jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Michael A. Skinnider | R. Greg Stacey | David S. Wishart | Leonard J. Foster
Source: ChemRxiv (preprint, 2021-01-27)
Field: Materials Science — Materials Chemistry

Deep Generative Models Enable Navigation in Sparsely Populated Chemical Space Deep generative models are powerful tools for the exploration of chemical space, enabling the on-demand gener- ation of molecules with desired physical, chemical, or biological properties. However, these models are typically thought to require training datasets comprising hundreds of thousands, or even millions, of molecules. This per- ception limits the application of deep generative models in regions of chemical space populated by only a small number of examples. Here, we systematically evaluate and optimize generative models of molecules for low-data settings. We carry out a series of systematic benchmarks, training more than 5,000 deep generative models and evaluating over 2.6 billion generated molecules. We find that robust models can be learned from far fewer examples than has been widely assumed. We further identify strategies that dramatically reduce the number of molecules required to learn a model of equivalent quality, and demonstrate the application of these principles by learning models of chemical structures found in bacterial, plant, and fungal metabolomes. The structure of our experiments also allows us to benchmark the metrics used to evaluate generative models themselves. We find that many of the most widely used metrics in the field fail to capture model quality, but identify a subset of well-behaved metrics that provide a sound basis for model development. Collectively, our work provides a foundation for directly learning generative models in sparsely populated regions of chemical space. Chemical space ; Computer science ; Generative model ; Generative grammar ; Artificial intelligence ; Benchmark (surveying) ; Field (mathematics) ; Machine learning ; Space (punctuation) ; Quality (philosophy) ; Drug discovery ; Mathematics ; Geography ; Bioinformatics ; Biology Machine Learning in Materials Science ; Genomics and Phylogenetic Studies ; Microbial Natural Products and Biosynthesis
Authors: Shuaihang Hu | Wenchao Dan | Jinlei Liu | Peng Ha | Tong Zhou | Xinyuan Guo | Wei Hou
Source: Evidence-based Complementary and Alternative Medicine (article, 2021-04-01)
Field: Chemistry — Organic Chemistry

The Use of Traditional Chinese Medicine in Relieving EGFR-TKI-Associated Diarrhea Based on Network Pharmacology and Data Mining In this study, the role of traditional Chinese medicine (TCM) in relieving epidermal growth factor receptor-tyrosine kinase inhibitor- (EGFR-TKI-) associated diarrhea was discussed by network pharmacology and data mining. Prediction of drug targets by introducing the EGFR-TKI molecular structures into the SwissTargetPrediction platform and diarrhea-related targets in the DrugBank, GeneCards, DisGeNET, and OMIM databases were obtained. Compounds in the drug-disease target intersection were screened by absorption, distribution, metabolism, and excretion parameters and Lipinski's rule in Traditional Chinese Medicine Systems Pharmacology. TCM-containing compounds were selected, and information on the property, taste, and meridian tropism of these TCMs was summarized and analyzed. A target-compound-TCM network diagram was constructed, and core targets, compounds, and TCMs were selected. The core targets and components were docked by AutoDock Vina (Version 1.1.2) to explore the target combinations of related compounds and evaluate the docking activity of related targets and compounds. Twenty-three potential therapeutic TCM targets for the treatment of EGFR-TKI-related diarrhea were obtained. There were 339 compounds acting on potential therapeutic targets, involving a total of 402 TCMs. The results of molecular docking showed good binding between the core targets and compounds, and the binding between the core targets and compounds was similar to that of the core target and the recommended drug loperamide. TCMs have multitarget characteristics and are present in a variety of compounds used for relieving EGFR-TKI-associated diarrhea. Antitumor activity and the efficacy of alleviating diarrhea are the pharmacological basis of combining TCMs with EGFR-TKI in the treatment of non-small-cell lung cancer. The core targets, compounds, and TCMs can provide data to support experimental and clinical studies on the relief of EGFR-TKI-associated diarrhea in the future. DrugBank ; Pharmacology ; Drug ; Traditional Chinese medicine ; Medicine ; AutoDock ; Drug discovery ; Systems pharmacology ; Traditional medicine ; Bioinformatics ; Chemistry ; Biology ; In silico Quinazolinone synthesis and applications ; Synthesis and biological activity ; Pharmacological Effects of Natural Compounds
Authors: Qing Hao | Jivtesh Garg
Source: ES Materials & Manufacturing (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

A Review on Phonon Transport within Polycrystalline Materials As one fundamental problem in materials science research, thermal transport across grain boundaries is critical to many energy-related applications. Due to the complexity of grain boundaries, the current understanding on how a grain boundary interacts with heat carriers is still in its infancy. This review summarizes the current progresses of this important topic, with its further extension to general interfaces. One focus is on major modeling and simulation techniques to predict the thermal resistance of a grain boundary. The corresponding thermal measurements of individual grain boundaries and grain-boundary thermal engineering are also discussed. A better understanding of the grain-boundary phonon transport is critical to many energy-related applications, where the concerned thermal transport within a polycrystalline material can be largely suppressed by grain boundaries. Grain boundary ; Phonon ; Crystallite ; Materials science ; Grain boundary strengthening ; Thermal ; Condensed matter physics ; Boundary (topology) ; Grain size ; Engineering physics ; Thermodynamics ; Metallurgy ; Physics ; Microstructure ; Mathematics Thermal properties of materials ; Radiative Heat Transfer Studies ; Composite Material Mechanics
Authors: Qing Li | Tao Sun | Yigang Zhang | Jiawei Xian | Lidunka Vočadlo
Source: The Journal of Chemical Physics (article, 2021-11-01)
Field: Chemistry — Inorganic Chemistry

Atomic transport properties of liquid iron at conditions of planetary cores Atomic transport properties of liquid iron are important for understanding the core dynamics and magnetic field generation of terrestrial planets. Depending on the sizes of planets and their thermal histories, planetary cores may be subject to quite different pressures (P) and temperatures (T). However, previous studies on the topic mainly focus on the P-T range associated with the Earth's outer core; a systematic study covering conditions from small planets to massive exoplanets is lacking. Here, we calculate the self-diffusion coefficient D and viscosity η of liquid iron via ab initio molecular dynamics from 7.0 to 25 g/cm<sup>3</sup> and 1800 to 25 000 K. We find that D and η are intimately related and can be fitted together using a generalized free volume model. The resulting expressions are simpler than those from previous studies where D and η were treated separately. Moreover, the new expressions are in accordance with the quasi-universal atomic excess entropy (S<sub>ex</sub>) scaling law for strongly coupled liquids, with normalized diffusivity D<sup>⋆</sup> = 0.621 exp(0.842S<sub>ex</sub>) and viscosity η<sup>⋆</sup> = 0.171 exp(-0.843S<sub>ex</sub>). We determine D and η along two thermal profiles of great geophysical importance: the iron melting curve and the isentropic line anchored at the ambient melting point. The variations of D and η along these thermal profiles can be explained by the atomic excess entropy scaling law, demonstrating the dynamic invariance of the system under uniform time and space rescaling. Accordingly, scale invariance may serve as an underlying mechanism to unify planetary dynamos of different sizes. Astrobiology ; Materials science ; Chemical physics ; Nanotechnology ; Environmental science ; Chemistry ; Physics Radioactive element chemistry and processing ; Geological and Geochemical Analysis ; Geomagnetism and Paleomagnetism Studies
Authors: Yoshihiko Imanaka | Toshihisa Anazawa | Fumiaki Kumasaka | Hideyuki Jippo
Source: Scientific Reports (article, 2021-02-04)
Field: Materials Science — Materials Chemistry

Optimization of the composition in a composite material for microelectronics application using the Ising model Tailored material is necessary in many industrial applications since material properties directly determine the characteristics of components. However, the conventional trial and error approach is costly and time-consuming. Therefore, materials informatics is expected to overcome these drawbacks. Here, we show a new materials informatics approach applying the Ising model for solving discrete combinatorial optimization problems. In this study, the composition of the composite, aimed at developing a heat sink with three necessary properties: high thermal dissipation, attachability to Si, and a low weight, is optimized. We formulate an energy function equation concerning three objective terms with regard to the thermal conductivity, thermal expansion and specific gravity, with the composition variable and two constrained terms with a quadratic unconstrained binary optimization style equivalent to the Ising model and calculated by a simulated annealing algorithm. The composite properties of the composition selected from ten constituents are verified by the empirical mixture rule of the composite. As a result, an optimized composition with high thermal conductivity, thermal expansion close to that of Si, and a low specific gravity is acquired. Quadratic unconstrained binary optimization ; Thermal conductivity ; Ising model ; Microelectronics ; Computer science ; Composite number ; Materials science ; Dissipation ; Quadratic equation ; Mathematical optimization ; Thermodynamics ; Algorithm ; Mathematics ; Statistical physics ; Physics ; Composite material ; Nanotechnology Machine Learning in Materials Science ; Graph theory and applications ; Graph Theory and Algorithms
Authors: Zhenzhuo Lan | Shaama Mallikarjun Sharada
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

A framework for constructing linear free energy relationships to design molecular transition metal catalysts A computational framework for ligand-driven design of transition metal complexes is presented in this work. We propose a general procedure for the construction of active site-specific linear free energy relationships (LFERs), which are inspired from Hammett and Taft correlations in organic chemistry and grounded in the activation strain model (ASM). Ligand effects are isolated and quantified in terms of their contribution to interaction and strain energy components of ASM. Scalar descriptors that are easily obtainable are then employed to construct the complete LFER. We successfully demonstrate proof-of-concept by constructing and applying an LFER to CH activation with enzyme-inspired [Cu2O2]2+ complexes. The key benefit of using ASM is a built-in compensation or error cancellation between LFER prediction of interaction and strain terms, resulting in accurate barrier predictions for 37 of the 47 catalysts examined in this study. The LFER is also transferable with respect to level of theory and flexible towards the choice of reference system. The absence of interaction-strain compensation or poor model performance for the remaining systems is a consequence of the approximate nature of the chosen interaction energy descriptor and LFER construction of the strain term, which focuses largely on trends in substrate and not catalyst strain. Transition metal ; Catalysis ; Energy (signal processing) ; Chemical physics ; Materials science ; Computational chemistry ; Nanotechnology ; Chemistry ; Physics ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Advanced Chemical Physics Studies
Authors: Alex A. Aina | Alston J. Misquitta | Sarah L. Price
Source: The Journal of Chemical Physics (article, 2021-03-05)
Field: Chemistry — Physical and Theoretical Chemistry

A non-empirical intermolecular force-field for trinitrobenzene and its application in crystal structure prediction An anisotropic atom-atom distributed intermolecular force-field (DIFF) for rigid trinitrobenzene (TNB) is developed using distributed multipole moments, dipolar polarizabilities, and dispersion coefficients derived from the charge density of the isolated molecule. The short-range parameters of the force-field are fitted to first- and second-order symmetry-adapted perturbation theory dimer interaction energy calculations using the distributed density-overlap model to guide the parameterization of the short-range anisotropy. The second-order calculations are used for fitting the damping coefficients of the long-range dispersion and polarization and also for relaxing the isotropic short-range coefficients in the final model, DIFF-srL2(rel). We assess the accuracy of the unrelaxed model, DIFF-srL2(norel), and its equivalent without short-range anisotropy, DIFF-srL0(norel), as these models are easier to derive. The model potentials are contrasted with empirical models for the repulsion-dispersion fitted to organic crystal structures with multipoles of iterated stockholder atoms (ISAs), FIT(ISA,L4), and with Gaussian Distributed Analysis (GDMA) multipoles, FIT(GDMA,L4), commonly used in modeling organic crystals. The potentials are tested for their ability to model the solid state of TNB. The non-empirical models provide more reasonable relative lattice energies of the three polymorphs of TNB and propose more sensible hypothetical structures than the empirical force-field (FIT). The DIFF-srL2(rel) model successfully has the most stable structure as one of the many structures that match the coordination sphere of form III. The neglect of the conformational flexibility of the nitro-groups is a significant approximation. This methodology provides a step toward force-fields capable of representing all phases of a molecule in molecular dynamics simulations. Multipole expansion ; Chemistry ; Intermolecular force ; Force field (fiction) ; Isotropy ; Anisotropy ; London dispersion force ; Molecular physics ; Statistical physics ; Computational chemistry ; Physics ; Molecule ; Quantum mechanics Crystallography and molecular interactions ; Luminescence and Fluorescent Materials ; Nonlinear Optical Materials Research
Authors: Johannes Popp | Michael Haider | Martin Franckié | Jérôme Faist | Christian Jirauschek
Source: Optical and Quantum Electronics (article, 2021-05-06)
Field: Chemistry — Spectroscopy

Bayesian optimization of quantum cascade detectors Abstract A Bayesian optimization algorithm in combination with a scattering based simulation approach is used for the optimization of quantum cascade detectors (QCDs). QCDs operate in the mid-infrared and terahertz regime and are, together with quantum cascade lasers, appropriate for the integration into on-chip applications such as gas sensors. Our modeling approach is based on a rate equation model and a Kirchhoff resistance network for noise modeling, using scattering rates calculated with Fermi’s golden rule, or alternatively extracted from an ensemble Monte Carlo transport approach. The appropriate surrogate model of Bayesian optimization is based on Gaussian process regression, which can handle noisy offsets on the objective function evaluations inherent in ensemble Monte Carlo simulations. Here, we focus on the optimization of a matured mid-infrared QCD design detecting at 4.7 $$\upmu {\mathrm{m}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mi>m</mml:mi> </mml:mrow> </mml:math> . For optimization we choose as figure of merit the specific detectivity, which is a measure for the signal-to-noise ratio. As the trade-off between high extraction efficiency and low detector conductance is important for good detection performance, we search for the perfect layer composition and vary the thicknesses of different cascade layers. Due to the high-temperature requirements interesting for cost-effective and mobile on-chip sensing applications, a simulation temperature of 300 K is selected. Our optimization strategy yields an improvement of specific detectivity by a factor of $${\sim 2-3}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>∼</mml:mo> <mml:mn>2</mml:mn> <mml:mo>-</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> at room temperature using two different parameter sets. Furthermore, we investigate the sensitivity of our approach to fabrication tolerances, showing robustness of the optimized designs against growth fluctuations under fabrication conditions. Bayesian optimization ; Quantum cascade laser ; Algorithm ; Cascade ; Detector ; Computer science ; Surrogate model ; Monte Carlo method ; Physics ; Artificial intelligence ; Machine learning ; Statistics ; Optics ; Terahertz radiation ; Mathematics Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Air Quality Monitoring and Forecasting
Authors: Adrichem De Jong | Marc Jansen | Jeffrey Van Dijk | Jeanine Meyer
Source: Fusion of Multidisciplinary Research An International Journal (article, 2021-02-24)
Field: Materials Science — General Materials Science

Analysis of Innovative Practices in Advanced Materials and Structural Engineering The field of structural engineering is undergoing significant transformations, driven by innovations in materials science and new engineering methods. This abstract presents a comprehensive analysis of these innovative practices within advanced materials and structural engineering, emphasizing their impact on building safer, more efficient, and sustainable structures. Advancements in materials such as ultra-high-performance concrete, self-healing materials, and smart sensors have revolutionized the approach to constructing and monitoring infrastructure. For instance, the integration of shape memory alloys and engineered cementitious composites contributes to structures that can adapt to stress and heal autonomously, enhancing longevity and resilience against environmental threats. Additionally, the development of lightweight, high-strength materials has enabled the design of more intricate and bold architectural forms while maintaining structural integrity. The adoption of digital tools and technologies like Building Information Modeling (BIM) and 3D printing has further facilitated the precise and rapid fabrication of complex components, optimizing both material usage and labor costs. Moreover, computational design and simulation have allowed for the efficient testing and optimization of structural models under various scenarios, reducing the risks associated with physical prototyping. This analysis highlights the synergy between material innovations and advanced engineering techniques, outlining their pivotal roles in advancing the capabilities of modern structural design. These developments not only push the boundaries of architectural aesthetics but also significantly contribute to eco-friendly and disaster-resilient construction practices. Engineering ; Construction engineering ; Computer science Material Properties and Applications ; Advanced ceramic materials synthesis ; Concrete and Cement Materials Research
Authors: Christopher Mayer-Bacon | Neyiasuo Agboha | Mickey Muscalli | Stephen J. Freeland
Source: International Journal of Molecular Sciences (review, 2021-03-10)
Field: Chemistry — Spectroscopy

Evolution as a Guide to Designing xeno Amino Acid Alphabets Here, we summarize a line of remarkably simple, theoretical research to better understand the chemical logic by which life's standard alphabet of 20 genetically encoded amino acids evolved. The connection to the theme of this Special Issue, "Protein Structure Analysis and Prediction with Statistical Scoring Functions", emerges from the ways in which current bioinformatics currently lacks empirical science when it comes to xenoproteins composed largely or entirely of amino acids from beyond the standard genetic code. Our intent is to present new perspectives on existing data from two different frontiers in order to suggest fresh ways in which their findings complement one another. These frontiers are origins/astrobiology research into the emergence of the standard amino acid alphabet, and empirical xenoprotein synthesis. Alphabet ; Genetic code ; Complement (music) ; Theme (computing) ; Computer science ; Simple (philosophy) ; Amino acid ; Protein evolution ; Code (set theory) ; Computational biology ; Amino acid residue ; Order (exchange) ; Data science ; Theoretical computer science ; Biology ; Programming language ; Genetics ; Epistemology ; Linguistics ; Peptide sequence ; Gene ; World Wide Web Mass Spectrometry Techniques and Applications ; RNA and protein synthesis mechanisms ; Protein Structure and Dynamics
Authors: Youngchun Kwon | Dongseon Lee | Youn-Suk Choi | Seokho Kang
Source: Scientific Reports (article, 2021-10-25)
Field: Chemistry — Spectroscopy

Molecular search by NMR spectrum based on evaluation of matching between spectrum and molecule Inferring molecular structures from experimentally measured nuclear magnetic resonance (NMR) spectra is an important task in many chemistry applications. Herein, we present a novel method implementing an automated molecular search by NMR spectrum. Given a query spectrum and a pool of candidate molecules, the matching score of each candidate molecule with respect to the query spectrum is evaluated by introducing a molecule-to-spectrum estimation procedure. The candidate molecule with the highest matching score is selected. This procedure does not require any prior knowledge of the corresponding molecular structure nor laborious manual efforts by chemists. We demonstrate the effectiveness of the proposed method on molecular search using <sup>13</sup>C NMR spectra. Molecule ; Matching (statistics) ; Spectrum (functional analysis) ; NMR spectra database ; Spectral line ; Nuclear magnetic resonance spectroscopy ; Nuclear magnetic resonance ; Computer science ; Chemistry ; Physics ; Mathematics Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Various Chemistry Research Topics