jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qiyu Yang | Congzi Xia | Yuanwu Cui | Xiaoqiuyan Zhang | Tianyu Zhang | Yong Huang | Diwei Liu | Yanyu Wei | Yubin Gong | Guangyou Fang | Zhenyou Wang | XueQuan Chen | Min Hu
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Fusion of Terahertz Spectroscopy and Raman Spectroscopy Combined with Support Vector Machine to Distinguish Different Pericarpium Citri Reticulatae Species Raman spectroscopy ; Spectroscopy ; Terahertz spectroscopy and technology ; Terahertz radiation ; Support vector machine ; Analytical Chemistry (journal) ; Materials science ; Nuclear magnetic resonance ; Chemistry ; Physics ; Computer science ; Artificial intelligence ; Optics ; Optoelectronics ; Chromatography ; Astronomy Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Kazushige Hyodo | Kenta Hongo | Tom Ichibha | Ryo Maezono
Source: Journal of Alloys and Compounds (article, 2024-08-30)
Field: Materials Science — Materials Chemistry

Solid-liquid phase boundary of oxide solid solutions using neural network potentials We propose a cost-effective computational approach for predicting the phase boundary of oxide solid solutions, i.e., melting point, by identifying the point where the free energies of the solid and liquid phases are equivalent. To evaluate the melting point, we computed the temperature-dependent free energies of both phases using the thermodynamic integration (TI) method. This was combined with the reverse-scaling technique, employing the Einstein model for the solid and the scaled Uhlenbeck–Ford model for the liquid as the reference systems. The change in free energy between the real and reference states in the present TI method was calculated as the ensemble average of the configurations sampled from molecular dynamics (MD) simulations using neural network potentials (NNPs) trained on first-principles density functional theory (DFT) data. Initially, we benchmarked copper (Cu) as a typical metal. Tungsten (W) and magnesium oxide (MgO) were then tested as typical high melting-point metals and oxides, respectively, achieving good agreement with both ab initio MD (AIMD) results and experimental data. We then applied our established approach to a BaO–CaO oxide solid solution, observing that the computed phase boundary aligns well with Calphad predictions from previous studies. The integration of NNPs into phase boundary prediction is essential for reducing computational costs while ensuring accuracy, compared with AIMD-based approaches. • Neural network-based potentials are proposed for cost-effective and high-accuracy prediction tools of phase diagrams. • The above concepts are validated for solid–liquid phase boundaries of metal-oxides with high melting points. • Accuracies of phase boundaries calculated by molecular dynamics using neural network potentials are comparable to those of the Calphad method. Oxide ; Artificial neural network ; Phase boundary ; Phase (matter) ; Materials science ; Boundary (topology) ; Solid solution ; Chemistry ; Computer science ; Mathematics ; Metallurgy ; Mathematical analysis ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics ; Calcium Carbonate Crystallization and Inhibition
Authors: Yunsung Lim | Hyunsoo Park | Aron Walsh | Jihan Kim
Source: ChemRxiv (preprint, 2024-12-13)
Field: Chemistry — Inorganic Chemistry

Accelerating CO₂ Direct Air Capture Screening for Metal-Organic Frameworks with a Transferable Machine Learning Force Field Direct air capture (DAC) of CO₂ is necessary for climate change mitigation, but it faces challenges from low atmospheric CO₂ concentrations and competition from water vapor. Metal-organic frameworks (MOFs) are attractive candidates for DAC owing to their exceptionally high surface area, tunable porosity, and potential for adsorption-based capture processes with relatively low regeneration cost. Identifying optimal MOFs is hindered by their structural complexity, the vastness of their chemical space, and the expense of accurate simulations. Here, we present a machine learning force field (MACE-DAC) tailored for CO2 and H2O interactions in MOFs by finetuning the foundation model MACE-MP-0. To address smoothing issues and catastrophic forgetting, we curated the diverse GoldDAC dataset and introduced a continual learning loss function. To efficiently sample gas configurations, we developed the DAC-SIM package that uses MLFFs to achieve ab initio quality thermodynamics based on Widom insertion at computational speeds comparable to classical force fields. High-throughput screening on more than 8,000 synthesized MOF structures was performed to identify optimal MOFs and extract important chemical features. This approach overcomes prior limitations in describing CO2/MOF and H₂O/MOF interactions, providing a scalable and accurate framework for accelerating DAC research for porous materials. Metal-organic framework ; Scalability ; Computer science ; Porosity ; Smoothing ; Adsorption ; Nanotechnology ; Materials science ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Phase Equilibria and Thermodynamics ; Catalytic Processes in Materials Science
Authors: Yang Yang | Cheng Chen | Yunhao Fan | Gia-Wei Chern
Source: arXiv (Cornell University) (preprint, 2024-12-30)
Field: Materials Science — Materials Chemistry

Enhanced coarsening of charge density waves induced by electron correlation: Machine-learning enabled large-scale dynamical simulations The phase ordering kinetics of emergent orders in correlated electron systems is a fundamental topic in non-equilibrium physics, yet it remains largely unexplored. The intricate interplay between quasiparticles and emergent order-parameter fields could lead to unusual coarsening dynamics that is beyond the standard theories. However, accurate treatment of both quasiparticles and collective degrees of freedom is a multi-scale challenge in dynamical simulations of correlated electrons. Here we leverage modern machine learning (ML) methods to achieve a linear-scaling algorithm for simulating the coarsening of charge density waves (CDWs), one of the fundamental symmetry breaking phases in functional electron materials. We demonstrate our approach on the square-lattice Hubbard-Holstein model and uncover an intriguing enhancement of CDW coarsening which is related to the screening of on-site potential by electron-electron interactions. Our study provides fresh insights into the role of electron correlations in non-equilibrium dynamics and underscores the promise of ML force-field approaches for advancing multi-scale dynamical modeling of correlated electron systems. Scale (ratio) ; Electron ; Charge (physics) ; Correlation ; Physics ; Charge density wave ; Statistical physics ; Condensed matter physics ; Computational physics ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Quantum and electron transport phenomena
Authors: Ruihao Yuan | Bo Wang | Jinshan Li | Peng Sun | Zhen Liu | Turab Lookman | Dezhen Xue
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Enabled Design of Ferroelectrics with Multiple Properties via a Landau Model Computer science ; Cognitive science ; Psychology ; Neuroscience Machine Learning in Materials Science
Authors: Fahimeh Derakhshanfard | Amir Mehralizadeh
Source: SHILAP Revista de lepidopterología (article, 2024-02-01)
Field: Materials Science — Polymers and Plastics

Investigation of Tri-Calcium Phosphates Effect in Multi-Stage Production of Expandable Polystyrene with Applications of Artificial Neural Networks There are some polymers, like expandable polystyrene, which are mostly used in industries. The production of EPS had some issues, which made the process of its production hard and reduced the polymer produced. In this research, besides of implementation of the initiator injection method, adding Tri-Calcium Phosphate (TCP) with different percentages (3, 6, and 9%) in different states (polymerization of the first stage in 2.5, 3, 3.5, and 4 hours and the amount of used initiator in 70, 75, 80, and 100% of the conventional method, and the number of injections in 6, 8, 10, and 12 times) has been tested, and different tests have been conducted on the produced polymer. Artificial neural networks have simulated the results of obtained data from experiments, and the results of the RBF network had better prediction compared with the MLP network because of having more scientific foundations and filtering noises; therefore, the points that have not been experimented on can be predicted by it. Investigating the experimental data shows that in a constant percentage of TCP, by changing the initiator amount and dosing times, and increasing the time of polymerization, the PDI, absorbed pentane, residual monomer amount, and tension in yield point change. TCP change in different laboratory conditions changes the quality of the polymer, with attention to the market's needs, and the importance of each item. The information from this research can be used accordingly. Artificial neural network ; Polystyrene ; Yield (engineering) ; Monomer ; Residual ; Polymer Polymer Science and PVC ; biodegradable polymer synthesis and properties ; Injection Molding Process and Properties
Authors: Hasnul Hadi, M.H. | Ker, Pin Jern | Lee, Hui Jing | Leong, Yang Sing | Thiviyanathan, Vimal A. | Hannan, M.A. | Jamaludin, Md. Zaini | Mahdi, M.A.
Source: Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia) (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Measuring color index of transformer oil-enabling single-wavelength spectroscopy with artificial neural network-fuzzy logic model Conventionally, the color index of transformer oil is determined by a color comparator based on the American Society for Testing and Materials (ASTM) D 1500 standard. The equipment requires humans to operate, which leads to human error and limited number of samples tested per day. This work proposes the utilization of single-wavelength spectroscopy with 405 nm laser diode using artificial neural network (ANN) to determine the color index of transformer oil. Two ANN models were developed using data collected from 50 oil samples with different optical pathlengths of 1 to 10 mm, and laser output powers of 1 to 15 mW. The first model classified the input into different color indices and another model correlated the input parameters through regression analysis to determine the color index. A hybrid ANN-fuzzy logic model was also developed to improve the color index prediction. The root-mean-squared error (RMSE) obtained from the prediction by ANN regressor and ANN classifier are 0.5602 and 0.6416, respectively. The hybrid ANN-fuzzy logic model improves the RMSE especially for optical pathlengths < 5 mm, which is required for measuring samples with high color index. This proposed method reduces the dependency on complex optoelectronic hardware to obtain highly accurate results.Note to Practitioners - Unlike the conventional testing method for color index of transformer oil that requires human observation, the findings of this study enables the possibility of compact and smart portable device through the utilization of single wavelength spectroscopy with machine learning models. With no human involvement, more computational power with lesser hardware dependency, the maintenance cost and error can be reduced. This proposed method can potentially be applied to measure the color of other amber-colored liquid products such as olive oil, honey and others. Artificial neural network ; Transformer ; Transformer oil ; Metrology ; Laser ; Light-emitting diode ; Spectroscopy Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Power Transformer Diagnostics and Insulation
Authors: Zhan‐Bin Chen
Source: International Journal of Quantum Chemistry (article, 2024-05-21)
Field: Chemistry — Spectroscopy

Investigation of the cross sections for electron collision ionization of complex molecules Abstract An accurate and computationally efficient determination of the cross sections for electron collision ionization of molecules has various applications, such as plasma physics and atmospheric science. In the case of large molecules, ab initio calculations are often difficult and time‐consuming. Here, we develop a feed forward neural network to predict the electron impact ionization cross sections of complex molecules. The training (predicting) set in the method consists of a series of theoretical ionization cross sections for small (large) molecules obtained from the combined model, which integrates the Binary‐Encounter‐Bethe and Deutsch‐Märk models. Several complex systems or targets involving electron collision ionization are evaluated, including molecules such as CH, CH, CH, CH, C, CHO, and CHO. The root mean square errors of the trained and predicted cross sections by the neural network (compared to the values from the combined model) are found to be approximately .0086 and .0930 (in 10 cm), respectively, (using the CHO molecule as an example), indicating our results are very high accuracy. The excellent agreement between the predicted values and the actual values indicates that the neural network is a practical and powerful tool for determining the electron collision ionization cross sections of complex molecules and can provide valuable insights into the dynamics process. Apart from its fundamental importance, this study has far‐reaching implications for gas discharge, low‐temperature plasmas, and fusion edge plasmas and so forth. Ionization ; Atomic physics ; Electron ; Electron ionization ; Collision ; Chemistry ; Molecule ; Ab initio ; Binary number ; Plasma ; Physics ; Ion ; Computer science ; Nuclear physics Mass Spectrometry Techniques and Applications ; Advanced Chemical Physics Studies ; Analytical chemistry methods development
Authors: Adhari AlZaabi | Stephen Piccolo | Steven W. Graves | Marc D.H. Hansen
Source: Cancers (article, 2024-06-27)
Field: Chemistry — Spectroscopy

Differential Serum Peptidomics Reveal Multi-Marker Models That Predict Breast Cancer Progression Here, we assess how the differential expression of low molecular weight serum peptides might predict breast cancer progression with high confidence. We apply an LC/MS-MS-based, unbiased 'omics' analysis of serum samples from breast cancer patients to identify molecules that are differentially expressed in stage I and III breast cancer. Results were generated using standard and machine learning-based analytical workflows. With standard workflow, a discovery study yielded 65 circulating biomarker candidates with statistically significant differential expression. A second study confirmed the differential expression of a subset of these markers. Models based on combinations of multiple biomarkers were generated using an exploratory algorithm designed to generate greater diagnostic power and accuracy than any individual markers. Individual biomarkers and the more complex multi-marker models were then tested in a blinded validation study. The multi-marker models retained their predictive power in the validation study, the best of which attained an AUC of 0.84, with a sensitivity of 43% and a specificity of 88%. One of the markers with <i>m</i>/<i>z</i> 761.38, which was downregulated, was identified as a fibrinogen alpha chain. Machine learning-based analysis yielded a classifier that correctly categorizes every subject in the study and demonstrates parameter constraints required for high confidence in classifier output. These results suggest that serum peptide biomarker models could be optimized to assess breast cancer stage in a clinical setting. Breast cancer ; Biomarker ; Biomarker discovery ; Oncology ; Classifier (UML) ; Predictive marker ; Confidence interval ; Cancer ; Bioinformatics ; Internal medicine ; Machine learning ; Computational biology ; Artificial intelligence ; Medicine ; Computer science ; Biology ; Proteomics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: A. D. Gimalieva | A. B. Ostrovskii | S. V. Salii | M. S. Kirsanova
Source:  (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Creating a database to analyse the results of molecular lines obseravations in star-forming regions We created the preliminary database of integrated intensities of molecular lines 13CS (2 - 1), SiO(2 - 1), CCH (N = 1 - 0, J = 3/2 - 1/2, F = 2 - 1), N2H+ (1 - 0), H13CO+ (1 - 0), HCN (j = 1 - 0, F = 0 - 1), HN13C (1 - 0), HNCO (4(0,4) - 3(0,3)), CH3CN (51 - 41 E), CH3OH (42,2 - 31,2) observed in sources that are in diherent phases of star formation. These values are going to be used for cluster analysis with machine learning methods. Computer science ; Star (game theory) ; Database ; Astrophysics ; Physics Spectroscopy and Laser Applications ; Molecular Spectroscopy and Structure ; Astrophysics and Star Formation Studies
Authors: Qi Ge | Huai-Liang Zheng | Chenxi Liu | Li Ma | Kai‐Uwe Schröder
Source: arXiv (Cornell University) (preprint, 2024-03-20)
Field: Materials Science — Materials Chemistry

Canonical Descriptors for Periodic Lattice Truss Materials For decades, aspects of the topological architecture, and of the mechanical as well as other physical behaviors of periodic lattice truss materials (PLTMs) have been massively studied. Their approximate infinite design space presents a double-edged sword, implying on one hand dramatic designability in fulfilling the requirement of various performance, but on the other hand unexpected intractability in determining the best candidate with tailoring properties. In recent years, the development of additive manufacturing and artificial intelligence spurs an explosion in the methods exploring the design space and searching its boundaries. However, regrettably, a normative description with sufficient information of PLTMs applying to machine learning has not yet been constructed, which confines the inverse design to some discrete and small scrutinized space. In the current paper, we develop a system of canonical descriptors for PLTMs, encoding not only the geometrical configurations but also mechanical properties into matrix forms to establish good quantitative correlations between structures and mechanical behaviors. The system mainly consists of the geometry matrix for the lattice node configuration, density, stretching and bending stiffness matrices for the lattice strut properties, as well as packing matrix for the principal periodic orientation. All these matrices are theoretically derived based on the intrinsic nature of PLTMs, leading to concise descriptions and sufficient information. The characteristics, including the completeness and uniqueness, of the descriptors are analyzed. In addition, we discuss how the current system of descriptors can be applied to the database construction and material discovery, and indicate the possible open problems. Truss ; Lattice (music) ; Mathematics ; Pure mathematics ; Structural engineering ; Physics ; Engineering Quasicrystal Structures and Properties
Authors: Meroua Safa Mechouche | Fateh Merouane | Ahmed Addad | Lydia Karmazin | Rabah Boukherroub | Nadjem Lakhdari
Source: Research Square (preprint, 2024-04-30)
Field: Materials Science — Materials Chemistry

Enhanced Biosynthesis of Coated Silver Nanoparticles using Isolated Bacteria from Heavy Metal Soils and their Photothermal- based antibacterial activity: Integrating Response Surface Methodology (RSM) Hybrid Artificial Neural Network (ANN)- Genetic Algorithm (GA) Strategies Response surface methodology ; Silver nanoparticle ; Biocompatibility ; Nanoparticle ; Photothermal therapy ; Fourier transform infrared spectroscopy ; Antibacterial activity ; Materials science ; Nuclear chemistry ; Chemical engineering ; Nanotechnology ; Chemistry ; Bacteria ; Chromatography ; Organic chemistry ; Biology Nanoparticles: synthesis and applications ; Nanoplatforms for cancer theranostics ; Advanced Nanomaterials in Catalysis
Authors: Luis Rangel DaCosta | Mary Scott
Source: Microscopy and Microanalysis (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Generating and Sampling Complex Nanostructures with Construction Zone In modern computational workflows for TEM modeling, we are increasingly working with both more numerous and more complex atomic nanostructures.On the visualization and simulation end, we have a need to use atomic structures which resemble and mimic experimental nanostructures as closely as possible.In computational workflows relying on the development of machine learning models, we need to sample many different atomic structures-potentially tens of thousands or even more-in order to satisfy the data-intensive training needs of modern ML techniques.Reproducibly generating and managing atomic structures at all scales remains a critical task in developing new computational workflows for TEM modeling and analysis.Recently, we have introduced Construction Zone (CZ, [1,2]), an open-source Python package for creating and sampling arbitrarily complex atomic nanostructures.CZ relies on a CAD-like module system, where objects are described jointly with Volumes and Generators, which define the extent of an object and how atoms are arranged within the object, respectively.Alongside module utilities which handle simple features like object arrangement and transformations, CZ offers ability to define complex object generation routines to allow for sampling nanostructures with intricate structural features, such as planar defects, and advanced surface modifications.Altogether, these modules provide a flexible set of tools for designing bespoke atomic structures with concise code that abstracts away generation complexity in favor of descriptive materials science interpretability.CZ has been used to demonstrate the effectiveness of training neural networks with simulated HRTEM data [2], where it was integrated into a high-throughput nanoparticle TEM simulation pipeline.Lastly, CZ is performant and readily integrates with popular tools in the computational materials science and TEM simulation ecosystem, including abTEM, Prismatic, ASE, pymatgen,.In this poster, we discuss the internal strategy behind CZ and the interaction between its features, as well as how it can be incorporated into complex nanostructure sampling workflows.Specifically, we will focus on several new features and developments of the CZ package which aim to improve the reproducibility of atomic generation workflows, including metadata tracking, a structure configuration specification system, and a unit testing suite. Sampling (signal processing) ; Nanostructure ; Materials science ; Nanotechnology ; Computer science ; Telecommunications Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electronic and Structural Properties of Oxides