jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Paolo Giusto | Daniel Cruz | Yael Rodriguez | Regina Rothe | Nadezda V. Tarakina
Source: ChemRxiv (preprint, 2021-12-16)
Field: Materials Science — Materials Chemistry

Red Carbon: a Carbon-Oxygen-based covalent semiconductor for selective amine sensing The requirements for organic semiconductor materials and new methods for their synthesis at low temperature have risen over the last decades, especially due to concerns of sustainability. Herein, we present an innovative method for the synthesis of a so-called “red carbon” thin film, being composed of carbon and oxygen, only. This material was already described by Kappe and Ziegler at the beginning of the 20th century, but now can complement the current research on covalent organic semiconductor materials. The herein described red carbon can be homogeneous deposited on glass substrates as thin ilms which reveal a highly ordered structure. The films are highly reactive towards amines and were employed as amine vapor sensors for a scope of analogous amines. The gas-to-solid phase reaction causes a significant change of the films optical properties in all cases, blue-shifting the bandgap and the photoluminescence spectra from the red to the near UV range. The irreversible chemical reaction between the thin film and the vapor was also exploited for the preparation of nitrogen containing thin carbon films. We expect the herein presented red carbon material is of interest not only for sensing applications, but also in optoelectronics. Carbon fibers ; Materials science ; Thin film ; Semiconductor ; Photoluminescence ; Amine gas treating ; Nanotechnology ; Band gap ; Carbon film ; Chemical engineering ; Organic chemistry ; Chemistry ; Optoelectronics ; Composite material Covalent Organic Framework Applications ; Conducting polymers and applications ; Porphyrin and Phthalocyanine Chemistry
Authors: Daniel Schwalbe‐Koda | Avelino Corma | Yuriy Román‐Leshkov | Manuel Moliner | Rafael Gómez‐Bombarelli
Source: ChemRxiv (preprint, 2021-09-24)
Field: Chemistry — Inorganic Chemistry

Data-driven design of bi-selective OSDAs for intergrowth zeolites Zeolites are inorganic materials with wide industrial applications due to their topological diversity. Tailoring confinement effects in zeolite pores, for instance by crystallizing intergrown frameworks, can improve their catalytic and transport properties, but controlling zeolite crystallization often relies on heuristics. In this work, we use computational simulations and data mining to design organic structure-directing agents (OSDAs) to favor the synthesis of intergrown zeolites. First, we propose design principles to identify OSDAs which are selective towards both end members of the disordered structure. Then, we mine a database of hundreds of thousands of zeolite-OSDA pairs and downselect OSDA candidates to synthesize known intergrowth zeolites such as CHA/AFX, MTT/TON, and BEC/ISV. The computationally designed OSDAs balance phase competition metrics and shape selectivity towards the frameworks, thus bypassing expensive dual-OSDA approaches typically used in the synthesis of intergrowths. Finally, we propose potential OSDAs to obtain hypothesized disordered frameworks such as AEI/SAV. This work may accelerate zeolite discovery through data-driven synthesis optimization and design. Zeolite ; Heuristics ; Crystallization ; Materials science ; Work (physics) ; Phase (matter) ; Nanotechnology ; Computer science ; Chemical engineering ; Mechanical engineering ; Catalysis ; Chemistry ; Engineering Zeolite Catalysis and Synthesis ; Polyoxometalates: Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Konstantin G. Nikolaev | Nikolay V. Ryzhkov | Carlos Gershenson | Ekaterina V. Skorb
Source: Reviews on advanced materials and technologies (article, 2021-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Chemical Systems for Life Science The use of dynamic, adaptive materials with feedback control is a tendency of the past decade. Life sciences and medicine require materials with the controlled and responsive assembly of various components on the scales from molecular to macroscopic and even robotics. The main idea of this review is the use of synthetic systems as regulatory networks that facilitate the integration of chemical and biological materials. The synthetic systems, which are inspired by biochemical regulatory networks, help synthetic material to adapt to environmentand to interact with living matter cooperatively. The first step in realizing this concept is designing simple model systems. The simplicity means that the system should contain a minimal number of components but should be robust and sustainable to perform the required functions through logic operations and feedback loops. Here we suggest specific examples of robust systems for the selected functionality: compartmentalized signaling cascades, computation with light-induced chemical gradients andadvanced biomimetic mixed organic-inorganic materials, and self-regulation in chemical-biological systems. The main challenges for the given examples are discussed, and future prospects of logic operation with chemical systems are provided. d , d B k B A t (3) 3 d , d C k C B t (4) Computer science Various Chemistry Research Topics
Authors: James Goff | Bryant Y. Li | Susan B. Sinnott | Ismaïla Dabo
Source: Physical review. B./Physical review. B (preprint, 2021-08-09)
Field: Materials Science — Materials Chemistry

Quantifying multipoint ordering in alloys A central problem in multicomponent lattice systems is to systematically quantify multipoint ordering. Ordering in such systems is often described in terms of pairs, even though this is not sufficient when three-point and higher-order interactions are included in the Hamiltonian. Current models and parameters for multipoint ordering are often only applicable for very specific cases or require approximating a subset of correlated occupational variables on a lattice as being uncorrelated. In this paper, cluster order parameters are introduced to systematically quantify arbitrary multipoint ordering motifs in substitutional systems through direct calculations of normalized cluster probabilities. These parameters can describe multipoint chemical ordering in crystal systems with multiple sublattices, multiple components, and systems with reduced symmetry. These are defined in this paper and applied to quantify four-point chemical ordering motifs in platinum/palladium alloy nanoparticles that are of practical interest to the synthesis of catalytic nanocages. Impacts of chemical ordering on nanocage stability are discussed. It is demonstrated that approximating four-point probabilities from superpositions of lower-order pair probabilities is not sufficient in cases where three- and four-body terms are included in the energy expression. Conclusions about the formation mechanisms of nanocages may change significantly when using common pair approximations. Nanocages ; Statistical physics ; Hamiltonian (control theory) ; Crystallographic defect ; Lattice (music) ; Cluster (spacecraft) ; Alloy ; Cluster expansion ; Materials science ; Mathematics ; Computer science ; Physics ; Thermodynamics ; Condensed matter physics ; Chemistry ; Mathematical optimization ; Catalysis Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Chemical Physics Studies
Authors: Qiuxing Li | Li Huang | Paijin Zhu | Min Zhong | Shuxia Xu
Source: Research Square (preprint, 2021-12-06)
Field: Materials Science — Materials Chemistry

Rapid Adsorption Removal of Triclosan and p-chloro-m-xylenol In Water By Nitrogen Doped Magnetic Porous Carbon Adsorption ; Triclosan ; Nitrogen ; Porosity ; Carbon fibers ; Doping ; Chemistry ; Materials science ; Chemical engineering ; Inorganic chemistry ; Nuclear chemistry ; Composite material ; Organic chemistry ; Medicine Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Electrochemical sensors and biosensors
Authors: WooSeok Jeong | Carlo Alberto Gaggioli | Laura Gagliardi
Source:  (preprint, 2021-08-03)
Field: Materials Science — Materials Chemistry

Active Learning Configuration Interaction for Excited States Calculations of Polycyclic Aromatic Hydrocarbons We present the active learning configuration interaction (ALCI) method for multiconfigurational calculations based on large active spaces. ALCI leverages the use of an active learning procedure to find important electronic configurations among the full configuration space generated within an active space. We tested it for the calculation of singlet-singlet excited states of acenes and pyrene, by using different machine learning algorithms. The ALCI method yields excitation energies within 0.2–0.3 eV from those obtained by traditional complete active space configuration interaction (CASCI) calculations (affordable for active spaces up to 16 electrons in 16 orbitals), by including only a small fraction of the CASCI configuration space in the calculations. For larger active spaces (up to 26 electrons in 26 orbitals), not affordable with traditional CI methods, ALCI captures the trends of experimental excitation energies. Overall ALCI provides satisfactory approximations to large active-space wave functions with up to ten orders of magnitude fewer configurations. These ALCI wave functions are promising and affordable starting points for subsequent second order perturbation theory or pair-density functional theory calculations. Complete active space ; Configuration interaction ; Excited state ; Atomic orbital ; Configuration space ; Wave function ; Excitation ; Density functional theory ; Singlet state ; Space (punctuation) ; Perturbation theory (quantum mechanics) ; Physics ; Atomic physics ; Electron ; Molecular physics ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Sourabh Shinde | Taukir Momin | Vispi Karkaria | P. B. Karandikar
Source: E3S Web of Conferences (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Polypropylene as an innovative substitute for jar material of horizontal axis, multi-jar ball milling machine to grind electrode materials for energy storage devices It is approximated that there will be a considerable amount of increase in worlds energy needs. So, there is a need of high energy storing devices when the load is low or moderate. Supercapacitors (SC) can be the competent options to serve this purpose. The paper recounts the performance of supercapacitors (SC) churned out from a horizontal axis multi-jar ball milling machine based on variations in material properties of ball milling jars. To store ample amount of energy, it is very essential to develop energy storing devices with large capacities and large ratings. The performance of energy storage devices is investigated using Manganese dioxide and Activated carbon as electrode materials. Majority of the times the granular size of raw-electrode materials is not enough fine to use them as electrodes directly. As a result of this, it is essential to crush the raw-electrode materials of the SC into fine powder. The energy storing capacity of SCs depends on size of particle, specific area, the of material and quantity to weight ratio of electrode material. In this paper an alternative material ‘Polypropylene’ is used as a substitute for Aluminum jars in horizontal axis multi-jar ball milling machine to crush the electrode materials. The effectiveness of both the jars is compared by evaluating the performance parameters of the SCs manufactured from electrode materials crushed in each of these jars. It is observed that the performance of SCs depends on type of ball milling machine, number of ball milling balls used, ball milling time, diameter of ball milling jars, speed of ball milling and material of ball mill jars properties. So, all other parameters are kept the same except the material of ball mill jars to scrutinize the effect of ball milling jars on performance of SCs. Ball mill ; Materials science ; Electrode ; Polypropylene ; Raw material ; Ball (mathematics) ; Grind ; Specific energy ; Composite material ; Supercapacitor ; Particle size ; Energy storage ; Mechanical engineering ; Metallurgy ; Capacitance ; Grinding ; Chemical engineering ; Engineering Supercapacitor Materials and Fabrication ; Electric and Hybrid Vehicle Technologies ; Advanced Battery Technologies Research
Authors: A O Odysseos | H See Awan | Y Ozturk | A Dizaji | A Pal | K Amreen | S Dubey | S Goel | N Patil | S See Bankapur | P Pattnaik | M Bhaiyya | V Paul | N Sawaftah | Y Pei | P Yang | F Pein | A Bartsch | C Steinem | A Munk | T Pham | S Poddar | U See Kasiviswanathan | K Priyadarshani | S Singh | K Puttananjegowda | A Takshi | S Thomas | M Rad | A Amerizadeh | A Rahawy | S Nakhla | M Rai | S See Shende | A Raj | S Kumar | G Raja | N See Ayyanar | L Rajan | M See Hari | A | M Rajan | R Raut | P Rewatkar | H Rong | S Rouzegar | H Arjmandi | C Roychaudhuri | N Das | H Rudsari | M Veletic | J Bergsland | I Balasingham | S Sahu | S Mahapatra | M Salam | M Saleh | H See Ammar | M Salve | N Awad | G Husseini | M Schafer | S See Lotter | R Schober | T Shalaby | A Shamloo | A See Amerizadeh | N Sharifi | Z Gong | G Holmes | Y Chen | N Sharma | S Shende | R Bhagat | A Gade | H Shi | K Nie | B Dong | M Long | Z Liu | S Shi | Y Yan | J Xiong
Source: IEEE Transactions on NanoBioscience (article, 2021-10-01)
Field: Materials Science — Materials Chemistry

2021 Index IEEE Transactions on NanoBioscience Vol. 20 This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the co-authors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under the primary entry in the Author Index. Index (typography) ; Computer science ; World Wide Web Advanced Nanomaterials in Catalysis
Authors: An Su | Ling Wang | Xinqiao Wang | Chengyun Zhang | Yejian Wu | Hongliang Duan
Source: ChemRxiv (preprint, 2021-06-23)
Field: Materials Science — Materials Chemistry

Reproducing the invention of a named reaction: zero-shotprediction of unseen chemical reactions While state-of-art models can predict reactions through the transfer learning of thousands of samples with the same reaction types as those of the reactions to predict, how to prepare such models to predict "unseen" reactions remain an unanswered question. We aim to study the Transformer model's ability to predict "unseen" reactions following "zero-shot reaction prediction (ZSRP)", a concept derived from zero-shot learning and zero-shot translation. We reproduce the human invention of the Chan-Lam coupling reaction where the inventor was inspired by the Suzuki reaction when improving Barton's bismuth arylation reaction. After being fine-tuned with the samples from these two "existing" reactions, the USPTO-trained Transformer can predict "unseen" Chan-Lam coupling reactions with 55.7% top-1 accuracy. Our model also mimics the later stage of the history of this reaction, where the initial case of this reaction was generalized to more reactants and reagents via the "one-shot/few-shot reaction prediction(OSRP/FSRP)" approaches. Transformer ; Coupling reaction ; Shot (pellet) ; Computer science ; Artificial intelligence ; Chemistry ; Physics ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Topic Modeling ; Machine Learning in Bioinformatics
Authors: Alexandru Woinaroschy | ALEXANDRU WOINAROSCHY
Source: Journal of Engineering Sciences and Innovation (article, 2021-11-17)
Field: Materials Science — Materials Chemistry

Interdisciplinarity: artificial intelligence and chemical engineering There are exposed implications of artificial intelligence in addressing important problems from a technical and economic point of view whose traditional solution would not be possible or would require a huge computational time: selection of the most efficient catalyst, and determination of the optimal sequence of processes for the separation into individual components of the multicomponent mixtures in the liquid state. Artificial intelligence ; Computer science ; Selection (genetic algorithm) ; Sequence (biology) ; Point (geometry) ; Management science ; Engineering ; Mathematics ; Chemistry Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Process Optimization and Integration
Authors: Shunning Li | Zhefeng Chen | Zhi Wang | Mouyi Weng | Jianyuan Li | Mingzheng Zhang | Jing Lu | Kang Xu | Feng Pan
Source: Research Square (preprint, 2021-02-03)
Field: Materials Science — Materials Chemistry

Atomic-scale one-dimensional materials identified from graph theory Abstract The past decades have witnessed an exponential growth in the discovery of functional materials, benefited from our unprecedented capabilities in characterizing their structure, chemistry, and morphology with the aid of advanced imaging, spectroscopic and computational techniques. Among these materials, atomic-scale low-dimensional compounds, as represented by the two-dimensional (2D) atomic layers, one-dimensional (1D) atomic chains and zero-dimensional (0D) atomic clusters, have long captivated scientific interest due to their unique topological motifs and exceptional properties. Their tremendous potentials in various applications make it a pressing urgency to establish a complete database of their structural information, especially for the underexplored 1D species. Here we apply graph theory in combination with first-principles high-throughput calculations to identify atomic-scale 1D materials that can be conceptually isolated from their parent bulk crystals. In total, two hundred and fifty 1D atomic chains are shown to be potentially exfoliable. We demonstrate how the lone electron pairs on cations interact with the p-orbitals of anions and hence stabilize their edge sites. Data analysis of the 2D and 1D materials also reveals the dependence of electronic band gap on the cationic percolation network determined by graph theory. The library of 1D compounds systematically identified in this work will pave the way for the predictive discovery of material systems for quantum engineering, and can serve as a source of stimuli for future data-driven design and understanding of functional materials with reduced dimensionality. Atomic units ; Scale (ratio) ; Graph ; Graph theory ; Theoretical physics ; Computer science ; Statistical physics ; Theoretical computer science ; Physics ; Mathematics ; Quantum mechanics ; Combinatorics 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Graphene research and applications
Authors: Lukas Turcani | Andrew Tarzia | Filip Szczypiński | Kim E. Jelfs
Source: ChemRxiv (preprint, 2021-03-09)
Field: Materials Science — Materials Chemistry

Stk: An Extendable Python Framework for Automated Molecular and Supramolecular Structure Assembly and Discovery Computational software workflows are emerging as all-in-one solutions to speed up the discovery of new materials.Many computational approaches require the generation of realistic structural models for property prediction and candidate screening. However, molecular and supramolecular materials represent classes of materials with many potential applications for which there is no go-to database of existing structures or general protocol for generating structures. Here, we report a new version of the supramolecular toolkit, stk, an open-source, extendable and modular Python framework for general structure generation of (supra)molecular structures. Our construction approach follows a bottom-up process and minimises the input required from the user, making stk user-friendly and applicable to many material classes. This version of stk includes metal-containing structures and rotaxanes as well as general implementation and interface improvements. Additionally, this version includes built-in tools for exploring chemical space with an evolutionary algorithm and tools for database generation and visualisation. The latest version of stk is freely available at github.com/lukasturcani/stk Python (programming language) ; Workflow ; Computer science ; Modular design ; Software ; Supramolecular chemistry ; Visualization ; Graphical user interface ; Interface (matter) ; Computational science ; Programming language ; Data mining ; Database ; Chemistry ; Operating system Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Catalysis and Oxidation Reactions
Authors: Grigoriy A. Mun | Gulzhakhan Yeligbayeva | Ulantay Nakan | Aigul Amitova | Ibragim Suleimenov
Source: Periodicals of Engineering and Natural Sciences (PEN) (article, 2021-09-30)
Field: Chemistry — Spectroscopy

Some peculiarities of the kinetics of interaction of cationic hydrogels based on copolymers of vinyl esters of monoethanolamine and ethylene glycol with copper ions Over the past few decades, formation of polymer hydrogels complexes with metal ions, including transition ones, attracts the steady attention of researchers. The relevance of such study is due to the fact that the kinetics of swelling and contraction of hydrogels in various media has been studied in many works, however, this question remains insufficiently studied. Cationic hydrogels based on copolymer of vinyl ethers of monoethanolamine (VEMEA) and ethylene glycol (VEEG) as the study subject in the present work have been used. The kinetics of interaction cationic hydrogels based on copolymer of vinyl ethers of monoethanolamine and ethylene glycol with copper ions in aqueous solutions has been studied. It was shown that swelling behavior such hydrogel in this process is characterized two stages. At the initial stages of the interaction of the gel with the copper salt solution, the interaction in question is determined mainly by the displacement of water from the swollen mesh, due to the difference in osmotic pressures inside and outside the sample. At this stage, the kinetics of the gel contraction is described by the same laws as for polyelectrolyte hydrogel in the alkali metal solution. At the second stage, the network charge density increases due to the formation of a complex, and the degree of the gel swelling begins to increase with time. On the basis of the results obtained, it can be assumed that in systems of this kind, metastable states with differences in character from truly equilibrium states, persisting for a long time, can be formed. Ethylene glycol ; Cationic polymerization ; Copolymer ; Copper ; Kinetics ; Polymer chemistry ; Ion ; Ethylene ; Self-healing hydrogels ; Materials science ; Chemistry ; Chemical engineering ; Organic chemistry ; Catalysis ; Engineering ; Polymer ; Physics Adsorption, diffusion, and thermodynamic properties of materials ; Analytical Chemistry and Chromatography