jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nicolai Ree | Mads Koerstz | Kurt V. Mikkelsen | Jan H. Jensen
Source:  (preprint, 2021-10-07)
Field: Chemistry — Physical and Theoretical Chemistry

Virtual screening of norbornadiene-based molecular solar thermal energy storage systems using a genetic algorithm We present a computational methodology for the screening of a chemical space of 10²⁵ substituted norbornadiene molecules for promising kinetically stable molecular solar thermal (MOST) energy storage systems with high energy densities that absorb in the visible part of the solar spectrum. We use semiempirical tight-binding methods to construct a dataset of nearly 34,000 molecules and train graph convolutional networks to predict energy densities, kinetic stability, and absorption spectra and then use the models together with a genetic algorithm to search the chemical space for promising MOST energy storage systems. We identify 15 kinetically stable molecules, five of which have energy densities greater than 0.45 MJ/kg and the main conclusion of this study is that the largest energy density that can be obtained for a single norbornadiene moiety with the substituents considered here, while maintaining a long half-life and absorption in the visible spectrum, is around 0.55 MJ/kg. Norbornadiene ; Molecule ; Kinetic energy ; Thermal ; Absorption spectroscopy ; Absorption (acoustics) ; Chemical physics ; Materials science ; Biological system ; Chemistry ; Physics ; Optics ; Thermodynamics ; Quantum mechanics ; Organic chemistry ; Biology Photochemistry and Electron Transfer Studies ; Perovskite Materials and Applications ; Free Radicals and Antioxidants
Authors: Masakiyo Mukai | Kazunori Nagao | Shigeru Yamaguchi | Hirohisa Ohmiya
Source:  (preprint, 2021-08-04)
Field: Materials Science — Materials Chemistry

Molecular Field Analysis Using Computational-Screening Data in Asymmetric N-Heterocyclic Carbene-Copper Catalysis toward Data-driven in silico Catalyst Optimization A molecular-field-based regression analysis using computational screening data for N-heterocyclic carbene (NHC)-Cu-catalyzed asymmetric carbonyl additions of a silylboronate to aldehydes is reported. A computational screening was performed to collect enantioselectivity data (ΔΔG‡: energy differences between the transitions states leading to each enantiomer) via transition-state (TS) calculations using density functional theory (DFT) methods. A molecular field analysis (MFA) was carried out using the obtained calculated ΔΔG‡ values and TS structures (30 samples in total). Important structural infor-mation for enantioselectivity extracted by the MFA was visualized on the TS structures, which provided insight into an asymmetric induction mechanism. Based on the obtained information, chiral NHC ligands were designed, which showed improved enantioselectivity in these carbonyl additions. Carbene ; Catalysis ; Chemistry ; Enantiomer ; Density functional theory ; Computational chemistry ; In silico ; Combinatorial chemistry ; Stereochemistry ; Organic chemistry Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Asymmetric Hydrogenation and Catalysis
Authors: Michaël Moret | Francesca Grisoni | Paul Katzberger | Gisbert Schneider
Source:  (preprint, 2021-10-27)
Field: Materials Science — Materials Chemistry

Perplexity-based molecule ranking and bias estimation of chemical language models Chemical language models (CLMs) can be employed to design molecules with desired properties. CLMs generate new chemical structures in the form of textual representations, such as the simplified molecular input line entry systems (SMILES) strings, in a rule-free manner. However, the quality of these de novo generated molecules is difficult to assess a priori. In this study, we apply the perplexity metric to determine the degree to which the molecules generated by a CLM match the desired design objectives. This model-intrinsic score allows identifying and ranking the most promising molecular designs based on the probabilities learned by the CLM. Using perplexity to compare “greedy” (beam search) with “explorative” (multinomial sampling) methods for SMILES generation, certain advantages of multinomial sampling become apparent. Additionally, perplexity scoring is performed to identify undesired model biases introduced during model training and allows the development of a new ranking system to remove those undesired biases. Perplexity ; Computer science ; Multinomial distribution ; Ranking (information retrieval) ; Metric (unit) ; Sampling (signal processing) ; Artificial intelligence ; Language model ; Quality (philosophy) ; Machine learning ; Natural language processing ; Statistics ; Mathematics ; Detector Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Martín Leandro Paleico | Jörg Behler
Source: Machine Learning Science and Technology (preprint, 2021-02-15)
Field: Materials Science — Materials Chemistry

A bin and hash method for analyzing reference data and descriptors in machine learning potentials Abstract In recent years the development of machine learning potentials (MLPs) has become a very active field of research. Numerous approaches have been proposed, which allow one to perform extended simulations of large systems at a small fraction of the computational costs of electronic structure calculations. The key to the success of modern MLPs is the close-to first principles quality description of the atomic interactions. This accuracy is reached by using very flexible functional forms in combination with high-level reference data from electronic structure calculations. These data sets can include up to hundreds of thousands of structures covering millions of atomic environments to ensure that all relevant features of the potential energy surface are well represented. The handling of such large data sets is nowadays becoming one of the main challenges in the construction of MLPs. In this paper we present a method, the bin-and-hash (BAH) algorithm, to overcome this problem by enabling the efficient identification and comparison of large numbers of multidimensional vectors. Such vectors emerge in multiple contexts in the construction of MLPs. Examples are the comparison of local atomic environments to identify and avoid unnecessary redundant information in the reference data sets that is costly in terms of both the electronic structure calculations as well as the training process, the assessment of the quality of the descriptors used as structural fingerprints in many types of MLPs, and the detection of possibly unreliable data points. The BAH algorithm is illustrated for the example of high-dimensional neural network potentials using atom-centered symmetry functions for the geometrical description of the atomic environments, but the method is general and can be combined with any current type of MLP. Computer science ; Bin ; Hash function ; Process (computing) ; Data mining ; Field (mathematics) ; Reference data ; Key (lock) ; Quality (philosophy) ; Identification (biology) ; Artificial intelligence ; Machine learning ; Algorithm ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Linhan Du | Xiaoyu Hu | Diannan Lu | Zheng Liu
Source: Molecules (article, 2021-11-23)
Field: Materials Science — Materials Chemistry

Ionic Transport Triggered by Asymmetric Illumination on 2D Nano-Membrane Ionic transport and ion sieving are important in the field of separation science and engineering. Based on the rapid development of nanomaterials and nano-devices, more and more phenomena occur on the nanoscale devices in the field of thermology, optics, mechanics, etc. Recently, we experimentally observed a novel ion transport phenomenon in nanostructured graphene oxide membrane (GOM) under asymmetric illumination. We first build a light-induced carriers' diffusion model based on our previous experimental results. This model can reveal the light-induced ion transport mechanism and predict the carriers' diffusion behavior under different operational situations and material characters. The voltage difference increases with the rise of illuminate asymmetry, photoresponsivity, recombination coefficient, and carriers' diffusion coefficient ratio. Finally, we discuss the ion transport behavior with different surface charge densities using MD simulation. Moderate surface charge decreases the ion transport with the same type of charge due to the electrostatic repulsion; however, excess surface charge blocks both cation and anion because a thicker electrical double layer decreases effective channel height. Research here provides referenced operational and material conditions to obtain a greater voltage difference between the membrane sides. Also, the mechanism of ion transport and ion sieving can guide us to modify membrane material according to different aims. Ion ; Diffusion ; Ionic bonding ; Ion transporter ; Chemical physics ; Membrane ; Materials science ; Surface charge ; Asymmetry ; Electric field ; Graphene ; Nanofluidics ; Nanomaterials ; Voltage ; Transport phenomena ; Nano- ; Nanotechnology ; Charge carrier ; Chemistry ; Optoelectronics ; Mechanics ; Thermodynamics ; Physics ; Physical chemistry Graphene research and applications ; Nanopore and Nanochannel Transport Studies ; Energy Harvesting in Wireless Networks
Authors: Nisha Mehta | Thomas Fellowes | Jonathan White | Lars Goerigk
Source: ChemRxiv (preprint, 2021-03-16)
Field: Chemistry — Physical and Theoretical Chemistry

The CHAL336 Benchmark Set: How Well Do Quantum-Chemical Methods Describe Chalcogen-Bonding Interactions? We present the CHAL336 benchmark set—the most comprehensive database for the assessment of chalcogen-bonding (CB) interactions. After careful selection of suitable systems and identification of three high-level reference methods, the set comprises 336 dimers each consisting of up to 49 atoms and covers both σ- and π-hole interactions across four categories: chalcogen-chalcogen, chalcogen-π, chalcogen-halogen, and chalcogen-nitrogen interactions. In a subsequent study of DFT methods, we re-emphasize the need for using proper London dispersion corrections when treating noncovalent interactions. We also point out that the deterioration of results and systematic overestimation of interaction energies for some dispersion-corrected DFT methods does not hint at problems with the chosen dispersion correction, but is a consequence of large density-driven errors. We conclude this work by performing the most detailed DFT benchmark study for CB interactions to date. We assess 109 variations of dispersion-corrected and -uncorrected DFT methods, and carry out a detailed analysis of 80 of them. Double-hybrid functionals are the most reliable approaches for CB interactions, and they should be used whenever computationally feasible. The best three double hybrids are SOS0-PBE0-2-D3(BJ), revDSD-PBEP86-D3(BJ), and B2NCPLYP-D3(BJ). The best hybrids in this study are ωB97M-V, PW6B95-D3(0), and PW6B95-D3(BJ). We do not recommend using the popular B3LYP functional nor the MP2 approach, which have both been frequently used to describe CB interactions in the past. We hope to inspire a change in computational protocols surrounding CB interactions that leads away from the commonly used, popular methods to the more robust and accurate ones recommended herein. We would also like to encourage method developers to use our set for the investigation and reduction of density-driven errors in new density functional approximations. Chalcogen ; Benchmark (surveying) ; Set (abstract data type) ; Dispersion (optics) ; Basis set ; Density functional theory ; Computer science ; Non-covalent interactions ; Computational chemistry ; Chemistry ; Materials science ; Bioinformatics ; Molecule ; Physics ; Crystallography ; Hydrogen bond ; Quantum mechanics ; Biology ; Organic chemistry Crystallography and molecular interactions ; Machine Learning in Materials Science ; Organic and Molecular Conductors Research
Authors: Cara Murphy | John P. Kerekes
Source: Journal of Spectral Imaging (article, 2021-06-11)
Field: Chemistry — Analytical Chemistry

1D conditional generative adversarial network for spectrum-to-spectrum translation of simulated chemical reflectance signatures The classification of trace chemical residues through active spectroscopic sensing is challenging due to the lack of physics-based models that can accurately predict spectra. To overcome this challenge, we leveraged the field of domain adaptation to translate data from the simulated to the measured domain for training a classifier. We developed the first 1D conditional generative adversarial network (GAN) to perform spectrum-to-spectrum translation of reflectance signatures. We applied the 1D conditional GAN to a library of simulated spectra and quantified the improvement in classification accuracy on real data using the translated spectra for training the classifier. Using the GAN-translated library, the average classification accuracy increased from 0.622 to 0.723 on real chemical reflectance data, including data from chemicals not included in the GAN training set. Classifier (UML) ; Generative grammar ; Computer science ; Reflectivity ; Domain adaptation ; Generative adversarial network ; Artificial intelligence ; Training set ; Translation (biology) ; Pattern recognition (psychology) ; Spectral line ; Machine learning ; Data mining ; Deep learning ; Optics ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Vijayakumar Vijayakumar | B. R. Manjunath | Sharanagouda Hiregoudar | Dinesha BL | M Vidya | G Pramod
Source: International Journal of Chemical Studies (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Characterization and antibacterial efficacy of silver nanoparticles extracted from neem (Azadirachta indica) leaf extract Use of silver and silver salts is as old as human civilization but the green synthesis of silver nanoparticles (Ag NPs) has only recently been recognized. AgNPs were synthesized using aqueous extract of neem leaves and silver salt. The synthesized AgNPs were characterized using particle size analyser, UV-visible spectroscopy, XRD, SEM and AFM. The size of AgNPs was found to be within the range of 20-60 nm. The absorbance peak was in the range of 420-450 nm. SEM and AFM analysis showed that, the synthesized AgNPs are spherical in shape and surface roughness is 20.40 nm. Further the XRD analysis confirms the nano crystalline phase of silver with cubic crystal structure nature. The EDS analysis confirmed the presence of silver nanoparticles. AgNPs were specifically used in agriculture and medicine as antibacterial, antifungal and antioxidants. Antibacterial activity of synthesized AgNPs was carried out for both gram positive and gram negative bacteria and compared with standard silver nanoparticles. Hence, AgNPs could be use in the food and agricultural filed for commercial usage. Silver nanoparticle ; Nuclear chemistry ; Absorbance ; Azadirachta ; Antibacterial activity ; Silver Nano ; Nanoparticle ; Silver nitrate ; Chemistry ; Aqueous solution ; Materials science ; Nanotechnology ; Bacteria ; Chromatography ; Organic chemistry ; Botany ; Nano- Nanoparticles: synthesis and applications ; Moringa oleifera research and applications
Authors: J. Y. C. Tam | Tim Lorsbach | Sebastian Schmidt | Jörg Wicker
Source: ChemRxiv (preprint, 2021-03-29)
Field: Chemistry — Spectroscopy

Holistic Evaluation of Biodegradation Pathway Prediction: Assessing Multi-Step Reactions and Intermediate Products The prediction of metabolism and biotransformation pathways of xenobiotics is a highly desired tool in environmental and life sciences. There are several systems that currently predict single transformation steps or complete pathways as series of parallel and subsequent steps. Their accuracy is often evaluated on the level of a single transformation step. Such an approach cannot account for some specific challenges that are related to the nature of the biotransformation experiments. This is particularly true for missing transformation products in the reference data that occur only in low concentrations, e.g. transient intermediates or higher-generation metabolites. Furthermore, some rulebased prediction systems evaluate accuracy only based on the defined set of transformation rules. Therefore, the performance of different models cannot be directly compared. Biotransformation ; Transformation (genetics) ; Biochemical engineering ; Computer science ; Xenobiotic ; Biological system ; Biodegradation ; Transient (computer programming) ; Data mining ; Computational biology ; Chemistry ; Biology ; Engineering ; Biochemistry ; Organic chemistry Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Statistical and Computational Modeling
Authors: Mengistu Jemberu Dagnaw | M. Gopal
Source: Environmental Health Engineering and Management (article, 2021-12-07)
Field: Chemistry — Spectroscopy

Detection to trace aluminum ion of pharmaceutical wastewater using synthesis of Schiff-based chemosensor Background: The aim of this research was to develop a fluorogenic sensor for Al3+ions, which have been identified as a possible food and drinking water pollutant by the WHO and considered to be harmful to human health. Methods: The sensing mechanism was based on excited-state intramolecular proton transfer, with the intramolecular rotation restriction occurring after binding with the analyte. The probe attaches Al3+selectively and emits strong emission in 4:1 H2 O/MeOH (v/v) solution while irradiated at 400 nm in the presence of a wide number of cations, acting as a "turn-on" fluorescence chemosensor. The range of detection for Al3+is 3.3 nM (3 method), which is more than 200 times more responsive than the WHO suggested limit of 7.4 mM (3σ method). Mass spectra, job plot, and Benesi-Hildebrand plot were used to determine the formation of the 1:1 metal-to-ligand complex. Results: Aluminum (Al) ion content in effluent obtained from the pharmaceutical sector is 0.381 mM, which is a trace amount. A separate in vitro experiment indicates that the probe can precisely perceive Al3+ions in a cell line. The sensor-based method is developed to detect 3.3 nM of Al3+ions, which is significantly less than the WHO max. Conclusion: The probe to detect Al3+ions in live cells. HL becomes a flexible sensor for recognizing intracellular Al3+in human liver cancer cell line Hep G2 and human lung fibroblast cell lines by fluorescence cell imaging procedures, and the probe’s non-toxicity has been proven by MTT tests up to 100M. Detection limit ; Chemistry ; Fluorescence ; Metal ions in aqueous solution ; Ion ; Intramolecular force ; Analyte ; Photochemistry ; Analytical Chemistry (journal) ; Chromatography ; Stereochemistry ; Organic chemistry Molecular Sensors and Ion Detection ; Analytical Chemistry and Sensors ; Inorganic and Organometallic Chemistry
Authors: Jacob Terence Blaskovits | Kun‐Han Lin | Raimón Fabregat | Iwona Swiderska | Hélène Wu | Clémence Corminbœuf
Source: ChemRxiv (preprint, 2021-01-19)
Field: Materials Science — Materials Chemistry

Is a Single Conformer Sufficient to Describe the Reorganization Energy of Amorphous Organic Transport Materials? The reorganization energy (λ), which quantifies the structural rearrangement of a molecule when accommodating a charge, is a key parameter in the evaluation of charge mobility in molecular solids. However, it is unclear how λ is influenced by conformational isomerism, which co-exist in amorphous solids. Here, we examine the conformational space of a family of model amorphous organic hole transport materials (HTMs), derived from triphenylamine in a core-arm template, and probe the effect of conformational complexity on λ. We observe an extreme dependence of λ on the conformer geometry of sterically congested HTMs, which to the best of our knowledge has not been described previously. These results serve as a cautionary tale that, while extracting the reorganization energy from a single molecular conformer optimized in the gas phase may be appropriate for rigid and sterically unencumbered structures, it is not for many state-of-the-art HTMs that contain multiple bulky substituents. Conformational isomerism ; Steric effects ; Chemical physics ; Amorphous solid ; Triphenylamine ; Molecule ; Materials science ; Crystallography ; Nanotechnology ; Computational chemistry ; Chemistry ; Stereochemistry ; Optoelectronics ; Organic chemistry Luminescence and Fluorescent Materials ; Organic and Molecular Conductors Research ; Organic Light-Emitting Diodes Research
Authors: Sven H. Giese | Ludwig Sinn | Fritz Wegner | Juri Rappsilber
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-03-08)
Field: Chemistry — Spectroscopy

Retention Time Prediction Using Neural Networks Increases Identifications in Crosslinking Mass Spectrometry Abstract Crosslinking mass spectrometry (Crosslinking MS) has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the spectra limits the numbers of protein-protein interactions (PPIs) that can be confidently identified. Here, we successfully leveraged chromatographic retention time (RT) information to aid the identification of crosslinked peptides from spectra. Our Siamese machine learning model xiRT achieved highly accurate RT predictions of crosslinked peptides in a multi-dimensional separation of crosslinked E. coli lysate. We combined strong cation exchange (SCX), hydrophilic strong anion exchange (hSAX) and reversed-phase (RP) chromatography and reached R 2 0.94 in RP and a margin of error of 1 fraction for hSAX in 94%, and SCX in 85% of the predictions. Importantly, supplementing the search engine score with retention time features led to a 1.4-fold increase in PPIs at a 1% false discovery rate. We also demonstrate the value of this approach for the more routine analysis of a crosslinked multiprotein complexes. An increase of 1.7-fold in heteromeric crosslinked residue-pairs was achieved at 1% residue-pair FDR for Fanconi anaemia monoubiquitin ligase complex, solely using reversed-phase RT. Retention times are a powerful complement to mass spectrometric information to increase the sensitivity of Crosslinking MS analyses. Chemistry ; Mass spectrometry ; Retention time ; Chromatography ; Lysis ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Danijela Ilić Komatina | Aleksandra Minić | Jovana Bugarinović | Dragana Stevanović
Source: Bulletin of Natural Sciences Research (article, 2021-01-01)
Field: Chemistry — Organic Chemistry

The preparation and characterization of some novel ferocenil derivatives This paper provides a report on an easily performable synthesis via one-pot reaction, between the newly synthesized 3-arylamino-1-ferrocenylpropan-1-ols and PhNCO. We have used the acetic acid as the catalyst and ultrasound irradiation as the reaction promoter. We have evaluated the reaction score on three examples. In order to gain a more comprehensive insight into the role of a-ferrocenyl carbocation, we have described a plausible mechanism of the transformations in question. Moreover, we have purified the prepared by using column chromatography. Besides, we provide a detailed characterization of the new compounds (products and starting material), which was performed by IR, NMR and elemental analyses. Carbocation ; Catalysis ; Elemental analysis ; Characterization (materials science) ; Acetic acid ; Chemistry ; Combinatorial chemistry ; Column chromatography ; Organic chemistry ; Materials science ; Nanotechnology Multicomponent Synthesis of Heterocycles ; Synthesis and Biological Activity ; Chemical Synthesis and Reactions