jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matthew F. Hardman | Sally Eaton‐Magaña | Christopher M. Breeding | Troy Ardon | Ulrika F. S. D’Haenens-Johansson
Source: Diamond and Related Materials (article, 2022-10-28)
Field: Materials Science — Materials Chemistry

Evaluating the defects in CVD diamonds: A statistical approach to spectroscopy Chemical vapor deposition ; Photoluminescence ; Fluorescence ; Analytical Chemistry (journal) ; Magenta ; Materials science ; Diamond ; Ultraviolet ; Mineralogy ; Chemistry ; Optics ; Optoelectronics ; Environmental chemistry ; Physics ; Composite material Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Laser-induced spectroscopy and plasma
Authors: Forrest A. L. Laskowski | Daniel B. McHaffie | Kimberly A. See
Source: ChemRxiv (preprint, 2022-07-08)
Field: Materials Science — Materials Chemistry

Identification of Potential Solid-State Li-Ion Conductors with Semi-Supervised Learning Despite ongoing efforts to identify high-performance electrolytes for solid-state Li-ion batteries, thousands of prospective Li-containing structures remain unexplored. Here, we employ a semi-supervised learning approach to expedite identification of ionic conductors. We screen 180 unique descriptor representations and use agglomerative clustering to cluster ~26,000 Li-containing structures. The clusters are then labeled with experimental ionic conductivity data to assess the fitness of the descriptors. By inspecting clusters containing the highest conductivity labels, we identify 212 promising structures that are further screened using bond valence site energy and nudged elastic band calculations. Li3BS3 is identified as a potential high-conductivity material and selected for experimental characterization. With sufficient defect engineering, we show that Li3BS3 is a superionic conductor with room temperature ionic conductivity greater than 1 mS cm-1. While the semi-supervised method shows promise for identification of superionic conductors, the results illustrate a continued need for descriptors that explicitly encode for defects. Ionic conductivity ; Electrical conductor ; Conductivity ; Ionic bonding ; Fast ion conductor ; Conductor ; Ion ; Identification (biology) ; Cluster analysis ; Valence (chemistry) ; Materials science ; Cluster (spacecraft) ; Electrolyte ; Chemical physics ; Computer science ; Artificial intelligence ; Chemistry ; Physical chemistry Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: J. Terence Blaskovits | Rubén Laplaza | Sergi Vela | Clémence Corminbœuf
Source: ChemRxiv (preprint, 2022-12-05)
Field: Materials Science — Materials Chemistry

Data-driven discovery of organic electronic materials enabled by hybrid top-down/bottom-up design The high-throughput molecular exploration and screening of organic electronic materials often starts with either a 'top-down' mining of existing repositories, or the 'bottom-up' assembly of fragments based on predetermined rules and known synthetic templates. In both instances, the datasets used are often produced on a case-by-case basis, and require the high-quality computation of electronic properties and extensive user input: curation in the top-down approach, and the construction of a fragment library and introduction of rules for linking them in the bottom-up approach. Both approaches are time-consuming and require significant computational resources. Here, we generate a top-down set of 117K synthesized molecules containing their optimized structures, associated electronic and topological properties and chemical composition, and use these structures as a vast library of molecular building blocks for bottom-up fragment-based materials design. A tool is developed to automate the coupling of these building block units based on their available C(sp2/sp)-H bonds, thus providing a fundamental link between the two philosophies of dataset construction. Statistical models are trained on this dataset and a subset of the resulting hybrid top-down/bottom-up compounds, which enable on-the-fly prediction of key ground state (frontier molecular orbital gaps) and excited state (S1 and T1 energies) properties from molecular geometries with high accuracy across all known p-block organic compound space. With access to ab initio-quality optical properties in hand, it is possible to apply this bottom-up pipeline using existing compounds as molecular building blocks to any materials design campaign. To illustrate this, we construct and screen over a million molecular candidates for efficient intramolecular singlet fission, the leading candidates of which provide insight into the structural features that may promote this multiexciton-generating process. Top-down and bottom-up design ; Computer science ; Block (permutation group theory) ; Chemical space ; Pipeline (software) ; Set (abstract data type) ; Computational science ; Data mining ; Theoretical computer science ; Drug discovery ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organic Electronics and Photovoltaics
Authors: Patricia Poths | Geng Sun | Philippe Sautet | Anastassia N. Alexandrova
Source: The Journal of Physical Chemistry C (article, 2022-01-20)
Field: Materials Science — Materials Chemistry

Interpreting the <i>Operando</i> X-ray Absorption Near-Edge Structure of Supported Cu and CuPd Clusters in Conditions of Oxidative Dehydrogenation of Propane: Dynamic Changes in Composition and Size Supported subnano-cluster catalysts are highly dynamic, developing true active sites only under the pressures and temperatures of reaction conditions. Operando X-ray absorption near-edge structure (XANES) spectroscopy can track changes in the oxidation state and the local environment of cluster atoms, providing insight into the development of these active sites. While bulk metal, oxide, and hydroxide standards are often used for fitting experimental XANES spectra to obtain average oxidation states, we recently showed that computed cluster standards of relevant compositions are a more suitable basis, producing more accurate fits. Here, we theoretically interpret the operando XANES of supported Cu3Pd and Cu4 clusters during temperature-programmed reaction (TPRx) of oxidative dehydrogenation of propane. We use an expanded basis set including both globally optimized computed clusters and bulk standards. Not only can we track reversible composition/oxidation state change with temperature, but also the irreversible growth of the bulk fraction upon heating, which we attribute to cluster sintering. This has important implications for the mechanism of the catalyzed reaction and the nature of the available active sites. We propose that operando XANES provides most significant insight into the nature of supported cluster catalysts in reaction conditions when interpreted using mixed computed cluster and bulk standards. XANES ; Dehydrogenation ; Catalysis ; Cluster (spacecraft) ; Oxidation state ; Propane ; Oxide ; Chemistry ; Absorption (acoustics) ; Materials science ; Spectral line ; Physical chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Herole RA | Rakesh Kumar Jat | Rajendra Dnyandeo Dighe
Source: Tropical Journal of Pharmaceutical and Life Sciences (review, 2022-12-27)
Field: Chemistry — Organic Chemistry

A REVIEW ON ACRIDINE, XANTHENE AND ITS DERIVATIVES: SYNTHESIS, PHYSICAL AND PHARMACOLOGICAL PROPERTIES Acridine derivatives are one of the oldest category of bioactive, globally used as anticancer, antiprotozoal agents and antibacterial. The class of acridine derivatives constitutes an interesting group of nitrogen-containing tricyclic compounds that caught the scientific group’s attention, mainly due to its wide range of pharmaceutical properties. The synthesis of new polycyclic acridine skeletons fused with a five or six-membered rings, have been extensively studied because they play important roles in some DNA-intercalating anticancer drugs. Benzoacridine derivatives have been recently synthesized by a number of methods via one-pot multi-component condensation reactions of dimedone, naphthylamines, and aldehydes in different conditions, for example, using triethylbenzylammonium chloride/H2O, ionic liquid, under microwave irradiation, or ultrasound irradiation. A xanthene is found to most multifaceted heterocyclic ring having as it is having variety of activity and utilization. Xanthine derivatives are medications used to treat bronchospasm caused by lung conditions such as asthma. Xanthenes dyes shows antiviral activity, anti-tubercular, anticancer, anti-microbial, malonate derivatives was having anti-spasmodic activity. Benzoxanthenes are tetracyclic dibenzopyrans with diverse biological and therapeutic properties such as antibacterial, antitumor, anti-inflammatory, antiviral, pesticidal activities, and antimalarial. Xanthene ; Acridine ; Acridine derivatives ; Chemistry ; Combinatorial chemistry ; Organic chemistry ; Stereochemistry Multicomponent Synthesis of Heterocycles ; Synthesis and biological activity
Authors: Alexandra Jakab-Nácsa | Emőke Sikora | Ádám Prekob | László Vanyorek | Milán Szöri | Renáta Zsanett Boros | Károly Nehéz | M Szabo | László Farkas | Béla Viskolcz
Source: Catalysts (article, 2022-04-22)
Field: Chemistry — Organic Chemistry

Comparison of Catalysts with MIRA21 Model in Heterogeneous Catalytic Hydrogenation of Aromatic Nitro Compounds The vast majority of research and development activities begins with a detailed literature search to explore the current state-of-the-art. However, this search becomes increasingly difficult as we go into the information revolution of 21st century. The aim of the work is to establish a functional and practical mathematical model of catalyst characterization and exact comparison of catalysts. This work outlines the operation of the MIskolc RAnking 21 (MIRA21) model through the reaction of nitrobenzene catalytic hydrogenation to aniline. A total of 154 catalysts from 45 research articles were selected, studied, characterized, ranked, and classified based on four classes of descriptors: catalyst performance, reaction conditions, catalyst conditions, and sustainability parameters. MIRA21 is able to increase the comparability of different types of catalysts and support catalyst development. According to the model, 8% of catalysts received D1 (top 10%) classification. This ranking model is able to show the most effective catalyst systems that are suitable for the production of aniline. Catalysis ; Aniline ; Nitrobenzene ; Ranking (information retrieval) ; Catalytic hydrogenation ; Comparability ; Chemistry ; Organic chemistry ; Computer science ; Mathematics ; Information retrieval Nanomaterials for catalytic reactions ; Catalysis and Hydrodesulfurization Studies ; Asymmetric Hydrogenation and Catalysis
Authors: Xianjun Yang | Zhuo Ya | Julia L. Zuo | Xinlu Zhang | Stephen Wilson | Linda Petzold
Source:  (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Text Scientific action graphs extraction from materials synthesis procedures is important for reproducible research, machine automation, and material prediction. But the lack of annotated data has hindered progress in this field. We demonstrate an effort to annotate Polycrystalline Materials Synthesis Procedures PcMSP from 305 open access scientific articles for the construction of synthesis action graphs. This is a new dataset for material science information extraction that simultaneously contains the synthesis sentences extracted from the experimental paragraphs, as well as the entity mentions and intra-sentence relations. A two-step human annotation and inter-annotator agreement study guarantee the high quality of the PcMSP corpus. We introduce four natural language processing tasks: sentence classification, named entity recognition, relation classification, and joint extraction of entities and relations. Comprehensive experiments validate the effectiveness of several state-of-the-art models for these challenges while leaving large space for improvement. We also perform the error analysis and point out some unique challenges that require further investigation. We will release our annotation scheme, the corpus, and codes to the research community to alleviate the scarcity of labeled data in this domain. Computer science ; Relationship extraction ; Artificial intelligence ; Information extraction ; Sentence ; Natural language processing ; Annotation ; Field (mathematics) ; Point (geometry) ; Scheme (mathematics) ; Information retrieval Machine Learning in Materials Science ; Topic Modeling ; Software Engineering Research
Authors: Rosangela Santalucia | Paolo Negro | Tiziano Vacca | Francesco Pellegrino | Alessandro Damin | Federico Cesano | Domenica Scarano
Source: Catalysts (article, 2022-11-11)
Field: Materials Science — Materials Chemistry

In Situ Assembly of Well-Defined MoS2 Slabs on Shape-Tailored Anatase TiO2 Nanostructures: Heterojunctions Role in Phenol Photodegradation MoS2/TiO2-based nanostructures have attracted extensive attention due to their high performance in many fields, including photocatalysis. In this contribution, MoS2 nanostructures were prepared via an in situ bottom-up approach at the surface of shape-controlled TiO2 nanoparticles (TiO2 nanosheets and bipyramids). Furthermore, a multi-technique approach by combining electron microscopy and spectroscopic methods was employed. More in detail, the morphology/structure and vibrational/optical properties of MoS2 slabs on TiO2 anatase bipyramidal nanoparticles, mainly exposing {101} facets, and on TiO2 anatase nanosheets exposing both {001} and {101} facets, still covered by MoS2, were compared. It was shown that unlike other widely used methods, the bottom-up approach enabled the atomic-level growth of well-defined MoS2 slabs on TiO2 nanostructures, thus aiming to achieve the most effective chemical interactions. In this regard, two kinds of synergistic heterojunctions, namely, crystal face heterojunctions between anatase TiO2 coexposed {101} and {001} facets and semiconductor heterojunctions between MoS2 and anatase TiO2 nanostructures, were considered to play a role in enhancing the photocatalytic activity, together with a proper ratio of (101), (001) coexposed surfaces. Anatase ; Heterojunction ; Materials science ; Photodegradation ; Nanostructure ; Photocatalysis ; Nanotechnology ; Nanoparticle ; Semiconductor ; Chemical engineering ; Optoelectronics ; Chemistry ; Catalysis ; Organic chemistry 2D Materials and Applications ; Advanced Photocatalysis Techniques ; MXene and MAX Phase Materials
Authors: Robert Jones | Kevin Rossi | Claudio Zeni | Mirko Vanzan | Igor Vasiljevic | Alejandro Santana‐Bonilla | Francesca Baletto
Source: Faraday Discussions (article, 2022-06-29)
Field: Materials Science — Materials Chemistry

Structural characterisation of nanoalloys for (photo)catalytic applications with the Sapphire library A non-trivial interplay rules the relationship between the structure and the chemophysical properties of a nanoparticle. In this context, characterization experiments, molecular dynamics simulations and electronic structure calculations may allow the variables that determine a given property to be pinpointed. Conversely, a rigorous computational characterization of the geometry and chemical ordering of metallic nanoparticles and nanoalloys enables discrimination of which descriptors could be linked with their stability and performance. To this end, we introduce a modular and open-source library, <b>Sapphire</b>, which may classify the structural characteristics of a given nanoparticle through several structural analysis techniques and order parameters. A special focus is geared towards using geometrical descriptors to make predictions on a given nanoparticle's catalytic activity. Sapphire ; Catalysis ; Focus (optics) ; Nanoparticle ; Nanotechnology ; Materials science ; Computer science ; Chemistry ; Physics ; Optics ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Photocatalysis Techniques
Authors: Saeed Rahmanian Koshkaki | Zahed Allahyari | Artem R. Oganov | Vladimir L. Solozhenko | Ilya B. Polovov | А. С. Белозеров | A. A. Katanin | В. И. Анисимов | Evgenii Tikhonov | Guang‐Rui Qian | Konstantin V. Maksimtsev | Andrey S. Mukhamadeev | А. В. Чукин | А. В. Королев | Н.В. Мушников | Hao Li
Source: The Journal of Chemical Physics (article, 2022-09-28)
Field: Materials Science — Materials Chemistry

Computational prediction of new magnetic materials The discovery of new magnetic materials is a big challenge in the field of modern materials science. We report the development of a new extension of the evolutionary algorithm USPEX, enabling the search for half-metals (materials that are metallic only in one spin channel) and hard magnetic materials. First, we enabled the simultaneous optimization of stoichiometries, crystal structures, and magnetic structures of stable phases. Second, we developed a new fitness function for half-metallic materials that can be used for predicting half-metals through an evolutionary algorithm. We used this extended technique to predict new, potentially hard magnets and rediscover known half-metals. In total, we report five promising hard magnets with high energy product (|BH|<sub>MAX</sub>), anisotropy field (H<sub>a</sub>), and magnetic hardness (κ) and a few half-metal phases in the Cr-O system. A comparison of our predictions with experimental results, including the synthesis of a newly predicted antiferromagnetic material (WMnB<sub>2</sub>), shows the robustness of our technique. Magnet ; Materials science ; Antiferromagnetism ; Magnetic field ; Robustness (evolution) ; Anisotropy ; Condensed matter physics ; Metal ; Nanotechnology ; Mechanical engineering ; Physics ; Metallurgy ; Chemistry Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Advancements in Battery Materials
Authors: Andrea Silva | Jiangming Cao | Tomáš Polcar | Denis Kramer
Source: npj Computational Materials (article, 2022-08-20)
Field: Materials Science — Materials Chemistry

Pettifor maps of complex ternary two-dimensional transition metal sulfides Abstract Alloying is an established strategy to tune the properties of bulk compounds for desired applications. With the advent of nanotechnology, the same strategy can be applied to 2D materials for technological applications, like single-layer transistors and solid lubricants. Here we present a systematic analysis of the phase behaviour of substitutional 2D alloys in the Transition Metal Disulfides (TMD) family. The phase behaviour is quantified in terms of a metastability metric and benchmarked against many-body expansion of the energy landscape. We show how the metastability metric can be directly used as starting point for setting up rational search strategies in phase space, thus allowing for targeted further computational prediction and analysis of properties. The results presented here also constitute a useful guideline for synthesis of TMDs binary alloys via a range of synthesis techniques. Metastability ; Ternary operation ; Metric (unit) ; Materials science ; Binary number ; Nanotechnology ; Phase transition ; Computer science ; Thermodynamics ; Chemistry ; Physics ; Mathematics ; Engineering Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Martin Z. Bazant | Hongbo Zhao | Haitao Deng | Alexander E. Cohen | Jongwoo Lim | Yiyang Li | Dimitrios Fraggedakis | Benben Jiang | Brian D. Storey | William C. Chueh | Richard D. Braatz
Source: Research Square (preprint, 2022-11-29)
Field: Materials Science — Materials Chemistry

Learning heterogeneous reaction kinetics from X-ray movies pixel-by-pixel Abstract Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to quantify, yet are essential in engineering many chemical systems, such as batteries [1] and electro-catalysts [2]. Experimental characterizations of such materials by operando microscopy produce rich image datasets [3, 4, 5, 6], but data driven methods to learn physics from these images are still lacking due to the complex coupling of reaction kinetics, surface chemistry, and phase separation [7]. Here, we show that heterogeneous reaction kinetics can be learned from in-situ scanning transmission X-ray microscopy (STXM) images of carbon-coated lithium iron phosphate (LFP) nanoparticles. Combining a large dataset of STXM images with a thermodynamically consistent electrochemical phase-field model, PDE-constrained optimization, and uncertainty quantification, we extract the free energy landscape and reaction kinetics and verify their consistency with theoretical mod els. We also simultaneously learn the spatial heterogeneity of the reaction rate, which closely matches the carbon-coating thickness profiles obtained through auger electron microscopy. Across 180,000 image pixels, the mean discrepancy with the learned model is remarkably small (&amp;lt;7%) and comparable to experimental noise. Our results open the possibility of learning nonequilibrium material properties beyond the reach of tra ditional experimental methods and offer a new non-destructive technique for characterizing and optimizing heterogeneous reactive surfaces. Pixel ; Kinetics ; Computer science ; X-ray ; Artificial intelligence ; Computer graphics (images) ; Computer vision ; Physics ; Optics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Computational Drug Discovery Methods
Authors: Dylan J. Walsh | Weizhong Zou | Ludwig Schneider | Reid Mello | Michael E. Deagen | Joshua A. Mysona | Tzyy‐Shyang Lin | Juan Pablo | Klavs F. Jensen | Debra J. Audus | Bradley D. Olsen
Source: ChemRxiv (preprint, 2022-09-15)
Field: Materials Science — Materials Chemistry

CRIPT: A Scalable Polymer Material Data Structure Polymeric materials are integral components of nearly every aspect of modern life. Today, polymer scientists and engineers devote significant resources to the design and development of these materials to meet growing societal needs. However, developing cheminformatic solutions for polymers has been difficult since they are large stochastic molecules with hierarchical structures spanning multiple length scales from chemical bonds to large molecular assemblies. Here we present the design for a general material data model that underpins the Community Resource for Innovation in Polymer Technology (CRIPT) data ecosystem. Among the key challenges that the data model addresses are the high complexity in defining a polymer structure and the intricacies involved with characterizing material properties. The core design of the data model is graph-based which provides flexibility, robustness, and scalability to support the community-driven mission. This approach to structuring material data provides the key advancements that the community needs to bring cheminformatics to polymer science and accelerate the development of new materials. Scalability ; Computer science ; Flexibility (engineering) ; Structuring ; Key (lock) ; Data science ; Robustness (evolution) ; Resource (disambiguation) ; Nanotechnology ; Systems engineering ; Materials science ; Database ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods