jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haidi Wang | Tao Li | Xiaofeng Liu | Weiduo Zhu | Zhao Chen | Zhongjun Li | Jinlong Yang
Source: Preprints.org (preprint, 2023-04-21)
Field: Materials Science — Materials Chemistry

mech2d: An Efficient Tool for High-Throughput Calculation of Mechanical Properties for Two-Dimensional Materials Two-dimensional (2D) materials have been a research hot topic in the passed decades due to their unique and fascinating properties. Among them, mechanical properties plays an important role for their application. However, there lacks an effective tool for high-throughput calculating, analysing and visualizing the mechanical properties of 2D materials. In this work, we present, mech2d package, a highly automated toolkit for calculating and analysing second-order elastic constants (SOECs) tensor and relevant properties of 2D materials with considering their symmetry. In the mech2d, the SOECs can be fitted by both strain-energy and stress-strain approach, where the energy or strain can be calculated by first-principles engine, such as VASP. As a key feature, the mech2d package can automatically submit and collect the tasks from local or remote machine with robust fault-tolerant ability, make it suitable for high-throughput calculation. The present code has been validated by several common 2D materials, including graphene, black phosphorene, GeSe2 and so on. Throughput ; Computer science ; Phosphorene ; Tensor (intrinsic definition) ; Work (physics) ; Computational science ; Mechanical engineering ; Materials science ; Graphene ; Nanotechnology ; Mathematics ; Geometry ; Engineering 2D Materials and Applications ; Boron and Carbon Nanomaterials Research ; Graphene research and applications
Authors: Charles Le Losq | Barbara Baldoni
Source: arXiv (Cornell University) (preprint, 2023-04-24)
Field: Materials Science — Ceramics and Composites

Machine learning modeling of the atomic structure and physical properties of alkali and alkaline-earth aluminosilicate glasses and melts The first version of the machine learning greybox model i-Melt was trained to predict latent and observed properties of K$_2$O-Na$_2$O-Al$_2$O$_3$-SiO$_2$ melts and glasses. Here, we extend the model compositional range, which now allows accurate predictions of properties for glass-forming melts in the CaO-MgO-K$_2$O-Na$_2$O-Al$_2$O$_3$-SiO$_2$ system, including melt viscosity (accuracy equal or better than 0.4 log$_{10}$ Pa$\cdot$s in the 10$^{-1}$-10$^{15}$ log$_{10}$ Pa$\cdot$s range), configurational entropy at glass transition ($\leq$ 1 J mol$^{-1}$ K$^{-1}$), liquidus ($\leq$ 60 K) and glass transition ($\leq$ 16 K) temperatures, heat capacity ($\leq$ 3 \%) as well as glass density ($\leq$ 0.02 g cm$^{-3}$), optical refractive index ($\leq$ 0.006), Abbe number ($\leq$ 4), elastic modulus ($\leq$ 6 GPa), coefficient of thermal expansion ($\leq$ 1.1 10$^{-6}$ K$^{-1}$) and Raman spectra ($\leq$ 25 \%). Uncertainties on predictions also are now provided. The model offers new possibilities to explore how melt/glass properties change with composition and atomic structure. Liquidus ; Aluminosilicate ; Glass transition ; Thermodynamics ; Thermal expansion ; Raman spectroscopy ; Alkali metal ; Mineralogy ; Alkaline earth metal ; Materials science ; Bulk modulus ; Analytical Chemistry (journal) ; Chemistry ; Physics ; Polymer ; Optics ; Composite material Glass properties and applications ; Material Science and Thermodynamics ; Mineralogy and Gemology Studies
Authors: Tao Yao | Mingzhe Li | Fucheng Zhang | Huanhuan Dong | Tao Chen | Jiang Fu | Tao Ding | Wenkun Zhu
Source: Research Square (preprint, 2023-11-10)
Field: Chemistry — Inorganic Chemistry

Phosphoric Acid Network Induced by Surface Ligands Shifts the Selectivity for Uranium Extraction over Single Atom Modified TiO2 Catalysts Uranyl ; Uranium ; Phosphoric acid ; Fluorine ; Selectivity ; Chemistry ; Actinide ; Inorganic chemistry ; Extraction (chemistry) ; Catalysis ; Nuclear chemistry ; Materials science ; Organic chemistry ; Metallurgy Radioactive element chemistry and processing ; Nuclear materials and radiation effects ; Radioactive contamination and transfer
Authors: Florian Dietrich | Xavier R. Advincula | Gianpaolo Gobbo | Michael A. Bellucci | Matteo Salvalaglio
Source: ChemRxiv (preprint, 2023-10-05)
Field: Materials Science — Materials Chemistry

Machine Learning Nucleation Collective Variables with Graph Neural Networks The efficient calculation of nucleation collective variables (CVs) is one of the main limitations to the application of enhanced sampling methods to the investigation of nucleation processes in realistic environments. Here we discuss the development of a graph-based model for the approximation of nucleation CVs, which enables orders-of-magnitude gains in computational efficiency in the on-the-fly evaluation of nucleation CVs. By performing simulations on a nucleating colloidal system mimicking a multistep nucleation process from solution, we assess the model's efficiency in both postprocessing and on-the-fly biasing of nucleation trajectories with pulling, umbrella sampling and metadynamics simulations. Moreover, we probe and discuss the transferability of graph-based models of nucleation CVs across systems by using the model of a CV based on sixth-order Steinhardt parameters trained on a colloidal system to drive the nucleation of crystalline copper from its melt. Our approach is general and potentially transferable to more complex systems as well as to different CVs. Nucleation ; Metadynamics ; Computer science ; Graph ; Statistical physics ; Process (computing) ; Biological system ; Artificial intelligence ; Materials science ; Chemistry ; Molecular dynamics ; Theoretical computer science ; Physics ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Protein Structure and Dynamics
Authors: Nicolas Castel | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2023-05-01)
Field: Materials Science — Materials Chemistry

Computation of Finite Temperature Mechanical Properties of Zeolitic Imidazolate Framework Glasses by Molecular Dynamics Mechanical properties of amorphous phases of metal-organic frameworks (MOF), 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 zeolitic imidazolate framework (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. Zeolitic imidazolate framework ; Molecular dynamics ; Materials science ; Amorphous solid ; Crystal (programming language) ; Amorphous metal ; Bulk modulus ; Modulus ; Force field (fiction) ; Work (physics) ; Computation ; Characterization (materials science) ; Density functional theory ; Chemical physics ; Thermodynamics ; Computational chemistry ; Nanotechnology ; Metal-organic framework ; Composite material ; Physical chemistry ; Chemistry ; Crystallography ; Physics ; Computer science ; Algorithm Lanthanide and Transition Metal Complexes ; Metal-Organic Frameworks: Synthesis and Applications ; Glass properties and applications
Authors: Fei Chen | Yi-Jiao Sun | Chaowei Yuan | Chang‐Wei Bai | Ke-An Zhu | Xin-Jia Chen | Bin-Bin Zhang | Qi Yang
Source: Research Square (preprint, 2023-04-25)
Field: Materials Science — Materials Chemistry

Local Electron Density Regulation of Iron Single Atoms by Adjacent Nitrogen Vacancies for Ultra-stable Photo-Fenton-Like Reactions Nitrogen ; Electron ; Chemistry ; Materials science ; Atomic physics ; Photochemistry ; Physics ; Nuclear physics Catalytic Processes in Materials Science ; Iron oxide chemistry and applications ; Catalysis and Oxidation Reactions
Authors: Megan Stanley | Marwin Segler
Source: ChemRxiv (preprint, 2023-07-10)
Field: Materials Science — Materials Chemistry

Fake it until you make it? Generative De Novo Design and Virtual Screening of Synthesizable Molecules Computational techniques, including virtual screening, de novo design, and generative models, play an increasing role in expediting DMTA cycles for modern molecular discovery. However, computationally proposed molecules must be synthetically feasible for laboratory testing. In this perspective, we offer a succinct introduction to the subject, showcase typical workflows to integrate synthesis planning, synthesizability scoring, and molecule generation. Finally, we address limitations and opportunities for future research. Expediting ; Workflow ; Computer science ; Virtual screening ; Generative grammar ; Perspective (graphical) ; Data science ; Artificial intelligence ; Systems engineering ; Drug discovery ; Engineering ; Chemistry ; Database Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Sinan Keten | Akash Pandey | Wei Chen
Source: Research Square (preprint, 2023-12-21)
Field: Materials Science — Biomaterials

Sequence-based data-constraint deep learning framework to predict spider dragline mechanical properties Spider ; Constraint (computer-aided design) ; Sequence (biology) ; Computer science ; Artificial intelligence ; Deep learning ; Engineering ; Biology ; Mechanical engineering Silk-based biomaterials and applications ; Gear and Bearing Dynamics Analysis ; Adhesion, Friction, and Surface Interactions
Authors: Akshay Subramanian | Kevin L. Greenman | Alexis Gervaix | Tzuhsiung Yang | Rafael Gómez‐Bombarelli
Source: arXiv (Cornell University) (preprint, 2023-03-14)
Field: Materials Science — Materials Chemistry

Automated patent extraction powers generative modeling in focused chemical spaces Deep generative models have emerged as an exciting avenue for inverse molecular design, with progress coming from the interplay between training algorithms and molecular representations. One of the key challenges in their applicability to materials science and chemistry has been the lack of access to sizeable training datasets with property labels. Published patents contain the first disclosure of new materials prior to their publication in journals, and are a vast source of scientific knowledge that has remained relatively untapped in the field of data-driven molecular design. Because patents are filed seeking to protect specific uses, molecules in patents can be considered to be weakly labeled into application classes. Furthermore, patents published by the US Patent and Trademark Office (USPTO) are downloadable and have machine-readable text and molecular structures. In this work, we train domain-specific generative models using patent data sources by developing an automated pipeline to go from USPTO patent digital files to the generation of novel candidates with minimal human intervention. We test the approach on two in-class extracted datasets, one in organic electronics and another in tyrosine kinase inhibitors. We then evaluate the ability of generative models trained on these in-class datasets on two categories of tasks (distribution learning and property optimization), identify strengths and limitations, and suggest possible explanations and remedies that could be used to overcome these in practice. Trademark ; Generative grammar ; Computer science ; Pipeline (software) ; Class (philosophy) ; Data science ; Artificial intelligence ; Domain (mathematical analysis) ; Generative model ; Field (mathematics) ; Patent visualisation ; Public domain ; Machine learning ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Junshan Lai | Jolene P. Reid
Source: ChemRxiv (preprint, 2023-06-05)
Field: Chemistry — Inorganic Chemistry

The Synthesis and Application of the Bulkiest Imidodiphosphorimidate Brønsted Acid Catalyst The development of BINOL-derived Brønsted acid catalysts has been profoundly guided by rational design, with carefully implemented structural changes leading to unique generations of catalysts with enhanced reaction capabilities. This approach to catalyst optimization has promoted the integration of knowledge gathered in optimizing prior eras of Brønsted acids and ultimately, the molecular features that have contributed to the success of previous designs are preserved. Of these, the large substituents at the 3 and 3’ positions of the BINOL backbone are the most critical with almost every newly developed structure possessing this feature. However, imidodiphosphorimidate (IDPi) catalysts are not synthetically well-suited to contain the same sterically bulky groups associated with the high selectivity imparted by previously implemented catalyst structures. Herein, we have leveraged the moderate size (as compared to TRIP and 9-anthryl) but the high applicability of the 9-phenanthryl substituent to synthesize an extremely sterically demanding IDPi. Using computed descriptors, we survey the catalyst properties of known structures to demonstrate this catalyst to be both unique and to the best of our knowledge, the bulkiest IDPi yet synthesized. The applicability of the catalyst was evaluated in the construction of stereochemically dense spirocycles generated via an asymmetric Prins-semipinacol reaction sequence. Transition state calculations were deployed to interrogate the origins of the superior enantioselectivity and these demonstrate the mechanistic hallmarks of the 9-phenanthryl substituent can be generalized to a genuinely different class of Brønsted acid catalyst. Steric effects ; Brønsted–Lowry acid–base theory ; Catalysis ; Substituent ; Chemistry ; Combinatorial chemistry ; Selectivity ; Prins reaction ; Computational chemistry ; Stereochemistry ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Asymmetric Synthesis and Catalysis ; Synthetic Organic Chemistry Methods
Authors: Xuan Zhang | Shenglong Xu | Shuiwang Ji
Source: arXiv (Cornell University) (preprint, 2023-05-25)
Field: Materials Science — Materials Chemistry

A Score-Based Model for Learning Neural Wavefunctions Quantum Monte Carlo coupled with neural network wavefunctions has shown success in computing ground states of quantum many-body systems. Existing optimization approaches compute the energy by sampling local energy from an explicit probability distribution given by the wavefunction. In this work, we provide a new optimization framework for obtaining properties of quantum many-body ground states using score-based neural networks. Our new framework does not require explicit probability distribution and performs the sampling via Langevin dynamics. Our method is based on the key observation that the local energy is directly related to scores, defined as the gradient of the logarithmic wavefunction. Inspired by the score matching and diffusion Monte Carlo methods, we derive a weighted score matching objective to guide our score-based models to converge correctly to ground states. We first evaluate our approach with experiments on quantum harmonic traps, and results show that it can accurately learn ground states of atomic systems. By implicitly modeling high-dimensional data distributions, our work paves the way toward a more efficient representation of quantum systems. Wave function ; Statistical physics ; Quantum Monte Carlo ; Monte Carlo method ; Diffusion Monte Carlo ; Artificial neural network ; Quantum ; Computer science ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Hybrid Monte Carlo ; Statistics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Quantum many-body systems
Authors: Nuri Oh | Seongchan Kim | Sooyeon Yeon | Minwoo Lee | Junyoung Jin | Seungki Shin | Namyoung Gwak | Inyoung Jeong | Hyunwoo Jang | Gyu Weon Hwang
Source: Research Square (preprint, 2023-01-11)
Field: Materials Science — Materials Chemistry

Chemically and Electronically Active Metal Ions on InAs Quantum Dots for Infrared Detectors Quantum dot ; Infrared ; Detector ; Ion ; Materials science ; Optoelectronics ; Metal ; Nanotechnology ; Physics ; Optics ; Metallurgy ; Quantum mechanics Quantum Dots Synthesis And Properties ; Gas Sensing Nanomaterials and Sensors ; ZnO doping and properties
Authors: Anatoly M. Belostotskii
Source: RSC Advances (article, 2023-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Delocalization quantitatively mapped for prototypic organic nitroanions as well as azidoform anions Delocalization of occupied orbitals impacts the chemical bonding in the simplest known pernitroanions [(NO<sub>2</sub>)<sub>3</sub>C]<sup>-</sup> (1) and [(NO<sub>2</sub>)<sub>2</sub>N]<sup>-</sup> (2) as well as other functionalized organic anions. By quantitatively mapping it onto molecular backbones of 1, 2, [CH<sub>2</sub>NO<sub>2</sub>]<sup>-</sup> (3), [CH<sub>3</sub>NNO<sub>2</sub>]<sup>-</sup> (4) and [C(N<sub>3</sub>)]<sup>-</sup> (6) anions (all modeled by QM calculations), the Weinhold's NBO analysis refines their chemical structure, enabling to explain and even predict their essential chemical behaviour. In detail, the HOMO of 1 and 2 is associated with the central atom to the degree of 70.7% and 80.4%, respectively, while the HOMO localization on O atoms for 3 and 4 is 85.3% and 81.1%, respectively. Predomination of C-alkylation for 1 and that of O-alkylation for 3 in non-coordinating solvents thus becomes clear. The important news is that the easiness of homolytically disrupting the N-N bond in 2, a constituent of inexpensive powerful explosives, is because of the occupancy of the related σ*orbital increases with stretching this bond. The same is true for electrocyclic extrusion of NO<sub>3</sub><sup>-</sup> from this molecule. This antibonding effect may be assumed to be the common cause of the proneness of aliphatic nitro compounds to decompose. Pyramidal anion 6 is a highly localized carbanion. Its isomer of molecular symmetry <i>C</i><sub>S</sub> has a unique chemical structure of its azido substituents: each of them is represented by one high-weight resonance structure, <i>e.g.</i>, N-N[triple bond, length as m-dash]N. The prediction is that the dinitrogen-eliminating decomposition of this isomer is more facile than of the isomer of <i>C</i><sub>3</sub> symmetry. In summary, this study affords three novel particular insights into the chemical structure and reactivity of these anions: chemically telling delocalization-augmented molecular structures, a reasonable hypothesis of the common cause of thermally triggered instability of aliphatic nitro compounds, and discovered one-resonance structure azido groups. Delocalized electron ; Chemistry ; Computational chemistry ; Organic chemistry Chemical Reactions and Mechanisms ; Photoreceptor and optogenetics research ; Chemical Reaction Mechanisms
Authors: Martin Z. Bazant | Lydéric Bocquet | Fabio Cicoira | Daniel Felipe Duarte Sánchez | Emer B. Farrell | Christian Holm | Siretanu Igor | Amritha Janardanan | Felipe Jiménez‐Ángeles | Robert P. Johnson | Tim M. Kamsma | Frédéric Kanoufi | Alexei A. Kornyshev | Serge G. Lemay | Yan Levin | Sophie Marbach | Mónica Olvera de la Cruz | Susan Perkin | Giovanni Pireddu | Paul Robin | Benjamin Rotenberg | Alexander Schlaich | Zuzanna S. Siwy | Derek Stein | Alice Thorneywork | Markus Valtiner | René van Roij | Gilad Yossifon | Yujia Zhang
Source: Faraday Discussions (article, 2023-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Iontronics under confinement: general discussion Lydéric Bocquet opened a general discussion of the paper by Markus Valtiner: These are very nice and important experiments (https://doi.org/10.1039/d3fd00038a). Can you comment on the dynamics? The dynamics in the experiments seems to be substantially faster than the diffusion time scale (among Psychology Electron and X-Ray Spectroscopy Techniques
Authors: E. F. Sheka
Source: arXiv (Cornell University) (preprint, 2023-12-01)
Field: Chemistry — Organic Chemistry

Digitalization of Free-Radical Polymerization Current paper presents the first attempt of the digitalization of a complex polymerization process representing free-radical polymerization of vinyl monomers in the presence of small additives of stable radicals TEMPO and C60 fullerene. Chain-reaction essence of the chemical process; quantum-chemical concept of elementary reactions; digital-twin presentation of their participants; virtual thermodynamic and kinetic descriptors of the latters; digital polymeryzation passports of individually composed reaction solutions lay the foundation of the digitalization carried out. Polymerization-passport predictions made for a number of solutions with different vinyl monomers and/or different initiating free radicals are fully consistent with chemical reality. Polymerization ; Radical ; Monomer ; Radical polymerization ; Quantum chemical ; Kinetic chain length ; Chemistry ; Process (computing) ; Polymer chemistry ; Materials science ; Photochemistry ; Computer science ; Organic chemistry ; Molecule ; Polymer Photopolymerization techniques and applications
Authors: Xiaowen Han | Xinru Zuo | Ying Liu | Zixing Wang | Kefeng Cai
Source: Journal of Composites Science (article, 2023-11-03)
Field: Materials Science — Materials Chemistry

Preparation and Properties of Flexible CuI/Polyvinylpyrrolidone Nanocomposite Thermoelectric Film A facile preparation method for flexible p-type CuI/polyvinylpyrrolidone (PVP) nanocomposite thermoelectric (TE) film is developed. First, CuI powder was synthesized by a one-pot method; second, PVP was coated in situ with the CuI powder; third, the CuI/PVP nanocomposite film was prepared on a nylon membrane by vacuum filtration and then hot-pressing. Transmission electron microscopy (TEM) observation indicates that the film consists of CuI nanograins with an average size of ~15 nm and PVP distributed at the inner wall of nanopores and the surface of the CuI nanograins. The composite film shows a large Seebeck coefficient of ~605 µVK−1 and a power factor of ~8.05 µWm−1K−2 at 300 K. The nanocomposite film also exhibits excellent flexibility (~96% of initial electrical conductivity retention after being bent 1000 times along a 4 mm radius rod). A single-leg TE module outputs a voltage of ~3.6 mV when the temperature difference is 6 K. This work provides a fast, simple, and environmentally friendly method by which to prepare flexible CuI/PVP nanocomposite TE film with a large Seebeck coefficient, which could be used as a wearable sensor. Polyvinylpyrrolidone ; Nanocomposite ; Materials science ; Seebeck coefficient ; Thermoelectric effect ; Nanotechnology ; Composite material ; Chemical engineering ; Polymer chemistry ; Thermal conductivity Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials ; Thermal properties of materials