jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xinyi Wu | Yun Zhang | Jiahui Yu | Chengyun Zhang | Haoran Qiao | Yejian Wu | Zhipeng Wu | Xinqiao Wang | Hongliang Duan
Source: ChemRxiv (preprint, 2022-02-09)
Field: Materials Science — Materials Chemistry

Virtual data augmentation method for reaction prediction in small dataset scenario To improve the performance of data-driven reaction prediction models, a new data augmentation method for augmenting data volumes is presented that aims to add fake data in training dataset. This method is only pay attention to small-scale reactions, and manually generates fake data which is chemical and credible molecules by replacing functional groups in reaction sites. And we call this method as virtual data augmentation. Additionally, the transformer model is introduced to explore the effectiveness of virtual data augmentation method in the task of reaction prediction based on small data sets. We apply our method to five classic coupling reactions, the results show that the overall performance of the transformer-baseline model and transformer-transfer learning model combined with virtual data augmentation method is obviously improved, compared to raw datasets. Especially for Suzuki reaction, combining transfer learning strategy and virtual data augmentation method, reaches top-1 accuracy of 97.8%. To sum up, virtual data augmentation can be used as a measure to face up the problem of insufficient data and significantly improves the performance of reaction prediction. Computer science ; Transformer ; Raw data ; Data mining ; Transfer of learning ; Task (project management) ; Machine learning ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Tahereh Shah Mansouri | Hui Wang | Davide Mariotti | Paul Maguire
Source: Journal of Physics D Applied Physics (preprint, 2022-02-22)
Field: Chemistry — Spectroscopy

Methane detection to 1 ppm using machine learning analysis of atmospheric pressure plasma optical emission spectra Abstract Optical emission spectroscopy from a small-volume, 5 μ l, atmospheric pressure RF-driven helium plasma was used in conjunction with partial least squares-discriminant analysis for the detection of trace concentrations of methane gas. A limit of detection of 1 ppm was obtained and sample concentrations up to 100 ppm CH 4 were classified using a nine-category model. A range of algorithm enhancements were investigated including regularization, simple data segmentation and subset selection, feature selection via Variable Importance in Projection and wavelength variable compression in order to address the high dimensionality and collinearity of spectral emission data. These approaches showed the potential for significant reduction in the number of wavelength variables and the spectral resolution/bandwidth. Wavelength variable compression exhibited reliable predictive performance, with accuracy values &gt;97%, under more challenging multi-session train—test scenarios. Simple modelling of plasma electron energy distribution functions highlights the complex cross-sensitivities between the target methane, its dissociation products and atmospheric impurities and their impact on excitation and emission. Collinearity ; Feature selection ; Emission spectrum ; Wavelength ; Atmospheric pressure ; Chemistry ; Spectral line ; Spectroscopy ; Linear discriminant analysis ; Analytical Chemistry (journal) ; Computational physics ; Optics ; Physics ; Artificial intelligence ; Computer science ; Mathematics ; Statistics Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Wataru Matsuoka | Yu Harabuchi | Satoshi Maeda
Source: ChemRxiv (preprint, 2022-01-10)
Field: Materials Science — Materials Chemistry

Virtual-Ligand-Assisted Screening Strategy to Discover Enabling Ligands for Transition Metal Catalysis The ligand screening process, in which an optimal ligand for a reaction of interest is identified from an enormous and diverse set of candidate molecules, is of particular importance in the development of transition metal catalysis. Conventionally, this process has been performed by experimental trial-and-error cycles, which require significant time and resources. Herein, we report a novel strategy called “virtual-ligand-assisted (VLA) screening” that enables practical in silico ligand screening based on the transition state theory. We developed a virtual ligand, PCl*3, which parameterizes both the electronic and steric effects of monodentate phosphorus(III) ligands in quantum chemical calculations, and used it to assess how these effects perturb the energy profile of a reaction. This parameter-based ligand screening approach allowed us to identify the optimal electronic and steric effects for a reaction of interest, thereby affording guiding principles for rational ligand design. The VLA screening strategy was demonstrated for the selectivity-determining step of the rhodium-catalyzed hydroformylation of a terminal olefin, and phosphorus(III) ligands with potentially high linear or branched selectivities were designed. These findings indicate that VLA screening is a promising approach for streamlining the ligand screening process. Ligand (biochemistry) ; Steric effects ; Virtual screening ; Combinatorial chemistry ; Hydroformylation ; Chemistry ; Catalysis ; Denticity ; Electronic effect ; Computational chemistry ; Stereochemistry ; Rhodium ; Pharmacophore ; Metal ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Surface Chemistry and Catalysis
Authors: Yoshiaki Uchida | Shizuo Kaji | Naoto Nakano
Source: Research Square (preprint, 2022-04-27)
Field: Chemistry — Spectroscopy

Chemical-Data-Driven Validation of Physical Theories of Liquid Crystals Abstract Physicists have established a general approach for explaining the phase transition behavior using order parameters in phenomenological theories. Order parameters are generally accounted for by single molecular properties in mean-field theories. Such theories build on physicists’ hypotheses, which are validated by agreement between the observed and predicted values, such as the theoretical estimation of phase transition temperatures. In this study, we report chemical-data-driven methods for finding and scrutinizing physical assumptions on which physical theories are based. We demonstrate the effectiveness of our methodology by validating the assumption of the widely accepted phenomenological Landau theory for liquid-crystalline (LC) phases, wherein the symmetry of the lower-temperature phase explains phase transition. Furthermore, our method suggests that the importance of molecular flexibility is underestimated in the major mean-field theories for LC phases, compared with the widely incorporated molecular shape and intermolecular electrostatic interactions. Statistical physics ; Data science ; Theoretical physics ; Epistemology ; Computer science ; Physics ; Philosophy Molecular spectroscopy and chirality ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Rakesh D. Amrutkar | T Selvam | C James | P Dniandev | S Valzita | A Pinner | P Biginelli | K Saotome | A Singh | M Yelshanskaya | A Sobolevsky | L Tang | T Gamal El-Din | J Payandeh | G Martinez | T Heard | T Scheuer | . Catterall | W | A Peters | M Milevskiy | W Lee | M Curry | C Smart | J Saunus | . Monteith | G | S Khedkar | P Auti | L Kurnutala | S Soghomonyan | S Bergese | M Fujisawa | R Yorikane | Y Matsuoka | H Koike | K Ueno | M Mohamed | A Darweesh | A Elwahy | I Abdelhamid | S Valente | P Mellini | F Spallotta | V Carafa | A Nebbioso | L Polletta | . Mai | A | H Komoda | T Inoue | K Node | A Idhayadhulla | R Kumar | A Nasser | S Kavimani | S Indhumathy | B Kumar | S Yuvaraj | A Srivastava | V Chaturvedi | Y Manju | B Suresh | M Nanjan | N Desai | A Trivedi | H Somani | K Bhatt | R Juan | M Soriano-Ursa | J Espinosa-Raya | J Correa-Basurto | J Trujillo-Ferrara | R Miranda-Ruvalcaba | . Gmez-Pliego | R | C Sunkel | M Fau De Casa-Juana | L Santos | M Mar Gomez | M Villarroya | M Gonzalez-Morales | . Ortega | M | C Kappe | S Jain | J Joseph | S Singhal | B Sain | S Forli | R Huey | M Pique | M Sanner | D Goodsell | A Olson | D Hecht
Source: Journal of Pharmaceutical Research (article, 2022-12-28)
Field: Chemistry — Organic Chemistry

Green Synthesis, Characterization, In Silico ADMET Profiling, Molecular Docking Studies of 3, 4-Dihydropyrimidin-2-(1H)-ones Against Voltage-Gated Calcium Channel (4MS2) Receptor The present work involves design, synthesis and evaluation of derivatives of 3,4-dihydropyrimidine 2 one. Using pyrimdine as a core structure for design of novel pyrimidine derivatives which are calcium channel blocker, Derivatives of 3,4-dihydropyrinidine-2-one was synthesized by microwave assisted synthesis method. There are ten derivatives are synthesized by microwave assisted synthesis in laboratory. The entire synthesized compound was tested using Insilco parameters like Pass online data, SwissADME parameters and toxicity parameters by using protox II. All the synthesized compound tested by pass online software for its biological activity. All the synthesized compounds reflelects various activities like antihypertensive, antianginal, antiviral, antifungal and all of them are calcium channel blockers. In silico ; Combinatorial chemistry ; Pyrimidine ; Chemistry ; Docking (animal) ; Calcium channel ; Antifungal ; Calcium ; Stereochemistry ; Biochemistry ; Organic chemistry ; Biology Multicomponent Synthesis of Heterocycles ; Synthesis and biological activity ; Inorganic and Organometallic Chemistry
Authors: V. S. Volcheck | M. S. Baranava | V. R. Stempitsky
Source: Proceedings of the National Academy of Sciences of Belarus Physical-Technical Series (article, 2022-10-08)
Field: Materials Science — Materials Chemistry

Thermal conductivity of wurtzite gallium nitride This paper reviews the theoretical and experimental works concerning one of the most important parameters of wurtzite gallium nitride – thermal conductivity. Since the heat in gallium nitride is transported almost exclusively by phonons, its thermal conductivity has a temperature behavior typical of most nonmetallic crystals: the thermal conductivity increases proportionally to the third power of temperature at lower temperatures, reaches its maximum at approximately 1/20 of the Debye temperature and decreases proportionally to temperature at higher temperatures. It is shown that the thermal conductivity of gallium nitride (depending on fabrication process, crystallographic direction, concentration of impurity and other defects, isotopical purity) varies significantly, emphasizing the importance of determining this parameter for the samples that closely resemble those being used in specific applications. For isotopically pure undoped wurtzite gallium nitride, the thermal conductivity at room temperature has been estimated as high as 5.4 W/(cm·K). The maximum room temperature value measured for bulkshaped samples of single crystal gallium nitride has been 2.79 W/(cm·K). Wurtzite crystal structure ; Gallium nitride ; Thermal conductivity ; Materials science ; Gallium ; Nitride ; Conductivity ; Chemistry ; Nanotechnology ; Composite material ; Metallurgy ; Zinc ; Physical chemistry Thermal properties of materials ; GaN-based semiconductor devices and materials ; Ga2O3 and related materials
Authors: Yumin Zhang | Imanuel Bier | Venkatasubramanian Viswanathan
Source: ChemRxiv (preprint, 2022-06-01)
Field: Materials Science — Materials Chemistry

Predicting Electrolyte Conductivity Directly from Molecular-Level Interactions Ionic conductivity is a fundamental property to evaluate an electrolyte blend’s per- formance. Though it is a macroscopic quantity, ionic conductivity is derived from molecular interactions including the stoichiometry and relative population of ion solvation clusters. There is growing interest in constructing low-cost, high-throughput electrolyte design frameworks that can evaluate the properties of novel electrolyte blends in silico. A framework that can accurately predict ionic conductivity without empirical fitting would therefore be transformative. This study outlines such an approach built on a chemical physics formalism with parameters computed by classical molecular dynamic (MD) simulations. Quantitative and qualitative agreements are achieved for strong and weak electrolytes, respectively. This work provides the basis for conductivity predictions for novel battery electrolyte systems, facilitating in silico screening before synthesis. Electrolyte ; Conductivity ; Ionic conductivity ; Solvation ; In silico ; Chemical physics ; Materials science ; Population ; Ionic bonding ; Molecular dynamics ; Chemistry ; Ion ; Computational chemistry ; Electrode ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Ionic liquids properties and applications ; Advanced Battery Materials and Technologies
Authors: Darian T. Yang | Angela M. Gronenborn | Lillian T. Chong
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-01-06)
Field: Materials Science — Materials Chemistry

Development and Validation of Fluorinated Amino Acid Parameters for use with the AMBER ff15ipq Protein Force Field Abstract We developed force field parameters for fluorinated aromatic amino acids enabling molecular dynamics (MD) simulations of fluorinated proteins. These parameters are tailored to the AMBER ff15ipq protein force field and enable the modeling of 4, 5, 6, and 7F-tryptophan, 3F- and 3,5F-tyrosine, and 4F- or 4-CF 3 -phenylalanine. The parameters include 181 unique atomic charges derived using the Implicitly Polarized Charge (IPolQ) scheme in the presence of SPC/E b explicit water molecules and 9 unique bond, angle, or torsion terms. Our simulations of benchmark peptides and proteins maintain expected conformational propensities on the μ s-timescale. In addition, we have developed an open-source Python program to calculate fluorine relaxation rates from MD simulations. The extracted relaxation rates from protein simulations are in good agreement with experimental values determined by 19 F NMR. Collectively, our results illustrate the power and robustness of the IPolQ lineage of force fields for modeling structure and dynamics of fluorine containing proteins at the atomic level. Force field (fiction) ; Molecular dynamics ; Chemistry ; Tryptophan ; Amino acid ; Computational chemistry ; Torsion (gastropod) ; Fluorine ; Python (programming language) ; Chemical physics ; Molecule ; Phenylalanine ; Robustness (evolution) ; Physics ; Computer science ; Organic chemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced NMR Techniques and Applications
Authors: Shiyan Wang | Huiying Guo | Bei Wang | Sai Liu | Shundong Yuan
Source: Research Square (preprint, 2022-03-18)
Field: Chemistry — Analytical Chemistry

A Mechanism Study Of Sodium Dodecylbenzene Sulfonate On Oil Recovery: Effect Of Branched Chain Structure Abstract The effects of the branched structures of SDBS molecules on oil recovery are investigated by molecular dynamics method. The relative density of oil molecules shows that SDBS molecule with benzene ring located near the center of alkyl chain has the best effect on oil displacement. Dynamic trajectories show that the water and SDBS molecules gradually occupy the calcite surface and replace the oil droplets. In this process, more water molecules gather nearby the polar groups of SDBS, indicating that the polar group has a significant effect on the water infiltration and the formation of water channels. Contact angle between SDBS molecule and calcite surface indicates that compared to straight chains, the branched structure tends to spread on the calcite interface. Moreover, adsorption energies of the simulation systems further prove that as the aromatic ring is closer to the middle of the alkyl chain, the oil displacement effect is better. Dodecylbenzene ; Sulfonate ; Mechanism (biology) ; Sodium ; Chemistry ; Chain (unit) ; Organic chemistry ; Physics Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Surfactants and Colloidal Systems
Authors: Kirill Zinovjev
Source: ChemRxiv (preprint, 2022-09-06)
Field: Materials Science — Materials Chemistry

Electrostatic Embedding of Machine Learning Potentials This work presents a variant of an electrostatic embedding scheme that allows the embedding of arbitrary machine learned potentials trained on molecular systems in vacuo. The scheme is based on physically motivated models of electronic density and polarizability, resulting in a generic model without relying on an exhaustive training set. The scheme only requires in vacuo single point QM calculations to provide training densities and molecular dipolar polarizabilities. As an example, the scheme is applied to create an embedding model for the QM7 dataset using Gaussian Process Regression with only 445 reference atomic environments. The model was tested on SARS-CoV-2 protease complex with PF-00835231, resulting in predicted embedding energy RMSE of 2 kcal/mol, compared to explicit DFT/MM calculations. Embedding ; Polarizability ; Scheme (mathematics) ; Dipole ; Gaussian ; Computer science ; Statistical physics ; Set (abstract data type) ; Algorithm ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Computational chemistry ; Physics ; Chemistry ; Mathematics ; Molecule ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Toru Aonishi | Hiroki Tanimoto | Xu Hongkun | Masaishiro Mizumaki | Yoshiki Seno | Jumpei Uchiwada | Ryo Yamagami | Hiroyuki Kumazoe | Kazunori Iwamitsu | Yuta Kimura | Koji Amezawa | Ichiro Akai
Source: The Brain & Neural Networks (article, 2022-03-05)
Field: Materials Science — Surfaces, Coatings and Films

Analysis of 2D X-ray Absorption Spectrum Data with Non-negative Matrix Factorization 電気自動車などの性能向上のため，リチウムイオン電池の急速充電・高出力といった高レート特性の向上が強く求められている．高レートでの使用条件下で十分なパフォーマンスが達成できない原因一つとして，充放電中に化学反応が不均一に進むことが挙げられる．このような充放電時の不均一な化学反応を，2次元X線吸収スペクトル法により視覚化する試がある．この解説では，近年我々が取り組んでいる非負値行列因子分解（Non-negative Matrix Factorization（NMF））による2次元X線吸収スペクトルデータ解析を紹介する．本手法により，従来手法とは異なる吸収スペクトルの差異に基づく化学反応の不均一性の検出ができる．しかしながら，NMFで抽出したスペクトルの特徴が，我々に解釈可能な物理的特徴量と一致しているかどうかという問題に直面する．これは，データ駆動科学の多くの事例で直面している共通の問題である．我々は，NMFにより分解したスペクトルベイズ分光を適用することにより，我々が解釈可能な物理的特徴量に変換することで，この問題の解決を試みる． Factorization ; Matrix decomposition ; Spectrum (functional analysis) ; Matrix (chemical analysis) ; Physics ; Mathematics ; Algorithm ; Chemistry ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques
Authors: Guillermo Restrepo
Source: Bulletin for the history of chemistry (article, 2022-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Computational history of chemistry In this essay it is shown how mathematical and computational approaches can be used to model the underlying mechanisms of historical processes, which transform the structure, dynamics and function of chemistry. By chemical knowledge, I refer to a complex dynamical system emerging from the interaction of the social, material and semiotic systems of chemistry. Besides instantiating some watershed events of the history of chemistry in this framework, the increasing availability of large datasets amenable to computational exploration is discussed, as well as the suitable mathematical theories to carry out these studies. I show how this framework allows for exploring possible alternative histories of chemistry by perturbing its past, leading to solving questions of the sort "what would have happened if." This not only sheds light on the past of chemistry, it rather allows modelling the future of the discipline, with its societal and pedagogical reaches. This approach complements conventional methodologies for the history of chemistry and becomes an interdisciplinary field of research for linguists, mathematicians, physicists, historians and chemists, to name but a few scholars and scientists. History of chemistry ; Chemistry ; Computer science ; Epistemology ; History of science ; Philosophy History and advancements in chemistry ; Philosophy and History of Science ; Biomedical Text Mining and Ontologies
Authors: Alexis M. Fenton | Fikile R. Brushett
Source: ChemRxiv (preprint, 2022-12-30)
Field: Chemistry — Electrochemistry

Leveraging graphical models to enhance in situ analyte identification via multiple voltammetric techniques Voltammetry is a powerful analytical technique for evaluating electrochemical reactions and holds particular promise for interrogating electrolyte solutions suitable for energy storage technologies, including examining features such as state-of-charge and state-of-health. However, individual voltammetry techniques are likely to be subcomponents of broader analytical workflows that incorporate complementary methods to diagnose evolving electrolyte solutions of uncertain composition. As such, we demonstrate that jointly evaluating electrolyte solutions with distinct voltammetric modes can enhance the capabilities and sensitivities of characterization protocols. Specifically, by considering macroelectrode cyclic square wave and microelectrode cyclic voltammograms in sequential (“one after another”) and simultaneous (“all at once”) manners, the composition of an electrolyte solution may be estimated with greater accuracy, and analytes that exhibit near identical electrode potentials may be more readily differentiated. We explore means of further improving this method, finding that protocol accuracy increases when multiple voltammetry techniques are included in the training dataset. We also observe that the algorithm typically becomes more confident—but not necessarily more accurate—when the potential mesh granularity becomes finer. Overall, these studies show that the sequential and simultaneous methods may hold utility when evaluating multiple voltammetry datasets that, in turn, may be leveraged to streamline diagnostic workflows used to examine electrolyte solutions within electrochemical technologies. Granularity ; Analyte ; Cyclic voltammetry ; Electrolyte ; Voltammetry ; Computer science ; Workflow ; Supporting electrolyte ; Electrochemistry ; Electrode ; Characterization (materials science) ; Identification (biology) ; Biological system ; Protocol (science) ; Chemistry ; Nanotechnology ; Materials science ; Chromatography Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Analytical Chemistry and Sensors
Authors: See Afzal | T-Dei Dec | See Babu | S Amizhtan | F Bahdad | O Hestad | M Runde | H Enoksen | N Magnusson | T Hikosaka | J See Tokunaga | D Hong | Y Li | Z Hong | F See Meng | I Hosier | P Lewin | G Wilson | W Hou | L Yang | Y Feng | F Yin | Y He | J Li | Y Hsiao | C Yang | H Hu | Z Jia | X Wang | S Hu | W Zhang | W Wang | Q Shao | Y Zhang | Q Zhang | S Huang | J Hu | Q Li | J He | Y Hu | M Dong | B Song | C Xia | J Xie | Y Liu | Y Xing | C Huang | F Zheng | S Chen | R Huang | T Huang | Y See Chen | Y Huang | W Sima | Z Huang | J Geng | Y Kong | J Liu | L Jiang | W Huangfu | Z Wang | B Hui | M Imani | X Zhou | T Ismail | A Saber | F Jacquier | T Vu-Cong | J Jankovic | J Lukic | J Planojevic | D Kolarski | D Janackovic | I Jarrar | E Cherney | S Jayaram | J Jasni | A See Mohd Hashim | S Ji | Z See Chen | Y Jia | X Yang | Z Liu | L Zhu | Y Ji | P Giangrande | W Zhao | V Madonna | X Zhang | H Zhang | M Galea
Source: IEEE Transactions on Dielectrics and Electrical Insulation (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

2022 Index IEEE Transactions on Dielectrics and Electrical Insulation Vol. 29 Index (typography) ; Dielectric ; Electrical engineering ; Materials science ; Electronic engineering ; Engineering physics ; Computer science ; Optoelectronics ; Engineering ; World Wide Web High voltage insulation and dielectric phenomena