jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joshua J. Goings | Sharon Hammes‐Schiffer
Source: ACS Central Science (article, 2020-09-08)
Field: Materials Science — Materials Chemistry

Nonequilibrium Dynamics of Proton-Coupled Electron Transfer in Proton Wires: Concerted but Asynchronous Mechanisms The coupling between electrons and protons and the long-range transport of protons play important roles throughout biology. Biomimetic systems derived from benzimidazole-phenol (BIP) constructs have been designed to undergo proton-coupled electron transfer (PCET) upon electrochemical or photochemical oxidation. Moreover, these systems can transport protons along hydrogen-bonded networks or proton wires through multiproton PCET. Herein, the nonequilibrium dynamics of both single and double proton transfer in BIP molecules initiated by oxidation are investigated with first-principles molecular dynamics simulations. Although these processes are concerted in that no thermodynamically stable intermediate is observed, the simulations predict that they are predominantly asynchronous on the ultrafast time scale. For both systems, the first proton transfer typically occurs ∼100 fs after electron transfer. For the double proton transfer system, typically the second proton transfer occurs hundreds of femtoseconds after the initial proton transfer. A machine learning algorithm was used to identify the key molecular vibrational modes essential for proton transfer: a slow, in-plane bending mode that dominates the overall inner-sphere reorganization, the proton donor-acceptor motion that leads to vibrational coherence, and the faster donor-hydrogen stretching mode. The asynchronous double proton transfer mechanism can be understood in terms of a significant mode corresponding to the two anticorrelated proton donor-acceptor motions, typically decreasing only one donor-acceptor distance at a time. Although these PCET processes appear concerted on the time scale of typical electrochemical experiments, attaching these BIP constructs to photosensitizers may enable the detection of the asynchronicity of the electron and multiple proton transfers with ultrafast two-dimensional spectroscopy. Understanding the fundamental PCET mechanisms at this level will guide the design of PCET systems for catalysis and energy conversion processes. Proton-coupled electron transfer ; Proton ; Chemical physics ; Electron transfer ; Chemistry ; Molecular dynamics ; Molecular vibration ; Molecular physics ; Electron ; Acceptor ; Atomic physics ; Proton transport ; Photochemistry ; Molecule ; Computational chemistry ; Physics Porphyrin and Phthalocyanine Chemistry ; Photochemistry and Electron Transfer Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Zejun Wang | Shu‐Shen Liu | Li Feng | Ya-Qian Xu
Source: The Science of The Total Environment (article, 2020-06-17)
Field: Chemistry — Electrochemistry

BNNmix: A new approach for predicting the mixture toxicity of multiple components based on the back-propagation neural network Biological system ; Artificial neural network ; Backpropagation ; Caenorhabditis elegans ; Component (thermodynamics) ; Chemistry ; Toxicity ; Computer science ; Thermodynamics ; Machine learning ; Organic chemistry ; Physics ; Biology Electrochemical Analysis and Applications ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Gunnar Alexander Schmitz | Emil Lund Klinting | Ove Christiansen
Source: The Journal of Chemical Physics (article, 2020-08-11)
Field: Materials Science — Materials Chemistry

A Gaussian process regression adaptive density guided approach for potential energy surface construction We present a new iterative scheme for potential energy surface (PES) construction, which relies on both physical information and information obtained through statistical analysis. The adaptive density guided approach (ADGA) is combined with a machine learning technique, namely, the Gaussian process regression (GPR), in order to obtain the iterative GPR-ADGA for PES construction. The ADGA provides an average density of vibrational states as a physically motivated importance-weighting and an algorithm for choosing points for electronic structure computations employing this information. The GPR provides an approximation to the full PES given a set of data points, while the statistical variance associated with the GPR predictions is used to select the most important among the points suggested by the ADGA. The combination of these two methods, resulting in the GPR-ADGA, can thereby iteratively determine the PES. Our implementation, additionally, allows for incorporating derivative information in the GPR. The iterative process commences from an initial Hessian and does not require any presampling of configurations prior to the PES construction. We assess the performance on the basis of a test set of nine small molecules and fundamental frequencies computed at the full vibrational configuration interaction level. The GPR-ADGA, with appropriate settings, is shown to provide fundamental excitation frequencies of an root mean square deviation (RMSD) below 2 cm<sup>-1</sup>, when compared to those obtained based on a PES constructed with the standard ADGA. This can be achieved with substantial savings of 65%-90% in the number of single point calculations. Ground-penetrating radar ; Kriging ; Weighting ; Algorithm ; Gaussian process ; Computer science ; Gaussian ; Mathematical optimization ; Mathematics ; Machine learning ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jia Liang | Shujia Yin | Chunlei Wan
Source: Annual Review of Materials Research (article, 2020-05-09)
Field: Materials Science — Materials Chemistry

Hybrid Thermoelectrics Constructing hybrid composites with organic and inorganic materials at different length scales provides unconventional opportunities in the field of thermoelectric materials, which are classified as hybrid crystal, superlattice, and nanocomposite. A variety of new techniques have been proposed to fabricate hybrid thermoelectric materials with homogeneous microstructures and intimate interfaces, which are critical for good thermoelectric performance. The combination of organic and inorganic materials at the nano or atomic scale can cause strong perturbation in the structural, electron, and phonon characteristics, providing new mechanisms to decouple electrical and thermal transport properties that are not attainable in the pure organic or inorganic counterparts. Because of their increasing thermoelectric performance, compositional diversity, mechanical flexibility, and ease of fabrication, hybrid materials have become the most promising candidates for flexible energy harvesting and solid-state cooling. Thermoelectric materials ; Materials science ; Thermoelectric effect ; Nanocomposite ; Nanotechnology ; Fabrication ; Hybrid material ; Microstructure ; Superlattice ; Characterization (materials science) ; Engineering physics ; Thermal conductivity ; Optoelectronics ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: Francesco Ricci | Alexander Dunn | Anubhav Jain | Gian‐Marco Rignanese | Geoffroy Hautier
Source: Journal of Materials Chemistry A (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Gapped metals as thermoelectric materials revealed by high-throughput screening Gapped metals present in their band structure a gap near the Fermi level. This key feature makes these metals comparable to degenerate semiconductors and thus suitable as thermoelectrics. The present screening searches them systematically. Semiconductor ; Thermoelectric materials ; Thermoelectric effect ; Degenerate energy levels ; Materials science ; Fermi level ; Key (lock) ; Condensed matter physics ; Feature (linguistics) ; Throughput ; Engineering physics ; Physics ; Optoelectronics ; Computer science ; Quantum mechanics ; Wireless ; Telecommunications Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Surface and Thin Film Phenomena
Authors: William Yi Wang | Bin Tang | De-Ye Lin | Chengxiong Zou | Ying Zhang | Shun‐Li Shang | Quanmei Guan | Jun Gao | Letian Fan | Hongchao Kou | Haifeng Song | Jijun Ma | Xidong Hui | Michael C. Gao | Zi‐Kui Liu | Jinshan Li
Source: Journal of materials research/Pratt's guide to venture capital sources (review, 2020-02-28)
Field: Materials Science — Materials Chemistry

A brief review of data-driven ICME for intelligently discovering advanced structural metal materials: Insight into atomic and electronic building blocks Abstract Materials science ; Nanotechnology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Titanium Alloys Microstructure and Properties
Authors: Thomas A. Manz
Source: RSC Advances (article, 2020-01-01)
Field: Chemistry — Organic Chemistry

Seven confluence principles: a case study of standardized statistical analysis for 26 methods that assign net atomic charges in molecules This article studies two kinds of information extracted from statistical correlations between methods for assigning net atomic charges (NACs) in molecules. First, relative charge transfer magnitudes are quantified by performing instant least squares fitting (ILSF) on the NACs reported by Cho <i>et al.</i> (<i>ChemPhysChem</i>, 2020, <b>21</b>, 688-696) across 26 methods applied to ∼2000 molecules. The Hirshfeld and Voronoi deformation density (VDD) methods had the smallest charge transfer magnitudes, while the quantum theory of atoms in molecules (QTAIM) method had the largest charge transfer magnitude. Methods optimized to reproduce the molecular dipole moment (<i>e.g.</i>, ACP, ADCH, CM5) have smaller charge transfer magnitudes than methods optimized to reproduce the molecular electrostatic potential (<i>e.g.</i>, CHELPG, HLY, MK, RESP). Several methods had charge transfer magnitudes even larger than the electrostatic potential fitting group. Second, confluence between different charge assignment methods is quantified to identify which charge assignment method produces the best NAC values for predicting <i>via</i> linear correlations the results of 20 charge assignment methods having a complete basis set limit across the dataset of ∼2000 molecules. The DDEC6 NACs were the best such predictor of the entire dataset. Seven confluence principles are introduced explaining why confluent quantitative descriptors offer predictive advantages for modeling a broad range of physical properties and target applications. These confluence principles can be applied in various fields of scientific inquiry. A theory is derived showing confluence is better revealed by standardized statistical analysis (<i>e.g.</i>, principal components analysis of the correlation matrix and standardized reversible linear regression) than by unstandardized statistical analysis. These confluence principles were used together with other key principles and the scientific method to make assigning atom-in-material properties non-arbitrary. The N@C<sub>60</sub> system provides an unambiguous and non-arbitrary falsifiable test of atomic population analysis methods. The HLY, ISA, MK, and RESP methods failed for this material. Confluence ; Net (polyhedron) ; Statistical analysis ; Molecule ; Computer science ; Chemistry ; Statistics ; Mathematics ; Programming language ; Geometry ; Organic chemistry Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Cho‐Tung Yip | Masaharu Isobe | Chor‐Hoi Chan | Simiao Ren | Kin-Ping Wong | Qingxiao Huo | Chun-Shing Lee | Yuen Hong Tsang | Yilong Han | Chi-Hang Lam
Source: Physical Review Letters (article, 2020-12-17)
Field: Materials Science — Materials Chemistry

Direct Evidence of Void-Induced Structural Relaxations in Colloidal Glass Formers Particle dynamics in supercooled liquids are often dominated by stringlike motions in which lines of particles perform activated hops cooperatively. The structural features triggering these motions, crucial in understanding glassy dynamics, remain highly controversial. We experimentally study microscopic particle dynamics in colloidal glass formers at high packing fractions. With a small polydispersity leading to glass-crystal coexistence, a void in the form of a vacancy in the crystal can diffuse reversibly into the glass and further induces stringlike motions. In the glass, a void takes the form of a quasivoid consisting of a few neighboring free volumes and is transported by the stringlike motions it induces. In fully glassy systems with a large polydispersity, similar quasivoid actions are observed. The mobile particles cluster into stringlike or compact geometries, but the compact ones can be further broken down into connected sequences of strings, establishing their general importance. Void (composites) ; Dispersity ; Supercooling ; Materials science ; Chemical physics ; String (physics) ; Colloidal crystal ; Vacancy defect ; Cluster (spacecraft) ; Colloid ; Particle dynamics ; Lattice (music) ; Condensed matter physics ; Colloidal particle ; Molecular dynamics ; Physics ; Composite material ; Thermodynamics ; Chemistry ; Theoretical physics ; Physical chemistry Material Dynamics and Properties ; Liquid Crystal Research Advancements ; Plant and animal studies
Authors: José Manuel Dı́az-Cruz | Núria Serrano | Clara Pérez‐Ràfols | Cristina Ariño | Miquel Esteban
Source: Journal of Solid State Electrochemistry (review, 2020-06-21)
Field: Chemistry — Electrochemistry

Electroanalysis from the past to the twenty-first century: challenges and perspectives Nanotechnology ; Engineering ethics ; History ; Engineering ; Materials science Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Advanced Chemical Sensor Technologies
Authors: Shunyang Wang | Tobias Kind | Dean J. Tantillo | Oliver Fiehn
Source: Journal of Cheminformatics (article, 2020-10-20)
Field: Chemistry — Spectroscopy

Predicting in silico electron ionization mass spectra using quantum chemistry Compound identification by mass spectrometry needs reference mass spectra. While there are over 102 million compounds in PubChem, less than 300,000 curated electron ionization (EI) mass spectra are available from NIST or MoNA mass spectral databases. Here, we test quantum chemistry methods (QCEIMS) to generate in silico EI mass spectra (MS) by combining molecular dynamics (MD) with statistical methods. To test the accuracy of predictions, in silico mass spectra of 451 small molecules were generated and compared to experimental spectra from the NIST 17 mass spectral library. The compounds covered 43 chemical classes, ranging up to 358 Da. Organic oxygen compounds had a lower matching accuracy, while computation time exponentially increased with molecular size. The parameter space was probed to increase prediction accuracy including initial temperatures, the number of MD trajectories and impact excess energy (IEE). Conformational flexibility was not correlated to the accuracy of predictions. Overall, QCEIMS can predict 70 eV electron ionization spectra of chemicals from first principles. Improved methods to calculate potential energy surfaces (PES) are still needed before QCEIMS mass spectra of novel molecules can be generated at large scale. Mass spectrum ; NIST ; Electron ionization ; Mass spectrometry ; Chemistry ; Spectral line ; PubChem ; Ionization energy ; Ionization ; Analytical Chemistry (journal) ; Computer science ; Physics ; Chromatography ; Organic chemistry ; Ion Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Marco Beccaria | Anna Luiza Mendes Siqueira | Adrien Maniquet | Pierre Giusti | Marco Piparo | Pierre‐Hugues Stefanuto | Jean‐François Focant
Source: Journal of Separation Science (review, 2020-11-13)
Field: Chemistry — Spectroscopy

Advanced mono‐ and multi‐dimensional gas chromatography–mass spectrometry techniques for oxygen‐containing compound characterization in biomass and biofuel samples A wide variety of biomass, from triglycerides to lignocellulosic-based feedstock, are among promising candidates to possibly fulfill requirements as a substitute for crude oils as primary sources of chemical energy feedstock. During the feedstock processing carried out to increase the H:C ratio of the products, heteroatom-containing compounds can promote corrosion, thus limiting and/or deactivating catalytic processes needed to transform the biomass into fuel. The use of advanced gas chromatography techniques, in particular multi-dimensional gas chromatography, both heart-cutting and comprehensive coupled to mass spectrometry, has been widely exploited in the field of petroleomics over the past 30 years and has also been successfully applied to the characterization of volatile and semi-volatile compounds during the processing of biomass feedstock. This review intends to describe advanced gas chromatography-mass spectrometry-based techniques, mainly focusing in the period 2011-early 2020. Particular emphasis has been devoted to the multi-dimensional gas chromatography-mass spectrometry techniques, for the isolation and characterization of the oxygen-containing compounds in biomass feedstock. Within this context, the most recent advances to sample preparation, derivatization, as well as gas chromatography instrumentation, mass spectrometry ionization, identification, and data handling in the biomass industry, are described. Gas chromatography ; Mass spectrometry ; Raw material ; Chemistry ; Biomass (ecology) ; Gas chromatography–mass spectrometry ; Context (archaeology) ; Derivatization ; Chromatography ; Lignocellulosic biomass ; Organic chemistry ; Lignin Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications ; Fermentation and Sensory Analysis
Authors: Kei Terayama | Masato Sumita | Ryo Tamura | Daniel T. Payne | Mandeep K. Chahal | Shinsuke Ishihara | Koji Tsuda
Source: Chemical Science (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Pushing property limits in materials discovery<i>via</i>boundless objective-free exploration Materials chemists develop chemical compounds to meet often conflicting demands of industrial applications. This process may not be properly modeled by black-box optimization because the target property is not well defined in some cases. Herein, we propose a new algorithm for automated materials discovery called BoundLess Objective-free eXploration (BLOX) that uses a novel criterion based on kernel-based Stein discrepancy in the property space. Unlike other objective-free exploration methods, a boundary for the materials properties is not needed; hence, BLOX is suitable for open-ended scientific endeavors. We demonstrate the effectiveness of BLOX by finding light-absorbing molecules from a drug database. Our goal is to minimize the number of density functional theory calculations required to discover out-of-trend compounds in the intensity-wavelength property space. Using absorption spectroscopy, we experimentally verified that eight compounds identified as outstanding exhibit the expected optical properties. Our results show that BLOX is useful for chemical repurposing, and we expect this search method to have numerous applications in various scientific disciplines. Property (philosophy) ; Computer science ; Astrobiology ; Physics ; Philosophy ; Epistemology Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Electronic and Structural Properties of Oxides
Authors: Robbin Bouwmeester | Ralf Gabriels | Niels Hulstaert | Lennart Martens | Sven Degroeve
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-03-29)
Field: Chemistry — Spectroscopy

DeepLC can predict retention times for peptides that carry as-yet unseen modifications Abstract The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex LC-MS identification workflows. However, due to the way peptides are encoded in current prediction models, accurate retention times cannot be predicted for modified peptides. This is especially problematic for fledgling open modification searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity. We here therefore present DeepLC, a novel deep learning peptide retention time predictor utilizing a new peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. We show that DeepLC performs similarly to current state-of-the-art approaches for unmodified peptides, and, more importantly, accurately predicts retention times for modifications not seen during training. Moreover, we show that DeepLC’s ability to predict retention times for any modification enables potentially incorrect identifications to be flagged in an open modification search of CD8-positive T-cell proteome data. DeepLC is available under the permissive Apache 2.0 open source license and comes with a user-friendly graphical user interface, as well as a Python package on PyPI, Bioconda, and BioContainers for effortless workflow integration. Workflow ; Computer science ; Ambiguity ; Retention time ; Identification (biology) ; Proteome ; Peptide ; Python (programming language) ; Artificial intelligence ; Machine learning ; Chemistry ; Chromatography ; Database ; Biology ; Programming language ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; vaccines and immunoinformatics approaches
Authors: Yaru Li | Jun Luo | Xiong Li | Mingbo Pu | Xiaoliang Ma | Xin Xie | Jianping Shi | Xiangang Luo
Source: IEEE photonics journal (article, 2020-02-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Switchable Quarter-Wave Plate and Half-Wave Plate Based on Phase-Change Metasurface Wave plates as important polarization control devices are widely used in the applications of biomedical imaging, fiber optical communication, astronomy, semiconductor industry and aerospace etc. To make the wave plate actively tunable, we propose a rectangular antenna-based metasurface based on the phase-changing material Ge <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Sb <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Te <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sub> (GST) with high transmittance and polarization conversion rate. Before the GST phase transition, the metasurface operates as a quarter-wave plate in the wavelength range of 10.0-11.9 μm. After the GST turning into crystalline state, the metasurface operates as a half-wave plate in the wavelength range of 10.3-10.9 μm. The physical mechanism for the high transmittance efficiency and polarization conversion rate attributes to the combination of the electric dipole and magnetic dipole resonances. Moreover, the adjustable wavelength range can be further extended by changing the geometry of antenna-based GST to selectively enhancing or suppressing the electric and magnetic resonances. This work provides a new strategy for tunable wave plate, which would have the potential in the application of multi-functional integrated optical systems. Waveplate ; Quarter (Canadian coin) ; Optics ; Phase change ; Phase (matter) ; Physics ; Engineering physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis