jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fujisaku Takahiro | Frederick T.-K. So | Igarashi Ryuji | Shirakawa Masahiro
Source: Institutional Repositories DataBase (IRDB) (article, 2021-07-01)
Field: Materials Science — Materials Chemistry

Machine-Learning Optimization of Multiple Measurement Parameters Nonlinearly Affecting the Signal Quality Determination of optimal measurement parameters is essential for measurement experiments. They can be manually optimized if the linear correlation between them and the corresponding signal quality is known or easily determinable. However, in practice, this correlation is often nonlinear and not known apriori; hence, complicated trial and error procedures are employed for finding optimal parameters while avoiding local optima. In this work, we propose a novel approach based on machine learning for optimizing multiple measurement parameters, which nonlinearly influence the signal quality. Optically detected magnetic resonance measurements of nitrogen-vacancy centers in fluorescent nanodiamonds were used as a proof-of-concept system. We constructed a suitable dataset of optically detected magnetic resonance spectra for predicting the optimal laser and microwave powers that deliver the highest contrast and signal-tonoise ratio values by means of linear regression, neural networks, and random forests. The model developed by the considered neural network turned out to have a significantly higher coefficient of determination than the other methods. The proposed method thus provided a novel approach for the rapid setting of measurement parameters that influence the signal quality in a nonlinear way, opening a gate for fields like nuclear magnetic resonance, electron paramagnetic resonance, and fluorescence microscopy to benefit from it SIGNAL (programming language) ; Artificial neural network ; Nonlinear system ; Correlation coefficient ; Observational error ; Signal-to-noise ratio (imaging) ; Quality (philosophy) ; Laser Diamond and Carbon-based Materials Research ; Nonlinear Optical Materials Studies ; Mechanical and Optical Resonators
Authors: Alistair J. Sterling | Russell C. Smith | Edward A. Anderson | Fernanda Duarte
Source: ChemRxiv (preprint, 2021-12-21)
Field: Chemistry — Organic Chemistry

Beyond strain release: Delocalisation-enabled organic reactivity Strain energy has long been recognised as a fundamental driving force for organic reactions. However, the release of strain alone is an insufficient predictor of reactivity, as seen in the equivalent strain energies but disparate reactivity of cyclopropane and cyclobutane. Here we show that electronic delocalisation is a key factor that operates alongside strain release to boost reactivity, significantly lowering the energy required for bond-breaking in cyclopropanes, cycloalkynes and cycloalkenes. Consideration of thermodynamic and delocalisation parameters explains the relative rates of reaction of molecules containing these functional groups, leading to a ‘hierarchy of delocalisation’ and a rule-of-thumb model that accurately predicts activation barriers. The implications of these principles are demonstrated in the context of the reactions of strained building blocks commonly encountered in total synthesis, medicinal chemistry, polymer science and bioconjugation. Reactivity (psychology) ; Cyclobutane ; Cyclopropane ; Strain (injury) ; Context (archaeology) ; Chemistry ; Substituent ; Computational chemistry ; Stereochemistry ; Organic chemistry ; Biology Cyclopropane Reaction Mechanisms ; Catalytic Alkyne Reactions ; Synthetic Organic Chemistry Methods
Authors: Jake Shechter
Source: Scholarworks (University of Massachusetts Amherst) (article, 2021-01-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Transitions Between Radial and Bipolar Liquid Crystal Drops in the Presence of Novel Surfactants Liquid crystals (LCs) are a class of molecules that form a variety of configurations easily influenced by external interactions. Of particular interest are rod-like LC molecules confined to a spherical geometry, which have a competition between interfacial tension and elastic deformations. The configuration of the liquid crystal inside a droplet can be controlled using surfactants, influencing the boundary conditions, in an oil-in-water emulsion. I tested the effects of novel surfactants on the configuration of the LC droplets. These novel surfactant molecules, synthesized by collaborators, are oligomers with either a variable length hydrophobic domain or protein sensitive hydrophilic domain. I tested the equilibrium configuration and dynamics of configurational changes, comparing experimental results to simulations performed by collaborators. We find that configuration transitions can be triggered by the addition of a control surfactant, sodium dodecyl sulfate (SDS), but not the novel surfactants. The SDS concentration at which the phase transition occurs appears to depend on the droplet diameter and I observed a hysteresis in the SDS concentration of the phase transition, both of which depend on the novel surfactant present. Liquid crystal ; Materials science ; Pulmonary surfactant ; Physics ; Thermodynamics ; Optoelectronics Liquid Crystal Research Advancements ; Surfactants and Colloidal Systems ; Fluid Dynamics and Heat Transfer
Authors: Emily A. Eikey | Alex M. Maldonado | Charles D. Griego | Guido Falk von Rudorff | John A. Keith
Source: ChemRxiv (preprint, 2021-11-22)
Field: Chemistry — Physical and Theoretical Chemistry

Quantum alchemy beyond singlets: Bonding in diatomic molecules with hydrogen Bonding energies are key for the relative stability of molecules in chemical space. Therefore methods employed to search for relevant molecules in chemical space need to capture the bonding behavior for a wide range of molecules, including radicals. In this work, we investigate the ability of quantum alchemy to do so for exploring hypothetical chemical compounds, here diatomic molecules involving hydrogen with various electronic structures. We evaluate equilibrium bond lengths, ionization ener- gies, and electron affinities of these fundamental systems. We compare and contrast how well manual quantum alchemy calculations, i.e. quantum mechanical calculations in which the nuclear charge is altered, and quantum alchemy approximations using a Taylor series expansion can predict these molecular properties. We also investigate the extent of error cancellation of these approaches in terms of ionization energies and electron affinities when using thermodynamic cycles. Our results suggest that the accuracy of Taylor series expansions are greatly improved by error cancellation in thermodynamic cycles, and errors also appear to be generally system-dependent. Taken together, this work provides insights into how quantum alchemy predictions us- ing a Taylor series expansion may be applied to future studies of non-singlet systems as well as which challenges remain open for these cases. Diatomic molecule ; Quantum chemistry ; Chemistry ; Molecule ; Ionization ; Quantum ; Taylor series ; Ionization energy ; Chemical physics ; Computational chemistry ; Physics ; Atomic physics ; Quantum mechanics ; Ion ; Mathematics Various Chemistry Research Topics ; Advanced Chemical Physics Studies ; Machine Learning in Materials Science
Authors: Mingxuan Gao | Wenxian Yang | Chenxin Li | Yuqing Chang | Yachen Liu | Shun Wang | Qingzu He | Chuan‐Qi Zhong | Jianwei Shuai | Rongshan Yu | Jiahuai Han
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-04-23)
Field: Chemistry — Spectroscopy

DreamDIA-XMBD: deep representation features improve the analysis of data-independent acquisition proteomics We developed DreamDIA-XMBD, a software suite for data-independent acquisition (DIA) data analysis. DreamDIA-XMBD adopts a data-driven strategy to capture comprehensive information from elution patterns of target peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. More specifically, in contrast to existing methods which use only 6 to 10 selected transitions from spectral library, DreamDIA-XMBD extracts additional features from dozens of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by non-linear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA-XMBD is written in Python, and is publicly available at https://github.com/xmuyulab/Dream-DIA-XMBD for high coverage and precision DIA data analysis. Computer science ; Python (programming language) ; Discriminative model ; Decoy ; Artificial intelligence ; Software ; Representation (politics) ; External Data Representation ; Data mining ; Pattern recognition (psychology) ; Machine learning ; Chemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Eric Taw | Jeffrey B. Neaton
Source:  (preprint, 2021-11-08)
Field: Chemistry — Inorganic Chemistry

Accelerated Discovery of CH4 Uptake Capacity MOFs using Bayesian Optimization High-throughput computational studies for discovery of metal-organic frameworks (MOFs) for separations and storage applications are often limited by the costs of computing thermodynamic quantities, with recent studies reliant ab initio results for a narrow selection of MOFs and empirical force-field methods for larger selections. Here, we conduct a proof-of-concept study using Bayesian optimization on CH4 uptake capacity of hypothetical MOFs for an existing dataset (Wilmer et al, Nature Chem. 2012, 4, 83). We show that less than 0.1% of the database needs to be screened with our Bayesian optimization approach to recover the top candidate MOFs. This opens the possibility of efficient screening of MOF databases using accurate ab-initio calculations for future adsorption studies on a minimal subset of MOFs. Furthermore, Bayesian optimization and the surrogate model presented here can offer interpretable material design insights and our framework will be applicable in the context of other target properties. Bayesian optimization ; Context (archaeology) ; Computer science ; Bayesian probability ; Selection (genetic algorithm) ; Ab initio ; Metal-organic framework ; Bayesian network ; Adsorption ; Chemistry ; Artificial intelligence Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: E.M. Yergaliyeva | L. A. Kаyukova | Анна В. Вологжанина | G.P. Baitursynova | V. V. Vazhev
Source: Chemical Journal of Kazakhstan (article, 2021-12-15)
Field: Chemistry — Organic Chemistry

DFT STUDIES OF STRUCTURAL PARAMETERS, VIBRATIONAL FREQUENCIES AND NMR SPECTRA OF 3-(1H-BENZO[D]IMIDAZOL-1-YL)-N'-(TOSYLOXY)PROPANIMIDAMIDE Amidoxime derivatives have practically valuable biological properties. We have previously obtained new spiropyrazolinium compounds by arylsulfo-chlorination of β-aminopropioamidoximes, but in case of β-(benzimidazol-1-yl)pro-pioamidoxime we have obtained O-substitution product – 3-(1H-benzo[d]imidazol-1-yl)-N'-(tosyloxy)pro-panimidamide. The aim of the work is predicting of structural parameters (bond lengths, bond angles), vibrational frequencies and NMR spectra of 3-(1H-benzo-[d]imidazol-1-yl)-N'-(tosyloxy)propanimidamide. The calculations were performed using Gaussian 09 package. Structural parameters and vibrational frequencies was calculated using DFT (B3LYP/B3PW91/WB97XD)/6-31G(d,p). 1H and 13C NMR was predicted using DFT B3LYP/6-31G(d,p)-GIAO in DMSO. All calculated values are in good agreement with experimental data. The calculated bond lengths and bond angles were compared with results of X-ray structural analysis. The best correlation coefficient was 0.981 (calcu-lations with B3LYP level). For bond angles, the best result was obtained with B3LYP level (0.990). For vibrational frequencies correlation coefficients between the calculated and experimental values were 0.997 (B3LYP), 0.996 (B3PW91) and 0.995 (WB97XD). The most accurate method was used for predic-ting NMR spectrum. The correlation coefficients between the experimental and calculated 1H and 13C chemical shifts were 0.949 and 0.999 respectively. Bond length ; Chemistry ; Molecular geometry ; Chemical shift ; Computational chemistry ; Carbon-13 NMR ; Proton NMR ; Density functional theory ; Molecule ; Physical chemistry ; Crystallography ; Stereochemistry ; Organic chemistry Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and biological activity ; Structural and Chemical Analysis of Organic and Inorganic Compounds
Authors: Martin Bornemann‐Pfeiffer | Jakob Wolf | Klas Meyer | Simon Kern | David Angelone | Artem I. Leonov | LeRoy Cronin | Franzicka Emmerling
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2021-10-01)
Field: Chemistry — Physical and Theoretical Chemistry

Standardization and Control of Grignard Reactions in a Universal Chemical Synthesis Machine using online NMR A big problem with the chemistry literature is that it is not standardized with respect to precise operational param- eters, and real time corrections are hard to make without expert knowledge. This lack of context means difficult reproducibility because many steps are ambiguous, and hence depend on tacit knowledge. Here we present the integration of online NMR into an automated chemical synthesis machine (CSM aka. “Chemputer” which is capable of small-molecule synthesis using a universal programming language) to allow automated analysis and adjustment of reactions on the fly. The system was validated and benchmarked by using Grignard reactions which were chosen due to their importance in synthesis. The system was monitored in real time using online-NMR, and spectra were measured continuously during the reactions. This shows that the synthesis being done in the Chemputer can be dynamically controlled in response to feedback optimizing the reaction conditions according to the user requirements. Standardization ; Control (management) ; Computer science ; Chemistry ; Artificial intelligence ; Operating system Various Chemistry Research Topics ; Advanced NMR Techniques and Applications ; NMR spectroscopy and applications
Authors: William Bang Lomholdt | Matthew Helmi Leth Larsen | Cuauhtémoc Núñez Valencia | Jakob Schiøtz | Thomas W. Hansen
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

Electron beam effects in high-resolution transmission electron microscopy investigations of catalytic nanoparticles High-resolution transmission electron microscopy (HRTEM) is a powerful tool for atomic scale investigations of catalytic nanoparticles. The dynamics of such catalytic nanoparticles are highly dependent on the environment: temperature, reactant gases and reactor pressure. It is possible to imitate such conditions in a transmission electron microscope (TEM). Electron beam effects play a substantial role in the interpretation of data produced in TEM investigations. There is a trade-off between optimal signal-to-noise ratio (SNR) and minimal beam damage. The current model system consists of gold nanoparticles supported on cerium dioxide. The aforementioned studies elucidate how the nanoparticles undergo changes with observation time and reactant gases present, and surface events as function of dose rate, respectively. Transmission electron microscopy ; Materials science ; High-resolution transmission electron microscopy ; Electron microscope ; Cathode ray ; Energy filtered transmission electron microscopy ; Electron ; Nanoparticle ; Scanning transmission electron microscopy ; Conventional transmission electron microscope ; Electron beam-induced deposition ; Scanning confocal electron microscopy ; Resolution (logic) ; Nanotechnology ; Optics ; Optoelectronics ; Physics ; Nuclear physics ; Computer science Catalytic Processes in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electrocatalysts for Energy Conversion
Authors: Hiroaki Ito | Takashi Matsui | Ryo Konno | Makoto Itakura | Yoshio Kodera
Source: Research Square (preprint, 2021-08-31)
Field: Chemistry — Spectroscopy

LC-MS Peak Assignment Based on Unanimous Selection by Six Machine Learning Algorithms Abstract Recent Mass spectrometry (MS)-based techniques enable deep proteome coverage with relative quantitative analysis, resulting in increased identification of very weak signals accompanied by increased data size of liquid chromatography (LC)–MS/MS spectra. However, the identification of weak signals using an assignment strategy with poorer performance resulted in imperfect quantification with misidentification of peaks and ratio distortions. Manually annotating a large number of signals within a very large dataset is not a realistic approach. In this study, therefore, we utilized machine learning algorithms to successfully extract a higher number of peptide peaks with high accuracy and precision. Our strategy evaluated each peak identified using six different algorithms; peptide peaks identified by all six algorithms (i.e., unanimously selected) were subsequently assigned as true peaks, which resulted in a reduction in the false-positive rate. Hence, exact and highly quantitative peptide peaks were obtained, providing better performance than obtained applying the conventional criteria or using a single machine learning algorithm. Algorithm ; Computer science ; Artificial intelligence ; Mass spectrometry ; Reduction (mathematics) ; Identification (biology) ; Imperfect ; Selection (genetic algorithm) ; Machine learning ; Mathematics ; Chemistry ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Ki Joon Kang | Jun Woo Bae | Hee Reyoung Kim
Source:  (preprint, 2021-07-16)
Field: Materials Science — Materials Chemistry

Tritium Recovery Using High Temperature Furnace in Melting of the Radioactive Aluminum Sample Aluminium ; Sample (material) ; Melting temperature ; Tritium ; Materials science ; Radiochemistry ; Nuclear engineering ; Environmental science ; Metallurgy ; Chemistry ; Composite material ; Nuclear physics ; Engineering ; Physics ; Chromatography Fusion materials and technologies ; Nuclear Materials and Properties ; Nuclear reactor physics and engineering
Authors: Chengyun Zhang | Xiang Cai | Haoran Qiao | Yun Zhang | Yejian Wu | Xinqiao Wang | Xie Hai-ying | Feng Luo | Hongliang Duan
Source:  (preprint, 2021-07-27)
Field: Materials Science — Materials Chemistry

Self-supervised molecular pretraining strategy for low-resource reaction prediction scenarios In the face of low-resource reaction training samples, we construct a chemical platform for addressing small-scale reaction prediction problem. By using a self-supervised pretraining strategy called MASS, the transformer model can absorb the chemical information about 1 billion molecules and then finetunes on small-scale reaction prediction, which is different from previous works that only rely on reaction samples. To demonstrate the broad applicability of our approach, we adopt three dif-ferent name reactions in our work. In the Baeyer-Villiger, Heck and Sharpless asymmetric epoxidation reaction prediction tasks, the average accuracies increase by 5.7%, 10.8%, 4.8% respectively, marking an important step to low-resource reaction prediction. Computer science ; Resource (disambiguation) ; Construct (python library) ; Scale (ratio) ; Reaction conditions ; Transformer ; Artificial intelligence ; Machine learning ; Chemistry ; Organic chemistry ; Engineering ; Physics ; Catalysis Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Computational Drug Discovery Methods
Authors: Aleksandr O. Lykhin | Donald G. Truhlar | Laura Gagliardi
Source:  (preprint, 2021-09-13)
Field: Chemistry — Organic Chemistry

Dipole Moment Calculations Using Multiconfiguration Pair-Density Functional Theory and Hybrid Multiconfiguration Pair-Density Functional Theory The dipole moment is the molecular property that most directly indicates molecular polarity. The accuracy of computed dipole moments depends strongly on the quality of the calculated electron density, and the breakdown of single-reference methods for strongly correlated systems can lead to poor predictions of the dipole moments in those cases. Here, we derive the analytical expression for obtaining the electric dipole moment by multiconfiguration pair density functional theory (MC-PDFT), and we assess the accuracy of MC-PDFT for predicting dipole moments at equilibrium and nonequilibrium geometries. We show that MC-PDFT dipole moment curves have reasonable behavior even for stretched geometries, and they significantly improve upon the CASSCF results by capturing more electron correlation. The analysis of a dataset consisting of 18 first-row transition metal diatomics and 6 main-group polyatomic molecules with multireference character suggests that MC-PDFT and its hybrid extension (HMC-PDFT) perform comparably to CASPT2 and MRCISD+Q methods and have a mean unsigned deviation of 0.2–0.3 D with respect to the best available dipole moment reference values. We explored the dependence of the predicted dipole moments upon the choice of the on-top density functional and active space, and we recommend the tPBE and hybrid tPBE0 on-top choices for the functionals combined with the moderate correlated participating orbital scheme for selecting the active space. With these choices, the mean unsigned deviations (in debyes) of the calculated equilibrium dipole moments from the best estimates are 0.77 for CASSCF, 0.29 for MC-PDFT, 0.24 for HMC-PDFT, 0.28 for CASPT2, and 0.25 for MRCISD+Q. These results are encouraging because the computational cost of MC-PDFT or HMC-PDFT is largely reduced compared to the CASPT2 and MRCISD+Q methods. Dipole ; Moment (physics) ; Density functional theory ; Electric dipole moment ; Bond dipole moment ; Statistical physics ; Physics ; Chemistry ; Computational chemistry ; Atomic physics ; Quantum mechanics Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: A. Castiñeiras | Isabel García‐Santos | Rocío Torres-Iglesias
Source:  (article, 2021-11-14)
Field: Chemistry — Physical and Theoretical Chemistry

Multicomponent Crystalline Solid Forms of Pyridinecarboxamides and DL-2-Hydroxy-2-phenylacetic Acid We have prepared co-crystals of racemic DL-2-Hydroxy-2-phenylacetic acid (DL-Mandelic acid, DL-H2ma) with achiral 2-Pyridinecarboxamide (picolinamide, pic), 3-Pyridinecarboxamide (nicotinamide, nam), and 4-Pyridinecarboxamide (isonicotinamide, inam); they have been characterized by elemental analysis, single crystal and powder X-ray, IR spectroscopy and 1H and 13C NMR. The crystal packing is stabilized primarily by hydrogen bonding and, in some cases, through &pi;&ndash;&pi; stacking interactions. The analysis of crystal structures reveals the existence of the characteristic heterosynthons with the binding motif R22(8) (primary amide-carboxilic acid) between pyridinecarboxamide molecules and the acid. Other synthons involve hydrogen bonds such as (carboxyl)O-H&middot;&middot;&middot;N(pyridine) and (hydroxyl)O-H&middot;&middot;&middot;N(pyridine). Isonicotinamide ; Hydrogen bond ; Crystal structure ; Synthon ; Phenylacetic acid ; Pyridine ; Chemistry ; Stacking ; Amide ; Stereochemistry ; Molecule ; Crystallography ; Medicinal chemistry ; Organic chemistry Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Nonlinear Optical Materials Research