jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Croix J. Laconsay | Dean J. Tantillo
Source: ChemRxiv (preprint, 2023-04-04)
Field: Chemistry — Organic Chemistry

Modulating Escape Channels of CycloheptatrienylRhodium Carbenes to Form Semibullvalene We report the various escape channels available to dirhodium carbene intermediates from cycloheptatrienyl diazo compounds with density functional theory. An intramolecular cyclopropanation would, in principle, provide a new route to semibullvalenes. A full exploration of the potential energy surface reveals that methylating carbon-7 suppresses a competing β-hydride migration pathway to heptafulvene products, giving semibullvalene formation a reasonable chance. During our explorations, we additionally discovered unusual spirononatriene, spironorcaradiene, and metal-stabilized 9-barbaralyl cation structures as local minima. Intramolecular force ; Diazo ; Chemistry ; Cyclopropanation ; Carbene ; Rhodium ; Potential energy surface ; Density functional theory ; Carbenoid ; Hydride ; Computational chemistry ; Metal ; Chemical physics ; Photochemistry ; Stereochemistry ; Medicinal chemistry ; Catalysis ; Organic chemistry ; Molecule Cyclopropane Reaction Mechanisms ; Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Judy Meiksin | Kunwar Abhikeern | Ekaterina Antimirova | Grace Hu | Atif Javaid | Cullen Walsh | Elizabeth Wilson | Richard L. C. Wu
Source: MRS Bulletin (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Materials Research Society celebrates 50th anniversary at 2023 MRS Spring Meeting &amp; Exhibit Spring (device) ; Engineering physics ; Nanotechnology ; Engineering ethics ; Environmental ethics ; Polymer science ; Materials science ; Engineering ; Philosophy ; Mechanical engineering Machine Learning in Materials Science
Authors: J.A.R. Schmidt | Emma H. Wolpert | Grace M. Sparrow | Erin R. Johnson | Kim E. Jelfs
Source: ChemRxiv (preprint, 2023-08-11)
Field: Chemistry — Organic Chemistry

The effect of [n]-helicene length on crystal packing Chiral $\pi$-conjugated organic molecules hold potential for emerging technologies as they are capable of introducing novel functionalities into electronic devices owing to their strong chiroptical properties. However, capitalizing on chiral molecules for electronic devices is reliant on their molecular packing—a factor that impacts their charge-transport properties. The solid-state behaviour of molecules is sensitive to subtle differences in molecular interactions, chirality, and shape, but these relationships are not fully understood. Here, we employ crystal structure prediction as a tool to probe the lattice-energy landscape for a family of chiral organic molecules: [$n$]helicenes, where $n$ ranges from 3 to 12. Our results show excellent agreement between the crystal structure prediction landscapes and experimentally reported structures. By analysing the packing motifs within the polymorph landscapes, we begin to develop an understanding of how helicene length affects the shape and $\pi-\pi$ stacking interactions seen in the polymorphs. Further, we propose how helicene length can be used as a tool to design new functional organic electronics. Helicene ; Stacking ; Molecule ; Chemical physics ; Chirality (physics) ; Materials science ; Conjugated system ; Organic electronics ; Crystal (programming language) ; Crystallography ; Nanotechnology ; Polymer ; Chemistry ; Chiral symmetry ; Physics ; Organic chemistry ; Transistor ; Computer science Synthesis and Properties of Aromatic Compounds ; Molecular spectroscopy and chirality ; Surface Chemistry and Catalysis
Authors: Aaron R. Finney | Matteo Salvalaglio
Source: arXiv (Cornell University) (preprint, 2023-07-04)
Field: Chemistry — Electrochemistry

Properties of aqueous electrolyte solutions at carbon electrodes: effects of concentration and surface charge on solution structure, ion clustering and thermodynamics in the electric double layer Surfaces are able to control physical-chemical processes in multi-component solution systems and, as such, find application in a wide range of technological devices. Understanding the structure, dynamics and thermodynamics of non-ideal solutions at surfaces, however, is particularly challenging. Here, we use Constant Chemical Potential Molecular Dynamics simulations to gain insight into aqueous NaCl solutions in contact with graphite surfaces at high concentrations and under the effect of applied surface charges: conditions where mean-field theories describing interfaces cannot (typically) be reliably applied. We discover an asymmetric effect of surface charge on the electric double layer structure and resulting thermodynamic properties, which can be explained by considering the affinity of the surface for cations and anions and the cooperative adsorption of ions that occurs at higher concentrations. We characterise how the sign of the surface charge affects ion densities and water structure in the double layer and how the capacitance of the interface - a function of the electric potential drop across the double layer - is largely insensitive to the bulk solution concentration. Notably, we find that negatively charged graphite surfaces induce an increase in the size and concentration of extended liquid-like ion clusters confined to the double layer. Finally, we discuss how concentration and surface charge affect the activity coefficients of ions and water at the interface, demonstrating how electric fields in this region should be explicitly considered when characterising the thermodynamics of both solute and solvent at the solid/liquid interface. Electric field ; Electrolyte ; Chemical physics ; Ion ; Aqueous solution ; Chemistry ; Surface charge ; Adsorption ; Graphite ; Differential capacitance ; Activity coefficient ; Double layer (biology) ; Thermodynamics ; Capacitance ; Analytical Chemistry (journal) ; Electrode ; Layer (electronics) ; Physical chemistry ; Chromatography ; Organic chemistry Electrochemical Analysis and Applications ; Spectroscopy and Quantum Chemical Studies ; Electrostatics and Colloid Interactions
Authors: James R. Neilson | Matthew J. McDermott | Kristin A. Persson
Source: arXiv (Cornell University) (preprint, 2023-03-21)
Field: Materials Science — Materials Chemistry

Modernist Materials Synthesis: Finding Thermodynamic Shortcuts with Hyperdimensional Chemistry Synthesis remains a challenge for advancing materials science. A key focus of this challenge is how to enable selective synthesis, particularly as it pertains to metastable materials. This perspective addresses the question: how can ``spectator'' elements, such as those found in double ion exchange (metathesis) reactions, enable selective materials synthesis? By observing reaction pathways as they happen (\emph{in situ}) and calculating their energetics using modern computational thermodynamics, we observe transient, crystalline intermediates that suggest that many reactions attain a local thermodynamic equilibrium dictated by local chemical potentials far before achieving a global equilibrium set by the average composition. Using this knowledge, one can thermodynamically ``shortcut'' unfavorable intermediates by including additional elements beyond those of the desired target, providing access to a greater number of intermediates with advantageous energetics and selective phase nucleation. Ultimately, data-driven modeling that unites first-principles approaches with experimental insights will refine the accuracy of emerging predictive retrosynthetic models for complex materials synthesis. Metastability ; Retrosynthetic analysis ; Salt metathesis reaction ; Metathesis ; Nucleation ; Thermodynamic equilibrium ; Chemistry ; Biochemical engineering ; Computer science ; Nanotechnology ; Materials science ; Thermodynamics ; Physics ; Total synthesis ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Inorganic Chemistry and Materials
Authors: Stepan Melnyk | Yurii Melnyk | Halyna Mahorivska | Olena Fuchyla
Source: Chemistry & Chemical Technology (article, 2023-12-25)
Field: Chemistry — Organic Chemistry

Kinetic Aspects of Catalytic Interactions Involving Pentyl Acetate and Ethanolamine A conversion scheme for pentyl acetate, ethanolamine, and the products resulting from their interaction through aminolysis, transesterification, and O-N-acyl migration reactions catalyzed by homogeneous and heterogeneous Brønsted-Lowry bases and acids is proposed. It has been determined that acid and base catalysts significantly enhance the aminolysis reaction of esters with amino alcohols when compared to the non-catalytic process. The impact of the catalyst on each reaction has been assessed. Aminolysis ; Ethanolamine ; Catalysis ; Transesterification ; Homogeneous ; Chemistry ; Organic chemistry ; Brønsted–Lowry acid–base theory ; Base (topology) Chemical Reaction Mechanisms ; Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Reactions
Authors: Simon Wintersteller | Olesya Yarema | Dhananjeya Kumaar | Florian M. Schenk | Оlga V. Safonova | Paula M. Abdala | Vanessa Wood | Maksym Yarema
Source: Research Square (preprint, 2023-09-26)
Field: Materials Science — Materials Chemistry

Unravelling the Amorphous Structure, Nanoscale Effects, and Crystallization Mechanism of GeTe Phase Change Memory Material Abstract The reversible phase transitions in phase-change memory devices can switch on the order of nanoseconds, suggesting a close structural resemblance between the amorphous and crystalline phases. In contrast, the amorphous structure is generally assumed to consist of a highly random ordering of atoms, quite distant to its crystalline counterpart. This work uses in-situ high-temperature x-ray absorption spectroscopy (XAS) and theoretical calculations to quantify the amorphous structure of bulk and nanoscale GeTe. Based on XAS experiments, we develop a theoretical model of the amorphous GeTe structure, consisting of a disordered fcc -type Te sublattice and randomly arranged chains of Ge atoms in a tetrahedral coordination. Strikingly, our intuitive and scalable model provides an accurate description of the structural dynamics in phase-change memory materials, observed experimentally. Specifically, we present a detailed crystallization mechanism through the formation of an intermediate, partially stable, ‘ideal glass’ state and demonstrate differences between bulk and nanoscale GeTe leading to size-dependent crystallization temperature. Amorphous solid ; Crystallization ; Materials science ; Chemical physics ; X-ray absorption spectroscopy ; Nanoscopic scale ; Phase (matter) ; Polyamorphism ; Phase transition ; Crystallography ; Nanotechnology ; Absorption spectroscopy ; Condensed matter physics ; Thermodynamics ; Chemistry ; Optics ; Physics Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Transition Metal Oxide Nanomaterials
Authors: Lucas Foppa | Matthias Scheffler
Source: arXiv (Cornell University) (preprint, 2023-11-17)
Field: Materials Science — Materials Chemistry

Towards a Multi-Objective Optimization of Subgroups for the Discovery of Materials with Exceptional Performance Artificial intelligence (AI) can accelerate the design of materials by identifying correlations and complex patterns in data. However, AI methods commonly attempt to describe the entire, immense materials space with a single model, while it is typical that different mechanisms govern the materials behaviors across the materials space. The subgroup-discovery (SGD) approach identifies local rules describing exceptional subsets of data with respect to a given target. Thus, SGD can focus on mechanisms leading to exceptional performance. However, the identification of appropriate SG rules requires a careful consideration of the generality-exceptionality tradeoff. Here, we discuss challenges to advance the SGD approach in materials science and analyse the tradeoff between exceptionality and generality based on a Pareto front of SGD solutions. Generality ; Computer science ; Identification (biology) ; Space (punctuation) ; Focus (optics) ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Nicolas Castel | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2023-04-12)
Field: Materials Science — Materials Chemistry

Computation of Finite Temperature Mechanical Properties of ZIF Glasses by Molecular Dynamics Mechanical properties of amorphous phases of metal-organic frameworks, such as MOF glasses, are difficult to determine experimentally. Moreover, computational characterization is limited by the level of theory chosen for the description of interatomic interactions and is often computationally expensive. In this work, we have extensively investigated the computation of finite temperature mechanical properties of ZIF-4 in the crystal and glass phases. We critically assessed computational methodologies including ab initio molecular dynamics, reactive force fields, and classical force fields, based on a variety of glass models. We find that ZIF-4 glasses have a larger bulk modulus than the crystal and confirm previous studies that the density is larger for the glass phases. Moreover, we confirm in the case of ZIF glasses the relationship between density and bulk modulus, showing that obtaining models of correct density is key to the prediction of physical properties for these systems. Molecular dynamics ; Amorphous solid ; Computation ; Materials science ; Crystal (programming language) ; Work (physics) ; Modulus ; Amorphous metal ; Elastic modulus ; Characterization (materials science) ; Bulk modulus ; Statistical physics ; Thermodynamics ; Physics ; Nanotechnology ; Computational chemistry ; Computer science ; Chemistry ; Composite material ; Crystallography ; Algorithm Material Dynamics and Properties ; Lanthanide and Transition Metal Complexes ; Glass properties and applications
Authors: Jingheng Deng | Shuming Bai
Source: ChemRxiv (preprint, 2023-10-09)
Field: Materials Science — Materials Chemistry

Learning Intermolecular Electronic Coupling from a Molecular-Orbital-Pair-based Descriptor In this work, we proposed a graphic molecular-orbital-pair-based descriptor to predict intermolecular electronic couplings (ECs). We transform the numeric grid points data of the molecular orbitals (MOs) involved in the charge or energy transfer process by reducing the thousands of grid points to a few points based on the lobes of MO. The simplified representation consists of two parts, the integral value over the space and the coordinates of centroid of each lobe. To express the intermolecular interaction for a donor-acceptor pair, each simplified MO representation from the donor is paired to the one from the acceptor. Then, the obtained MO-pair representation is the descriptor for learning the intermolecular ECs, including hole transfer (HT), electron transfer (ET) and Dexter energy transfer (DET) coupling, with a multi-layer perceptron (MLP) model. The accuracy of our model reached at a promising level of 0.1~5 meV comparing with the quantum chemistry calculations. The data size dependence tests shows that our model has exhibited good ability on low-shot learning. Therefore, the graphic MO-pair-based descriptor has been proved to be capable to characterize MO interaction features. Intermolecular force ; Representation (politics) ; Molecular orbital ; Acceptor ; Coupling (piping) ; Chemistry ; Atomic orbital ; Electron transfer ; Orbital overlap ; Molecular physics ; Chemical physics ; Physics ; Computer science ; Topology (electrical circuits) ; Materials science ; Electron ; Molecule ; Quantum mechanics ; Mathematics ; Physical chemistry Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Computational Drug Discovery Methods
Authors: Marco Krummenacher | Moritz Gubler | Jonas A. Finkler | Hannes Huber | Martin Sommer-Jörgensen | Stefan Goedecker
Source: arXiv (Cornell University) (preprint, 2023-09-15)
Field: Materials Science — Materials Chemistry

Performing highly efficient Minima Hopping structure predictions using the Atomic Simulation Environment (ASE) In the dynamic field of materials science, the quest to find optimal structures with low potential energy is of great significance. Over the past two decades, the minima hopping algorithm has emerged as a successful tool in this pursuit. We present a robust, user friendly and efficient implementation of the minima hopping algorithm as a Python library, enhancing in this way the global structure optimization simulations significantly. Our implementation significantly accelerates the exploration the potential energy surfaces, leveraging an MPI parallelization scheme that allows for multi level parallelization. In this scheme, multiple minima hopping processes are running simultaneously communicating their findings to a single database and, therefore, sharing information with each other about which parts of the potential energy surface have already been explored. Also multiple features from several existing implementations such as variable cell shape molecular dynamics and combined atomic position and cell geometry optimization for bulk systems, enhanced temperature feedback and fragmentation fixing for clusters are included in this implementation. Finally, this implementation takes advantage of the Atomic Simulation Environment (ASE) Python library allowing for high flexibility regarding the underlying energy and force evaluation. Maxima and minima ; Python (programming language) ; Computer science ; Computational science ; Molecular dynamics ; Energy minimization ; Implementation ; Fragmentation (computing) ; Force field (fiction) ; Efficient energy use ; Distributed computing ; Algorithm ; Chemistry ; Mathematics ; Computational chemistry ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Liangliang Xu | Ning Xu | Yue Wang | Xiaojuan Hu | Zhongkang Han
Source: Research Square (preprint, 2023-06-29)
Field: Materials Science — Materials Chemistry

Unraveling the Formation Mechanism of Oxygen Vacancy on the Surface of Transition Metal-Doped Ceria Utilizing Artificial Intelligence Electronegativity ; Vacancy defect ; Dopant ; Oxygen ; Chemical physics ; Oxide ; Mechanism (biology) ; Transition metal ; Doping ; Materials science ; Nanotechnology ; Catalysis ; Chemistry ; Optoelectronics ; Crystallography ; Metallurgy ; Physics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: T Chuang | Y Lai | O Gupte | V Jayaram | J Ha | J Haarla | J Ala-Laurinaho | M Lahti | M Varonen | M Kantanen | J Holmberg | V Viikari | M Hamasha | K Alzoubi | M Obeidat | E Hmasha | A Alkhazali | S Hamasha | M See Jian | R See Al Athamneh | M Belhadi | B Han | C Wang | J Han | L Han | B Zhang | Z Han | S Ge | W Li | Z Zhao | J Shen | D Hani | R Al Athamneh | G Hantos | J Hegedus | M Haque | M Khan | L Harle | Z Ahmad | N Harris | A Valavan | T Hartmann | L Tsui | Y Hassab | M Schierholz | S Hayakawa | K Yasui | F He | Y Feng | J Dai | J Li | P He | S Zhang | W He | S Wu | W Wang | X He | Z See Yan | Y He | X Yu | M Lukacs | B Bodnar | G Lipak | A Poppe | W Heinrich | G Phung | T Henner | P Zhao | K Hettak | D King | C Hildmann | L Buttner | T Hilt | J Souriau | D Hobby | N See Cassada | J Hong | D Zou | W Hong | Y See Youn | B Hou | C Hou | F Wang | C Hu | G Hu | J Ma | Y Wang
Source: IEEE Transactions on Components Packaging and Manufacturing Technology (article, 2023-12-01)
Field: Materials Science — Polymers and Plastics

2023 Index IEEE Transactions on Components, Packaging and Manufacturing Technology Vol. 13 Index (typography) ; Business ; Computer science ; World Wide Web Synthesis and properties of polymers ; Electronic Packaging and Soldering Technologies
Authors: K Anitha | M Ramachandraiah
Source: International Journal of Chemical Studies (review, 2023-05-01)
Field: Materials Science — Polymers and Plastics

Exploring hybrid materials combining organic and inorganic components for versatile biosensors: A review Hybrid materials combining organic and inorganic components have emerged as transformative tools in biosensor technology, addressing critical needs in diagnostics, environmental monitoring, and food safety. These materials synergize the biocompatibility and specificity of organic elements, such as enzymes and polymers, with the stability and conductivity of inorganic components like metals, metal oxides, and carbon nanomaterials. By enabling superior signal transduction and sensitivity, hybrid materials overcome limitations of conventional biosensors. This review explores the fundamentals of hybrid materials, including their composition, unique properties, and synthesis techniques. The integration of organic and inorganic components, particularly at their interfaces, enhances compatibility and functionality, resulting in performance improvements such as signal amplification and increased selectivity. Various types of hybrid materials, including polymer hybrids, biomolecule-inorganic hybrids, metal-organic frameworks (MOFs), and composite nanoparticles, are discussed with their biosensing applications. These materials have facilitated breakthroughs in healthcare, including glucose monitoring and cancer diagnostics, as well as in environmental and food safety sectors. Challenges like stability, scalability, and regulatory barriers are highlighted, along with future opportunities for artificial intelligence-driven optimization and interdisciplinary collaboration. Hybrid materials thus represent a bridge between innovation and real-world application, driving advancements in sustainable and versatile biosensing technologies. Biosensor ; Nanotechnology ; Environmental science ; Biochemical engineering ; Materials science ; Environmental chemistry ; Chemistry ; Engineering Conducting polymers and applications ; Electrochemical sensors and biosensors ; Advanced Nanomaterials in Catalysis
Authors: Jason E. Hein | Yao Shi | Joshua S. Derasp | Tristan Maschmeyer
Source: Research Square (preprint, 2023-09-18)
Field: Chemistry — Organic Chemistry

Phase Transfer Catalysts Shift the Pathway to Transmetalation in Biphasic Suzuki-Miyaura Cross-Couplings Transmetalation ; Catalysis ; Phase (matter) ; Chemistry ; Political science ; Organic chemistry Catalytic Cross-Coupling Reactions ; Coordination Chemistry and Organometallics ; Organoboron and organosilicon chemistry