jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Camila Pontes | Miguel de Souza Andrade | José Fiorote | Werner Treptow
Source: Scientific Reports (article, 2021-03-25)
Field: Chemistry — Spectroscopy

Trivial and nontrivial error sources account for misidentification of protein partners in mutual information approaches The problem of finding the correct set of partners for a given pair of interacting protein families based on multi-sequence alignments (MSAs) has received great attention over the years. Recently, the native contacts of two interacting proteins were shown to store the strongest mutual information (MI) signal to discriminate MSA concatenations with the largest fraction of correct pairings. Although that signal might be of practical relevance in the search for an effective heuristic to solve the problem, the number of MSA concatenations with near-native MI is large, imposing severe limitations. Here, a Genetic Algorithm that explores possible MSA concatenations according to a MI maximization criteria is shown to find degenerate solutions with two error sources, arising from mismatches among (i) similar and (ii) non-similar sequences. If mistakes made among similar sequences are disregarded, type-(i) solutions are found to resolve correct pairings at best true positive (TP) rates of 70%-far above the very same estimates in type-(ii) solutions. A machine learning classification algorithm helps to show further that differences between optimized solutions based on TP rates are not artificial and may have biological meaning associated with the three-dimensional distribution of the MI signal. Type-(i) solutions may therefore correspond to reliable results for predictive purposes, found here to be more likely obtained via MI maximization across protein systems having a minimum critical number of amino acid contacts on their interaction surfaces (N > 200). Mutual information ; Computer science ; Computational biology ; Data science ; Information retrieval ; Artificial intelligence ; Biology Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Gene Regulatory Network Analysis
Authors: Lukas Spieß | Peter de Peinder | H. van den Bijgaart
Source: Foods (article, 2021-05-18)
Field: Chemistry — Analytical Chemistry

Advances in Atypical FT-IR Milk Screening: Combining Untargeted Spectra Screening and Cluster Algorithms Fourier-transform mid-infrared spectrometry is an attractive technology for screening adulterated liquid milk products. So far, studies on how infrared spectroscopy can be used to screen spectra for atypical milk composition have either used targeted methods to test for specific adulterants, or have used untargeted screening methods that do not reveal in what way the spectra are atypical. In this study, we evaluate the potential of combining untargeted screening methods with cluster algorithms to indicate in what way a spectrum is atypical and, if possible, why. We found that a combination of untargeted screening methods and cluster algorithms can reveal meaningful and generalizable categories of atypical milk spectra. We demonstrate that spectral information (e.g., the compositional milk profile) and meta-data associated with their acquisition (e.g., at what date and which instrument) can be used to understand in what way the milk is atypical and how it can be used to form hypotheses about the underlying causes. Thereby, it was indicated that atypical milk screening can serve as a valuable complementary quality assurance tool in routine FTIR milk analysis. Cluster (spacecraft) ; Algorithm ; Chemistry ; Computer science ; Computational biology ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Emad Alharbi | Paul S. Bond | Radu Călinescu | Kevin Cowtan
Source: Acta Crystallographica Section D Structural Biology (article, 2021-11-29)
Field: Materials Science — Materials Chemistry

Predicting the performance of automated crystallographic model-building pipelines Proteins are macromolecules that perform essential biological functions which depend on their three-dimensional structure. Determining this structure involves complex laboratory and computational work. For the computational work, multiple software pipelines have been developed to build models of the protein structure from crystallographic data. Each of these pipelines performs differently depending on the characteristics of the electron-density map received as input. Identifying the best pipeline to use for a protein structure is difficult, as the pipeline performance differs significantly from one protein structure to another. As such, researchers often select pipelines that do not produce the best possible protein models from the available data. Here, a software tool is introduced which predicts key quality measures of the protein structures that a range of pipelines would generate if supplied with a given crystallographic data set. These measures are crystallographic quality-of-fit indicators based on included and withheld observations, and structure completeness. Extensive experiments carried out using over 2500 data sets show that the tool yields accurate predictions for both experimental phasing data sets (at resolutions between 1.2 and 4.0 Å) and molecular-replacement data sets (at resolutions between 1.0 and 3.5 Å). The tool can therefore provide a recommendation to the user concerning the pipelines that should be run in order to proceed most efficiently to a depositable model. Pipeline (software) ; Pipeline transport ; Software ; Computer science ; Data mining ; Set (abstract data type) ; Completeness (order theory) ; Data structure ; Visualization ; Protein structure prediction ; Experimental data ; Data set ; Phaser ; Protein structure ; Algorithm ; Artificial intelligence ; Engineering ; Mathematics ; Chemistry ; Programming language Enzyme Structure and Function ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Anderson J. C. Sena | Rodrigo M. S. de Oliveira | Júlio A. S. do Nascimento
Source: Energies (article, 2021-10-20)
Field: Materials Science — Materials Chemistry

Frequency Resolved Partial Discharges Based on Spectral Pulse Counting A partial discharge (PD) classification methodology based counting PD pulses in the spectral domain is proposed and presented in this paper. The spectral counting data are processed using the proposed PD Spectral Pulse Counting Mapping technique (PD-SPCM), which leads to a Frequency-Resolved Partial Discharges (FRPD) map. The proposed map is then used for PD detection and classification. In this work, corona and slot FRPDs are presented in frequency bands up to 500 MHz, obtained from laboratory measurements performed using two hydro-generator stator bars. The electromagnetic signals from the PDs were captured using a patch antenna designed for this purpose and a spectral analyzer. The corona and slot PDs were chosen because one can be mistakenly classified as the other because they may present similar Phase Resolved PD (PRPD) maps and may occupy shared spectral bands. Furthermore, corona and slot PDs can occur concurrently. The obtained results show that the corona and slot PDs can be properly identified using the developed methodology, even when they occur simultaneously. This is possible because, as it is experimentally demonstrated, corona and slot PDs have appreciable levels of spectral pulse counting in particular bands of the frequency spectrum. Corona (planetary geology) ; Partial discharge ; Corona discharge ; Spectrum analyzer ; Optics ; Pulse (music) ; Generator (circuit theory) ; Electromagnetic pulse ; Materials science ; Physics ; Acoustics ; Voltage ; Detector High voltage insulation and dielectric phenomena ; Electrostatic Discharge in Electronics ; Lightning and Electromagnetic Phenomena
Authors: William K. Epting | Yinkai Lei | Jerry Hunter Mason | Harry Abernathy | Thomas Kalapos | Gregory Hackett
Source: ECS Transactions (article, 2021-07-07)
Field: Materials Science — Materials Chemistry

Optimization of SOFC Electrode Microstructures with Long Term Performance Modeling and Machine Learning To understand the relationship between SOFC electrode microstructure and long-term performance, we developed a fully resolved 4D model for simulating microstructural and performance changes over 1,000 hours of cell operation by combining a phase-field coarsening model with spatially resolved microstructural analysis and continuum-level multiphysics performance modeling. This framework was used to simulate the long-term performance of hundreds of LSM-YSZ cathodes and Ni-YSZ anodes. These electrode microstructures were synthetically generated to intentionally span an 11-dimensional space of initial microstructural parameters. These high-dimensional results were analyzed with a machine learning model using a novel figure of merit that accounts for both the initial and long-term performance. This demonstrated that e.g. the LSM/YSZ ratio was the most impactful parameter in the cathode microstructure, with lower LSM/YSZ ratios generally being beneficial. The trained model also produces tailored recommendations for improvements to existing electrodes, providing a valuable tool for SOFC electrode developers and manufacturers. Microstructure ; Multiphysics ; Materials science ; Electrode ; Anode ; Cathode ; Yttria-stabilized zirconia ; Term (time) ; Composite material ; Computer science ; Finite element method ; Cubic zirconia ; Ceramic ; Structural engineering ; Electrical engineering ; Engineering ; Physics Advancements in Solid Oxide Fuel Cells ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: Yasuyuki Joko | Shinya Honda
Source: Mechanics of Advanced Materials and Structures (article, 2021-07-13)
Field: Materials Science — Polymers and Plastics

Dehydration dynamics model with Monte Carlo Method for a front-loading washer/dryer In this study, we aimed to improve the efficiency of an anti-vibration design for front-loading washer/dryers and propose a dehydration dynamics model that combines a mechanical model with a random algorithm. In addition to the anti-vibration structure, front-loading washer-dryers are equipped with dehydration start control where dehydration is repeated until the level of vibration detected during dehydration falls below the threshold. The proposed model focuses on the probability distribution of clothing behavior during dehydration and models the clothing behavior by using a random number that follows the probability distribution. Then, we present the reusability of the probability distribution and the difference from a mechanical model that does not consider clothing. In this way, in the upstream design without the actual machine, the anti-vibration structure and dehydration start control could be simultaneously designed, improving the efficiency of the anti-vibration design. Washer ; Dehydration ; Vibration ; Monte Carlo method ; Probability distribution ; Engineering ; Structural engineering ; Simulation ; Materials science ; Computer science ; Mechanical engineering ; Mathematics ; Physics ; Acoustics ; Chemistry Textile materials and evaluations ; Color Science and Applications ; Material Properties and Processing
Authors: Muhammad Syafiq Abdullah | Sławomir Kozieł | Stanisław Szczepański
Source: Electronics (article, 2021-07-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Normalized Partial Scattering Cross Section for Performance Evaluation of Low-Observability Scattering Structures The development of diffusion metasurfaces created new opportunities to elevate the stealthiness of combat aircraft. Despite the potential significance of metasurfaces, their rigorous design methodologies are still lacking, especially in the context of meticulous control over the scattering of electromagnetic (EM) waves through geometry parameter tuning. Another practical issue is insufficiency of the existing performance metrics, specifically, monostatic and bistatic evaluation of the reflectivity, especially at the design stage of metasurfaces. Both provide limited insight into the RCS reduction properties, with the latter being dependent on the selection of the planes over which the evaluation takes place. This paper introduces a novel performance metric for evaluating scattering characteristics of a metasurface, referred to as Normalized Partial Scattering Cross Section (NPSCS). The metric involves integration of the scattered energy over a specific solid angle, which allows for a comprehensive assessment of the structure performance in a format largely independent of the particular arrangement of the scattering lobes. We demonstrate the utility of the introduced metric using two specific metasurface architectures. In particular, we show that the integral-based metric can be used to discriminate between the various surface configurations (e.g., checkerboard versus random), which cannot be conclusively compared using traditional methods. Consequently, the proposed approach can be a useful tool in benchmarking radar cross section reduction performance of metamaterial-based, and other types of scattering structures. Scattering ; Radar cross-section ; Metric (unit) ; Observability ; Context (archaeology) ; Computer science ; Bistatic radar ; Cross section (physics) ; Radar ; Optics ; Physics ; Mathematics ; Engineering ; Radar imaging ; Telecommunications ; Applied mathematics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Ilshat Mirsayapov | Evgeny Khorkov | Rustam Minzianov
Source: E3S Web of Conferences (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Research of the stress-strain state of a reinforced concrete beamless floor In the article, the authors present the results of a study of a flat reinforced concrete beamless floor of an existing building. The experiment consisted of loading a slab in span with water tanks. There were 15 loading stages in total, on average 4 kN. During the experiment, data was obtained on the relative deformations of concrete and reinforcement in the span and on the support. Also, observations of the growth of vertical deformations and the growth of cracks in the span were introduced. Structural engineering ; Span (engineering) ; Slab ; Reinforcement ; Reinforced concrete ; Geotechnical engineering ; Stress (linguistics) ; Stress–strain curve ; Materials science ; Geology ; Engineering ; Finite element method Structural mechanics and materials ; Advanced Energy Technologies and Civil Engineering Innovations ; Geodetic Measurements and Engineering Structures
Authors: Madushanka Manathunga | Chi Jin | Vinícius Wilian D. Cruzeiro | Yipu Miao | Dawei Mu | Kamesh Arumugam | Kristopher Keipert | Hasan Metin Aktulga | Kenneth M. Merz | Andreas Goetz
Source: ChemRxiv (preprint, 2021-02-09)
Field: Chemistry — Spectroscopy

Harnessing the Power of Multi-GPU Acceleration into the Quantum Interaction Computational Kernel Program We report a new multi-GPU capable ab initio Hartree-Fock/density functional theory implementation integrated into the open source QUantum Interaction Computational Kernel (QUICK) program. Details on the load balancing algorithms for electron repulsion integrals and exchange correlation quadrature across multiple GPUs are described. Benchmarking studies carried out on up to 4 GPU nodes, each containing 4 NVIDIA V100-SMX2 type GPUs demonstrate that our implementation is capable of achiev- ing excellent load balancing and high parallel efficiency. For representative medium to large size protein/organic molecular sys- tems, the observed efficiencies remained above 86%. The accelerations on NVIDIA A100, P100 and K80 platforms also have real- ized parallel efficiencies higher than 74%, paving the way for large-scale ab initio electronic structure calculations. Computer science ; Kernel (algebra) ; Computational science ; Parallel computing ; Ab initio ; Quantum ; Benchmarking ; Acceleration ; Physics ; Mathematics ; Quantum mechanics Advanced NMR Techniques and Applications ; Electron Spin Resonance Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Koya Sakuma | Shintaro Minami
Source: BMC Bioinformatics (article, 2021-09-27)
Field: Materials Science — Materials Chemistry

Enumeration and comprehensive in-silico modeling of three-helix bundle structures composed of typical αα-hairpins The enumeration of the dominant subsets of possible backbone structures for small single-chain three-helical bundle topologies revealed that the compact foldable structures are discontinuously and sparsely distributed in the conformational space. Additionally, although the designs have not been experimentally validated in the present research, the comprehensive set of computational structural models generated also offers protein designers the opportunity to skip building similar structures by themselves and enables them to quickly focus on building specialized designs using the prebuilt structure models. The backbone and best design models in this study are publicly accessible from the following URL: https://doi.org/10.5281/zenodo.4321632 . Bundle ; Helix (gastropod) ; In silico ; Helix bundle ; Protein design ; Crystallography ; Protein structure ; Topology (electrical circuits) ; Structural motif ; Chain (unit) ; Computational biology ; Computer science ; Mathematics ; Biology ; Chemistry ; Physics ; Materials science ; Genetics ; Combinatorics ; Gene Enzyme Structure and Function ; Protein Structure and Dynamics ; Glycosylation and Glycoproteins Research
Authors: Masakazu Kondo
Source: Molecular Informatics (article, 2021-08-25)
Field: Materials Science — Materials Chemistry

Developing a Generative Model Utilizing Self‐attention Networks: Application to Materials/Drug Discovery A new generative model, in which the Variational Autoencoder network is combined with the Transformer architecture, is developed. The proposed model, the Variational Autoencoding Transformer (VAT), is applied to the task of generating molecules, showing that, with proper training, the VAT model can not only produce similar molecules to input ones with high accuracy but also generate new molecules from a predefined prior almost perfectly. A desirable aspect of our VAT is that no heuristic setting is necessary for optimal performance, which suggests that the model can readily be available to a variety of datasets. As practical directions toward materials/drug discovery, two strategies: a fine-tuning method for directed molecular generation and a method of mixing molecules in the latent space, are demonstrated. Computer science ; Transformer ; Autoencoder ; Generative grammar ; Chemical space ; Generative model ; Heuristic ; Drug discovery ; Machine learning ; Artificial intelligence ; Artificial neural network ; Data mining ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Minghao Guo | Wan Shou | Liane Makatura | Timothy Erps | Michael Foshey | Wojciech Matusik
Source: arXiv (Cornell University) (preprint, 2021-05-05)
Field: Materials Science — Materials Chemistry

Polygrammar: Grammar for Digital Polymer Representation and Generation Polymers are widely-studied materials with diverse properties and applications determined by different molecular structures. It is essential to represent these structures clearly and explore the full space of achievable chemical designs. However, existing approaches are unable to offer comprehensive design models for polymers because of their inherent scale and structural complexity. Here, we present a parametric, context-sensitive grammar designed specifically for the representation and generation of polymers. As a demonstrative example, we implement our grammar for polyurethanes. Using our symbolic hypergraph representation and 14 simple production rules, our PolyGrammar is able to represent and generate all valid polyurethane structures. We also present an algorithm to translate any polyurethane structure from the popular SMILES string format into our PolyGrammar representation. We test the representative power of PolyGrammar by translating a dataset of over 600 polyurethane samples collected from literature. Furthermore, we show that PolyGrammar can be easily extended to the other copolymers and homopolymers such as polyacrylates. By offering a complete, explicit representation scheme and an explainable generative model with validity guarantees, our PolyGrammar takes an important step toward a more comprehensive and practical system for polymer discovery and exploration. As the first bridge between formal languages and chemistry, PolyGrammar also serves as a critical blueprint to inform the design of similar grammars for other chemistries, including organic and inorganic molecules. Computer science ; Representation (politics) ; Context (archaeology) ; Grammar ; String (physics) ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Data Visualization and Analytics ; Software Engineering Research
Authors: Seiji Takeda | Toshiyuki Hama | Hsiang-Han Hsu | Akihiro Kishimoto | Makoto Kogoh | Takumi Hongo | Kumiko Fujieda | Hideaki Nakashika | Dmitry Yu. Zubarev | Daniel P. Sanders | Jed W. Pitera | Junta Fuchiwaki | Daiju Nakano
Source: arXiv (Cornell University) (preprint, 2021-08-06)
Field: Materials Science — Materials Chemistry

Molecule Generation Experience: An Open Platform of Material Design for Public Users Artificial Intelligence (AI)-driven material design has been attracting great attentions as a groundbreaking technology across a wide spectrum of industries. Molecular design is particularly important owing to its broad application domains and boundless creativity attributed to progresses in generative models. The recent maturity of molecular generative models has stimulated expectations for practical use among potential users, who are not necessarily familiar with coding or scripting, such as experimental engineers and students in chemical domains. However, most of the existing molecular generative models are Python libraries on GitHub, that are accessible for only IT-savvy users. To fill this gap, we newly developed a graphical user interface (GUI)-based web application of molecular generative models, Molecule Generation Experience, that is open to the general public. This is the first web application of molecular generative models enabling users to work with built-in datasets to carry out molecular design. In this paper, we describe the background technology extended from our previous work. Our new online evaluation and structural filtering algorithms significantly improved the generation speed by 30 to 1,000 times with a wider structural variety, satisfying chemical stability and synthetic reality. We also describe in detail our Kubernetes-based scalable cloud architecture and user-oriented GUI that are necessary components to achieve a public service. Finally, we present actual use cases in industrial research to design new photoacid generators (PAGs) as well as release cases in educational events. Generative grammar ; Computer science ; Scripting language ; Python (programming language) ; Generative Design ; Generative model ; Scalability ; Human–computer interaction ; User interface ; Cloud computing ; Software engineering ; World Wide Web ; Data science ; Artificial intelligence ; Engineering ; Database ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation