. This success has been attributed to the combined effect of the high hydrolytic lability and specific distribution of germanium within the zeolitic framework, e.g., favoring the double four-ring (D4R) structural units. While experimental determination of germanium distributions remains limited, their in silico investigation has been hampered by the high computational cost of ab initio calculations. To overcome these limitations, we have developed neural network potentials (NNPs) that can efficiently explore a wide range of distributions in germanosilicate zeolites, while maintaining the accuracy of the ab initio (dispersion-corrected GGA DFT) training dataset. Through comprehensive screening of low-energy germanium distributions for five zeolite topologies (UTL, BEC, UOV, IWW and *CTH) across a broad range of Si/Ge ratios, we have identified a key factor governing the distribution of germanium in these D4R-containing zeolites, which is the tendency of germanium to cluster. The clustering initiates at the D4R units, leading to a preference to occupy these units at low to medium Ge loading (Si/Ge > 5). However, at high Ge loading, the Ge tends to phase separate into Ge-rich and Ge-poor regions, regardless of the specific structural unit. The zeolite topology was shown to be capable of modulating these trends in germanium distribution (e.g., UTL strongly favors D4R occupation even for low Si/Ge ratios), which suggests the possibility to develop design strategies for targeted zeolite synthesis. The NNPs presented herein enable rapid evaluation of these design strategies across a wide range of candidate zeolite structures and experimentally relevant Si/Ge ratios. Germanium ; Artificial neural network ; Materials science ; Computer science ; Artificial intelligence ; Optoelectronics ; Silicon X-ray Diffraction in Crystallography ; Zeolite Catalysis and Synthesis ; Thallium and Germanium Studies
Authors: Khushi Mittal | Kanwarpartap Singh Gill | Rahul Chauhan | Manish Sharma | G Sunil
Source:  (article, 2024-04-18)
Field: Chemistry — Analytical Chemistry

In-Depth Analysis of Exploratory Data Utilizing an InceptionV3 Convolutional Neural Network (CNN) Framework and Deep Learning Techniques for Predicting the Quality of Red Wine This research delves into the crucial role of the Exploratory Data Processing (EDP) phase as a fundamental initial step in predicting red wine quality through the application of an InceptionV3 Cnn technique within the realm of Machine Learning. The dataset encompasses a diverse array of chemical attributes and sensory elements inherent to red wines. The Exploratory Data Analysis (EDA) process involves a comprehensive examination of the data, encompassing meticulous scrutiny, data purification, and rigorous statistical analysis, all geared towards ensuring data integrity and quality. Visualizations play a pivotal role in facilitating the identification and exploration of patterns and connections within the provided information. Feature engineering and dimensionality reduction techniques are employed to enhance the model's predictive capabilities. The choice of the InceptionV3 Cnn algorithm is motivated by its interpretability and ability to capture non-linear interactions. The performance of the trained model is evaluated using established criteria to validate its robustness and generalization capabilities. The EDP-centric methodology employed in this study establishes the groundwork for an advanced and precise prediction framework for evaluating red wine quality, offering valuable insights to the wine industry. Computer science ; Convolutional neural network ; Artificial intelligence ; Wine ; Machine learning ; Interpretability ; Robustness (evolution) ; Data mining ; Quality (philosophy) ; Deep learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Tianle Wang | Jorge Ramirez | Cristina García–Cardona | Thomas Proffen | Shantenu Jha | Sudip K. Seal
Source:  (article, 2024-12-15)
Field: Materials Science — Materials Chemistry

An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry Structure determination workloads in neutron diffractometry are computationally expensive and routinely require several hours to many days to determine the structure of a material from its neutron diffraction patterns. The potential for machine learning models trained on simulated neutron scattering patterns to significantly speed up these tasks have been reported recently. However, the amount of simulated data needed to train these models grows exponentially with the number of structural parameters to be predicted and poses a significant computational challenge. To overcome this challenge, we introduce a novel batch-mode active learning (AL) policy that uses uncertainty sampling to simulate training data drawn from a probability distribution that prefers labelled examples about which the model is least certain. We confirm its efficacy in training the same models with ∼ 75% less training data while improving the accuracy. We then discuss the design of an efficient stream-based training workflow that uses this AL policy and present a performance study on two heterogeneous platforms to demonstrate that, compared with a conventional training workflow, the streaming workflow delivers ∼ 20% shorter training time without any loss of accuracy. Pipeline (software) ; Computer science ; Training (meteorology) ; Neutron ; Training set ; Artificial intelligence ; Machine learning ; Physics ; Nuclear physics ; Operating system Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Olivier Vo Van | Vincent Laurent
Source: Procedia Structural Integrity (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Impact of daily and seasonal temperature variation on rolling contact fatigue damage in the rail Rolling contact fatigue is the most frequent degradation mode observed on the rail. Many mechanical studies have revealed the main physical parameters that generate the crack initiation, which are all linked with the track geometry, the dynamic of the wheel and contact conditions. Except for extreme climate, exogenous phenomena such as ambient temperature have not been analysed. The main reason is that temperature variations induce stress cycles in the rail that are under the endurance limit of the material. Therefore, most fatigue life estimation methods do not consider these cycles. In this paper, 6 years of daily temperature measurement over all the French railway network are used and integrated in a machine learning model developed for computing residual lifespan of each rail segment before crack initiation by rolling contact fatigue. Temperature data are aggregated using the continuum damage mechanics model developed by Lemaitre and Chaboche that gives the possibility to let low amplitude cycles to contribute to fatigue damage. Classification models are then implemented, and importance variables are finally quantifed. Results show that daily temperature variations have more impact than seasonal variations and that its contribution to fatigue is significant. Variation (astronomy) ; Environmental science ; Materials science ; Forensic engineering ; Engineering ; Physics Metal Alloys Wear and Properties ; Mechanical and Thermal Properties Analysis ; Gear and Bearing Dynamics Analysis
Authors: Matt Nagle | Hannah Conroy Broderick | Christelle Vedel | Michel Destrade | Michael Fop | Aisling Ní Annaidh
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Polymers and Plastics

A Gaussian Process Approach for Rapid Evaluation of Skin Tension Tension (geology) ; Process (computing) ; Computer science ; Biochemical engineering ; Process engineering ; Materials science ; Engineering ; Composite material ; Programming language Textile materials and evaluations
Authors: Yihao Hu | Jiyuan Yang | Shi Liu
Source: arXiv (Cornell University) (preprint, 2024-01-11)
Field: Materials Science — Materials Chemistry

Giant piezoelectric effects of topological structures in stretched ferroelectric membranes Freestanding ferroelectric oxide membranes emerge as a promising platform for exploring the interplay between topological polar ordering and dipolar interactions that are continuously tunable by strain. Our investigations combining density functional theory (DFT) and deep-learning-assisted molecular dynamics simulations demonstrate that DFT-predicted strain-driven morphotropic phase boundary involving monoclinic phases manifest as diverse domain structures at room temperatures, featuring continuous distributions of dipole orientations and mobile domain walls. Detailed analysis of dynamic structures reveals that the enhanced piezoelectric response observed in stretched PbTiO$_3$ membranes results from small-angle rotations of dipoles at domain walls, distinct from conventional polarization rotation mechanism and adaptive phase theory inferred from static structures. We identify a ferroelectric topological structure, termed "dipole spiral," which exhibits a giant intrinsic piezoelectric response ($&gt;$320 pC/N). This helical structure, possessing a rotational zero-energy mode, unlocks new possibilities for exploring chiral phonon dynamics and dipolar Dzyaloshinskii-Moriya-like interactions. Ferroelectricity ; Dipole ; Piezoelectricity ; Condensed matter physics ; Materials science ; Density functional theory ; Molecular dynamics ; Polarization (electrochemistry) ; Phase boundary ; Monoclinic crystal system ; Polar ; Chemical physics ; Phase (matter) ; Topology (electrical circuits) ; Physics ; Crystallography ; Dielectric ; Crystal structure ; Computational chemistry ; Chemistry ; Optoelectronics Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Multiferroics and related materials
Authors: Yifan Sun | Y. Miyawaki | Masaya Kumagai | Shun Fujieda | Hiroaki Muta | Ken Kurosaki | Yuji Ohishi
Source: Journal of Nuclear Materials (article, 2024-03-27)
Field: Materials Science — Materials Chemistry

Thermophysical characterization of UFe3B2 and USiNi: An experimental study Thermal conductivity ; Credibility ; Nuclear engineering ; Uranium ; Fabrication ; Characterization (materials science) ; Computer science ; Reliability (semiconductor) ; Nuclear fuel ; Materials science ; Sample (material) ; Process (computing) ; Nuclear fuel cycle ; Process engineering ; Environmental science ; Nanotechnology ; Fuel cycle ; Engineering ; Physics ; Thermodynamics ; Composite material Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fusion materials and technologies
Authors: João P. M. Sousa | Maria J. Ramos | Pedro Alexandrino Fernandes
Source: The Journal of Physical Chemistry B (article, 2024-08-26)
Field: Chemistry — Spectroscopy

QM/MM Study of the Reaction Mechanism of <scp>L</scp>-Tyrosine Hydroxylation Catalyzed by the Enzyme CYP76AD1 We have studied the hydroxylation mechanism of l-Tyr by the heme-dependent enzyme CYP76AD1 from the sugar beet (<i>Beta vulgaris</i>). This enzyme has a promising biotechnological application in modified yeast strains to produce medicinal alkaloids, an alternative to the traditional opium poppy harvest. A generative machine learning software based on AlphaFold was used to build the structure of CYP76AD1 since there are no structural data for this specific enzyme. After model validation, l-Tyr was docked in the active site of CYP76AD1 to assemble the reactive complex, whose catalytic distances remained stable throughout the 100 ns of MD simulation. Subsequent QM/MM calculations elucidated that l-Tyr hydroxylation occurs in two steps: hydrogen abstraction from l-Tyr by CpdI, forming an l-Tyr radical, and subsequent radical rebound, corresponding to a rate-limiting step of 16.0 kcal·mol<sup>-1</sup>. Our calculations suggest that the hydrogen abstraction step should occur in the doublet state, while the radical rebound should happen in the quartet state. The clarification of the reaction mechanism of CYP76AD1 provides insights into the rational optimization of the biosynthesis of alkaloids to eliminate the use of opium poppy. Hydroxylation ; Chemistry ; Stereochemistry ; Enzyme ; Hydrogen atom abstraction ; Catalysis ; Biochemistry ; Organic chemistry ; Hydrogen Analytical Chemistry and Chromatography ; Pharmacogenetics and Drug Metabolism ; Synthesis and Biological Evaluation
Authors: Miki Kaneko | Yu Takano | Tôru Saitô
Source: Chemistry Letters (article, 2024-01-31)
Field: Chemistry — Organic Chemistry

C–H bond dissociation enthalpy prediction with machine learning reinforced semi-empirical quantum mechanical calculations Abstract We introduce a combined fast semi-empirical quantum mechanical and machine learning (SQM/ML) approach capable of matching the C–H bond dissociation enthalpies (BDEs) computed with the highly accurate (RO)CBS-QB3 method. The usefulness of our proposed SQM/ML model is corroborated by the fact that a single C–H BDE of a molecule is calculated in seconds and the mean absolute error amounts to only 1 to 2 kcal/mol. Chemistry ; Dissociation (chemistry) ; Bond-dissociation energy ; Enthalpy ; Molecule ; Quantum ; Computational chemistry ; Thermodynamics ; Quantum chemistry ; Quantum chemical ; Physical chemistry ; Quantum mechanics ; Organic chemistry Free Radicals and Antioxidants ; Advanced Chemical Physics Studies ; Machine Learning in Materials Science
Authors: Yunchao Fang | Qian Qian Zhao | Ryan B. Sills | Ahmed Aziz Ezzat
Source: APL Machine Learning (article, 2024-06-01)
Field: Materials Science — Materials Chemistry

Constructing coarse-grained models with physics-guided Gaussian process regression Coarse-grained models describe the macroscopic mean response of a process at large scales, which derives from stochastic processes at small scales. Common examples include accounting for velocity fluctuations in a turbulent fluid flow model and cloud evolution in climate models. Most existing techniques for constructing coarse-grained models feature ill-defined parameters whose values are arbitrarily chosen (e.g., a window size), are narrow in their applicability (e.g., only applicable to time series or spatial data), or cannot readily incorporate physics information. Here, we introduce the concept of physics-guided Gaussian process regression as a machine-learning-based coarse-graining technique that is broadly applicable and amenable to input from known physics-based relationships. Using a pair of case studies derived from molecular dynamics simulations, we demonstrate the attractive properties and superior performance of physics-guided Gaussian processes for coarse-graining relative to prevalent benchmarks. The key advantage of Gaussian-process-based coarse-graining is its ability to seamlessly integrate data-driven and physics-based information. Granularity ; Gaussian process ; Statistical physics ; Gaussian ; Kriging ; Cloud physics ; Computer science ; Process (computing) ; Algorithm ; Theoretical computer science ; Physics ; Machine learning ; Cloud computing Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Protein Structure and Dynamics
Authors: Guangrui Ding | Chang Liu | Jiaze Yin | Xinyan Teng | Yuying Tan | Hongjian He | Haonan Lin | Lei Tian | Ji‐Xin Cheng
Source: arXiv (Cornell University) (preprint, 2024-09-16)
Field: Chemistry — Analytical Chemistry

Self-Supervised Elimination of Non-Independent Noise in Hyperspectral Imaging Hyperspectral imaging has been widely used for spectral and spatial identification of target molecules, yet often contaminated by sophisticated noise. Current denoising methods generally rely on independent and identically distributed noise statistics, showing corrupted performance for non-independent noise removal. Here, we demonstrate Self-supervised PErmutation Noise2noise Denoising (SPEND), a deep learning denoising architecture tailor-made for removing non-independent noise from a single hyperspectral image stack. We utilize hyperspectral stimulated Raman scattering and mid-infrared photothermal microscopy as the testbeds, where the noise is spatially correlated and spectrally varied. Based on single hyperspectral images, SPEND permutates odd and even spectral frames to generate two stacks with identical noise properties, and uses the pairs for efficient self-supervised noise-to-noise training. SPEND achieved an 8-fold signal-to-noise improvement without having access to the ground truth data. SPEND enabled accurate mapping of low concentration biomolecules in both fingerprint and silent regions, demonstrating its robustness in sophisticated cellular environments. Hyperspectral imaging ; Noise (video) ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Image (mathematics) Spectroscopy and Chemometric Analyses
Authors: Iyekekpolor Osamudiame Moses
Source: Journal of Multidisciplinary Science MIKAILALSYS (article, 2024-10-16)
Field: Chemistry — Physical and Theoretical Chemistry

The Rise of Biochemical Sensors: Technology for Real-Time Health Monitoring (Applications and Future Scope) Biochemical sensors integrated into wearable health technology represent one of the most transformative innovations of the 21st century. This review delves into the evolution, principles, and real-time applications of biochemical sensors and their role in personalized health monitoring. Wearable biochemical sensors, capable of continuously measuring various biomarkers like glucose, lactate, cortisol, and electrolytes, are revolutionizing healthcare by enabling proactive management of chronic diseases, including diabetes, cardiovascular disorders, and mental health issues. Advances in biosensing technologies, coupled with the use of AI and machine learning algorithms, have enhanced the sensitivity and accuracy of these devices, ensuring that critical health data is available in real-time. From glucose monitoring devices like Abbott's FreeStyle Libre to the latest nanomaterial-based sensors, these innovations are reshaping healthcare delivery by shifting the focus from hospital-centered treatments to patient-centric, continuous monitoring systems. This review provides an in-depth analysis of the technological advancements, challenges, and future directions in biochemical sensors, focusing on key technologies such as electrochemical, optical, and enzymatic sensors. It also highlights the critical role of AI in interpreting the complex data generated by these sensors, paving the way for more efficient diagnostics and predictive healthcare models. Furthermore, the paper explores how these sensors have been applied in infectious disease detection, particularly during the COVID-19 pandemic, and discusses their potential to enhance global health surveillance systems. In conclusion, wearable biochemical sensors represent a significant leap forward in the pursuit of personalized medicine, offering real-time diagnostics and timely interventions for disease management. The future of healthcare is closely tied to the ongoing innovations in sensor technology, with the promise of even more advanced and multifunctional devices on the horizon. Scope (computer science) ; Computer science ; Systems engineering ; Risk analysis (engineering) ; Process engineering ; Business ; Engineering Various Chemistry Research Topics ; Advanced Chemical Sensor Technologies
Authors: Stefano Polimena | Gianvito Pio | Maria Cefola | Michela Palumbo | Michelangelo Ceci | G. Attolico
Source: Information Processing in Agriculture (article, 2024-09-03)
Field: Chemistry — Analytical Chemistry

A novel random forest-based approach for the non-destructive and explainable estimation of ammonia and chlorophyll in fresh-cut rocket leaves The perceived visual quality of fruits and vegetables plays a central role in the choices made by retail customers. Machine learning (ML) approaches based on image analysis have been recently proposed to overcome the poor efficiency and subjectivity of human visual evaluation as well as the expensiveness and destructiveness of physical and chemical methods that measure internal indicators. In this paper, we propose a ML method based on Random Forests for estimating the chlorophyll and ammonia contents (considered, in the literature, reliable indicators of product freshness) from images of fresh-cut rocket leaves. Our approach copes with specific issues raised by (i) the non-uniform distributions of ammonia and chlorophyll values and (ii) the need to provide insights into the features that produce a particular model outcome, aiming to enhance its trustworthiness. Our experiments, performed on real images of fresh-cut rocket leaves, proved that the proposed approach significantly outperforms 7 competitor methods, obtaining an improvement of the RSE results of 6.6% for the prediction of the ammonia and of 10.4% for the prediction of the chlorophyll over its best competitor. Moreover, a specific analysis of the explainability of the predictions showed that the learned models are based on reasonable features, empowering their acceptance in real-world applications. Rocket (weapon) ; Random forest ; Chlorophyll ; Ammonia ; Estimation ; Remote sensing ; Environmental science ; Engineering ; Computer science ; Artificial intelligence ; Geology ; Aerospace engineering ; Chemistry ; Botany ; Biology ; Systems engineering ; Biochemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis