jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tyler Sours | Ambarish Kulkarni
Source: ChemRxiv (preprint, 2022-09-29)
Field: Materials Science — Materials Chemistry

Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials Machine learning potentials (MLPs) capable of accurately describing complex ab initio potential energy surfaces (PES) have revolutionized the field of multiscale atomistic modeling. In this work, using an extensive density functional theory (DFT) dataset (denoted as Si-ZEO22) consisting of 187 unique silica topologies found in the International Zeolite Association (IZA) database, we have trained a DeePMD-kit MLP to model the dynamics of silica frameworks. The performance of our model is evaluated by calculating various properties that probe the accuracy of the energy and force predictions. This MLP demonstrates impressive agreement with DFT for predicting zeolite structural properties, energy-volume trends, and phonon density of states. Furthermore, our model achieves reasonable predictions for stress-strain relationships without including DFT stress data during training. These results highlight the ability of MLPs to capture the flexibility of zeolite frameworks and motivates further MLP development for nanoporous materials with near-ab initio accuracy. Nanoporous ; Ab initio ; Density functional theory ; Artificial neural network ; Zeolite ; Materials science ; Flexibility (engineering) ; Computer science ; Force field (fiction) ; Artificial intelligence ; Computational chemistry ; Nanotechnology ; Chemistry ; Mathematics Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Advanced NMR Techniques and Applications
Authors: Yanliang Zhao | Jikai Sun | Li Yang | Dong Zhai | Lei Sun | Wei Deng
Source: Chemical Physics Letters (article, 2022-06-26)
Field: Materials Science — Materials Chemistry

Umbrella sampling with machine learning potentials applied for solid phase transition of GeSbTe Phase transition ; Umbrella sampling ; Molecular dynamics ; Sampling (signal processing) ; Energy landscape ; Chemical physics ; Statistical physics ; Phase (matter) ; Process (computing) ; Materials science ; Ab initio ; Amorphous solid ; Folding (DSP implementation) ; Vacancy defect ; Computer science ; Chemistry ; Computational chemistry ; Condensed matter physics ; Physics ; Crystallography ; Thermodynamics ; Engineering ; Quantum mechanics ; Mechanical engineering ; Computer vision Machine Learning in Materials Science ; Phase-change materials and chalcogenides ; Material Dynamics and Properties
Authors: Murilo Moreira | Matthias Hillenkamp | Giorgio Divitini | Luiz H. G. Tizei | Caterina Ducati | M. A. Cotta | Varlei Rodrigues | D. Ugarte
Source: Microscopy and Microanalysis (article, 2022-04-18)
Field: Materials Science — Surfaces, Coatings and Films

Improving Quantitative EDS Chemical Analysis of Alloy Nanoparticles by PCA Denoising: Part II. Uncertainty Intervals Analytical studies of nanoparticles (NPs) are frequently based on huge datasets derived from hyperspectral images acquired using scanning transmission electron microscopy. These large datasets require machine learning computational tools to reduce dimensionality and extract relevant information. Principal component analysis (PCA) is a commonly used procedure to reconstruct information and generate a denoised dataset; however, several open questions remain regarding the accuracy and precision of reconstructions. Here, we use experiments and simulations to test the effect of PCA processing on data obtained from AuAg alloy NPs a few nanometers wide with different compositions. This study aims to address the reliability of chemical quantification after PCA processing. Our results show that the PCA treatment mitigates the contribution of Poisson noise and leads to better quantification, indicating that denoised results may be reliable from the point of view of both uncertainty and accuracy for properly planned experiments. However, the initial data need to be of sufficient quality: these results can only be obtained if the signal-to-noise ratio of input data exceeds a minimal value to avoid the occurrence of random noise bias in the PCA reconstructions. Principal component analysis ; Dimensionality reduction ; Computer science ; Pattern recognition (psychology) ; Hyperspectral imaging ; Noise (video) ; Reliability (semiconductor) ; Artificial intelligence ; Curse of dimensionality ; Noise reduction ; Data mining ; Physics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Electrochemical Analysis and Applications
Authors: Koki Obinata | Tomofumi Nakayama | Atsushi Ishikawa | Keitaro Sodeyama | Kenji Nagata | Yasuhiko Igarashi | Masato Okada
Source: Science and Technology of Advanced Materials Methods (article, 2022-09-26)
Field: Materials Science — Materials Chemistry

Data integration for multiple alkali metals in predicting coordination energies based on Bayesian inference Building machine learning models using a dataset calculated by first principles calculations is an important approach to explore the next-generation batteries. In previous studies, the rechargeable secondary battery dataset was constructed for Li ion by density functional theory (DFT) calculation, and after that, extended to five alkali metal ions. This dataset can be regarded as consisting of five alkali metal ion groups, and it is one of the interests to know which approach is preferred to build individual models specialized for each alkali metal ion or build a single model by integrating the datasets. We quantitatively evaluate the possibility of data integration in the framework of Bayesian model selection and show that the integration of datasets is suitable. In addition, extracting new knowledge using feature selection is also important in exploring next-generation batteries. In order to further advance the knowledge extraction, the reliability of the selected features should be considered to avoid misinterpretation. We evaluate the confidence level of feature selection by calculating the posterior probabilities of features using Bayesian model averaging (BMA). We found that the confidence level of feature selection increases when the number of data increases. Inference ; Alkali metal ; Bayesian probability ; Bayesian inference ; Computer science ; Data mining ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; X-ray Diffraction in Crystallography
Authors: Yaser Açıkbaş | Matem Erdoğan | Rıfat Çapan | Cansu Özkaya | Yasemin Bayğu | Nilgün Kabay | Yaşar Gök | Gürkan Küçükyıldız
Source: Applied Nanoscience (article, 2022-12-21)
Field: Materials Science — Materials Chemistry

Preparation and characterization of the phthalocyanine–zinc(II) complex-based nanothin films: optical and gas-sensing properties Phthalocyanine ; Materials science ; Surface plasmon resonance ; Thin film ; Analytical Chemistry (journal) ; Nanotechnology ; Organic chemistry ; Nanoparticle ; Chemistry Porphyrin and Phthalocyanine Chemistry ; Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors
Authors: Mouad Ramil | Caroline Boudier | Alexandra M. Goryaeva | Mihai‐Cosmin Marinica | Jean‐Bernard Maillet
Source: Journal of Chemical Theory and Computation (article, 2022-09-08)
Field: Materials Science — Materials Chemistry

On Sampling Minimum Energy Path Sampling the minimum energy path (MEP) between two minima of a system is often hindered by the presence of an energy barrier separating the two metastable states. As a consequence, direct sampling based on molecular dynamics or Markov Chain Monte Carlo methods becomes inefficient, the crossing of the energy barrier being associated to a rare event. Augmented sampling methods based on the definition of collective variables or reaction coordinates allow us to circumvent this limitation at the price of an arbitrary choice of the dimensionality reduction algorithm. We couple the statistical sampling techniques, namely, metadynamics and invertible neural networks, with autoencoders so as to gradually learn the MEP and the collective variable at the same time. Learning is achieved through a succession of two steps: statistical sampling of the most probable path between the two minima and redefinition of the collective variable from the updated data points. The prototypical Mueller potential with nearly orthogonal minima is employed to demonstrate the ability of such coupling to unravel a complex MEP. Maxima and minima ; Sampling (signal processing) ; Computer science ; Metadynamics ; Path (computing) ; Rare events ; Statistical physics ; Energy landscape ; Oversampling ; Curse of dimensionality ; Energy (signal processing) ; Markov chain Monte Carlo ; Monte Carlo method ; Importance sampling ; Algorithm ; Filter (signal processing) ; Molecular dynamics ; Mathematics ; Artificial intelligence ; Physics ; Statistics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Núbia Rosa da Silva | Victor Deklerck | Jan Baetens | Jan Van den Bulcke | Maaike De Ridder | Mélissa Rousseau | Odemir Martinez Bruno | Hans Beeckman | Joris Van Acker | Bernard De Baets | Jan Verwaeren
Source: Research Square (preprint, 2022-01-05)
Field: Chemistry — Organic Chemistry

Improved Wood Species Identification Based On Multi-View Imagery of The Three Anatomical Planes Identification (biology) ; Computer vision ; Artificial intelligence ; Geography ; Computer science ; Computational biology ; Biology ; Ecology Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Remote Sensing and Land Use
Authors: Jeroen Koopman | Stefan Grimme
Source: ChemRxiv (preprint, 2022-05-10)
Field: Chemistry — Spectroscopy

Calculation of mass spectra with the QCxMS method for negatively and multiply charged molecules Detailed information about the structural composition of an unknown chemical analyte can be obtained routinely and reliably by using mass spectrometry (MS). Analysis and validation of an MS experiment are usually performed by comparison to reference spectra, which are stored in databases that contain a large number of entries for common molecules. This procedure relies on the quality and completeness of the entries and if structures (classes) are missing, measured spectra cannot be properly matched. To close this gap, and to enable detailed mechanistic analysis, the Quantum Chemical Mass Spectrometry (QCxMS) program has been developed. It enables fully automatic calculations of electron ionization (EI), dissociative electron attachment (DEA), and positive ion collision induced dissociation (CID) mass spectra of singly charged molecular ions. In this work, the extension to negative and multiple ion charge for the CID run mode is presented. QCxMS is now capable of calculating structures carrying any charge, without the need for pre-tabulated fragmentation pathways or machine-learning of database spectra. Mass spectra of four single negatively charged, as well as two multiple positively charged organic ions with molecular sizes ranging from 12 to 92 atoms were computed and compared to reference spectra taken from the literature. The underlying Born-Oppenheimer molecular dynamics (MD) calculations were conducted using the extended tight-binding semi-empirical quantum mechanical GFN2-xTB method while for some small molecules, ab-initio DFT-based MD simulations were performed. Detailed insights into the fragmentation pathways were gained and the effects of the computed charge assignments on the resulting spectrum are discussed. Especially for the negative ion mode, the influence of the deprotonation site to create the anion was found to be substantial. Doubly charged fragments could successfully be calculated for the first time while higher charged structures introduced severe assignment problems. Overall, this extension of the QCxMS program further enhances its applicability and underlines its value as a sophisticated toolkit for CID-based tandem MS structure elucidation. Mass spectrum ; Fragmentation (computing) ; Chemistry ; Electron ionization ; Ion ; Mass spectrometry ; Ionization ; Collision-induced dissociation ; Polyatomic ion ; Spectral line ; Dissociation (chemistry) ; Ab initio ; Molecule ; Ab initio quantum chemistry methods ; Atomic physics ; Tandem mass spectrometry ; Physics ; Physical chemistry ; Computer science ; Quantum mechanics Mass Spectrometry Techniques and Applications ; Analytical chemistry methods development ; Analytical Chemistry and Chromatography
Authors: Aaron A. Kohnert | Brian D. Wirth | Chris Wolverton | Prasanna V. Balachandran | Laurent Capolungo
Source: MRS Bulletin (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

Modeling materials under coupled extremes: Enabling better predictions of performance Multiphysics ; Computer science ; Systems engineering ; Engineering Fusion materials and technologies ; Nuclear Materials and Properties ; Nuclear reactor physics and engineering
Authors: В. В. Горбачев | Evgeny Nikulchev | Alexander N. Kosenkov | Andrey A. Sokolov | И. В. Завалишин | И.А. Никитин
Source: International Journal of Environmental Research and Public Health (article, 2022-11-23)
Field: Chemistry — Organic Chemistry

Estimating the Mass of Food Components Necessary for the Utilization of Free Radical Particles in the Human Body The article proposes an algorithm for an approximate assessment of the molar volume of free radicals generated in the human body per day. It takes into account the act of breathing, physical activity, food consumption, the influence of unfavorable environmental conditions, exposure to xenobiotics, as well as bad habits (alcohol and tobacco smoking). A calculation of the required set of the most commonly used food products for the disposal of free radicals was made. The calculation is a structure of four blocks with the possibility of adding optional data from human population genetic studies, environmental conditions, etc. In the proposed algorithm, the results of antiradical activity (ARA) of food products are used as input, including the results of predicting antiradical activity using artificial neural networks (ANN), which we published earlier. Based on the accepted values of one equivalent (in terms of the activity of 1 μmol of ascorbic acid), it was shown (for our data) that for the utilization of all free radicals produced in the human body per day, it will take an average of ≈260 to ≈540 g of food components in terms of dry mass (including proteins, fats, carbohydrates, etc.). At the same time, for the utilization of consumed xenobiotics, from 220 mg (in terms of vitamin C) to 260 mg (in terms of acetylcysteine -NAC) of additional plastic components or 11.5-13.0 g of essential amino acids will be required, which must be taken into account when calculating diets. This approach will be useful in the development of new functional foods, as well as in assessing the possible impact of diets on human health. Another applied point of this study is related to the possibility of using these data for better detailing and selection of food products for people working in conditions of increased radiation (in space conditions), in contact with harmful substances (chemical synthesis and production), for people practicing increased physical activity (bodybuilding and sports), and for the purposes of medical nutritional therapy. Xenobiotic ; Ascorbic acid ; Radical ; Population ; Food science ; Chemistry ; Set (abstract data type) ; Biological system ; Toxicology ; Biochemistry ; Computer science ; Biology ; Environmental health ; Medicine Free Radicals and Antioxidants ; Phytochemicals and Antioxidant Activities ; Biochemical effects in animals
Authors: Yujie Zhu | Chuanyang Li | Shaojie Wang | Manxi Li | Junluo Li | Shixun Hu | Qi Li | Jinliang He
Source: Journal of Physics D Applied Physics (article, 2022-12-29)
Field: Materials Science — Materials Chemistry

Charge trap depth prediction of grafted polypropylene system using machine learning Abstract Carbon neutrality is a state of dynamic equilibrium in carbon dioxide emissions. In insulation material aspect, polypropylene (PP) with grafted side chain segments possesses recyclability and may have better electrical property under high temperature compared with traditional norecyclable insulation insulation material like cross-linked polyethylene, which will reduce the carbon emission and help carbon neutrality processing. However, focused on the ideal grafted PP chain, the bandgap ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi>E</mml:mi> <mml:mrow> <mml:mi mathvariant="normal">g</mml:mi> </mml:mrow> </mml:msub> </mml:math> ) parameter is no longer suitable for describing the electrical properties of this system for the small grafting ratio. The induced grafted monomer might generate new band energy level and reduce the bandgap but with improvement on the electrical properties, which is not consistent with the positive correlation of the bandgap and the electrical properties. Alternatively, the trap depth, as one of the important electrical performance parameters, can reflect its influence on conductivity and breakdown. In this paper, the trap depth of the grafted PP chain is predicted by machine learning method with recursive feature elimination-based fingerprints. The paper analyzes the influence of different component groups and the ratioes of PP monomers on the charge trap depth. The grafted monomers with strong positive correlations on the electron/hole trap depth are given, and the electrical conductivity of PP-g-VK polymer has been tested for validation from the side. This paper provides a predictable parameter for the analysis of grafted systems and accelerates future predictions of performance parameters such as conductivity and breakdown strength. Materials science ; Trap (plumbing) ; Polypropylene ; Band gap ; Electrical resistivity and conductivity ; Composite material ; Analytical Chemistry (journal) ; Algorithm ; Polymer chemistry ; Computer science ; Electrical engineering ; Optoelectronics ; Chemistry ; Physics ; Organic chemistry High voltage insulation and dielectric phenomena ; Advanced Sensor and Energy Harvesting Materials ; Electrostatic Discharge in Electronics
Authors: Jiwoo An | Gabriella Guzman-Joyce | Alexandra Brooks | Kailey To | Lisa Vu | Cynthia J. Luxford
Source: Journal of Chemical Education (article, 2022-01-10)
Field: Chemistry — Physical and Theoretical Chemistry

Cluster Analysis of Learning Approaches and Course Achievement of General Chemistry Students at a Hispanic Serving Institution Introductory chemistry courses tend to suffer from high failure rates. Success in an introductory course has been linked to how students approach learning. The aim of this study was to explore how students’ learning approach was related to their success in the introductory chemistry courses measured by the ACS exam and the final course grade. Additionally, the study sought to understand how the level of success varied between Hispanic and non-Hispanic students at a Hispanic Serving Institution (HSI). Students in general chemistry courses were surveyed at the beginning and the end of the semester using a shortened version of the Revised Approaches to Studying Inventory (RASI). The survey consisted of 18 Likert-scale items that measured three approaches to learning: surface, strategic, and deep. K-means cluster analysis was performed to explore patterns of students’ learning approaches and how they relate to success in the introductory chemistry courses. Five cluster profiles emerged, and the results indicated students with high strategic, high deep, and low surface learning approach scores had the highest average score of 53.8% on the ACS final exam, while students who had low strategic, low deep, and high surface leaning approach scores had the lowest average score of 42.1% on the ACS final exam. Overall cluster characteristics between Hispanic and non-Hispanic student groups were identical. However, for the ideal clustering of low surface and high strategic and deep learning approach scores, there was a small but statistically significant (p < 0.05) difference in the average ACS exam scores between the two demographic groups. The non-Hispanic group showed a mean ACS exam score of 55.3%, whereas a mean score of 50.7% was observed for the Hispanic student group. Additionally, the average final course grade for all but one cluster was slightly lower for the Hispanic group. The results of this study serve as another motivation to understand how to better support underserved students in chemistry courses at an HSI. Furthermore, these findings contribute to the existing literature of undergraduate students’ learning approaches, particularly in relation to Hispanic and non-Hispanic demographic groups. Likert scale ; Mathematics education ; Psychology ; Cluster (spacecraft) ; Scale (ratio) ; Chemistry ; Computer science ; Cartography ; Geography Various Chemistry Research Topics ; Evaluation of Teaching Practices ; Innovative Teaching Methods
Authors: Xiaohan Zhang | Chunyi Zhang | Cui Zhang | Ping Zhang | Wei Kang
Source: Physical review. B./Physical review. B (article, 2022-01-10)
Field: Materials Science — Materials Chemistry

Finite-temperature phonon dispersion and vibrational dynamics of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>BaTiO</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math> from first-principles molecular dynamics A systematic investigation of the finite-temperature phonon dispersion, including its smearing and temperature effects, is carried out using autocorrelation function method together with first-principles molecular dynamics method along the [001] direction of $\mathrm{Ba}\mathrm{Ti}{\mathrm{O}}_{3}$, as an illustrating example of entropy-stabilized structures. A unique anharmonicity in the cubic phase mainly derived from the interactions between titanium and oxygen atoms is revealed, which provides extremely strong damping and smearing to longitudinal optical phonons but no discernible effect on the shift of phonon energies. The anharmonicity gives rise to a nearly constant density distribution in a cubic region around the equilibrium position of the relative motion of titanium atoms with respect to oxygen atoms. These results may help to gain a further insight into complex interactions in entropy-stabilized structures and provide an essential benchmark reference to the development of the promising machine-learning based molecular dynamics methods for the investigation of phonon properties. Anharmonicity ; Phonon ; Physics ; Materials science ; Molecular dynamics ; Condensed matter physics ; Quantum mechanics Thermal properties of materials ; High-pressure geophysics and materials ; Ferroelectric and Piezoelectric Materials