jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Simon Müller | Thomas Nevolianis | Miquel García‐Ratés | Christoph Riplinger | Kai Leonhard | Ирина Смирнова
Source: RWTH Publications (RWTH Aachen) (preprint, 2024-07-03)
Field: Chemistry — Organic Chemistry

Predicting solvation free energies for neutral molecules in any solvent with openCOSMO-RS The accurate prediction of solvation free energies is critical for understanding various phenomena in the liquid phase, including reaction rates, equilibrium constants, activity coefficients, and partition coefficients. Despite extensive research, precise prediction of solvation free energies remains challenging. In this study, we introduce openCOSMO-RS 24a, an improved version of the open-source COSMO-RS model, capable of predicting solvation free energies alongside other liquid-phase properties. We parameterize openCOSMO-RS 24a using quantum chemical calculations from ORCA 6.0, leveraging a comprehensive dataset that includes solvation free energies, partition coefficients, and infinite dilution activity coefficients for various solutes and solvents at 25 °C. Additionally, we develop a Quantitative Structure-Property Relationships model to predict molar volumes of the solvents, an essential requirement for predicting solvation free energies from structure alone. Our results show that openCOSMO-RS 24a achieves an average absolute deviation of 0.45 kcal/mol for solvation free energies, 0.76 for partition coefficients, and 0.51 for infinite dilution activity coefficients, demonstrating improvements over the previous openCOSMO-RS 22 parameterization and comparable results to COSMOtherm 24 TZVP. A new command line interface for openCOSMO-RS 24a was developed which allows easy acces to the solvation energy model directly from within ORCA 6.0. This represents a significant advancement in the predictive modeling of solvation free energies and other solution-phase properties, providing researchers with a robust tool for applications in chemical and materials science. Solvation ; Molecule ; Solvent ; Computational chemistry ; Chemistry ; Chemical physics ; Organic chemistry Free Radicals and Antioxidants ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Raheel Hammad | Snehith Adabala | Soumya K. Ghosh
Source: arXiv (Cornell University) (preprint, 2024-08-12)
Field: Materials Science — Materials Chemistry

Surface Termination and Band Alignment in 2D Heterostructures Heterostructures are ubiquitous in many optoelectronic devices and as photocatalysts. One of the key features of a heterojunction is the proper band alignment between the two materials. Estimation of the correct relative band positions with density functional theory (DFT) based electronic structure calculations is often constrained by the accuracy and cost associated with the various DFT functionals. In this study, we introduce a novel computational approach that achieves band alignments closely matching experimental results with the widely used PBE functional. We specifically examine the well-documented MoO3/MoS2 system, a type-II heterojunction. In our setup, the MoS2 layers are kept as it is but for MoO3 the individual layers are chosen differently. These alternative layers have higher surface energy, and hence, the band edges are higher than the conventional layers. This shift in band edges of the alternative MoO3 layers changes the band alignment in MoO3/MoS2 heterojunction from type-III to the experimentally observed type-II character. We also extend this computational strategy to additional systems, demonstrating its versatility and effectiveness. Heterojunction ; Surface (topology) ; Materials science ; Optoelectronics ; Geometry ; Mathematics Graphene research and applications ; Ga2O3 and related materials
Authors: Natalia A. Kabanova | Ekaterina A. Grishina | Vladislav T. Osipov | Valeria E. Pavlova | Maria A. Solodovnikova | Artem A. Kabanov | Vladislav A. Blatov
Source: Research Square (preprint, 2024-09-06)
Field: Materials Science — Materials Chemistry

Topological relations between crystal structures: a route to predicting inorganic materials Topology (electrical circuits) ; Crystal (programming language) ; Materials science ; Computer science ; Engineering ; Electrical engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: P. Bernát Szabó | Zeno Schätzle | Mike Entwistle | Frank Noé
Source: arXiv (Cornell University) (preprint, 2024-05-27)
Field: Materials Science — Materials Chemistry

An improved penalty-based excited-state variational Monte Carlo approach with deep-learning ansatzes We introduce several improvements to the penalty-based variational quantum Monte Carlo (VMC) algorithm for computing electronic excited states of Entwistle $\textit{et al.}$ [M. T. Entwistle $\textit{et al.}$, Nat. Commun. $\textbf{14}$, 274 (2023)], and demonstrate that the accuracy of the updated method is competitive with other available excited-state VMC approaches. A theoretical comparison of the computational aspects of these algorithms is presented, where several benefits of the penalty-based method are identified. Our main contributions include an automatic mechanism for tuning the scale of the penalty terms, an updated form of the overlap penalty with proven convergence properties, and a new term that penalizes the spin of the wave function, enabling the selective computation of states with a given spin. With these improvements, along with the use of the latest self-attention-based ansatz, the penalty-based method achieves a mean absolute error below 1 kcal/mol for the vertical excitation energies of a set of 26 atoms and molecules, without relying on variance matching schemes. Considering excited states along the dissociation of the carbon dimer, the accuracy of the penalty-based method is on par with that of natural-excited-state (NES) VMC, while also providing results for larger sections of the potential energy surface. Additionally, the accuracy of the original penalty-based method is improved for a conical intersection of ethylene, with the predicted angle of the intersection agreeing well with both NES-VMC and multi-reference configuration interaction. Monte Carlo method ; Variational Monte Carlo ; Excited state ; Computer science ; Quantum Monte Carlo ; Statistical physics ; Physics ; Mathematics ; Nuclear physics ; Statistics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; CCD and CMOS Imaging Sensors
Authors: Daniel Schwalbe‐Koda | Sébastien Hamel | Babak Sadigh | Fei Zhou | Vincenzo Lordi
Source: arXiv (Cornell University) (preprint, 2024-04-18)
Field: Materials Science — Materials Chemistry

Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification (UQ), or extracting physical insights from large datasets. However, atomistic ML often relies on unsupervised learning or model predictions to analyze information contents from simulation or training data. Here, we introduce a theoretical framework that provides a rigorous, model-free tool to quantify information contents in atomistic simulations. We demonstrate that the information entropy of a distribution of atom-centered environments explains known heuristics in ML potential developments, from training set sizes to dataset optimality. Using this tool, we propose a model-free UQ method that reliably predicts epistemic uncertainty and detects out-of-distribution samples, including rare events in systems such as nucleation. This method provides a general tool for data-driven atomistic modeling and combines efforts in ML, simulations, and physical explainability. Computer science ; Uncertainty quantification ; Statistical physics ; Thermodynamics ; Artificial intelligence ; Machine learning ; Physics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Panpan Li | Yanpeng Yang | Denglei Gao | Fei Lu | Yi Ding | Bo Zhou | Shoujie Liu | Ji Nie | M. Li | Yuting Du | Yong Xu | Sha Li | Xi Wang | Jiannian Yao
Source:  (preprint, 2024-01-30)
Field: Materials Science — Materials Chemistry

Engineering Pt1-Al2O3 enhances catalytic performance of naphtha reforming Pt nanoparticle with high Pt-Pt coordination number and degenerate d orbitals, has a strong affinity to naphtha and relevant intermediates, leading to severe carbon deposition and catalyst deactivation during naphtha reforming. The catalytic performance of Pt catalyst could be finely tuned by tailoring its d-electron structure. Herein, we propose a straightforward chlorinating strategy to transform PtOx nanoparticle into Pt single atoms with a specific Cl-Pt-O coordination. The adsorption of benzene on Pt is weakened with the Cl-Pt-O coordination, which facilitates aromatics desorption and hampers carbon deposition. Density functional theory (DFT) calculation indicates that the z-axis of Pt 5d orbitals is occupied and selectively exposes xy-plane orbital (dxy and dx2-y2), which weakens d-π hybridization between Pt 5d orbitals and π orbital of benzene ring. The d-electron regulation through chlorinating is a readily scalable strategy to synthesis single atom catalysts, and optimize naphtha reforming catalytic performance. Naphtha ; Catalysis ; Benzene ; Atomic orbital ; Chemistry ; Atom (system on chip) ; Adsorption ; Chemical engineering ; Materials science ; Nanotechnology ; Electron ; Physical chemistry ; Organic chemistry ; Computer science ; Physics Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Zeolite Catalysis and Synthesis
Authors: Huican Mao | Xiang Zhu | Guangmao Li | Jie Pang | Junfeng Hao | Liqi Wang | Hailong Yu | Youguo Shi | Fan Wu | Shilie Pan | Ruijuan Xiao | Hong Li | Liquan Chen
Source: arXiv (Cornell University) (preprint, 2024-05-06)
Field: Materials Science — Materials Chemistry

A family of air-stable chalcogenide solid electrolytes in Li$_2$BMQ$_4$ (B = Ca, Sr and Ba; M = Si, Ge and Sn; Q = O, S and Se) systems Combining high-throughput first-principles calculations and experimental measurements, we have identified a novel family of fast lithium-ion chalcogenide conductors in Li$_2$BMQ$_4$ (2114, B = Ca, Sr and Ba; M = Si, Ge and Sn; Q = O, S and Se) systems. Our calculations demonstrate that most of the thermodynamically and kinetically stable sulfides and selenides in this new system exhibit ultralow Li$^+$ ion migration activation energy (0.16 eV ~ 0.56 eV) and considerable bandgaps varying between ~ 2 eV and 3.5 eV. We have successfully synthesized Li$_2$BaSnS$_4$ and Li$_2$SrSiS$_4$, and they exhibit excellent moisture stability through H$_2$S gas measurements. Electrochemical impedance measurements indicate 2114 systems show the typical features of solid ionic conductors, with a room-temperature Li$^+$ conductivity close to 5$\times$10$^{-4}$ mS/cm aligning with our molecular dynamics simulations. Furthermore, we have theoretically investigated the substitution of Cl$^-$ at S$^{2-}$ site. The doped compounds display significantly higher conductivity, with an increase of about three orders of magnitude (up to a maximum of 0.72 mS/cm) compared to the undoped compounds. These findings offer valuable insights for the further exploration of potential chalcogenide solid electrolyte materials with robust air stability and enhanced ionic conductivity for practical applications in lithium-ion batteries. Chalcogenide ; Fast ion conductor ; Solid solution ; Materials science ; Electrolyte ; Chemistry ; Inorganic chemistry ; Crystallography ; Physical chemistry ; Metallurgy ; Electrode Solid-state spectroscopy and crystallography ; Inorganic Chemistry and Materials ; Advanced Battery Materials and Technologies
Authors: Marwan M. Aran | Shaymaa H. Abdulrahman
Source: Bulletin of the Chemical Society of Ethiopia (article, 2024-10-25)
Field: Chemistry — Electrochemistry

Electrical conductance study of Schiff base in different solvents and temperatures: DFT calculation The electrical conductivities of the N-(2-chlorobenzylidene)benzylamine (NCB) as Schiff bases (SB) compound in water and methanol were studied in different temperatures, the mixtures of methanol and water studied in various percentages of methanol at 298 K. Initially, the relationship between equivalent conductivity and the square root of molar concentration was plotted to find electrolyte types using Kohlrausch equations. The plot indicates that NCB was weakly associated with water, methanol, and mixtures. Lee-Wheaton equation treated the experimental results of symmetrical electrolytes (1:1) to calculate the conductivity confines: equivalent conductance at infinite dilution Λₒ, association constant KA, and the main distance between ions in solution (R) at best-fit values of (ϬΛ). Thermodynamic quantities for the ion association reaction (∆Go, ∆Ho and ∆So) have been measured. Also, the theoretical calculation was used to optimize the energy of the molecule in the gas phase then measured many descriptors by using (AM1, PM3) then DFT. In addition, recalculated the optimization energy in water, and methanol. After theoretical calculation and analysis, it was found that the Λₒ value is related to the molecular volume of the system (molecules assembled in the solvent). As the molecular volume increases, the Λₒ value decreases due to association. KEY WORDS: Schiff bases, Electrical conductivities, Lee-Wheaton equation, Thermodynamic parameter, DFT Bull. Chem. Soc. Ethiop. 2025, 39(1), 177-187. DOI: https://dx.doi.org/10.4314/bcse.v39i1.15 Chemistry ; Schiff base ; Conductance ; Electrical resistivity and conductivity ; Computational chemistry ; Chemical physics ; Polymer chemistry ; Condensed matter physics Electrochemical Analysis and Applications ; Inorganic and Organometallic Chemistry ; Analytical Chemistry and Sensors
Authors: Ramzi Jalgham | Sihem Ouchenane | Omar Dagdag | Houria Ghodbane | Himadri Sekhar Das | Anjana Ghosh | Gourisankar Roymahapatra | Muneer M. Ba‐Abbad
Source: ES Energy & Environments (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Precise Prediction on the Corrosion Prevention Ability of 1,2,4- Triazole Derivatives: An artificial Neural Network Approach Artificial neural network ; Corrosion ; Computer science ; Artificial intelligence ; Materials science ; Metallurgy Corrosion Behavior and Inhibition
Authors: Xinqiao Jia | Huixia Zhang | Yanfeng Liu | Guihong Zuo
Source: Journal of Inorganic Materials (article, 2024-12-12)
Field: Materials Science — Materials Chemistry

Cu<sub>2</sub>O/Cu Hollow Spherical Heterojunction Photocatalysts Prepared by Wet Chemical Approach Cu 2 O/Cu , , , , Cu (SPR) Cu 2 O/Cu , Cu 2 O/Cu (MO)(ENR) 5 , Cu 2 O/Cu Materials science ; Heterojunction ; Copper ; Chemical engineering ; Nanotechnology ; Optoelectronics ; Metallurgy Copper-based nanomaterials and applications ; Nanomaterials for catalytic reactions
Authors: С. А. Денисов | V. Kondrashev
Source:  (article, 2024-04-24)
Field: Materials Science — Materials Chemistry

ДИНАМИКА ИСПОЛЬЗОВАНИЯ РЕСУРСОВ ЦКП «ИНФОРМАТИКА» ДЛЯ РЕШЕНИЯ ЗАДАЧ МАТЕРИАЛОВЕДЕНИЯ The paper presents the experience of using the high-performance computing infrastructure of the of the CKP «Informatics» by research teams of the Federal Research Center Computer Science and Control of the Russian Academy of Sciences (FRC CSC RAS) when conducting research and solving problems in the field of materials science. Computer science Advanced Materials and Semiconductor Technologies ; Electric Power Systems and Control ; Mathematics, Computing, and Information Processing
Authors: Subrata Ghosh | Massimiliano Righi | Andrea Macrelli | Francesco Goto | Agozzino, Marco | Gianlorenzo Bussetti | Valeria Russo | Andrea Li Bassi | Carlo S. Casari
Source: arXiv (Cornell University) (preprint, 2024-10-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Low-density functionalized amorphous carbon nanofoam as binder-free Supercapacitor electrode Nanoporous carbon materials containing small domains of sp2-carbon with highly disordered structures are promising for supercapacitor applications. Herein, we synthesize amorphous carbon nanofoam with 98% volumetric void fraction and low mass density of around 30 mg/cm3 by pulsed laser deposition at room temperature. With the unavoidable oxygen functional groups on the nanoporous surface, carbon nanofoam and nitrogen-functionalized carbon nanofoams are directly grown on the desired substrate under different background gases (Ar, N2, N2-H2), and employed as supercapacitor electrodes. Among the background gases used in synthesis, the use of nitrogen yields nanofoam with higher thickness and more N-content with higher graphitic-N. From the test of amorphous carbon nanofoam supercapacitor device, nitrogenated amorphous carbon electrode shows a higher areal capacitance of 4.1 mF/cm2 at 20 mV/s in aqueous electrolyte, a better capacitance retention at higher current, and excellent cycle stability (98%) over 10000 charge-discharge cycles are achieved compared to not-functionalized counterpart prepared under Ar background gas (2.7 mF/cm2 and cycle stability of 88%). Supercapacitor ; Materials science ; Carbon nanofoam ; Electrode ; Composite material ; Carbon fibers ; Amorphous solid ; Composite number ; Electrochemistry ; Porosity ; Chemistry ; Crystallography Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Electrospun Nanofibers in Biomedical Applications
Authors: He LI | Yong XU | WenHui DUAN | RuiJuan XIAO | HongMing WENG
Source: Zhongguo kexue. Wulixue Lixue Tianwenxue (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence and data-driven computational simulation <p indent="0mm">“Big data + artificial intelligence” represents a novel research paradigm that will have a profound impact on future scientific research. In the field of computational physics, artificial intelligence and data-driven approaches are fostering the formation of new methodologies, and consequently giving rise to new significant scientific problems. This article will explore the emerging research area of artificial intelligence and data-driven computational simulations, review related research progress, and provide a perspective on future developments. Big data ; Computer science ; Field (mathematics) ; Perspective (graphical) ; Data science ; Artificial intelligence ; Computational intelligence ; Management science ; Marketing and artificial intelligence ; Computational model ; Engineering ; Intelligent decision support system ; Data mining Machine Learning in Materials Science ; Scientific Computing and Data Management ; Topic Modeling
Authors: Olumide Bolarinwa Ayodele
Source: Energies (article, 2024-06-29)
Field: Materials Science — Materials Chemistry

Plasma-Assisted One-Step Direct Methanol Conversion to Ethylene Glycol and Hydrogen: Process Intensification This perspective reports a process intensification strategy that converts methanol into ethylene glycol (MeOH-2-EG) in a single step to circumvent multi-step naphtha cracking into ethylene followed by ethylene epoxidation to ethylene oxide (EO) and the subsequent hydrolysis of EO to ethylene glycol (EG). Due to the thermodynamic restriction for the direct MeOH-2-EG, plasma-assisted catalysis was introduced, and platinum group metals were identified as prospective transition metal catalysts that can achieve the formation of strong metal hydride bonds and guarantee the controlled C–C coupling of two plasma-activated hydroxymethyl radicals (*CH2OH) from methanol, both of which are essential for the single-step MeOH-2-EG. Ethylene glycol ; Methanol ; Process (computing) ; Hydrogen ; Process engineering ; Ethylene ; Plasma ; Chemistry ; Chemical engineering ; Materials science ; Computer science ; Organic chemistry ; Engineering ; Catalysis ; Physics ; Operating system Catalytic Processes in Materials Science ; Catalysts for Methane Reforming
Authors: Henry Jiaheng Lyu
Source: Forum on research and innovation management. (article, 2024-10-13)
Field: Chemistry — Inorganic Chemistry

Application and Optimization of Novel Asymmetric Catalysts in Drug Synthesis This study aims to develop a novel asymmetric catalyst and explore its application and optimization in drug synthesis. By designing specific organometallic frameworks and optimizing the catalytic active centers, the study successfully enhanced the efficiency and selectivity of drug synthesis. The experiments included measuring catalytic efficiency, evaluating product selectivity, and testing the recyclability of the catalyst. The results indicate that the novel catalyst not only excels in improving synthesis efficiency but also demonstrates excellent selectivity and high potential for reuse. The study also investigates various factors affecting catalyst performance and provides constructive suggestions for future research directions. Catalysis ; Drug ; Computer science ; Combinatorial chemistry ; Chemistry ; Medicine ; Organic chemistry ; Pharmacology Asymmetric Hydrogenation and Catalysis ; Surface Chemistry and Catalysis ; Chemical Synthesis and Analysis
Authors: Aaditya Rau | Christopher A. Schuh | Raùl Radovitzky
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quantification of Elastic Incompatibilities at Triple Junctions Via Physics-Based Surrogate Models Physics ; Triple junction ; Computer science ; Statistical physics ; Optoelectronics Magnetic Properties and Applications ; Model Reduction and Neural Networks ; Force Microscopy Techniques and Applications
Authors: Nicolas Castel | Dune André | Connor Edwards | Jack D. Evans | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2024-01-03)
Field: Materials Science — Materials Chemistry

Machine Learning Interatomic Potentials for Amorphous Zeolitic Imidazolate Frameworks The detailed understanding of the microscopic structure of amorphous phases of metal-organic frameworks (MOFs) remains a widely open question: characterization of these systems is very difficult, both from the experimental and computational point of view. In molecular simulations, approaches have been proposed that rely either on reactive force field, that lack chemical accuracy, or first-principles calculations, that are too computationally expensive. Here, we have found an innovative solution to these problems by training a machine-learnt potential for the description of disordered phases of a zeolitic imidazolate framework (ZIF). We then used it to produce high-quality atomistic models of ZIF glasses, with accuracy close to density functional theory (DFT) but at far lower computational cost in production runs. Zeolitic imidazolate framework ; Amorphous solid ; Force field (fiction) ; Imidazolate ; Density functional theory ; Molecular dynamics ; Field (mathematics) ; Materials science ; Computer science ; Characterization (materials science) ; Nanotechnology ; Computational chemistry ; Metal-organic framework ; Chemistry ; Artificial intelligence ; Physical chemistry ; Mathematics ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metal-Organic Frameworks: Synthesis and Applications