jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andreas Erlebach | Petr Nachtigall | Lukáš Grajciar
Source: npj Computational Materials (article, 2022-08-19)
Field: Materials Science — Materials Chemistry

Accurate large-scale simulations of siliceous zeolites by neural network potentials Abstract The computational discovery and design of zeolites is a crucial part of the chemical industry. Finding highly accurate while computational feasible protocol for identification of hypothetical siliceous frameworks that could be targeted experimentally is a great challenge. To tackle this challenge, we trained neural network potentials (NNP) with the SchNet architecture on a structurally diverse database of density functional theory (DFT) data. This database was iteratively extended by active learning to cover not only low-energy equilibrium configurations but also high-energy transition states. We demonstrate that the resulting reactive NNPs retain DFT accuracy for thermodynamic stabilities, vibrational properties, as well as reactive and non-reactive phase transformations. As a showcase, we screened an existing zeolite database and revealed &gt;20k additional hypothetical frameworks in the thermodynamically accessible range of zeolite synthesis. Hence, our NNPs are expected to be essential for future high-throughput studies on the structure and reactivity of siliceous zeolites. ReaxFF ; Density functional theory ; Artificial neural network ; Zeolite ; Computer science ; Chemistry ; Computational chemistry ; Molecular dynamics ; Machine learning ; Catalysis Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; X-ray Diffraction in Crystallography
Authors: Xinyue Dai | Yu Chen
Source: Advanced Materials (review, 2022-08-02)
Field: Materials Science — Biomaterials

Computational Biomaterials: Computational Simulations for Biomedicine Abstract With the flourishing development of material simulation methods (quantum chemistry methods, molecular dynamics, Monte Carlo, phase field, etc.), extensive adoption of computing technologies (high‐throughput, artificial intelligence, machine learning, etc.), and the invention of high‐performance computing equipment, computational simulation tools have sparked the fundamental mechanism‐level explorations to predict the diverse physicochemical properties and biological effects of biomaterials and investigate their enormous application potential for disease prevention, diagnostics, and therapeutics. Herein, the term “computational biomaterials” is proposed and the computational methods currently used to explore the inherent properties of biomaterials, such as optical, magnetic, electronic, and acoustic properties, and the elucidation of corresponding biological behaviors/effects in the biomedical field are summarized/discussed. The theoretical calculation of the physiochemical properties/biological performance of biomaterials applied in disease diagnosis, drug delivery, disease therapeutics, and specific paradigms such as biomimetic biomaterials is discussed. Additionally, the biosafety evaluation applications of theoretical simulations of biomaterials are presented. Finally, the challenges and future prospects of such computational simulations for biomaterials development are clarified. It is anticipated that these simulations would offer various methodologies for facilitating the development and future clinical translations/utilization of versatile biomaterials. Biomedicine ; Nanotechnology ; Computer science ; Computational model ; Biochemical engineering ; Materials science ; Engineering ; Artificial intelligence ; Bioinformatics Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques ; Advanced Sensor and Energy Harvesting Materials
Authors: Jie Gong | Sharon Lynn Chu | Rohan K. Mehta | Alan J. H. McGaughey
Source: npj Computational Materials (article, 2022-07-01)
Field: Materials Science — Materials Chemistry

XGBoost model for electrocaloric temperature change prediction in ceramics Abstract An eXtreme Gradient Boosting (XGBoost) machine learning model is built to predict the electrocaloric (EC) temperature change of a ceramic based on its composition (encoded by Magpie elemental properties), dielectric constant, Curie temperature, and characterization conditions. A dataset of 97 EC ceramics is assembled from the experimental literature. By sampling data from clusters in the feature space, the model can achieve a coefficient of determination of 0.77 and a root mean square error of 0.38 K for the test data. Feature analysis shows that the model captures known physics for effective EC materials. The Magpie features help the model to distinguish between materials, with the elemental electronegativities and ionic charges identified as key features. The model is applied to 66 ferroelectrics whose EC performance has not been characterized. Lead-free candidates with a predicted EC temperature change above 2 K at room temperature and 100 kV/cm are identified. Ceramic ; Dielectric ; Materials science ; Curie temperature ; Electronegativity ; Feature vector ; Thermodynamics ; Condensed matter physics ; Artificial intelligence ; Computer science ; Composite material ; Chemistry ; Physics ; Optoelectronics Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Odysseas Sierepeklis | Jacqueline M. Cole
Source: Scientific Data (article, 2022-10-22)
Field: Materials Science — Materials Chemistry

A thermoelectric materials database auto-generated from the scientific literature using ChemDataExtractor An auto-generated thermoelectric-materials database is presented, containing 22,805 data records, automatically generated from the scientific literature, spanning 10,641 unique extracted chemical names. Each record contains a chemical entity and one of the seminal thermoelectric properties: thermoelectric figure of merit, ZT; thermal conductivity, κ; Seebeck coefficient, S; electrical conductivity, σ; power factor, PF; each linked to their corresponding recorded temperature, T. The database was auto-generated using the automatic sentence-parsing capabilities of the chemistry-aware, natural language processing toolkit, ChemDataExtractor 2.0, adapted for application in the thermoelectric-materials domain, following a rule-based sentence-simplification step. Data were mined from the text of 60,843 scientific papers that were sourced from three scientific publishers: Elsevier, the Royal Society of Chemistry, and Springer. To the best of our knowledge, this is the first automatically-generated database of thermoelectric materials and their properties from existing literature. The database was evaluated to have a precision of 82.25% and has been made publicly available to facilitate the application of data science in the thermoelectric-materials domain, for analysis, design, and prediction. Scientific literature ; Information retrieval ; Database ; Data science ; Computer science ; Geology ; Paleontology Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Rahul Nikhar | Krzysztof Szalewicz
Source: Nature Communications (article, 2022-06-02)
Field: Chemistry — Physical and Theoretical Chemistry

Reliable crystal structure predictions from first principles An inexpensive and reliable method for molecular crystal structure predictions (CSPs) has been developed. The new CSP protocol starts from a two-dimensional graph of crystal's monomer(s) and utilizes no experimental information. Using results of quantum mechanical calculations for molecular dimers, an accurate two-body, rigid-monomer ab initio-based force field (aiFF) for the crystal is developed. Since CSPs with aiFFs are essentially as expensive as with empirical FFs, tens of thousands of plausible polymorphs generated by the crystal packing procedures can be optimized. Here we show the robustness of this protocol which found the experimental crystal within the 20 most stable predicted polymorphs for each of the 15 investigated molecules. The ranking was further refined by performing periodic density-functional theory (DFT) plus dispersion correction (pDFT+D) calculations for these 20 top-ranked polymorphs, resulting in the experimental crystal ranked as number one for all the systems studied (and the second polymorph, if known, ranked in the top few). Alternatively, the polymorphs generated can be used to improve aiFFs, which also leads to rank one predictions. The proposed CSP protocol should result in aiFFs replacing empirical FFs in CSP research. Crystal structure prediction ; Crystal (programming language) ; Ab initio ; Density functional theory ; Molecule ; Materials science ; Robustness (evolution) ; Crystal structure ; Statistical physics ; Chemical physics ; Computer science ; Computational chemistry ; Chemistry ; Crystallography ; Physics ; Quantum mechanics Crystallography and molecular interactions ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Hua Zhang | Lin Zhao | Jingjing Jiang | Jie Zheng | Yang Li | Yanyan Li | Jian Zhou | Tianshu Liu | Jianmin Xu | Wenhui Lou | Weige Yang | Lijie Tan | Weiren Liu | Yiyi Yu | Meiling Ji | Yaolin Xu | Yan Lü | Xiaomu Li | Zhen Liu | Rong Tian | Cheng Hu | Shumang Zhang | Qinsheng Hu | Yangdong Deng | Hao Ying | Sheng Zhong | Xingdong Zhang | Yunbing Wang | Hua Wang | Jingwei Bai | Xiaoying Li | Xiangfeng Duan
Source: Nature Communications (article, 2022-02-01)
Field: Chemistry — Spectroscopy

Multiplexed nanomaterial-assisted laser desorption/ionization for pan-cancer diagnosis and classification As cancer is increasingly considered a metabolic disorder, it is postulated that serum metabolite profiling can be a viable approach for detecting the presence of cancer. By multiplexing mass spectrometry fingerprints from two independent nanostructured matrixes through machine learning for highly sensitive detection and high throughput analysis, we report a laser desorption/ionization (LDI) mass spectrometry-based liquid biopsy for pan-cancer screening and classification. The Multiplexed Nanomaterial-Assisted LDI for Cancer Identification (MNALCI) is applied in 1,183 individuals that include 233 healthy controls and 950 patients with liver, lung, pancreatic, colorectal, gastric, thyroid cancers from two independent cohorts. MNALCI demonstrates 93% sensitivity at 91% specificity for distinguishing cancers from healthy controls in the internal validation cohort, and 84% sensitivity at 84% specificity in the external validation cohort, with up to eight metabolite biomarkers identified. In addition, across those six different cancers, the overall accuracy for identifying the tumor tissue of origin is 92% in the internal validation cohort and 85% in the external validation cohort. The excellent accuracy and minimum sample consumption make the high throughput assay a promising solution for non-invasive cancer diagnosis. Cohort ; Pancreatic cancer ; Cancer ; Oncology ; Medicine ; Mass spectrometry ; Internal medicine ; Metabolite ; Cancer research ; Chemistry ; Chromatography Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Javier Corpas | Pablo Mauleón | Ramón Goméz Arrayás | Juan C. Carretero
Source: Advanced Synthesis & Catalysis (article, 2022-03-04)
Field: Chemistry — Organic Chemistry

<i>E/Z</i> Photoisomerization of Olefins as an Emergent Strategy for the Control of Stereodivergence in Catalysis Abstract The stereoselective photoisomerization of olefins via Energy Transfer (E n T) sensitization bears significant potential in the context of rational design of catalytic stereodivergent methodologies. Whereas approaches for controlling the access to E‐ and Z ‐ isomers depend highly on the nature of the catalyst – and therefore are not easily implemented as a general strategy – E n T catalysis has emerged in recent years as an increasingly powerful tool for olefin geometry control in a nearly perfect step‐economic fashion. Moreover, this approach presents both high functional group tolerance and, most notably, has demonstrated ample multicatalytic compatibility. This feature has enabled the development of tandem stereodivergent catalytic strategies, which are the focus of this Review. magnified image Photoisomerization ; Chemistry ; Catalysis ; Olefin fiber ; Tandem ; Rational design ; Context (archaeology) ; Stereoselectivity ; Combinatorial chemistry ; Nanotechnology ; Isomerization ; Organic chemistry Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Polyoxometalates: Synthesis and Applications
Authors: Yanxiao Sun | Luyue Jiang | Zhe Wang | Zhenfei Hou | Liyan Dai | Yankun Wang | Jinyan Zhao | Ya‐Hong Xie | Libo Zhao | Zhuangde Jiang | Wei Ren | Gang Niu
Source: ACS Nano (article, 2022-12-12)
Field: Materials Science — Materials Chemistry

Multiwavelength High-Detectivity MoS<sub>2</sub> Photodetectors with Schottky Contacts Photodetection is one of the vital functions for the multifunctional "More than Moore" (MtM) microchips urgently required by Internet of Things (IoT) and artificial intelligence (AI) applications. The further improvement of the performance of photodetectors faces various challenges, including materials, fabrication processes, and device structures. We demonstrate in this work MoS<sub>2</sub> photodetectors with a nanoscale channel length and a back-gate device structure. With the mechanically exfoliated six-monolayer-thick MoS<sub>2</sub>, a Schottky contact between source/drain electrodes and MoS<sub>2</sub>, a high responsivity of 4.1 × 10<sup>3</sup> A W<sup>-1</sup>, and a detectivity of 1.34 × 10<sup>13</sup> cm Hz<sup>1/2</sup> W<sup>-1</sup> at 650 nm were achieved. The devices are also sensitive to multiwavelength lights, including 520 and 405 nm. The electrical and optoelectronic properties of the MoS<sub>2</sub> photodetectors were studied in depth, and the working mechanism of the devices was analyzed. The photoinduced Schottky barrier lowering (PIBL) was found to be important for the high performance of the phototransistor. Photodetector ; Responsivity ; Photodetection ; Materials science ; Optoelectronics ; Schottky barrier ; Fabrication ; Photodiode ; Schottky diode ; Monolayer ; Nanotechnology 2D Materials and Applications ; Nanowire Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Lichen Liu | Miguel López‐Haro | José A. Pérez‐Omil | Mercedes Boronat | José J. Calvino | Avelino Corma
Source: Nature Communications (article, 2022-02-10)
Field: Materials Science — Materials Chemistry

Direct assessment of confinement effect in zeolite-encapsulated subnanometric metal species Subnanometric metal species confined inside the microporous channels/cavities of zeolites have been demonstrated as stable and efficient catalysts. The confinement interaction between the metal species and zeolite framework has been proposed to play the key role for stabilization, though the confinement interaction is elusive to be identified and measured. By combining theoretical calculations, imaging simulation and experimental measurements based on the scanning transmission electron microscopy-integrated differential phase contrast imaging technique, we have studied the location and coordination environment of isolated iridium atoms and clusters confined in zeolite. The image analysis results indicate that the local strain is intimately related to the strength of metal-zeolite interaction and a good correlation is found between the zeolite deformation energy, the charge state of the iridium species and the local absolute strain. The direct observation of confinement with subnanometric metal species encapsulated in zeolites provides insights to understand their structural features and catalytic consequences. Zeolite ; Microporous material ; Chemical physics ; Materials science ; Metal ; Transmission electron microscopy ; Confined space ; Molecular dynamics ; Iridium ; Nanotechnology ; Catalysis ; Chemistry ; Computational chemistry ; Composite material Catalytic Processes in Materials Science ; Advanced Chemical Physics Studies ; Advanced Electron Microscopy Techniques and Applications
Authors: Mohzibudin Z. Quazi | Nokyoung Park
Source: International Journal of Molecular Sciences (review, 2022-02-09)
Field: Materials Science — Biomaterials

Nanohydrogels: Advanced Polymeric Nanomaterials in the Era of Nanotechnology for Robust Functionalization and Cumulative Applications In the era of nanotechnology, the synthesis of nanomaterials for advanced applications has grown enormously. Effective therapeutics and functionalization of effective drugs using nano-vehicles are considered highly productive and selectively necessary. Polymeric nanomaterials have shown their impact and influential role in this process. Polymeric nanomaterials in molecular science are well facilitated due to their low cytotoxic behavior, robust functionalization, and practical approach towards in vitro and in vivo therapeutics. This review highlights a brief discussion on recent techniques used in nanohydrogel designs, biomedical applications, and the applied role of nanohydrogels in the construction of advanced therapeutics. We reviewed recent studies on nanohydrogels for their wide applications in building strategies for advantageously controlled biological applications. The classification of polymers is based on their sources of origin. Nanohydrogel studies are based on their polymeric types and their endorsed utilization for reported applications. Nanotechnology has developed significantly in the past decades. The novel and active role of nano biomaterials with amplified aspects are consistently being studied to minimize the deleterious practices and side effects. Here, we put forth challenges and discuss the outlook regarding the role of nanohydrogels, with future perspectives on delivering constructive strategies and overcoming the critical objectives in nanotherapeutic systems. Nanotechnology ; Surface modification ; Nanomaterials ; Applications of nanotechnology ; Nanomedicine ; Materials science ; Engineering ; Nanoparticle Nanoparticle-Based Drug Delivery ; Hydrogels: synthesis, properties, applications ; Graphene and Nanomaterials Applications
Authors: Arnau Call | Marco Cianfanelli | Pau Besalú‐Sala | Giorgio Olivo | Andrea Palone | Laia Vicens | Xavi Ribas | Josep M. Luis | Massimo Bietti | Miguel Costas
Source: Journal of the American Chemical Society (article, 2022-10-13)
Field: Chemistry — Organic Chemistry

Carboxylic Acid Directed γ-Lactonization of Unactivated Primary C–H Bonds Catalyzed by Mn Complexes: Application to Stereoselective Natural Product Diversification Reactions that enable selective functionalization of strong aliphatic C-H bonds open new synthetic paths to rapidly increase molecular complexity and expand chemical space. Particularly valuable are reactions where site-selectivity can be directed toward a specific C-H bond by catalyst control. Herein we describe the catalytic site- and stereoselective γ-lactonization of unactivated primary C-H bonds in carboxylic acid substrates. The system relies on a chiral Mn catalyst that activates aqueous hydrogen peroxide to promote intramolecular lactonization under mild conditions, via carboxylate binding to the metal center. The system exhibits high site-selectivity and enables the oxidation of unactivated primary γ-C-H bonds even in the presence of intrinsically weaker and a priori more reactive secondary and tertiary ones at α- and β-carbons. With substrates bearing nonequivalent γ-C-H bonds, the factors governing site-selectivity have been uncovered. Most remarkably, by manipulating the absolute chirality of the catalyst, γ-lactonization at methyl groups in <i>gem</i>-dimethyl structural units of rigid cyclic and bicyclic carboxylic acids can be achieved with unprecedented levels of diastereoselectivity. Such control has been successfully exploited in the late-stage lactonization of natural products such as camphoric, camphanic, ketopinic, and isoketopinic acids. DFT analysis points toward a rebound type mechanism initiated by intramolecular 1,7-HAT from a primary γ-C-H bond of the bound substrate to a highly reactive Mn<sup>IV</sup>-oxyl intermediate, to deliver a carbon radical that rapidly lactonizes through carboxylate transfer. Intramolecular kinetic deuterium isotope effect and <sup>18</sup>O labeling experiments provide strong support to this mechanistic picture. Chemistry ; Stereoselectivity ; Natural product ; Catalysis ; Diversification (marketing strategy) ; Carboxylic acid ; Stereochemistry ; Primary (astronomy) ; Organic chemistry Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis ; Synthesis and Catalytic Reactions
Authors: Hui Li | Jérôme Ollivier | Sophie M. Guillaume | Jean‐François Carpentier
Source: Angewandte Chemie International Edition (article, 2022-03-14)
Field: Materials Science — Biomaterials

Tacticity Control of Cyclic Poly(3‐Thiobutyrate) Prepared by Ring‐Opening Polymerization of Racemic β‐Thiobutyrolactone We report here on the ring-opening polymerization (ROP) of racemic β-thiobutyrolactone (rac-TBL), as the first chemical synthesis of poly(3-thiobutyrolactone) (P3TB), the thioester analogue of the ubiquitous poly(3-hydroxybutyrate) (P3HB). The ROP reactions proceed very fast (TOF >12 000 h<sup>-1</sup> at r.t.) in the presence of various metal-based catalysts. Remarkably, catalyst systems based on non-chiral yttrium complexes stabilized by tetradentate amino alkoxy- or diamino-bis(phenolate) ligands {ONXO<sup>R1,R2</sup> }<sup>2-</sup> (X=O, N) provide access to cyclic P3TB with either high isoselectivity (P<sub>m</sub> up to 0.90) or high syndiotactic bias (P<sub>r</sub> up to 0.70). The stereoselectivity can be controlled by manipulation of the substituents on the ligand platform and adequate choice of the reaction solvent and temperature as well. The cyclic polymer topology is evidenced by MALDI-ToF MS, NMR and TGA. Highly isotactic cyclic P3TB is a semi-crystalline material as revealed by DSC. Tacticity ; Alkoxy group ; Polymer chemistry ; Polymerization ; Catalysis ; Ring-opening polymerization ; Ligand (biochemistry) ; Materials science ; Stereoselectivity ; Solvent ; Ring (chemistry) ; Polymer ; Chemistry ; Organic chemistry biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis ; Polyoxometalates: Synthesis and Applications
Authors: V Bijina | P. J. Jandas | Sherin Joseph | J. Gopu | K. Abhitha | Honey John
Source: Polymer Bulletin (review, 2022-09-12)
Field: Materials Science — Biomaterials

Recent trends in industrial and academic developments of green tyre technology Environmentally friendly ; Production (economics) ; Greenhouse gas ; Emerging technologies ; Automotive industry ; Environmental pollution ; Industrial production ; Global warming ; Raw material ; Natural resource ; Business ; Engineering ; Environmental economics ; Architectural engineering ; Environmental science ; Computer science ; Climate change ; Environmental protection ; Economics ; Ecology biodegradable polymer synthesis and properties ; Polymer crystallization and properties ; Polymer composites and self-healing