jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaojuan Yu | Dumitru‐Claudiu Sergentu | Rulin Feng | Jochen Autschbach
Source: Inorganic Chemistry (article, 2021-11-06)
Field: Chemistry — Inorganic Chemistry

Covalency of Trivalent Actinide Ions with Different Donor Ligands: Do Density Functional and Multiconfigurational Wavefunction Calculations Corroborate the Observed “Breaks”? A comprehensive ab initio study of periodic actinide-ligand bonding trends for trivalent actinides is performed. Relativistic density functional theory (DFT) and complete active-space (CAS) self-consistent field wavefunction calculations are used to dissect the chemical bonding in the [AnCl<sub>6</sub>]<sup>3-</sup>, [An(CN)<sub>6</sub>]<sup>3-</sup>, [An(NCS)<sub>6</sub>]<sup>3-</sup>, [An(S<sub>2</sub>PMe<sub>2</sub>)<sub>3</sub>], [An(DPA)<sub>3</sub>]<sup>3-</sup>, and [An(HOPO)]<sup>-</sup> series of actinide (An = U-Es) complexes. Except for some differences for the early actinide complexes with DPA, bond orders and excess 5f-shell populations from donation bonding show qualitatively similar trends in 5f <sup><i>n</i></sup> active-space CAS vs DFT calculations. The influence of spin-orbit coupling on donation bonding is small for the tested systems. Along the actinide series, chemically soft vs chemically harder ligands exhibit clear differences in bonding trends. There are pronounced changes in the 5f populations when moving from Pu to Am or Cm, which correlate with previously noted "breaks" in chemical trends. Bonding involving 5f becomes very weak beyond Cm/Bk. We propose that Cm(III) is a borderline case among the trivalent actinides that can be meaningfully considered to be involved in ground-state 5f covalent bonding. Chemistry ; Actinide ; Density functional theory ; Wave function ; Covalent bond ; Complete active space ; Computational chemistry ; Chemical bond ; Lanthanide ; Ion ; Relativistic quantum chemistry ; Ab initio ; Crystallography ; Atomic physics ; Inorganic chemistry ; Organic chemistry ; Physics Radioactive element chemistry and processing ; Lanthanide and Transition Metal Complexes ; Inorganic Chemistry and Materials
Authors: Lars‐Åke Näslund | Mikko-Heikki Mikkelä | Esko Kokkonen | Martin Magnuson
Source: 2D Materials (article, 2021-08-18)
Field: Materials Science — Materials Chemistry

Chemical bonding of termination species in 2D carbides investigated through valence band UPS/XPS of Ti<sub>3</sub>C<sub>2</sub>T <sub>x</sub> MXene Abstract MXenes are technologically interesting 2D materials that show potential in numerous applications. The properties of the MXenes depend at large extent on the selection of elements that build the 2D MX-layer. Another key parameter for tuning the attractive material properties is the species that terminate the surfaces of the MX-layers. Although being an important parameter, experimental studies on the bonding between the MX-layers and the termination species are few and thus an interesting subject of investigation. Here we show that the termination species fluorine (F) bonds to the Ti 3 C 2 -surface mainly through Ti 3 p —F 2 p hybridization and that oxygen (O) bonds through Ti 3 p —O 2 p hybridization with a significant contribution of Ti 3 d and Ti 4 p . The study further shows that the Ti 3 C 2 -surface is not only terminated by F and O on the threefold hollow face-centered-cubic site. A significant amount of O sits on a bridge site bonded to two Ti surface atoms on the Ti 3 C 2 -surface. In addition, the results provide no support for hydroxide (OH) termination on the Ti 3 C 2 -surface. On the contrary, the comparison of the valence band intensity distribution obtained through ultraviolet- and x-ray photoelectron spectroscopy with computed spectra by density of states, weighed by matrix elements and sensitivity factors, reveals that OH cannot be considered as an inherent termination species in Ti 3 C 2 T x . The results from this study have implications for correct modeling of the structure of MXenes and the corresponding materials properties. Especially in applications where surface composition and charge are important, such as supercapacitors, Li-ion batteries, electrocatalysis, and fuel- and solar cells, where intercalation processes are essential. X-ray photoelectron spectroscopy ; Valence band ; Carbide ; Materials science ; Valence (chemistry) ; Chemical bond ; Semimetal ; Crystallography ; Chemistry ; Band gap ; Nuclear magnetic resonance ; Physics ; Metallurgy ; Optoelectronics MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene research and applications
Authors: Danhua Dai | Xianwei Wang | Yiwei Liu | Xiaoliang Yang | Clemens Glaubitz | Vasyl Denysenkov | Xiao He | Thomas F. Prisner | Jiafei Mao
Source: Nature Communications (article, 2021-11-25)
Field: Chemistry — Spectroscopy

Room-temperature dynamic nuclear polarization enhanced NMR spectroscopy of small biological molecules in water Nuclear magnetic resonance (NMR) spectroscopy is a powerful and popular technique for probing the molecular structures, dynamics and chemical properties. However the conventional NMR spectroscopy is bottlenecked by its low sensitivity. Dynamic nuclear polarization (DNP) boosts NMR sensitivity by orders of magnitude and resolves this limitation. In liquid-state this revolutionizing technique has been restricted to a few specific non-biological model molecules in organic solvents. Here we show that the carbon polarization in small biological molecules, including carbohydrates and amino acids, can be enhanced sizably by in situ Overhauser DNP (ODNP) in water at room temperature and at high magnetic field. An observed connection between ODNP <sup>13</sup>C enhancement factor and paramagnetic <sup>13</sup>C NMR shift has led to the exploration of biologically relevant heterocyclic compound indole. The QM/MM MD simulation underscores the dynamics of intermolecular hydrogen bonds as the driving force for the scalar ODNP in a long-living radical-substrate complex. Our work reconciles results obtained by DNP spectroscopy, paramagnetic NMR and computational chemistry and provides new mechanistic insights into the high-field scalar ODNP. Nuclear magnetic resonance spectroscopy ; Spectroscopy ; Molecule ; Chemistry ; Intermolecular force ; Transverse relaxation-optimized spectroscopy ; Paramagnetism ; Chemical shift ; Chemical physics ; Two-dimensional nuclear magnetic resonance spectroscopy ; Polarization (electrochemistry) ; Molecular dynamics ; Nuclear magnetic resonance ; Carbon-13 NMR ; Fluorine-19 NMR ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Stereochemistry ; Physics Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; NMR spectroscopy and applications
Authors: Dimitrios A. Koutsouras | Fabrizio Torricelli | Paschalis Gkoupidenis | Paul W. M. Blom
Source: Advanced Materials Technologies (article, 2021-08-27)
Field: Materials Science — Polymers and Plastics

Efficient Gating of Organic Electrochemical Transistors with In‐Plane Gate Electrodes Abstract Organic electrochemical transistors (OECTs) are electrolyte‐gated transistors, employing an electrolyte between their gate and channel instead of an insulating layer. For efficient gating, non‐polarizable electrodes, for example, Ag/AgCl, are typically used but unfortunately, this simple approach limits the options for multiple gate integration. Patterned polarizable Au gates on the other hand, show strongly reduced gating due to a large voltage drop at the gate/electrolyte interface. Here, an alternative, simple yet effective method for efficient OECT gating by scalable in‐plane gate electrodes, is demonstrated. The fact that poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) exhibits a volumetric capacitance in an electrolyte is made use of. As a result, the capacitance of PEDOT:PSS‐based gates can be strongly enhanced by increasing their thickness, thereby reducing the voltage loss at the gate/electrolyte interface. By combining spin coating and electrodeposition, planar electrodes of various thicknesses are created on a multi‐gated OECT chip and their effect on the gating efficiency, examined. It is shown that the gating performed by an in‐plane PEDOT:PSS electrode can be tuned to be comparable to the one obtained by a Ag/AgCl electrode. Overall, the realization of efficient gating with in‐plane electrodes paves the way toward integration of OECT‐based biosensors and “organ‐on‐a‐chip” platforms. Materials science ; Gating ; PEDOT:PSS ; Electrode ; Electrolyte ; Optoelectronics ; Transistor ; Capacitance ; Nanotechnology ; Voltage ; Chemistry ; Layer (electronics) ; Electrical engineering Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Electrochemical Analysis and Applications
Authors: Daniel Schwalbe‐Koda | Rafael Gómez‐Bombarelli
Source: The Journal of Chemical Physics (article, 2021-05-06)
Field: Chemistry — Inorganic Chemistry

Benchmarking binding energy calculations for organic structure-directing agents in pure-silica zeolites Molecular modeling plays an important role in the discovery of organic structure-directing agents (OSDAs) for zeolites. By quantifying the intensity of host-guest interactions, it is possible to select cost-effective molecules that maximize binding toward a given zeolite framework. Over the last few decades, a variety of methods and levels of theory have been used to calculate these binding energies. Nevertheless, there is no consensus on the best calculation strategy for high-throughput virtual screening undertakings. In this work, we compare binding affinities from density functional theory (DFT) and Dreiding force field calculations for 272 zeolite-OSDA pairs obtained from static and time-averaged simulations. Enabled by automation software, we show that Dreiding binding energies from the frozen pose method correlate best with DFT energies. They are also less sensitive to the choice of initial lattice parameters and optimization algorithms, as well as less computationally expensive than their time-averaged counterparts. Furthermore, we demonstrate that a broader exploration of the conformation space from molecular dynamics simulations does not provide significant improvements in binding energy trends over the frozen pose method despite being orders of magnitude more expensive. The code and benchmark data are open-sourced and provide robust and computationally efficient guidelines to calculating binding energies in zeolite-OSDA pairs. Binding energy ; Density functional theory ; Binding affinities ; Benchmarking ; Benchmark (surveying) ; Zeolite ; Molecular dynamics ; Energy minimization ; Computational chemistry ; Computer science ; Materials science ; Nanotechnology ; Computational science ; Chemistry ; Physics ; Catalysis ; Atomic physics Zeolite Catalysis and Synthesis ; Ammonia Synthesis and Nitrogen Reduction ; Advanced Chemical Physics Studies
Authors: Eric Musa | Francis Doherty | Bryan R. Goldsmith
Source: Current Opinion in Chemical Engineering (article, 2021-12-19)
Field: Materials Science — Materials Chemistry

Accelerating the structure search of catalysts with machine learning Catalysis ; Workflow ; Density functional theory ; Computer science ; Property (philosophy) ; Materials science ; Nanotechnology ; Artificial intelligence ; Chemistry ; Computational chemistry ; Database ; Organic chemistry Machine Learning in Materials Science ; Topic Modeling ; Catalysis and Oxidation Reactions
Authors: Yuanqing Mao | Hongliang Yang | Ye Sheng | Jiping Wang | Runhai Ouyang | Caichao Ye | Jiong Yang | Wenqing Zhang
Source: ACS Omega (article, 2021-05-26)
Field: Materials Science — Materials Chemistry

Prediction and Classification of Formation Energies of Binary Compounds by Machine Learning: An Approach without Crystal Structure Information It is well believed that machine learning models could help to predict the formation energies of materials if all elemental and crystal structural details are known. In this paper, it is shown that even without detailed crystal structure information, the formation energies of binary compounds in various prototypes at the ground states can be reasonably evaluated using machine-learning feature abstraction to screen out the important features. By combining with the "white-box" sure independence screening and sparsifying operator (SISSO) approach, an interpretable and accurate formation energy model is constructed. The predicted formation energies of 183 experimental and 439 calculated stable binary compounds (<i>E</i> <sub>hull</sub> = 0) are predicted using this model, and both show reasonable agreements with experimental and Materials Project's calculated values. The descriptor set is capable of reflecting the formation energies of binary compounds and is also consistent with the common understanding that the formation energy is mainly determined by electronegativity, electron affinity, bond energy, and other atomic properties. As crystal structure parameters are not necessary prerequisites, it can be widely applied to the formation energy prediction and classification of binary compounds in large quantities. Electronegativity ; Binary number ; Crystal structure prediction ; Crystal structure ; Crystal (programming language) ; Abstraction ; Feature (linguistics) ; Chemistry ; Energy (signal processing) ; Artificial intelligence ; Computer science ; Machine learning ; Statistical physics ; Physics ; Mathematics ; Crystallography ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Thermal and Kinetic Analysis
Authors: Liguo Wang | Lu Zhao | Xian Liu | Jianjie Fu | Aiqian Zhang
Source: Environmental Science & Technology (article, 2021-07-09)
Field: Materials Science — Materials Chemistry

SepPCNET: Deeping Learning on a 3D Surface Electrostatic Potential Point Cloud for Enhanced Toxicity Classification and Its Application to Suspected Environmental Estrogens Deep learning (DL) offers an unprecedented opportunity to revolutionize the landscape of toxicity prediction based on quantitative structure-activity relationship (QSAR) studies in the big data era. However, the structural description in the reported DL-QSAR models is still restricted to the two-dimensional level. Inspired by point clouds, a type of geometric data structure, a novel three-dimensional (3D) molecular surface point cloud with electrostatic potential (SepPC) was proposed to describe chemical structures. Each surface point of a chemical is assigned its 3D coordinate and molecular electrostatic potential. A novel DL architecture SepPCNET was then introduced to directly consume unordered SepPC data for toxicity classification. The SepPCNET model was trained on 1317 chemicals tested in a battery of 18 estrogen receptor-related assays of the ToxCast program. The obtained model recognized the active and inactive chemicals at accuracies of 82.8 and 88.9%, respectively, with a total accuracy of 88.3% on the internal test set and 92.5% on the external test set, which outperformed other up-to-date machine learning models and succeeded in recognizing the difference in the activity of isomers. Additional insights into the toxicity mechanism were also gained by visualizing critical points and extracting data-driven point features of active chemicals. Cloud computing ; Point cloud ; Toxicity ; Environmental science ; Environmental chemistry ; Chemistry ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Effects and risks of endocrine disrupting chemicals ; Computational Drug Discovery Methods
Authors: Simona Varvara | Camelia Berghian-Groșan | Roxana Bostan | R. Ciceo-Lucacel | Zohreh Salarvand | Milad Talebian | K. Raeissi | Javier Izquierdo | Ricardo M. Souto
Source: Electrochimica Acta (article, 2021-09-27)
Field: Materials Science — Materials Chemistry

Experimental characterization, machine learning analysis and computational modelling of the high effective inhibition of copper corrosion by 5‐(4‐pyridyl)‐1,3,4‐oxadiazole‐2‐thiol in saline environment Corrosion ; Copper ; Dielectric spectroscopy ; Raman spectroscopy ; Adsorption ; Corrosion inhibitor ; Erosion corrosion of copper water tubes ; Electrochemistry ; Aqueous solution ; X-ray photoelectron spectroscopy ; Polarization (electrochemistry) ; Chemistry ; Inorganic chemistry ; Materials science ; Metal ; Chemical engineering ; Physical chemistry ; Metallurgy ; Electrode Corrosion Behavior and Inhibition ; Electrochemical Analysis and Applications ; Concrete Corrosion and Durability
Authors: Xiaoyu Shi | Pratteek Das | Zhong‐Shuai Wu
Source: ACS Energy Letters (article, 2021-12-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Digital Microscale Electrochemical Energy Storage Devices for a Fully Connected and Intelligent World With the evolving digital era represented by 5G and Internet of Things technologies, microscale electronic terminals will enter every aspect of our daily lives. Meanwhile, they put forward all-around digital requirements for microscale electrochemical energy storage devices (MEESDs), including customizable implementation and precise description, to accurately match these terminals in various scenarios. In this Focus Review, we summarize the current status and latest progress of MEESDs from diverse aspects of key materials, device structures, fabrication processes, functional properties, and system integration in detail. On this basis, existing challenges and future research directions for MEESDs, including evaluation standards, analysis methods, and machine learning, are discussed in brief, through which we can expect to accelerate the development of digital MEESDs as an underlying energy foundation for a fully connected and intelligent world. Microscale chemistry ; Computer science ; Electrochemical energy storage ; Energy storage ; Systems engineering ; Internet of Things ; Key (lock) ; Data science ; Nanotechnology ; Embedded system ; Engineering ; Computer security ; Materials science ; Supercapacitor ; Electrochemistry Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced battery technologies research
Authors: Zhendong He | Bo Qiu | A-Li Luo | Jinghang Shi | Xiao Kong | Xia Jiang
Source: Monthly Notices of the Royal Astronomical Society (article, 2021-08-02)
Field: Chemistry — Spectroscopy

Deep learning applications based on SDSS photometric data: detection and classification of sources ABSTRACT Most astronomical source classification algorithms based on photometric data struggle to classify sources as quasars, stars, and galaxies reliably. To achieve this goal and build a new Sloan Digital Sky Survey photometric catalogue in the future, we apply a deep learning source detection network built on YOLO v4 object detection framework to detect sources and design a new deep learning classification network named APSCnet (astronomy photometric source classification network) to classify sources. In addition, a photometric background image generation network is applied to generate background images in the process of data sets synthesis. Our detection network obtains a mean average precision score of 88.02 when IOU = 0.5. As for APSCnet, in a magnitude range with 14–25, we achieve a precision of 84.1 ${{\ \rm per\ cent}}$ at 93.2 ${{\ \rm per\ cent}}$ recall for quasars, a precision of 94.5 ${{\ \rm per\ cent}}$ at 84.6 ${{\ \rm per\ cent}}$ recall for stars, and a precision of 95.8 ${{\ \rm per\ cent}}$ at 95.1 ${{\ \rm per\ cent}}$ recall for galaxies; and in a magnitude range with less than 20, we achieve a precision of 96.6 ${{\ \rm per\ cent}}$ at 94.7${{\ \rm per\ cent}}$ recall for quasars, a precision of 95.7${{\ \rm per\ cent}}$ at 97.4${{\ \rm per\ cent}}$ recall for stars, and a precision of 98.9 ${{\ \rm per\ cent}}$ at 99.2 ${{\ \rm per\ cent}}$ recall for galaxies. We have proved the superiority of our algorithm in the classification of astronomical sources through comparative experiments between multiple sets of methods. In addition, we also analysed the impact of point spread function on the classification results. These technologies may be applied to data mining of the next generation sky surveys, such as LSST, WFIRST, and CSST etc. Physics ; Quasar ; Galaxy ; Astrophysics ; Stars ; Sky ; Precision and recall ; Artificial intelligence ; Astronomy ; Computer science Spectroscopy and Laser Applications
Authors: Daniel Staros | Guoxiang Hu | Juha Tiihonen | Ravindra Nanguneri | Jaron T. Krogel | M. Chandler Bennett | Olle Heinonen | Panchapakesan Ganesh | Brenda M. Rubenstein
Source: The Journal of Chemical Physics (article, 2021-12-08)
Field: Materials Science — Materials Chemistry

A combined first principles study of the structural, magnetic, and phonon properties of monolayer CrI3 The first magnetic 2D material discovered, monolayer (ML) CrI<sub>3</sub>, is particularly fascinating due to its ground state ferromagnetism. However, because ML materials are difficult to probe experimentally, much remains unresolved about ML CrI<sub>3</sub>'s structural, electronic, and magnetic properties. Here, we leverage Density Functional Theory (DFT) and high-accuracy Diffusion Monte Carlo (DMC) simulations to predict lattice parameters, magnetic moments, and spin-phonon and spin-lattice coupling of ML CrI<sub>3</sub>. We exploit a recently developed surrogate Hessian DMC line search technique to determine CrI<sub>3</sub>'s ML geometry with DMC accuracy, yielding lattice parameters in good agreement with recently published STM measurements-an accomplishment given the ∼10% variability in previous DFT-derived estimates depending upon the functional. Strikingly, we find that previous DFT predictions of ML CrI<sub>3</sub>'s magnetic spin moments are correct on average across a unit cell but miss critical local spatial fluctuations in the spin density revealed by more accurate DMC. DMC predicts that magnetic moments in ML CrI<sub>3</sub> are 3.62 μ<sub>B</sub> per chromium and -0.145 μ<sub>B</sub> per iodine, both larger than previous DFT predictions. The large disparate moments together with the large spin-orbit coupling of CrI<sub>3</sub>'s I-p orbital suggest a ligand superexchange-dominated magnetic anisotropy in ML CrI<sub>3</sub>, corroborating recent observations of magnons in its 2D limit. We also find that ML CrI<sub>3</sub> exhibits a substantial spin-phonon coupling of ∼3.32 cm<sup>-1</sup>. Our work, thus, establishes many of ML CrI<sub>3</sub>'s key properties, while also continuing to demonstrate the pivotal role that DMC can assume in the study of magnetic and other 2D materials. Density functional theory ; Superexchange ; Condensed matter physics ; Magnetic moment ; Ferromagnetism ; Phonon ; Magnetism ; Magnetic anisotropy ; Chemistry ; Physics ; Magnetization ; Computational chemistry ; Quantum mechanics ; Magnetic field 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques