jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Simon León Krug | Danish Khan | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2024-09-26)
Field: Materials Science — Materials Chemistry

Alchemical harmonic approximation based potential for iso-electronic diatomics: Foundational baseline for $Δ$-machine learning We introduce the alchemical harmonic approximation (AHA) of the absolute electronic energy for charge-neutral iso-electronic diatomics at fixed interatomic distance $d_0$. To account for variations in distance, we combine AHA with this Ansatz for the electronic binding potential, $E(d)=(E_{u}-E_s) \left(\frac{E_c-E_s}{E_u-E_s}\right)^{\sqrt{d/d_0}}+E_s$, where $E_u,E_c,E_s$ correspond to the energies of united atom, calibration at $d_0$, and sum of infinitely separated atoms, respectively. Our model covers the entire two-dimensional electronic potential energy surface spanned by distance and difference in nuclear charge from which only one single point (with elements of nuclear charge $Z_1,Z_2$ and distance $d_0$) is drawn to calibrate $E_c$. Using reference data from pbe0/cc-pVDZ, we present numerical evidence for the electronic ground-state of all neutral diatomics with 8, 10, 12, 14 electrons. We assess the validity of our model by comparison to legacy interatomic potentials (Harmonic oscillator, Lennard-Jones, and Morse) within the most relevant range of binding (0.7 - 2.5 A), and find comparable accuracy if restricted to single diatomics, and significantly better predictive power when extrapolating to the entire iso-electronic series. We also investigated $Δ$-learning of the electronic absolute energy using our model as baseline. This baseline model results in a systematic improvement, effectively reducing training data needs for reaching chemical accuracy by up to an order of magnitude from $\sim$1000 to $\sim$100. By contrast, using AHA+Morse as a baseline hardly leads to any improvement, and sometimes even deteriorates the predictive power. Inferring the energy of unseen CO converges to a prediction error of $\sim$0.1 Ha in direct learning, and $\sim$0.04 Ha with our baseline. Diatomic molecule ; Baseline (sea) ; Harmonic ; Artificial intelligence ; Physics ; Computer science ; Machine learning ; Quantum mechanics ; Political science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Asma A. Al-Enzi | Omer I. Eid | M. E. M. Eisa
Source: Engineering Technology & Applied Science Research (article, 2024-10-09)
Field: Materials Science — Materials Chemistry

Structural, Electronic, and Mechanical Properties of Anatase and Rutile Titanium Dioxide Phases using the Density Functional Theory This study investigates the structural and electronic properties of the anatase and rutile TiO2 systems by employing the Quantum Espresso (QE) software using first-principles calculations based on Density Functional Theory (DFT). Optimized lattice constants (a = 3.788, 4.627 a.u. and c = 9.491, 2.979 a.u.) and the internal parameter u (0.209, 0.305), were obtained for anatase and rutile TiO2 phases, respectively. Unit cell volumes were also calculated. Furthermore, the Birch-Murnaghan equation of state was used to obtain the equilibrium volume (937.5, 428.3 a.u.3), the bulk modulus (198.5, 222.5 GPa), and the pressure derivative of the bulk modulus (4.18, 4.37) for both phases. The results are in good agreement with the experimental data and the theoretical results published in other studies. Finally, the energy band gap of both samples was calculated (1.8 and 1.6 eV, respectively) and compared with published results obtained from the Density Of Electron States (DOS). Rutile ; Anatase ; Titanium dioxide ; Density functional theory ; Materials science ; Titanium ; Chemical engineering ; Thermodynamics ; Mineralogy ; Composite material ; Metallurgy ; Computational chemistry ; Chemistry ; Engineering ; Organic chemistry ; Physics ; Photocatalysis Electronic and Structural Properties of Oxides ; ZnO doping and properties ; Machine Learning in Materials Science
Authors: Marcus Schwarting | Nathan A. Seifert | Michael J. Davis | Ben Blaiszik | Ian Foster | Kirill Prozument
Source: The Journal of Chemical Physics (article, 2024-07-25)
Field: Chemistry — Spectroscopy

Twins in rotational spectroscopy: Does a rotational spectrum uniquely identify a molecule? Rotational spectroscopy is the most accurate method for determining structures of molecules in the gas phase. It is often assumed that a rotational spectrum is a unique "fingerprint" of a molecule. The availability of large molecular databases and the development of artificial intelligence methods for spectroscopy make the testing of this assumption timely. In this paper, we pose the determination of molecular structures from rotational spectra as an inverse problem. Within this framework, we adopt a funnel-based approach to search for molecular twins, which are two or more molecules, which have similar rotational spectra but distinctly different molecular structures. We demonstrate that there are twins within standard levels of computational accuracy by generating rotational constants for many molecules from several large molecular databases, indicating that the inverse problem is ill-posed. However, some twins can be distinguished by increasing the accuracy of the theoretical methods or by performing additional experiments. Rotational partition function ; Spectroscopy ; Rotational spectroscopy ; Molecule ; Inverse ; Spectral line ; Molecular spectroscopy ; Rotational spectrum ; Rotational invariance ; Chemistry ; Molecular physics ; Computational chemistry ; Rotational–vibrational spectroscopy ; Physics ; Algorithm ; Computer science ; Mathematics ; Geometry ; Quantum mechanics Molecular Spectroscopy and Structure ; Spectroscopy and Laser Applications ; Advanced Chemical Physics Studies
Authors: Atike İnce Yardımcı | Yaser Açıkbaş | Rıfat Çapan
Source: Research & Design (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

SEM images of carbon nanotubes (CNTs): effect of voltage and spot size on image resolutions Scanning electron microscopy (SEM) is a method used to characterize 2D and 3D materials morphologically. It is of great importance with its high resolution, especially in the characterization of nano- and micro-sized materials. In this study, carbon nanotubes (CNTs) were analyzed with SEM, and high-quality images were obtained. With SEM analysis, it can be seen whether carbon nanotubes are formed in a regular structure or not, and whether there are agglomerations or distortions in the structure and could also be utilized to quantify the degree of purity within samples. The diameters of carbon nanotubes can be measured using SEM images. This study obtained the best imaging parameters by taking many SEM images at different magnifications under 3.5 and 7 kV voltage and 2, 2.5, and 3 spot values. The results showed that in the characterization of CNTs by SEM, low spot and low voltage values ​​are more suitable for detailed surface morphology imaging and obtaining high resolution. Carbon nanotube ; Materials science ; Nanotechnology ; Voltage ; Electrical engineering ; Engineering Carbon Nanotubes in Composites ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Amit Dutt | B. Pravallika | J. G. Manjunatha | Rajesh Goyal | Nakul Gupta | N. Eswara Prasad | Laith H. Alzubaidi
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Developing sustainable solutions with natural fiber reinforced composites The advances in technological developments in NFPCs are driven by the demands of a nation toward sustainability and ecologically friendly materials. Banana, eucalyptus, and kenaf-based material from natural fibers may confer several environmental benefits, including being biodegradable, having a reduced greenhouse gas, and carbon footprint. Despite these benefits, NFPCs exhibit drawbacks in mechanical performance. Poor interfacial adhesion, moisture absorption, and limited fire resistance are some examples of reasons hindering their broader use. Enhancement of fiber-matrix adhesion has been seen as a way of achieving enhanced mechanical properties of NFPCs, and the alkaline treatment using NaOH has come to be favored. Further, since such companies started using NFPCs as they are light in weight and green, such a review indicates a global trend towards sustainability, especially in the aerospace and automotive industries. Further innovation into these NFPCs will be a filling process with nano-clay and other nanoparticles for enhanced thermal and mechanical properties since such a material has immense potential of outperforming their rivals, which are mainly petroleum-based materials. In addition, review also discuss the increasing usage of biodegradable polymers such as polylactic acid, PLA reinforced with natural fibers to improve durability and mechanical performance, opening up new possibilities for various applications such as in construction and packaging and medicine and even in 3D printing. Advancements in NFPC technology are aptly highlighted as these materials can meet diverse needs evolving in several industries to ensure a greener tomorrow. Carbon footprint ; Kenaf ; Sustainability ; Polylactic acid ; Materials science ; Natural fiber ; Durability ; Automotive industry ; Composite material ; Environmentally friendly ; Greenhouse gas ; Fiber ; Polymer ; Engineering Natural Fiber Reinforced Composites ; Fiber-reinforced polymer composites ; Additive Manufacturing and 3D Printing Technologies
Authors: J Q Zhao | Eline Hermans | Kia Sepassi | Christophe Tistaert | Christel A. S. Bergström | Mazen Ahmad | Per Larsson
Source: Molecular Pharmaceutics (article, 2024-09-13)
Field: Materials Science — Materials Chemistry

Effect of Data Quality and Data Quantity on the Estimation of Intrinsic Solubility: Analysis Based on a Single-Source Data Set Aqueous solubility is one of the most important physicochemical properties of drug molecules and a major driving force for oral drug absorption. To date, the performance of in silico models for the estimation of solubility for novel chemical space is limited. To investigate possible reasons and remedies for this, the Johnson and Johnson in-house aqueous solubility data with over 40,000 compounds was leveraged. All data were generated through the same high-throughput assay, providing a unique opportunity to explore the relationship between data quality, quantity, and model estimations. Six intrinsic solubility data sets with different sizes and noise levels were generated by making use of three different approaches: (i) inclusion or exclusion of amorphous solid residue, (ii) measured or experimental log <i>D</i> to identify the intrinsic solubility, and (iii) adopting or omitting a quality check process in the data processing workflow. A random forest regressor was trained on the data sets with three different sets of descriptors calculated from RDKit, ADMET predictor, or Mordred, and the performances were evaluated with nested cross-validation as well as ten refined test sets. The models confirm, as expected, that with the same data set size, high-quality data leads to better model performance; however, also, models trained with larger data sets containing analytical variability can give equally accurate estimations compared to models trained with small, clean, and diverse data sets. However, noise introduced by including the presence of amorphous solid postsolubility measurement in the training data set cannot be overcome by increasing data size, as they are introducing a biased systematic positive error in the data set, confirming the importance of critical data review. Finally, two top-performing models were tested on the first test set from the second solubility challenge, achieving RMSE values of 0.74 and 0.72 and log <i>S</i> ± 0.5 of 46 and 48%, respectively. These results demonstrated improved performance compared to those reported in the findings of the competition, highlighting that a single-source curated data set can enhance the prediction of intrinsic solubility. Data set ; Quality (philosophy) ; Data quality ; Solubility ; Set (abstract data type) ; Data mining ; Chemistry ; Estimation ; Computer science ; Statistics ; Biological system ; Mathematics ; Biology ; Engineering ; Physics ; Organic chemistry Crystallization and Solubility Studies ; Machine Learning in Materials Science
Authors: Lukas C. Jarren | Eugen Gazenbiller | Visheet Arya | Rüdiger Reitz | Matthias Oechsner | Christian Feiler | Mikhail L. Zheludkevich | Daniel Höche
Source: Materials and Corrosion (article, 2024-10-08)
Field: Materials Science — Metals and Alloys

Machine Learning–Assisted Risk Assessment of Pitting Corrosion Susceptibility of AA1050 in Ethanol‐Containing Fuels ABSTRACT The ability to assess the risk of corrosion of metallic structures in particular environments holds considerable significance in the field of automotive industry. In recent years, machine learning has evolved into a crucial tool to evaluate the complex and multidimensional corrosion phenomena. In this paper, the special case of non‐aqueous alcoholate pitting corrosion of AA1050 in ethanol‐blended fuels with water and chloride contamination is examined via supervised machine learning techniques in order to distinguish between safe and unsafe conditions. The data space was created by conducting dedicated experiments with varying ethanol–fuel–water ratios, temperatures, and surface preparations. The classifier's performance rating of 0.87 (balanced accuracy) indicates an outstanding predictive ability and highlights the model's usefulness as decision support for subsequent experiments. Corrosion ; Pitting corrosion ; Ethanol ; Metallurgy ; Materials science ; Chemistry ; Biochemistry Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques
Authors: Zhengxuan Fan | Émile Maras | Maeva Cottura | Mihai‐Cosmin Marinica | Emmanuel Clouet
Source: Physical Review Materials (article, 2024-11-07)
Field: Materials Science — Materials Chemistry

Structure and coherency of bcc Nb precipitates in hcp Zr matrix from atomistic simulations We use atomistic simulations to study the structure and shape of niobium precipitates with a body-centered cubic crystallographic structure in a zirconium matrix with a hexagonal close-packed structure. We consider a Pitsch-Schrader orientation relationship, which is observed experimentally for Nb precipitates appearing under irradiation in zirconium alloys. In this regard, we have developed a Zr-Nb empirical potential using the embedded atom method. This parametrization offers a good description of the interfaces between Nb precipitates and the Zr matrix. The potential includes all the necessary physical ingredients to account for the coherency strain between both phases. The atomistic simulations reveal a significant decrease in the precipitate formation energy when they become semicoherent, with enough misfit dislocations at the interface to eliminate the lattice misfit between the Nb precipitates and the Zr matrix. The ``idealized'' equilibrium shape that minimizes the formation energy of these semicoherent precipitates matches the experimental shape: platelets lying in the Zr basal planes with a reduced dimension along the $[0\phantom{\rule{0.16em}{0ex}}0\phantom{\rule{0.16em}{0ex}}0\phantom{\rule{0.16em}{0ex}}1]$ axis. Our simulations suggest that the platelet shape observed in zirconium for irradiation-induced Nb-enriched precipitates simply results from a minimization of their energy cost. Materials science ; Crystallography ; Matrix (chemical analysis) ; Condensed matter physics ; Chemical physics ; Composite material Nuclear Materials and Properties ; Fusion materials and technologies ; Titanium Alloys Microstructure and Properties
Authors: Da Wu | Dexin Liu | Minghua Shi | Jiaqiang Wang | Han Zhao | Yeliang Dong
Source: Molecules (article, 2024-10-31)
Field: Materials Science — Materials Chemistry

The Molecular Design of a Macrocycle Descaling Agent Based on Azacrown and the Mechanism of Barium Sulfate Scale Removal The formation of barium sulfate scale is a persistent and formidable challenge across various industrial processes. In order to effectively mitigate this problem, this study proposed the development of an innovative azacrown ether-based macrocycle descaling agent. Using density functional theory, an in-depth analysis of the surface energy of different barium sulfate crystal facets was carried out, together with a detailed investigation into the adsorption properties of the functional groups on the (001) surface. A further comprehensive investigation was carried out to determine how changes in the nitrogen and oxygen atoms in the crown ether framework influence its adsorption affinity to barium ions. In addition, a detailed analysis was carried out to elucidate the molecular interactions between crown ethers with pyridine carboxylic acid side chains and barium sulfate. The newly developed decalcifying macrocycle descaling agent exhibited superior adsorption performance, achieving an adsorption energy for barium ions approximately -4.1512 ev higher than that of conventional DTPA decalcifiers. This remarkable improvement is mainly attributed to the pivotal role of electrostatic forces in the coordination process between the macrocycle descaling agent and barium ions, with an electrostatic potential value reaching -143.37 kcal/mol. This discovery not only introduces a novel approach to the removal of barium sulfate scale but also highlights the significant potential of macrocycle chemistry in industrial applications. Mechanism (biology) ; Chemistry ; Barium sulfate ; Barium ; Sulfate ; Scale (ratio) ; Combinatorial chemistry ; Biophysics ; Stereochemistry ; Organic chemistry ; Biology ; Physics Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Hao Shen | V. Paul‐Boncour | Ping Li | Lijun Jiang | Junxian Zhang
Source: Inorganics (article, 2024-02-07)
Field: Materials Science — Materials Chemistry

Structural Evolution and Hydrogen Sorption Properties of YxNi2−yMny (0.825 ≤ x ≤ 0.95, 0.1 ≤ y ≤ 0.3) Laves Phase Compounds The YxNi2−yMny system was investigated in the region 0.825 ≤ x ≤ 0.95, 0.1 ≤ y ≤ 0.3. The alloys were synthesized by induction melting and corresponding annealing. The substitution of Mn for Ni (y = 0.1) favors the formation of a C15 structure with disordered Y vacancies against the superstructure of Y0.95Ni2. For y = 0.2 and 0.3, Mn can substitute in both Y and Ni sites. Single-phase compounds with a C15 structure can be formed by adjusting both the Y and Mn contents. Their hydrogen absorption–desorption properties were measured by pressure–composition isotherm (PCI) measurements at 150 °C, and the hydrides were characterized at room temperature by X-ray diffraction and TG–DSC experiments. The PCIs show two plateaus corresponding to the formation of crystalline and amorphous hydrides. The heating of the amorphous hydrides leads to an endothermic desorption at first and then a recrystallization into Y(Ni, Mn)3 and YHx phases. At higher temperatures, the Y hydride desorbs, and a recombination into a Y(Ni, Mn)2 Laves phase compound is observed. For y = 0.1, vacancy formation in the Y site and partial Mn substitution in the Ni site enhance the structural stability and suppress the hydrogen-induced amorphization (HIA). However, for a larger Mn content (y ≥ 0.2), Mn substitutes also in the Y sites at the expense of Y vacancies. This yields worse structural stability upon hydrogenation than for y = 0.1, as the mean ratio r(Y, Mn)/r(Ni/Mn) becomes larger than for y = 0.1 r(Y, ☐)/r(Ni/Mn). Laves phase ; Materials science ; Hydride ; Amorphous solid ; Annealing (glass) ; Crystallography ; Desorption ; Hydrogen ; Analytical Chemistry (journal) ; Physical chemistry ; Chemistry ; Intermetallic ; Adsorption ; Metallurgy ; Alloy Hydrogen Storage and Materials ; Rare-earth and actinide compounds ; Metallurgical and Alloy Processes
Authors: Christopher Leist | Max Makurat | Andy Jiao | Xue Liu | Grégory F. Schneider | Ute Kaiser
Source: Microscopy and Microanalysis (article, 2024-10-15)
Field: Materials Science — Materials Chemistry

Control of Grain Boundary Formation in Atomically Resolved Nanocrystalline Carbon Monolayers: Dependence on Electron Energy In this study, we explore the dynamics of grain boundaries in nanocrystalline carbon monolayers, focusing on their variation with electron beam energy and electron dose rate in a spherical and chromatic aberration-corrected transmission electron microscope. We demonstrate that a clean surface, a high-dose rate, and a 60 keV electron beam are essential for precise local control over the dynamics of grain boundaries. The structure of these linear defects has been evaluated using neural network-generated polygon mapping. Nanocrystalline material ; Materials science ; Grain boundary ; Transmission electron microscopy ; Monolayer ; Electron beam-induced deposition ; Electron ; Cathode ray ; Electron microscope ; Energy filtered transmission electron microscopy ; Molecular physics ; Optics ; Nanotechnology ; Scanning transmission electron microscopy ; Chemistry ; Physics ; Composite material ; Microstructure Graphene research and applications ; Diamond and Carbon-based Materials Research ; Ion-surface interactions and analysis
Authors: Parastoo Fathi | Nasrollah Moghadam Charkari
Source: International Journal of Engineering (article, 2024-11-09)
Field: Chemistry — Analytical Chemistry

Herb Target Prediction Using Machine Learning Methods in a Heterogeneous Network Predicting herb-target interactions is crucial for advancing traditional Chinese medicine (TCM), but the existing methods often struggle with incomplete datasets and fail to fully leverage the network structure. The objective of this study is to develop a novel network-based approach that integrates both network topology as well as molecular data to further improve the accuracy of herb-target interaction predictions. We have constructed a heterogeneous network encompassing herbs, targets and symptoms, incorporating various network measures to assess edge weights. Molecular data for herbs has also been integrated into the model. Using six ensemble supervised learning models (GBM, XGBoost, LightGBM, CatBoost, etc.), the model has been trained to predict herb-target interactions. The proposed model achieved an AUROC of 88% and an AUPR of 90% on the HIT2 dataset, significantly outperforming the existing approaches. This research highlights the potential of integrating network structure and molecular data for accurate herb-target prediction, opening new avenues for drug discovery and personalized medicine in TCM. Computer science ; Herb ; Artificial intelligence ; Machine learning ; Medicinal herbs ; Traditional medicine ; Medicine Spectroscopy and Chemometric Analyses