jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jocelyn Sunseri | David Ryan Koes
Source: Journal of Chemical Information and Modeling (article, 2020-02-12)
Field: Materials Science — Materials Chemistry

libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications We describe libmolgrid, a general-purpose library for representing three-dimensional molecules using multidimensional arrays of voxelized molecular data. libmolgrid provides functionality for sampling batches of data suited to machine learning workflows, and it also supports temporal and spatial recurrences over that data to facilitate work with convolutional and recurrent neural networks. It was designed for seamless integration with popular deep learning frameworks and features optimized performance by leveraging graphics processing units (GPUs). libmolgrid is a free and open source project (GPLv2) that aims to democratize grid-based modeling in computational chemistry. Computer science ; Workflow ; Deep learning ; Graphics processing unit ; Grid ; Graphics ; Convolutional neural network ; Artificial intelligence ; Sampling (signal processing) ; General-purpose computing on graphics processing units ; Computational science ; Computer architecture ; Computer graphics (images) ; Parallel computing ; Database ; Computer vision Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Behnoosh Sattari Baboukani | Zhijiang Ye | Kristofer G. Reyes | Prathima C. Nalam
Source: Tribology Letters (article, 2020-04-08)
Field: Materials Science — Materials Chemistry

Prediction of Nanoscale Friction for Two-Dimensional Materials Using a Machine Learning Approach Materials science ; Molybdenum disulfide ; Tribology ; Graphene ; Boron nitride ; Lubrication ; Nanotechnology ; Dissipation ; Nanoscopic scale ; Molecular dynamics ; Mechanical engineering ; Biological system ; Composite material ; Thermodynamics Graphene research and applications ; Fuel Cells and Related Materials ; Lubricants and Their Additives
Authors: Ruihan Wang | Yeshuang Zhong | Leming Bi | Mingli Yang | Dingguo Xu
Source: ACS Applied Materials & Interfaces (article, 2020-11-11)
Field: Chemistry — Inorganic Chemistry

Accelerating Discovery of Metal–Organic Frameworks for Methane Adsorption with Hierarchical Screening and Deep Learning In recent years, machine learning (ML) methods have made significant progress, and ML models have been adopted in virtually all aspects of chemistry. In this study, based on the crystal graph convolutional neural networks algorithm, an end-to-end deep learning model was developed for predicting the methane adsorption properties of metal-organic frameworks (MOFs). High-throughput grand canonical Monte Carlo calculations were carried out on the computation-ready, experimental MOF database, which contains approximately 11 000 MOFs, to construct the data set. An area under the curve of 0.930 for the test set proved the reliability of the developed deep learning model. To assess the transferability of the model, we applied it to predict the methane adsorption volume for some randomly selected covalent organic frameworks and zeolitic imidazolate framework materials. The results indicated that the model could also be suitable for other porous materials. We also applied it to the hierarchical screening of a hypothetical MOFs database (∼330 000 MOFs). Four hypothetical MOFs were demonstrated to have the highest performance in methane adsorption. A calculated maximum working capacity of 145 cm<sup>3</sup>/cm<sup>3</sup> at 5-35 bar and 298 K indicated that the hypothetical MOF is close to the Department of Energy's 2015 target of 180 cm<sup>3</sup>/cm<sup>3</sup>. Further analyses on all screened out MOFs established correlations between some structural features with the working capacity. The successful incorporation of ML and hierarchical screening can accelerate the discovery of new materials not just for gas adsorption, but also other areas involving interactions in materials and molecules. Materials science ; Metal-organic framework ; Adsorption ; Methane ; Nanotechnology ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Hydrocarbon exploration and reservoir analysis
Authors: Seiji Kajita | Tomoyuki Kinjo | Tomoki Nishi
Source: Communications Physics (article, 2020-05-07)
Field: Materials Science — Materials Chemistry

Autonomous molecular design by Monte-Carlo tree search and rapid evaluations using molecular dynamics simulations Abstract Functional materials, especially those that largely differ from known materials, are not easily discoverable because both human experts and supervised machine learning need prior knowledge and datasets. An autonomous system can evaluate various properties a priori, and thereby explore unknown extrapolation spaces in high-throughput simulations. However, high-throughput evaluations of molecular dynamics simulations are unrealistically demanding. Here, we show an autonomous search system for organic molecules implemented by a reinforcement learning algorithm, and apply it to molecular dynamics simulations of viscosity. The evaluation is dramatically accelerated (by three orders of magnitude) using a femto-second stress-tensor correlation, which underlies the glass-transition model. We experimentally examine one of 55,000 lubricant oil molecules found by the system. This study indicates that merging simulations and physical models can open a path for simulation-driven approaches to materials informatics. Computer science ; Molecular dynamics ; Extrapolation ; A priori and a posteriori ; Monte Carlo method ; Throughput ; Path (computing) ; Artificial intelligence ; Chemistry ; Mathematics ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Software Engineering Research
Authors: Georgios Konstantopoulos | Elias P. Koumoulos | Costas A. Charitidis
Source: Materials & Design (article, 2020-04-03)
Field: Materials Science — Materials Chemistry

Classification of mechanism of reinforcement in the fiber-matrix interface: Application of Machine Learning on nanoindentation data Carbon fiber reinforced polymer manufacturing is emerging, with multiple studies to focus on the design of interfacial reinforcement to ensure the maximum of composite properties, but also respectively to be able to align with zero defect manufacturing. The controversy on the engineering approach is a data-driven task that can be efficiently tackled by involving Artificial Intelligence in order to establish unbiased structure-property relations. In the present study, nanoindentation mapping data were processed with Machine Learning classification models to identify the interfacial reinforcement. The data preparation included normalization and sorting out of highly similar data with k-means clustering, since nanoindentation on epoxy matrix does not enhance insight on the mechanism of reinforcement. The trained models included neural networks, classification trees, and support vector machines. Realization of models' performance was evaluated on the test dataset as screening to obtain best fitted models for each algorithm. Transfer learning potential was demonstrated by extrapolating the prediction of best trained models to a validation dataset at different indentation depth with support vector machines outperforming the other models. Overall accuracy was 67% on the test dataset, F1 Score was 65% in the prediction of reinforcement mechanism classes and 72% in case of pristine specimen, while accuracy on validation dataset was 72.7%. Prediction metrics were comparable to other case studies of real-world classification problems. Computational time-cost for tuning and training was sustainable and equal to 2.3 min. Reinforcement learning ; Artificial intelligence ; Machine learning ; Nanoindentation ; Support vector machine ; Cluster analysis ; Computer science ; Test data ; Artificial neural network ; Mechanism (biology) ; Materials science Machine Learning in Materials Science ; Advanced machining processes and optimization ; Force Microscopy Techniques and Applications
Authors: Ying Wu | Haipeng Duan | Hongxia Xi
Source: Chemistry of Materials (article, 2020-03-23)
Field: Chemistry — Inorganic Chemistry

Machine Learning-Driven Insights into Defects of Zirconium Metal–Organic Frameworks for Enhanced Ethane–Ethylene Separation Structural defects in metal–organic frameworks (MOFs) have the potential to yield desirable properties that could not be achieved by “defect-free” crystals, but previous works in this area have focused on limited versions of defects due to the difficulty of detecting defects in MOFs. In this work, a modeling library containing 425 defective UiO-66 (UiO-66-Ds) with a comprehensive population (in terms of concentration and distribution) of missing-linker defects was created. Taking ethane–ethylene separation as a case study, we demonstrated that machine learning could provide data-driven insight into how the defects control the performance of UiO-66-Ds in adsorption, separation, and mechanical stability. We found that the missing-linker ratio in real materials could be predicted from the gravimetric surface area and pore volume, making it a useful complement for the challenges of directly measuring the defect concentration. We further identified the “privileged” UiO-66-Ds that were optimal in overall properties and provided decision trees as guidance to access and design these top performers. This work offers a general strategy for fully exploring the defects in MOFs, providing long-term opportunities for the development of defect engineering in the adsorption community. Metal-organic framework ; Linker ; Adsorption ; Materials science ; Zirconium ; Ethylene ; Gravimetric analysis ; Nanotechnology ; Work (physics) ; Metal ; Chemical engineering ; Computer science ; Mechanical engineering ; Organic chemistry ; Chemistry ; Metallurgy ; Catalysis ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques
Authors: Peter Wirnsberger | Andrew J. Ballard | George Papamakarios | Stuart Abercrombie | Sébastien Racanière | Alexander Pritzel | Danilo Jimenez Rezende | Charles Blundell
Source: The Journal of Chemical Physics (article, 2020-10-13)
Field: Materials Science — Materials Chemistry

Targeted free energy estimation via learned mappings Free energy perturbation (FEP) was proposed by Zwanzig [J. Chem. Phys. 22, 1420 (1954)] more than six decades ago as a method to estimate free energy differences and has since inspired a huge body of related methods that use it as an integral building block. Being an importance sampling based estimator, however, FEP suffers from a severe limitation: the requirement of sufficient overlap between distributions. One strategy to mitigate this problem, called Targeted FEP, uses a high-dimensional mapping in configuration space to increase the overlap of the underlying distributions. Despite its potential, this method has attracted only limited attention due to the formidable challenge of formulating a tractable mapping. Here, we cast Targeted FEP as a machine learning problem in which the mapping is parameterized as a neural network that is optimized so as to increase the overlap. We develop a new model architecture that respects permutational and periodic symmetries often encountered in atomistic simulations and test our method on a fully periodic solvation system. We demonstrate that our method leads to a substantial variance reduction in free energy estimates when compared against baselines, without requiring any additional data. Parameterized complexity ; Energy (signal processing) ; Artificial neural network ; Variance (accounting) ; Free energy perturbation ; Perturbation (astronomy) ; Homogeneous space ; Deep neural networks ; Variation (astronomy) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Quantum many-body systems
Authors: Yingjie Yu | Fanqiang Bu | Huiling Zhou | Yongheng Wang | Jianxun Cui | Xing Wang | Guohui Nie | Haihua Xiao
Source: Materials Chemistry Frontiers (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Biosafety materials: an emerging new research direction of materials science from the COVID-19 outbreak We first officially propose the brand-new concept of “biosafety materials”, which utilizes materials science to solve biosafety problems. Biosafety ; Coronavirus disease 2019 (COVID-19) ; Outbreak ; 2019-20 coronavirus outbreak ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Nanotechnology ; Engineering ethics ; Virology ; Engineering ; Materials science ; Medicine ; Biotechnology ; Biology Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Nanoplatforms for cancer theranostics
Authors: Tobias Schubeis | Tanguy Le Marchand | Csaba Daday | Wojciech Kopeć | Kumar Tekwani Movellan | Jan Staněk | Tom S. Schwarzer | Kathrin Castiglione | Bert L. de Groot | Guido Pintacuda | Loren B. Andreas
Source: Proceedings of the National Academy of Sciences (article, 2020-08-19)
Field: Chemistry — Spectroscopy

A β-barrel for oil transport through lipid membranes: Dynamic NMR structures of AlkL The protein AlkL is known to increase permeability of the outer membrane of bacteria for hydrophobic molecules, yet the mechanism of transport has not been determined. Differing crystal and NMR structures of homologous proteins resulted in a controversy regarding the degree of structure and the role of long extracellular loops. Here we solve this controversy by determining the de novo NMR structure in near-native lipid bilayers, and by accessing structural dynamics relevant to hydrophobic substrate permeation through molecular-dynamics simulations and by characteristic NMR relaxation parameters. Dynamic lateral exit sites large enough to accommodate substrates such as carvone or octane occur through restructuring of a barrel extension formed by the extracellular loops. Barrel (horology) ; Membrane ; Materials science ; Chemistry ; Composite material ; Biochemistry Mass Spectrometry Techniques and Applications ; Lipid Membrane Structure and Behavior ; Molecular Sensors and Ion Detection
Authors: Isabell Bludau | Moritz Heusel | Max Frank | George Rosenberger | Robin Hafen | Amir Banaei‐Esfahani | Audrey van Drogen | Ben C. Collins | Matthias Gstaiger | Ruedi Aebersold
Source: Nature Protocols (article, 2020-07-20)
Field: Chemistry — Spectroscopy

Complex-centric proteome profiling by SEC-SWATH-MS for the parallel detection of hundreds of protein complexes Proteome ; Proteomics ; Computational biology ; Mass spectrometry ; Quantitative proteomics ; Human proteome project ; Systems biology ; Workflow ; Profiling (computer programming) ; Chemistry ; Computer science ; Biology ; Bioinformatics ; Biochemistry ; Chromatography ; Gene ; Database Advanced Proteomics Techniques and Applications ; Enzyme Structure and Function ; Mass Spectrometry Techniques and Applications
Authors: Valentina Abet | Filip Szczypiński | Marc A. Little | Valentina Santolini | Christopher D. Jones | Robert Evans | Craig Wilson | Xiaofeng Wu | Michael F. Thorne | Michael J. Bennison | Peng Cui | Andrew I. Cooper | Kim E. Jelfs | Anna G. Slater
Source: Angewandte Chemie International Edition (article, 2020-06-15)
Field: Chemistry — Organic Chemistry

Inducing Social Self‐Sorting in Organic Cages To Tune The Shape of The Internal Cavity Many interesting target guest molecules have low symmetry, yet most methods for synthesising hosts result in highly symmetrical capsules. Methods of generating lower symmetry pores are thus required to maximise the binding affinity in host-guest complexes. Herein, we use mixtures of tetraaldehyde building blocks with cyclohexanediamine to access low-symmetry imine cages. Whether a low-energy cage is isolated can be correctly predicted from the thermodynamic preference observed in computational models. The stability of the observed structures depends on the geometrical match of the aldehyde building blocks. One bent aldehyde stands out as unable to assemble into high-symmetry cages-and the same aldehyde generates low-symmetry socially self-sorted cages when combined with a linear aldehyde. We exploit this finding to synthesise a family of low-symmetry cages containing heteroatoms, illustrating that pores of varying geometries and surface chemistries may be reliably accessed through computational prediction and self-sorting. Sorting ; Chemistry ; Computer science Supramolecular Chemistry and Complexes ; Fuel Cells and Related Materials ; Covalent Organic Framework Applications
Authors: Petra Pál | Attila Bonyár | M. Vereš | L. Himics | László Balázs | Laura Juhász | István Csarnovics
Source: Sensors and Actuators A Physical (article, 2020-07-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A generalized exponential relationship between the surface-enhanced Raman scattering (SERS) efficiency of gold/silver nanoisland arrangements and their non-dimensional interparticle distance/particle diameter ratio The optimization of the geometrical properties of gold/silver nanoparticle arrangements to maximize their surface-enhanced Raman scattering (SERS) efficiency is studied in this work. For this purpose, the metallic nanostructures were created by thermally annealing gold and silver thin film layers deposited onto glass substrates. The SERS capabilities of the samples were evaluated by measuring an analyte solution of benzophenone with three different excitation laser wavelengths. Systematic investigations were carried out on different gold and silver nanoisland samples to determine how the SERS enhancement depends on the geometrical (particle diameter, interparticle distance) and optical parameters (plasmon wavelength) of the nanostructures, as well as on the wavelength of laser excitation. The importance of matching the excitation wavelength with the resonant plasmon absorbance properties of the surface was proved. However, it was also shown that the optimization of the geometrical properties of the nanoisland arrangements dominates over the selection of the excitation wavelength. A generalized exponential relationship between the SERS enhancement and the non-dimensional interparticle distance/particle diameter ratio was established. Optimal technological parameters for the fabrication of gold/silver nanoisland SERS substrates were proposed. Materials science ; Excitation ; Raman scattering ; Plasmon ; Wavelength ; Nanostructure ; Silver nanoparticle ; Discrete dipole approximation ; Absorbance ; Surface plasmon resonance ; Laser ; Scattering ; Optoelectronics ; Particle (ecology) ; Colloidal gold ; Raman spectroscopy ; Nanoparticle ; Nanotechnology ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Laser-Ablation Synthesis of Nanoparticles ; Nanoparticles: synthesis and applications
Authors: Vivek Srinivas | R. Banerjee | Hugo Lebrette | Jason C. Jones | Oskar Aurelius | In‐Sik Kim | Cindy C. Pham | Sheraz Gul | Kyle D. Sutherlin | Asmit Bhowmick | Juliane John | Esra Bozkurt | Thomas Fransson | Pierre Aller | Agata Butryn | Isabel Bogacz | Philipp S. Simon | Stephen M. Keable | Alexander Britz | Kensuke Tono | Kyung Sook Kim | Sang‐Youn Park | Sang Jae Lee | Jaehyun Park | Roberto Alonso‐Mori | Franklin D. Fuller | A. Batyuk | Aaron S. Brewster | Uwe Bergmann | Nicholas K. Sauter | Allen M. Orville | Vittal K. Yachandra | Junko Yano | John D. Lipscomb | Jan Kern | Martin Högbom
Source: Journal of the American Chemical Society (article, 2020-07-20)
Field: Chemistry — Inorganic Chemistry

High-Resolution XFEL Structure of the Soluble Methane Monooxygenase Hydroxylase Complex with its Regulatory Component at Ambient Temperature in Two Oxidation States Soluble methane monooxygenase (sMMO) is a multicomponent metalloenzyme that catalyzes the conversion of methane to methanol at ambient temperature using a nonheme, oxygen-bridged dinuclear iron cluster in the active site. Structural changes in the hydroxylase component (sMMOH) containing the diiron cluster caused by complex formation with a regulatory component (MMOB) and by iron reduction are important for the regulation of O<sub>2</sub> activation and substrate hydroxylation. Structural studies of metalloenzymes using traditional synchrotron-based X-ray crystallography are often complicated by partial X-ray-induced photoreduction of the metal center, thereby obviating determination of the structure of the enzyme in pure oxidation states. Here, microcrystals of the sMMOH:MMOB complex from <i>Methylosinus trichosporium</i> OB3b were serially exposed to X-ray free electron laser (XFEL) pulses, where the ≤35 fs duration of exposure of an individual crystal yields diffraction data before photoreduction-induced structural changes can manifest. Merging diffraction patterns obtained from thousands of crystals generates radiation damage-free, 1.95 Å resolution crystal structures for the fully oxidized and fully reduced states of the sMMOH:MMOB complex for the first time. The results provide new insight into the manner by which the diiron cluster and the active site environment are reorganized by the regulatory protein component in order to enhance the steps of oxygen activation and methane oxidation. This study also emphasizes the value of XFEL and serial femtosecond crystallography (SFX) methods for investigating the structures of metalloenzymes with radiation sensitive metal active sites. Chemistry ; Methane monooxygenase ; Component (thermodynamics) ; Methane ; Monooxygenase ; Photochemistry ; Environmental chemistry ; Organic chemistry ; Enzyme ; Thermodynamics Metal-Catalyzed Oxygenation Mechanisms ; Atmospheric and Environmental Gas Dynamics ; Petroleum Processing and Analysis
Authors: Suntharavathanan Mahalingam | Rupy Kaur Matharu | Shervanthi Homer‐Vanniasinkam | Mohan Edirisinghe
Source: Applied Physics Reviews (article, 2020-10-14)
Field: Materials Science — Biomaterials

Current methodologies and approaches for the formation of core–sheath polymer fibers for biomedical applications The application of polymer fibers has rocketed to unimaginable heights in recent years and occupies every corner of our day-to-day life, from knitted protective textile clothes to buzzing smartphone electronics. Polymer fibers could be obtained from natural and synthetic polymers at a length scale from the nanometer to micrometer range. These fibers could be formed into different configurations such as single, core–sheath, hollow, blended, or composite according to human needs. Of these several conformations of fibers, core–sheath polymer fibers are an interesting class of materials, which shows superior physical, chemical, and biological properties. In core–sheath fiber structures, one of the components called a core is fully surrounded by the second component known as a sheath. In this format, different polymers can be applied as a sheath over a solid core of another polymer, thus resulting in a variety of modified properties while maintaining the major fiber property. After a brief introduction to core–sheath fibers, this review paper focuses on the development of the electrospinning process to manufacture core–sheath fibers followed by illustrating the current methodology and approaches to form them on a larger scale, suitable for industrial manufacturing and exploitation. Finally, the paper reviews the applications of the core–sheath fibers, in particular, recent studies of core–sheath polymer fibers in tissue engineering (nerve, vascular grafts, cardiomyocytes, bone, tendons, sutures, and wound healing), growth factors and other bioactive component release, and drug delivery. Therefore, core–sheath structures are a revolutionary development in the field of science and technology, becoming a backbone to many emerging technologies and novel opportunities. Core (optical fiber) ; Polymer ; Materials science ; Electrospinning ; Textile ; Micrometer ; Composite material ; Fiber ; Nanotechnology ; Inner core ; Mechanical engineering ; Engineering Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Surface Modification and Superhydrophobicity