jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yifeng Jiang | Lai Chung Liu | Antoine Sarracini | Kamil M. Krawczyk | Jordan S. Wentzell | Cheng Lü | Ryan L. Field | Samir F. Matar | Wojciech Gawełda | Henrike M. Müller‐Werkmeister | R. J. Dwayne Miller
Source: Nature Communications (article, 2020-03-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Direct observation of nuclear reorganization driven by ultrafast spin transitions One of the most basic molecular photophysical processes is that of spin transitions and intersystem crossing between excited states surfaces. The change in spin states affects the spatial distribution of electron density through the spin orbit coupling interaction. The subsequent nuclear reorganization reports on the full extent of the spin induced change in electron distribution, which can be treated similarly to intramolecular charge transfer with effective reaction coordinates depicting the spin transition. Here, single-crystal [Fe<sup>II</sup>(bpy)<sub>3</sub>](PF<sub>6</sub>)<sub>2</sub>, a prototypical system for spin crossover (SCO) dynamics, is studied using ultrafast electron diffraction in the single-photon excitation regime. The photoinduced SCO dynamics are resolved, revealing two distinct processes with a (450 ± 20)-fs fast component and a (2.4 ± 0.4)-ps slow component. Using principal component analysis, we uncover the key structural modes, ultrafast Fe-N bond elongations coupled with ligand motions, that define the effective reaction coordinate to fully capture the relevant molecular reorganization. Intersystem crossing ; Excited state ; Intramolecular force ; Spin states ; Spin crossover ; Chemical physics ; Cluster (spacecraft) ; Ultrashort pulse ; Molecular physics ; Spin (aerodynamics) ; Excitation ; Electron transfer ; Atomic physics ; Chemistry ; Materials science ; Physics ; Crystallography ; Photochemistry ; Singlet state ; Laser Magnetism in coordination complexes ; Porphyrin and Phthalocyanine Chemistry ; Electron Spin Resonance Studies
Authors: Yasmine Guefrachi | Geetu Sharma | Dandan Xu | Gaurav Kumar | Katherine P. Vinter | Omar Abdelrahman | Xinyu Li | Saeed M. Alhassan | Paul J. Dauenhauer | Alexandra Navrotsky | Wei Zhang | Michael Tsapatsis
Source: Angewandte Chemie International Edition (article, 2020-03-02)
Field: Chemistry — Inorganic Chemistry

Steam‐Induced Coarsening of Single‐Unit‐Cell MFI Zeolite Nanosheets and Its Effect on External Surface Brønsted Acid Catalysis Commonly used methods to assess crystallinity, micro-/mesoporosity, Brønsted acid site density and distribution (in micro- vs. mesopores), and catalytic activity suggest nearly invariant structure and function for aluminosilicate zeolite MFI two-dimensional nanosheets before and after superheated steam treatment. Yet, pronounced reaction rate decrease for benzyl alcohol alkylation with mesitylene, a reaction that cannot take place in the zeolite micropores, is observed. Transmission electron microscopy images reveal pronounced changes in nanosheet thickness, aspect ratio and roughness indicating that nanosheet coarsening and the associated changes in the external (mesoporous) surface structure are responsible for the changes in the external surface catalytic activity. Superheated steam treatment of hierarchical zeolites can be used to alter nanosheet morphology and regulate external surface catalytic activity while preserving micro- and mesoporosity, and micropore reaction rates. Nanosheet ; Mesitylene ; Catalysis ; Zeolite ; Materials science ; Mesoporous material ; Chemical engineering ; Microporous material ; Brønsted–Lowry acid–base theory ; Crystallinity ; Aluminosilicate ; Alkylation ; Nanotechnology ; Chemistry ; Organic chemistry ; Composite material Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalytic Processes in Materials Science
Authors: Hélène Launay | Wenmin Huang | Stephen C. Maberly | Brigitte Gontero
Source: Frontiers in Plant Science (review, 2020-07-16)
Field: Materials Science — Biomaterials

Regulation of Carbon Metabolism by Environmental Conditions: A Perspective From Diatoms and Other Chromalveolates Diatoms belong to a major, diverse and species-rich eukaryotic clade, the Heterokonta, within the polyphyletic chromalveolates. They evolved as a result of secondary endosymbiosis with one or more Plantae ancestors, but their precise evolutionary history is enigmatic. Nevertheless, this has conferred them with unique structural and biochemical properties that have allowed them to flourish in a wide range of different environments and cope with highly variable conditions. We review the effect of pH, light and dark, and CO<sub>2</sub> concentration on the regulation of carbon uptake and assimilation. We discuss the regulation of the Calvin-Benson-Bassham cycle, glycolysis, lipid synthesis, and carbohydrate synthesis at the level of gene transcripts (transcriptomics), proteins (proteomics) and enzyme activity. In contrast to Viridiplantae where redox regulation of metabolic enzymes is important, it appears to be less common in diatoms, based on the current evidence, but regulation at the transcriptional level seems to be widespread. The role of post-translational modifications such as phosphorylation, glutathionylation, etc., and of protein-protein interactions, has been overlooked and should be investigated further. Diatoms and other chromalveolates are understudied compared to the Viridiplantae, especially given their ecological importance, but we believe that the ever-growing number of sequenced genomes combined with proteomics, metabolomics, enzyme measurements, and the application of novel techniques will provide a better understanding of how this important group of algae maintain their productivity under changing conditions. Biology ; Endosymbiosis ; Proteomics ; Polyphyly ; Gene ; Metabolic pathway ; Enzyme ; Biochemistry ; Transcriptome ; Computational biology ; Phylogenetics ; Clade ; Gene expression ; Plastid ; Chloroplast Diatoms and Algae Research ; Marine and coastal ecosystems ; Microbial Community Ecology and Physiology
Authors: Elena Gati | Li Xiang | Sergey L. Bud’ko | P. C. Canfield
Source: Annalen der Physik (article, 2020-08-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hydrostatic and Uniaxial Pressure Tuning of Iron‐Based Superconductors: Insights into Superconductivity, Magnetism, Nematicity, and Collapsed Tetragonal Transitions Abstract Iron‐based superconductors are well‐known for their intriguing phase diagrams, which manifest a complex interplay of electronic, magnetic, and structural degrees of freedom. Among the phase transitions observed are superconducting, magnetic, and several types of structural transitions, including a tetragonal‐to‐orthorhombic and a collapsed‐tetragonal transition. In particular, the widely observed tetragonal‐to‐orthorhombic transition is believed to be a result of an electronic order that is coupled to the crystalline lattice and is, thus, referred to as nematic transition. Nematicity is therefore a prominent feature of these materials, which signals the importance of the coupling of electronic and lattice properties. Correspondingly, these systems are particularly susceptible to tuning via pressure (hydrostatic, uniaxial, or some combination). Efforts to probe the phase diagrams of pressure‐tuned iron‐based superconductors are reviewed with a strong focus on recent insights into the phase diagrams of several members of this material class under hydrostatic pressure. These studies on FeSe, Ba(FeCo x ) 2 As 2 , Ca(FeCo x ) 2 As 2 , and CaK(FeNi x ) 4 As 4 are, to a significant extent, made possible by advances of what measurements can be adapted to their use under differing pressure environments. The potential impact of these tools for the study of the wider class of strongly correlated electron systems is pointed out. Superconductivity ; Magnetism ; Tetragonal crystal system ; Condensed matter physics ; Hydrostatic pressure ; Materials science ; Physics ; Phase (matter) ; Quantum mechanics ; Mechanics Iron-based superconductors research ; Rare-earth and actinide compounds ; Physics of Superconductivity and Magnetism
Authors: Jonas Elm
Source: The Journal of Physical Chemistry A (article, 2020-12-30)
Field: Materials Science — Materials Chemistry

Toward a Holistic Understanding of the Formation and Growth of Atmospheric Molecular Clusters: A Quantum Machine Learning Perspective The formation of atmospheric molecular clusters is an important stage in forming new particles in the atmosphere. Despite being a highly focused research area, the exact chemical species involved in the initial steps in new particle formation remain elusive. In this Perspective the main challenges and recent progression in the field are outlined with a special emphasis on the chemical complexity of the puzzle and prospect of modeling larger clusters. In general, there is a high demand for accurate and more complete quantum chemical data sets that can be applied in cluster distribution dynamics models and coupled to atmospheric chemical transport models. A view on how the community could reach this goal by applying data-driven machine learning approaches for more efficient exploration of cluster configurations is presented. A path toward larger clusters and direct molecular dynamics simulations of cluster formation and growth using machine learning models is discussed. Perspective (graphical) ; Cluster (spacecraft) ; Path (computing) ; Atmosphere (unit) ; Field (mathematics) ; Quantum chemical ; Computer science ; Statistical physics ; Nanotechnology ; Physics ; Artificial intelligence ; Meteorology ; Molecule ; Materials science ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Atmospheric chemistry and aerosols ; Advanced Chemical Physics Studies
Authors: Koichiro Kato | Tomohide Masuda | Chiduru Watanabe | Naoki Miyagawa | Hideo Mizouchi | Shumpei Nagase | Kikuko Kamisaka | Kanji Oshima | Satoshi Ono | Hiroshi Ueda | Atsushi Tokuhisa | Ryo Kanada | Masateru Ohta | Mitsunori Ikeguchi | Yasushi Okuno | Kaori Fukuzawa | Teruki Honma
Source: Journal of Chemical Information and Modeling (article, 2020-06-04)
Field: Materials Science — Materials Chemistry

High-Precision Atomic Charge Prediction for Protein Systems Using Fragment Molecular Orbital Calculation and Machine Learning Here, we have constructed neural network-based models that predict atomic partial charges with high accuracy at low computational cost. The models were trained using high-quality data acquired from quantum mechanics calculations using the fragment molecular orbital method. We have succeeded in obtaining highly accurate atomic partial charges for three representative molecular systems of proteins, including one large biomolecule (approx. 2000 atoms). The novelty of our approach is the ability to take into account the electronic polarization in the system, which is a system-dependent phenomenon, being important in the field of drug design. Our high-precision models are useful for the prediction of atomic partial charges and expected to be widely applicable in structure-based drug designs such as structural optimization, high-speed and high-precision docking, and molecular dynamics calculations. Partial charge ; Atomic charge ; Fragment molecular orbital ; Molecular dynamics ; Computer science ; Fragment (logic) ; Force field (fiction) ; Artificial neural network ; Biological system ; Charge (physics) ; Statistical physics ; Molecular orbital ; Physics ; Chemistry ; Algorithm ; Computational chemistry ; Artificial intelligence ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Rinkle Juneja | Abhishek K. Singh
Source: Journal of Materials Chemistry A (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Unraveling the role of bonding chemistry in connecting electronic and thermal transport by machine learning Electronic and thermal transport in materials originate from various forms of electron and ion interactions. Thermal ; Chemistry ; Ion ; Nanotechnology ; Chemical physics ; Materials science ; Physics ; Thermodynamics ; Organic chemistry Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Fuel Cells and Related Materials
Authors: Alessandro Simeone | Elliot Woolley | Josep Escrig | Nicholas J. Watson
Source: Sensors (article, 2020-06-29)
Field: Materials Science — Biomaterials

Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression Effectively cleaning equipment is essential for the safe production of food but requires a significant amount of time and resources such as water, energy, and chemicals. To optimize the cleaning of food production equipment, there is the need for innovative technologies to monitor the removal of fouling from equipment surfaces. In this work, optical and ultrasonic sensors are used to monitor the fouling removal of food materials with different physicochemical properties from a benchtop rig. Tailored signal and image processing procedures are developed to monitor the cleaning process, and a neural network regression model is developed to predict the amount of fouling remaining on the surface. The results show that the three dissimilar food fouling materials investigated were removed from the test section via different cleaning mechanisms, and the neural network models were able to predict the area and volume of fouling present during cleaning with accuracies as high as 98% and 97%, respectively. This work demonstrates that sensors and machine learning methods can be effectively combined to monitor cleaning processes. Fouling ; Process engineering ; Artificial neural network ; Process (computing) ; Computer science ; Artificial intelligence ; Engineering ; Machine learning ; Environmental science Calcium Carbonate Crystallization and Inhibition ; Electrical and Bioimpedance Tomography ; Mineral Processing and Grinding
Authors: Seyed Mohamad Moosavi | Henglu Xu | Linjiang Chen | Andrew I. Cooper | Berend Smit
Source: Chemical Science (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Geometric landscapes for material discovery within energy–structure–function maps Porous molecular crystals are an emerging class of porous materials formed by crystallisation of molecules with weak intermolecular interactions, which distinguishes them from extended nanoporous materials like metal-organic frameworks (MOFs). To aid discovery of porous molecular crystals for desired applications, energy-structure-function (ESF) maps were developed that combine <i>a priori</i> prediction of both the crystal structure and its functional properties. However, it is a challenge to represent the high-dimensional structural and functional landscapes of an ESF map and to identify energetically favourable and functionally interesting polymorphs among the 1000s to 10 000s of structures typically on a single ESF map. Here, we introduce geometric landscapes, a representation for ESF maps based on geometric similarity, quantified by persistent homology. We show that this representation allows the exploration of complex ESF maps, automatically pinpointing interesting crystalline phases available to the molecule. Furthermore, we show that geometric landscapes can serve as an accountable descriptor for porous materials to predict their performance for gas adsorption applications. A machine learning model trained using this geometric similarity could reach a remarkable accuracy in predicting the materials' performance for methane storage applications. Representation (politics) ; Basis (linear algebra) ; Function (biology) ; Energy (signal processing) ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Geometry ; Biology ; Evolutionary biology ; Statistics Machine Learning in Materials Science ; Hydrocarbon exploration and reservoir analysis ; Computational Drug Discovery Methods
Authors: Hao Sun | Luqi Liang | Chunlin Wang | Yi Wu | Fei Yang | Mingzhe Rong
Source: IEEE Access (article, 2020-01-01)
Field: Chemistry — Organic Chemistry

Prediction of the Electrical Strength and Boiling Temperature of the Substitutes for Greenhouse Gas SF₆ Using Neural Network and Random Forest Finding substitutes for sulfur hexafluoride (SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> ), a gas with extremely high global warming potential, has been a persistent effort for years in the field of high voltage power equipment, which focuses on the evaluation of the electrical strength and boiling temperature for the practical purpose. Following up the previous proposed linear regression models, this work introduces machine learning algorithms including artificial neural network (ANN) and random forest (RF) as the potential approaches to predict the electrical strength and boiling temperature. Based on a series of descriptors derived from the molecular structure of 74 molecules, the performance of three different methods: multiple linear regression, artificial neural network and random forest are compared and assessed in terms of the sensitivity to the sample size, prediction accuracy and stability, and the interpretability of predictors. Considering the available data are limited, random forest shows superior performance with higher robustness and efficiency. The same approaches were applied to the boiling temperature and random forest produced better results as well. Besides, the variable importance ranked by RF improves understanding of the correlation between the molecular properties and electrical strength. It provides important insights to analyze the properties of the SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> substitutes during the design and synthesis of the new eco-friendly gases in power equipment. Random forest ; Artificial neural network ; Robustness (evolution) ; Linear regression ; Interpretability ; Machine learning ; Computer science ; Boiling ; Artificial intelligence ; Process engineering ; Data mining ; Thermodynamics ; Engineering ; Chemistry ; Physics Chemical Thermodynamics and Molecular Structure ; Advanced Chemical Sensor Technologies ; Phase Equilibria and Thermodynamics
Authors: Clemens Rauer | Tristan Bereau
Source: The Journal of Chemical Physics (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

Hydration free energies from kernel-based machine learning: Compound-database bias We consider the prediction of a basic thermodynamic property—hydration free energies—across a large subset of the chemical space of small organic molecules. Our in silico study is based on computer simulations at the atomistic level with implicit solvent. We report on a kernel-based machine learning approach that is inspired by recent work in learning electronic properties but differs in key aspects: The representation is averaged over several conformers to account for the statistical ensemble. We also include an atomic-decomposition ansatz, which offers significant added transferability compared to molecular learning. Finally, we explore the existence of severe biases from databases of experimental compounds. By performing a combination of dimensionality reduction and cross-learning models, we show that the rate of learning depends significantly on the breadth and variety of the training dataset. Our study highlights the dangers of fitting machine-learning models to databases of a narrow chemical range. Curse of dimensionality ; Transferability ; Representation (politics) ; Chemical space ; Key (lock) ; Variety (cybernetics) ; Dimensionality reduction ; Work (physics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gaussian Processes and Bayesian Inference
Authors: Seema Mathew | P. B. Karandikar | N. R. Kulkarni
Source: Chemical Engineering & Technology (article, 2020-05-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modeling and Optimization of a Jackfruit Seed‐Based Supercapacitor Electrode Using Machine Learning Abstract Supercapacitors can be used for portable energy storage applications. In this study, machine learning techniques are applied to optimize the process of preparation of supercapacitor electrodes from chemically activated carbon made from jackfruit seeds. Experimental trials were carried out using statistical design of experiments. Artificial neural network was employed to generate the process model and a multiobjective optimization was attempted by means of swarm intelligence and the Derringer's desirability function. The optimized electrode demonstrated high capacitance and low resistance making it suitable for supercapacitors. The algorithm developed in the study can be adopted by process engineers for efficient optimization. Supercapacitor ; Process (computing) ; Capacitance ; Artificial neural network ; Process engineering ; Electrode ; Computer science ; Artificial intelligence ; Engineering ; Chemistry Supercapacitor Materials and Fabrication ; Electric and Hybrid Vehicle Technologies ; Electrospun Nanofibers in Biomedical Applications
Authors: Berk Onat | Christoph Ortner | James R. Kermode
Source: The Journal of Chemical Physics (article, 2020-10-12)
Field: Materials Science — Materials Chemistry

Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials Faithfully representing chemical environments is essential for describing materials and molecules with machine learning approaches. Here, we present a systematic classification of these representations and then investigate (i) the sensitivity to perturbations and (ii) the effective dimensionality of a variety of atomic environment representations and over a range of material datasets. Representations investigated include atom centered symmetry functions, Chebyshev Polynomial Symmetry Functions (CHSF), smooth overlap of atomic positions, many-body tensor representation, and atomic cluster expansion. In area (i), we show that none of the atomic environment representations are linearly stable under tangential perturbations and that for CHSF, there are instabilities for particular choices of perturbation, which we show can be removed with a slight redefinition of the representation. In area (ii), we find that most representations can be compressed significantly without loss of precision and, further, that selecting optimal subsets of a representation method improves the accuracy of regression models built for a given dataset. Curse of dimensionality ; Symmetry (geometry) ; Tensor (intrinsic definition) ; Sensitivity (control systems) ; Representation (politics) ; Atom (system on chip) ; Range (aeronautics) ; Variety (cybernetics) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Michiel Larmuseau | Michael Sluydts | Koenraad Theuwissen | Lode Duprez | Tom Dhaene | Stefaan Cottenier
Source: Scripta Materialia (article, 2020-10-23)
Field: Materials Science — Materials Chemistry

Race against the Machine: can deep learning recognize microstructures as well as the trained human eye? Deep learning ; Artificial intelligence ; Race (biology) ; Computer science ; Machine learning ; Magnification ; Image (mathematics) ; Human eye ; Pattern recognition (psychology) ; Data science Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques