jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zibo Zhao | Yingqi Wang | Ximei Yang | Jiamei Quan | Bastian C. Krüger | Paula Stoicescu | Reed Nieman | Daniel J. Auerbach | Alec M. Wodtke | Hua Guo | G. Barratt Park
Source: Nature Chemistry (article, 2023-05-22)
Field: Materials Science — Materials Chemistry

Spin-dependent reactivity and spin-flipping dynamics in oxygen atom scattering from graphite The formation of two-electron chemical bonds requires the alignment of spins. Hence, it is well established for gas-phase reactions that changing a molecule's electronic spin state can dramatically alter its reactivity. For reactions occurring at surfaces, which are of great interest during, among other processes, heterogeneous catalysis, there is an absence of definitive state-to-state experiments capable of observing spin conservation and therefore the role of electronic spin in surface chemistry remains controversial. Here we use an incoming/outgoing correlation ion imaging technique to perform scattering experiments for O(<sup>3</sup>P) and O(<sup>1</sup>D) atoms colliding with a graphite surface, in which the initial spin-state distribution is controlled and the final spin states determined. We demonstrate that O(<sup>1</sup>D) is more reactive with graphite than O(<sup>3</sup>P). We also identify electronically nonadiabatic pathways whereby incident O(<sup>1</sup>D) is quenched to O(<sup>3</sup>P), which departs from the surface. With the help of molecular dynamics simulations carried out on high-dimensional machine-learning-assisted first-principles potential energy surfaces, we obtain a mechanistic understanding for this system: spin-forbidden transitions do occur, but with low probabilities. Chemistry ; Spin (aerodynamics) ; Graphite ; Dynamics (music) ; Reactivity (psychology) ; Chemical physics ; Atom (system on chip) ; Scattering ; Oxygen ; Oxygen atom ; Condensed matter physics ; Molecule ; Quantum mechanics ; Thermodynamics ; Physics ; Organic chemistry Graphite, nuclear technology, radiation studies ; Graphene research and applications ; Ion-surface interactions and analysis
Authors: Yao Sheng | Mikhail V. Polynski | Mathan K. Eswaran | Bikun Zhang | Alvin M. H. Lim | Lili Zhang | Jianwen Jiang | Wen Liu | Sergey M. Kozlov
Source: Applied Catalysis B: Environmental (review, 2023-11-22)
Field: Materials Science — Materials Chemistry

A review of mechanistic insights into CO2 reduction to higher alcohols for rational catalyst design Catalysis ; Rational design ; Biochemical engineering ; Alcohol ; Chemistry ; Combinatorial chemistry ; Reduction (mathematics) ; Organic chemistry ; Nanotechnology ; Materials science ; Engineering ; Mathematics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Asymmetric Hydrogenation and Catalysis
Authors: Shaswat Mohanty | James Stevenson | Andrea Browning | Leif D. Jacobson | Karl Leswing | Mathew D. Halls | Mohammad Atif Faiz Afzal
Source: Scientific Reports (article, 2023-10-11)
Field: Materials Science — Materials Chemistry

Development of scalable and generalizable machine learned force field for polymers Understanding and predicting the properties of polymers is vital to developing tailored polymer molecules for desired applications. Classical force fields may fail to capture key properties, for example, the transport properties of certain polymer systems such as polyethylene glycol. As a solution, we present an alternative potential energy surface, a charge recursive neural network (QRNN) model trained on DFT calculations made on smaller atomic clusters that generalizes well to oligomers comprising larger atomic clusters or longer chains. We demonstrate the validity of the polymer QRNN workflow by modeling the oligomers of ethylene glycol. We apply two rounds of active learning (addition of new training clusters based on current model performance) and implement a novel model training approach that uses partial charges from a semi-empirical method. Our developed QRNN model for polymers produces stable molecular dynamics (MD) simulation trajectory and captures the dynamics of polymer chains as indicated by the striking agreement with experimental values. Our model allows working on much larger systems than allowed by DFT simulations, at the same time providing a more accurate force field than classical force fields which provides a promising avenue for large-scale molecular simulations of polymeric systems. Computer science ; Scalability ; Field (mathematics) ; Data science ; Operating system ; Mathematics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Protein Structure and Dynamics
Authors: Yuyang Wang | Changwen Xu | Zijie Li | Amir Barati Farimani
Source: Journal of Chemical Theory and Computation (article, 2023-06-30)
Field: Materials Science — Materials Chemistry

Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials Recent advances in equivariant graph neural networks (GNNs) have made deep learning amenable to developing fast surrogate models to expensive <i>ab initio</i> quantum mechanics (QM) approaches for molecular potential predictions. However, building accurate and transferable potential models using GNNs remains challenging, as the data are greatly limited by the expensive computational costs and level of theory of QM methods, especially for large and complex molecular systems. In this work, we propose denoise pretraining on nonequilibrium molecular conformations to achieve more accurate and transferable GNN potential predictions. Specifically, atomic coordinates of sampled nonequilibrium conformations are perturbed by random noises, and GNNs are pretrained to denoise the perturbed molecular conformations which recovers the original coordinates. Rigorous experiments on multiple benchmarks reveal that pretraining significantly improves the accuracy of neural potentials. Furthermore, we show that the proposed pretraining approach is model-agnostic, as it improves the performance of different invariant and equivariant GNNs. Notably, our models pretrained on small molecules demonstrate remarkable transferability, improving performance when fine-tuned on diverse molecular systems, including different elements, charged molecules, biomolecules, and larger systems. These results highlight the potential for leveraging denoise pretraining approaches to build more generalizable neural potentials for complex molecular systems. Non-equilibrium thermodynamics ; Computer science ; Molecule ; Neuroscience ; Statistical physics ; Artificial intelligence ; Physics ; Psychology ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Mahsa Zarei | Natalia Solomatova | Hoda Aghaei | Austin Rothwell | Jeffrey K. Wiens | Luke Melo | Travis G. Good | Sadegh Shokatian | Edward R. Grant
Source: Analytical Chemistry (article, 2023-09-12)
Field: Chemistry — Analytical Chemistry

Machine Learning Analysis of Raman Spectra To Quantify the Organic Constituents in Complex Organic–Mineral Mixtures Important decisions in local agricultural policy and practice often hinge on the soil's chemical composition. Raman spectroscopy offers a rapid noninvasive means to quantify the constituents of complex organic systems. But the application of Raman spectroscopy to soils presents a multifaceted challenge due to organic/mineral compositional complexity and spectral interference arising from overwhelming fluorescence. The present work compares methodologies with the capacity to help overcome common obstacles that arise in the analysis of soils. We created conditions representative of these challenges by combining varying proportions of six amino acids commonly found in soils with fluorescent bentonite clay and coarse mineral components. Referring to an extensive data set of Raman spectra, we compare the performance of the convolutional neural network (CNN) and partial least-squares regression (PLSR) multivariate models for amino acid composition. Strategies employing volume-averaged spectral sampling and data preprocessing algorithms improve the predictive power of these models. Our average test <i>R</i><sup>2</sup> for PLSR models exceeds 0.89 and approaches 0.98, depending on the complexity of the matrix, whereas CNN yields an <i>R</i><sup>2</sup> range from 0.91 to 0.97, demonstrating that classic PLSR and CNN perform comparably, except in cases where the signal-to-noise ratio of the organic component is very low, whereupon CNN models outperform. Artificially isolating two of the most prevalent obstacles in evaluating the Raman spectra of soils, we have characterized the effect of each obstacle on the performance of machine learning models in the absence of other complexities. These results highlight important considerations and modeling strategies necessary to improve the Raman analysis of organic compounds in complex mixtures in the presence of mineral spectral components and significant fluorescence. Partial least squares regression ; Raman spectroscopy ; Chemistry ; Principal component analysis ; Artificial intelligence ; Soil water ; Biological system ; Pattern recognition (psychology) ; Machine learning ; Computer science ; Soil science ; Environmental science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Metabolomics and Mass Spectrometry Studies
Authors: Brigitta Nagy | Ágnes Szabados-Nacsa | Gergő Fülöp | Anikó Turák Nagyné | Dorián László Galata | Attila Farkas | Lilla Alexandra Mészáros | Zsombor Kristóf Nagy | György Marosi
Source: International Journal of Pharmaceutics (article, 2023-01-18)
Field: Chemistry — Analytical Chemistry

Interpretable artificial neural networks for retrospective QbD of pharmaceutical tablet manufacturing based on a pilot-scale developmental dataset As the pharmaceutical industry increasingly adopts the Pharma 4.0. concept, there is a growing need to effectively predict the product quality based on manufacturing or in-process data. Although artificial neural networks (ANNs) have emerged as powerful tools in data-rich environments, their implementation in pharmaceutical manufacturing is hindered by their black-box nature. In this work, ANNs were developed and interpreted to demonstrate their applicability to increase process understanding by retrospective analysis of developmental or manufacturing data. The in vitro dissolution and hardness of extended-release, directly compressed tablets were predicted from manufacturing and spectroscopic data of pilot-scale development. The ANNs using material attributes and operational parameters provided better results than using NIR or Raman spectra as predictors. ANNs were interpreted by sensitivity analysis, helping to identify the root cause of the batch-to-batch variability, e.g., the variability in particle size, grade, or substitution of the hydroxypropyl methylcellulose excipient. An ANN-based control strategy was also successfully utilized to mitigate the batch-to-batch variability by flexibly operating the tableting process. The presented methodology can be adapted to arbitrary data-rich manufacturing steps from active substance synthesis to formulation to predict the quality from manufacturing or development data and gain process understanding and consistent product quality. Tableting ; Pharmaceutical manufacturing ; Quality by Design ; Artificial neural network ; Computer science ; Excipient ; Process engineering ; Process (computing) ; Quality (philosophy) ; Pharmaceutical industry ; Manufacturing engineering ; Data mining ; Artificial intelligence ; Engineering ; Particle size ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Drug Solubulity and Delivery Systems
Authors: Tobias Greisager Rehfeldt | Ralf Gabriels | Robbin Bouwmeester | Siegfried Gessulat | Benjamin A. Neely | Magnus Palmblad | Yasset Pérez‐Riverol | Tobias Schmidt | Juan Antonio Vizcaíno | Eric W. Deutsch
Source: Journal of Proteome Research (article, 2023-01-24)
Field: Chemistry — Spectroscopy

ProteomicsML: An Online Platform for Community-Curated Data sets and Tutorials for Machine Learning in Proteomics Data set acquisition and curation are often the most difficult and time-consuming parts of a machine learning endeavor. This is especially true for proteomics-based liquid chromatography (LC) coupled to mass spectrometry (MS) data sets, due to the high levels of data reduction that occur between raw data and machine learning-ready data. Since predictive proteomics is an emerging field, when predicting peptide behavior in LC-MS setups, each lab often uses unique and complex data processing pipelines in order to maximize performance, at the cost of accessibility and reproducibility. For this reason we introduce ProteomicsML, an online resource for proteomics-based data sets and tutorials across most of the currently explored physicochemical peptide properties. This community-driven resource makes it simple to access data in easy-to-process formats, and contains easy-to-follow tutorials that allow new users to interact with even the most advanced algorithms in the field. ProteomicsML provides data sets that are useful for comparing state-of-the-art machine learning algorithms, as well as providing introductory material for teachers and newcomers to the field alike. The platform is freely available at https://www.proteomicsml.org/, and we welcome the entire proteomics community to contribute to the project at https://github.com/ProteomicsML/ProteomicsML. Computer science ; Resource (disambiguation) ; Field (mathematics) ; Raw data ; Proteomics ; Machine learning ; Process (computing) ; Data science ; Set (abstract data type) ; Artificial intelligence ; Data mining ; Chemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Longfei Zhang | Rongfang Zhao | Yanzhou Wu | Zhiyang Zhang | Yan Chen | Meichun Liu | N. Zhou | Yunqing Wang | Xiuli Fu | Xuming Zhuang | Jianping Wang | Lingxin Chen
Source: Journal of Hazardous Materials (article, 2023-09-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultralow-background SERS substrates for reliable identification of organic pollutants and degradation intermediates Substrate (aquarium) ; Combinatorial chemistry ; Degradation (telecommunications) ; Chemistry ; Detection limit ; Nanotechnology ; Organic synthesis ; Density functional theory ; Materials science ; Organic chemistry ; Computer science ; Computational chemistry ; Catalysis ; Chromatography Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: Xuanjun Wu | Zijian Cao | Xiuyang Lü | Weiquan Cai
Source: Chemical Engineering Journal (article, 2023-01-28)
Field: Chemistry — Inorganic Chemistry

Prediction of methane adsorption isotherms in metal–organic frameworks by neural network synergistic with classical density functional theory Adsorption ; Methane ; Artificial neural network ; Metal-organic framework ; Langmuir adsorption model ; Biological system ; Thermodynamics ; Freundlich equation ; Chemistry ; Materials science ; Computer science ; Physical chemistry ; Artificial intelligence ; Organic chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Phase Equilibria and Thermodynamics ; Carbon Dioxide Capture Technologies
Authors: Qing‐Jie Li | Mahmut Nedim Cinbiz | Yin Zhang | Qi He | Geoffrey Beausoleil | Ju Li
Source: Acta Materialia (article, 2023-04-23)
Field: Materials Science — Materials Chemistry

Robust deep learning framework for constitutive relations modeling Robustness (evolution) ; Computer science ; Encoder ; Deep learning ; Mean squared error ; Artificial intelligence ; Algorithm ; Range (aeronautics) ; Computer engineering ; Mathematics ; Engineering Machine Learning in Materials Science ; Rock Mechanics and Modeling ; Drilling and Well Engineering
Authors: Maansi Aggarwal | Pranab Sahoo | Sriparna Saha | Prolay Das
Source: Journal of Agricultural and Food Chemistry (article, 2023-08-16)
Field: Materials Science — Materials Chemistry

Machine Learning-Mediated Ultrasensitive Detection of Citrinin and Associated Mycotoxins in Real Food Samples Discerned from a Photoluminescent Carbon Dot Barcode Array Economically viable remote sensing of foodborne contaminants using minimalistic chemical reagents and simultaneous automation calls for a concrete integration of a chemical detection strategy with artificial intelligence. In a first of its kind, we report the ultrasensitive detection of citrinin and associated mycotoxins like aflatoxin B1 and ochratoxin A using an Alizarin Red S (ARS) and cystamine-derived carbon dot (CD) that aptly amalgamate with machine learning algorithms for automation. The photoluminescence response of the CD as a function of various solvents and pH is used to generate array channels that are further modulated in the presence of the mycotoxins whose digital images were acquired to determine pixelation, essentially creating a barcode. The barcode was fed to machine learning algorithms that actualize and intertwine convoluted databases, demonstrating Extreme Gradient Boosting (XGBoost) as the optimized model out of eight algorithms tested. Spiked samples of wheat, rice, gram, maize, coffee, and milk were used to evaluate the testing model where an exemplary accuracy of 100% even at 10 pmol of mycotoxin concentration was achieved. Most importantly, the coexistence of mycotoxins could also be detected through the CD array and XGBoost synergy hinting toward a broader scope of the developed methodology for smart detection of foodborne contaminants. Citrinin ; Mycotoxin ; Ochratoxin A ; Barcode ; Aflatoxin ; Artificial intelligence ; Chemistry ; Computer science ; Food science Carbon and Quantum Dots Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Susumu Fujii | Katsuhiko Shimazaki | Akihide Kuwabara
Source: Acta Materialia (article, 2023-10-22)
Field: Materials Science — Materials Chemistry

Empirical interatomic potentials for ZrO2 and YSZ polymorphs: Application to a tetragonal ZrO2 grain boundary Yttria stabilized zirconia (YSZ) ceramics have been used for various engineering applications including structural ceramics, biomedical materials, and thermal barrier coatings. The versatile and excellent properties of YSZ stem from its unique microstructure consisting of monoclinic, tetragonal, and cubic phases, whose stability depends on yttria concentration and temperature. However, there are no empirical interatomic potentials (EIPs) that can reproduce the structures and energies of ZrO2 and YSZ polymorphs, limiting the atomic-scale investigation of lattice defect structures and their interactions that affect the YSZ microstructure and properties. Here, using a genetic algorithm and ab initio training datasets, we have optimized EIPs to sufficiently reproduce the structures and stability of ZrO2 and YSZ polymorphs, as well as the properties of the tetragonal and cubic phases at finite temperature. The potentials have also been applied to the search for a tetragonal grain boundary structure, showing that the obtained grain boundary structure is consistent with that obtained by ab initio calculations. The developed EIPs will aid in revealing the microstructure-property relationships in YSZ by performing large-scale and systematic calculations, which are practically difficult to perform with ab initio and machine-learning-potential calculations. Materials science ; Yttria-stabilized zirconia ; Tetragonal crystal system ; Microstructure ; Grain boundary ; Ab initio ; Ab initio quantum chemistry methods ; Interatomic potential ; Monoclinic crystal system ; Ceramic ; Chemical physics ; Thermodynamics ; Cubic zirconia ; Crystal structure ; Crystallography ; Computational chemistry ; Molecular dynamics ; Composite material ; Chemistry Machine Learning in Materials Science ; Nuclear Materials and Properties ; Electronic and Structural Properties of Oxides
Authors: Ruiyang Li | Eungkyu Lee | Tengfei Luo
Source: Physical Review Applied (article, 2023-06-15)
Field: Materials Science — Materials Chemistry

Physics-Informed Deep Learning for Solving Coupled Electron and Phonon Boltzmann Transport Equations Electron-phonon (e-ph) coupling and transport are ubiquitous in modern electronic devices. The coupled electron and phonon Boltzmann transport equations (BTEs) hold great potential for the simulation of thermal transport in metal and semiconductor systems. However, solving the BTEs is often computationally challenging owing to their high dimensional complexity and a wide span of heat carrier properties, which hinder large-scale thermal modeling at the device level. In this work, we present a physics-informed neural network framework for solving the coupled electron and phonon BTEs. Instead of relying on labeled data, the proposed framework directly learns the spatiotemporal solutions (i.e., the electron and phonon distribution functions) within a parameterized space by enforcing physical laws. The efficacy of this framework is demonstrated through its ability to accurately resolve temperature profiles in low-dimensional thermal transport problems and visualize the ultrafast electron and phonon dynamics in laser heating experiments on thin metal films. The results indicate that our approach can accurately describe nonequilibrium e-ph energy transfer with improved efficiency, opening alternative avenues for the predictive design and optimization of micro- and nanostructures. Phonon ; Boltzmann equation ; Coupling (piping) ; Boltzmann constant ; Electron ; Transport phenomena ; Electron temperature ; Statistical physics ; Physics ; Thermal ; Thermal conductivity ; Non-equilibrium thermodynamics ; Parameterized complexity ; Computational physics ; Materials science ; Computer science ; Condensed matter physics ; Mechanics ; Quantum mechanics ; Thermodynamics ; Algorithm Thermal properties of materials ; Heat Transfer and Optimization ; Model Reduction and Neural Networks
Authors: Cheng Hung Chu | Yu‐Hsin Chia | Hung‐Chuan Hsu | Sunil Vyas | Chen-Ming Tsai | Takeshi Yamaguchi | Takuo Tanaka | Huei‐Wen Chen | Yuan Luo | Pan‐Chyr Yang | Din Ping Tsai
Source: Nano Letters (article, 2023-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Intelligent Phase Contrast Meta-Microscope System Phase contrast imaging techniques enable the visualization of disparities in the refractive index among various materials. However, these techniques usually come with a cost: the need for bulky, inflexible, and complicated configurations. Here, we propose and experimentally demonstrate an ultracompact meta-microscope, a novel imaging platform designed to accomplish both optical and digital phase contrast imaging. The optical phase contrast imaging system is composed of a pair of metalenses and an intermediate spiral phase metasurface located at the Fourier plane. The performance of the system in generating edge-enhanced images is validated by imaging a variety of human cells, including lung cell lines BEAS-2B, CLY1, and H1299 and other types. Additionally, we integrate the ResNet deep learning model into the meta-microscope to transform bright-field images into edge-enhanced images with high contrast accuracy. This technology promises to aid in the development of innovative miniature optical systems for biomedical and clinical applications. Microscope ; Computer science ; Optics ; Phase-contrast imaging ; Visualization ; Contrast (vision) ; Phase (matter) ; Microscopy ; Artificial intelligence ; Phase imaging ; Materials science ; Optical microscope ; Phase contrast microscopy ; Computer vision ; Physics ; Scanning electron microscope Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Digital Holography and Microscopy