jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stefan Doerr | Maciej Majewski | Adrià Pérez | Andreas Krämer | Cecilia Clementi | Frank Noé | Toni Giorgino | Gianni De Fabritiis
Source: Journal of Chemical Theory and Computation (article, 2021-03-17)
Field: Materials Science — Materials Chemistry

TorchMD: A Deep Learning Framework for Molecular Simulations Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All force computations including bond, angle, dihedral, Lennard-Jones, and Coulomb interactions are expressed as PyTorch arrays and operations. Moreover, TorchMD enables learning and simulating neural network potentials. We validate it using standard Amber all-atom simulations, learning an ab initio potential, performing an end-to-end training, and finally learning and simulating a coarse-grained model for protein folding. We believe that TorchMD provides a useful tool set to support molecular simulations of machine learning potentials. Code and data are freely available at github.com/torchmd. Computer science ; Data science ; Deep learning ; Artificial intelligence Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Maik Pietzner | Eleanor Wheeler | Julia Carrasco-Zanini | Nicola D. Kerrison | Erin Oerton | Mine Koprulu | Jian’an Luan | Aroon D. Hingorani | Stephen A. Williams | Nicholas J. Wareham | Claudia Langenberg
Source: Nature Communications (article, 2021-11-24)
Field: Chemistry — Spectroscopy

Synergistic insights into human health from aptamer- and antibody-based proteomic profiling Affinity-based proteomics has enabled scalable quantification of thousands of protein targets in blood enhancing biomarker discovery, understanding of disease mechanisms, and genetic evaluation of drug targets in humans through protein quantitative trait loci (pQTLs). Here, we integrate two partly complementary techniques-the aptamer-based SomaScan<sup>®</sup> v4 assay and the antibody-based Olink assays-to systematically assess phenotypic consequences of hundreds of pQTLs discovered for 871 protein targets across both platforms. We create a genetically anchored cross-platform proteome-phenome network comprising 547 protein-phenotype connections, 36.3% of which were only seen with one of the two platforms suggesting that both techniques capture distinct aspects of protein biology. We further highlight discordance of genetically predicted effect directions between assays, such as for PILRA and Alzheimer's disease. Our results showcase the synergistic nature of these technologies to better understand and identify disease mechanisms and provide a benchmark for future cross-platform discoveries. Computational biology ; Phenome ; Aptamer ; Proteome ; Proteomics ; Biology ; Phenotype ; Biomarker discovery ; Toxicogenomics ; Biomarker ; Bioinformatics ; Genetics ; Gene ; Gene expression Advanced Proteomics Techniques and Applications ; Biotin and Related Studies ; RNA and protein synthesis mechanisms
Authors: Çiğdem Altıntaş | Ömer Faruk Altundal | Seda Keskın | Ramazan Yıldırım
Source: Journal of Chemical Information and Modeling (review, 2021-04-29)
Field: Chemistry — Inorganic Chemistry

Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of structural property and performance data for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure-performance relationships of materials but also for understanding their performance trends in different applications, specifically for gas storage and separation. In this review, we highlight the current state of the art in ML-assisted computational screening of MOFs for gas storage and separation and address both the opportunities and challenges that are emerging in this new field by emphasizing how merging of ML and MOF simulations can be useful. Computer science ; Metal-organic framework ; Field (mathematics) ; Nanotechnology ; Throughput ; Property (philosophy) ; Biochemical engineering ; Process engineering ; Materials science ; Chemistry ; Engineering ; Telecommunications Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Hira Fatima | Tawatchai Charinpanitkul | Kyo‐Seon Kim
Source: Nanomaterials (review, 2021-05-01)
Field: Materials Science — Biomaterials

Fundamentals to Apply Magnetic Nanoparticles for Hyperthermia Therapy The activation of magnetic nanoparticles in hyperthermia treatment by an external alternating magnetic field is a promising technique for targeted cancer therapy. The external alternating magnetic field generates heat in the tumor area, which is utilized to kill cancerous cells. Depending on the tumor type and site to be targeted, various types of magnetic nanoparticles, with variable coating materials of different shape and surface charge, have been developed. The tunable physical and chemical properties of magnetic nanoparticles enhance their heating efficiency. Moreover, heating efficiency is directly related with the product values of the applied magnetic field and frequency. Protein corona formation is another important parameter affecting the heating efficiency of MNPs in magnetic hyperthermia. This review provides the basics of magnetic hyperthermia, mechanisms of heat losses, thermal doses for hyperthermia therapy, and strategies to improve heating efficiency. The purpose of this review is to build a bridge between the synthesis/coating of magnetic nanoparticles and their practical application in magnetic hyperthermia. Magnetic nanoparticles ; Materials science ; Hyperthermia ; Hyperthermia therapy ; Nanoparticle ; Magnetic field ; Magnetic hyperthermia ; Coating ; Cancer therapy ; Nanotechnology ; Nuclear magnetic resonance ; Cancer ; Medicine ; Physics Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Daniela Meroni | Ridha Djellabi‬‬‬‬‬‬‬‬ | Muthupandian Ashokkumar | Claudia L. Bianchi‬ | Daria C. Boffito
Source: Chemical Reviews (review, 2021-11-24)
Field: Materials Science — Materials Chemistry

Sonoprocessing: From Concepts to Large-Scale Reactors Intensification of ultrasonic processes for diversified applications, including environmental remediation, extractions, food processes, and synthesis of materials, has received attention from the scientific community and industry. The mechanistic pathways involved in intensification of ultrasonic processes that include the ultrasonic generation of cavitation bubbles, radical formation upon their collapse, and the possibility of fine-tuning operating parameters for specific applications are all well documented in the literature. However, the scale-up of ultrasonic processes with large-scale sonochemical reactors for industrial applications remains a challenge. In this context, this review provides a complete overview of the current understanding of the role of operating parameters and reactor configuration on the sonochemical processes. Experimental and theoretical techniques to characterize the intensity and distribution of cavitation activity within sonoreactors are compared. Classes of laboratory and large-scale sonoreactors are reviewed, highlighting recent advances in batch and flow-through reactors. Finally, examples of large-scale sonoprocessing applications have been reviewed, discussing the major scale-up and sustainability challenges. Scale (ratio) ; Process engineering ; Ultrasonic sensor ; Context (archaeology) ; Biochemical engineering ; Chemistry ; Sustainability ; Cavitation ; Nanotechnology ; Systems engineering ; Engineering ; Mechanics ; Ecology ; Materials science Ultrasound and Cavitation Phenomena ; Environmental remediation with nanomaterials
Authors: Shangze Wu | Jaspreet Kaur | Tobias A. Karl | Xianhai Tian | Joshua P. Barham
Source: Angewandte Chemie International Edition (review, 2021-09-03)
Field: Chemistry — Organic Chemistry

Synthetic Molecular Photoelectrochemistry: New Frontiers in Synthetic Applications, Mechanistic Insights and Scalability Synthetic photoelectrochemistry (PEC) is receiving increasing attention as a new frontier for the generation and handling of reactive intermediates. PEC permits selective single-electron transfer (SET) reactions in a much greener way and broadens the redox window of possible transformations. Herein, the most recent contributions are reviewed, demonstrating exciting new opportunities, namely, the combination of PEC with other reactivity paradigms (hydrogen-atom transfer, radical polar crossover, energy transfer sensitization), scalability up to multigram scale, novel selectivities in SET super-oxidations/reductions and the importance of precomplexation to temporally enable excited radical ion catalysis. Photoelectrochemistry ; Scalability ; Nanotechnology ; Computer science ; Chemistry ; Materials science ; Electrochemistry ; Physical chemistry Radical Photochemical Reactions ; CO2 Reduction Techniques and Catalysts ; Advanced Photocatalysis Techniques
Authors: Hyeonseok Kim | Joonhwa Choi | Kyun Kyu Kim | Phillip Won | Sukjoon Hong | Seung Hwan Ko
Source: Nature Communications (article, 2021-08-10)
Field: Materials Science — Materials Chemistry

Biomimetic chameleon soft robot with artificial crypsis and disruptive coloration skin Development of an artificial camouflage at a complete device level remains a vastly challenging task, especially under the aim of achieving more advanced and natural camouflage characteristics via high-resolution camouflage patterns. Our strategy is to integrate a thermochromic liquid crystal layer with the vertically stacked, patterned silver nanowire heaters in a multilayer structure to overcome the limitations of the conventional lateral pixelated scheme through the superposition of the heater-induced temperature profiles. At the same time, the weaknesses of thermochromic camouflage schemes are resolved in this study by utilizing the temperature-dependent resistance of the silver nanowire network as the process variable of the active control system. Combined with the active control system and sensing units, the complete device chameleon model successfully retrieves the local background color and matches its surface color instantaneously with natural transition characteristics to be a competent option for a next-generation artificial camouflage. Camouflage ; Crypsis ; Superposition principle ; Materials science ; Structural coloration ; Nanomanufacturing ; Nanotechnology ; Interference (communication) ; Biomimetics ; Thermochromism ; Computer science ; Optics ; Optoelectronics ; Artificial intelligence ; Telecommunications ; Ecology ; Physics ; Photonic crystal ; Biology Photochromic and Fluorescence Chemistry ; Advanced Materials and Mechanics ; Advanced Sensor and Energy Harvesting Materials
Authors: He‐Xiu Xu | Guangwei Hu | Yanzhao Wang | Chaohui Wang | Mingzhao Wang | Shaojie Wang | Yongjun Huang | Patrice Genevet | Wei Huang | Cheng‐Wei Qiu
Source: Light Science & Applications (article, 2021-04-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Polarization-insensitive 3D conformal-skin metasurface cloak Abstract Electromagnetic metasurface cloaks provide an alternative paradigm toward rendering arbitrarily shaped scatterers invisible. Most transformation-optics (TO) cloaks intrinsically need wavelength-scale volume/thickness, such that the incoming waves could have enough long paths to interact with structured meta-atoms in the cloak region and consequently restore the wavefront. Other challenges of TO cloaks include the polarization-dependent operation to avoid singular parameters of composite cloaking materials and limitations of canonical geometries, e.g., circular, elliptical, trapezoidal, and triangular shapes. Here, we report for the first time a conformal-skin metasurface carpet cloak, enabling to work under arbitrary states of polarization (SOP) at Poincaré sphere for the incident light and arbitrary conformal platform of the object to be cloaked. By exploiting the foundry three-dimensional (3D) printing techniques to fabricate judiciously designed meta-atoms on the external surface of a conformal object, the spatial distributions of intensity and polarization of its scattered lights can be reconstructed exactly the same as if the scattering wavefront were deflected from a flat ground at any SOP, concealing targets under polarization-scanning detections. Two conformal-skin carpet cloaks working for partial- and full-azimuth plane operation are respectively fabricated on trapezoid and pyramid platforms via 3D printing. Experimental results are in good agreement with numerical simulations and both demonstrate the polarization-insensitive cloaking within a desirable bandwidth. Our approach paves a deterministic and robust step forward to the realization of interfacial, free-form, and full-polarization cloaking for a realistic arbitrary-shape target in real-world applications. Cloak ; Cloaking ; Conformal map ; Wavefront ; Optics ; Physics ; Polarization (electrochemistry) ; Metamaterial ; Geometry ; Mathematics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Electromagnetic Scattering and Analysis
Authors: Deepak Bamal | A. P. Singh | Gaurav Chaudhary | Monu Kumar | Manjeet Singh | Neelam Rani | Poonam Mundlia | Anita R. Sehrawat
Source: Nanomaterials (review, 2021-08-17)
Field: Materials Science — Materials Chemistry

Silver Nanoparticles Biosynthesis, Characterization, Antimicrobial Activities, Applications, Cytotoxicity and Safety Issues: An Updated Review Rapid advances in nanotechnology have led to its emergence as a tool for the development of green synthesized noble metal nanoparticles, especially silver nanoparticles (AgNPs), for applications in diverse fields such as human health, the environment and industry. The importance of AgNPs is because of their unique physicochemical and antimicrobial properties, with a myriad of activities that are applicable in various fields, including the pharmaceutical industry. Countries with high biodiversity require the collection and transformation of information about biological assets into processes, associations, methods and tools that must be combined with the sustainable utilization of biological diversity. Therefore, this review paper discusses the applicable studies of the biosynthesis of AgNPs and their antimicrobial activities towards microorganisms in different areas viz. medicine and agriculture. The confirmed antiviral properties of AgNPs promote their applicability for SARS-CoV-2 treatment, based on assimilating the virus' activities with those of similar viruses via in vivo studies. In this review, an insight into the cytotoxicity and safety issues of AgNPs, along with their future prospects, is also provided. Nanotechnology ; Antimicrobial ; Human health ; Silver nanoparticle ; Biotechnology ; Biochemical engineering ; Chemistry ; Nanoparticle ; Engineering ; Biology ; Materials science ; Microbiology ; Medicine Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications
Authors: Jörg Behler | Gábor Cśanyi
Source: The European Physical Journal B (article, 2021-07-01)
Field: Materials Science — Materials Chemistry

Machine learning potentials for extended systems: a perspective Abstract In the past two and a half decades machine learning potentials have evolved from a special purpose solution to a broadly applicable tool for large-scale atomistic simulations. By combining the efficiency of empirical potentials and force fields with an accuracy close to first-principles calculations they now enable computer simulations of a wide range of molecules and materials. In this perspective, we summarize the present status of these new types of models for extended systems, which are increasingly used for materials modelling. There are several approaches, but they all have in common that they exploit the locality of atomic properties in some form. Long-range interactions, most prominently electrostatic interactions, can also be included even for systems in which non-local charge transfer leads to an electronic structure that depends globally on all atomic positions. Remaining challenges and limitations of current approaches are discussed. Graphic Abstract Locality ; Exploit ; Perspective (graphical) ; Computer science ; Range (aeronautics) ; Charge (physics) ; Statistical physics ; Artificial intelligence ; Physics ; Engineering ; Aerospace engineering ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Pavlo O. Dral | Mario Barbatti
Source: Nature Reviews Chemistry (review, 2021-05-20)
Field: Materials Science — Materials Chemistry

Molecular excited states through a machine learning lens Excited state ; Computer science ; Quantum ; State (computer science) ; Molecular machine ; Nanotechnology ; Artificial intelligence ; Physics ; Materials science ; Quantum mechanics ; Algorithm Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Organic Electronics and Photovoltaics
Authors: Lei Tao | Vikas Varshney | Ying Li
Source: Journal of Chemical Information and Modeling (article, 2021-10-18)
Field: Materials Science — Materials Chemistry

Benchmarking Machine Learning Models for Polymer Informatics: An Example of Glass Transition Temperature In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the glass transition temperature <i>T</i><sub>g</sub> and other properties of polymers has attracted extensive attention. This data-centric approach is much more efficient and practical than the laborious experimental measurements when encountered a daunting number of polymer structures. Various ML models are demonstrated to perform well for <i>T</i><sub>g</sub> prediction. Nevertheless, they are trained on different data sets, using different structure representations, and based on different feature engineering methods. Thus, the critical question arises on selecting a proper ML model to better handle the <i>T</i><sub>g</sub> prediction with generalization ability. To provide a fair comparison of different ML techniques and examine the key factors that affect the model performance, we carry out a systematic benchmark study by compiling 79 different ML models and training them on a large and diverse data set. The three major components in setting up an ML model are structure representations, feature representations, and ML algorithms. In terms of polymer structure representation, we consider the polymer monomer, repeat unit, and oligomer with longer chain structure. Based on that feature, representation is calculated, including Morgan fingerprinting with or without substructure frequency, RDKit descriptors, molecular embedding, molecular graph, etc. Afterward, the obtained feature input is trained using different ML algorithms, such as deep neural networks, convolutional neural networks, random forest, support vector machine, LASSO regression, and Gaussian process regression. We evaluate the performance of these ML models using a holdout test set and an extra unlabeled data set from high-throughput molecular dynamics simulation. The ML model's generalization ability on an unlabeled data set is especially focused, and the model's sensitivity to topology and the molecular weight of polymers is also taken into consideration. This benchmark study provides not only a guideline for the <i>T</i><sub>g</sub> prediction task but also a useful reference for other polymer informatics tasks. Computer science ; Artificial intelligence ; Machine learning ; Support vector machine ; Feature (linguistics) ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Alaa El Din Mahmoud | Khairia M. Al-Qahtani | Sahab O. Alflaij | Salma F. Al-Qahtani | Faten A. Alsamhan
Source: Scientific Reports (article, 2021-06-15)
Field: Materials Science — Materials Chemistry

Green copper oxide nanoparticles for lead, nickel, and cadmium removal from contaminated water Environmentally friendly copper oxide nanoparticles (CuO NPs) were prepared with a green synthesis route without using hazardous chemicals. Hence, the extracts of mint leaves and orange peels were utilized as reducing agents to synthesize CuO NPs-1 and CuO NPs-2, respectively. The synthesized CuO NPs nanoparticles were characterized using scanning electron microscopy (SEM), Energy Dispersive X-ray Analysis (EDX), BET surface area, Ultraviolet-Visible spectroscopy (UV-Vis), and Fourier Transform Infrared Spectroscopy (FT-IR). Various parameters of batch experiments were considered for the removal of Pb(II), Ni(II), and Cd(II) using the CuO NPs such as nanosorbent dose, contact time, pH, and initial metal concentration. The maximum uptake capacity (q<sub>m</sub>) of both CuO NPs-1 and CuO NPs-2 followed the order of Pb(II) > Ni(II) > Cd(II). The optimum q<sub>m</sub> of CuO NPs were 88.80, 54.90, and 15.60 mg g<sup>-1</sup> for Pb(II), Ni(II), and Cd(II), respectively and occurred at sorbent dose of 0.33 g L<sup>-1</sup> and pH of 6. Furthermore, isotherm and kinetic models were applied to fit the experimental data. Freundlich models (R<sup>2</sup> > 0.97) and pseudo-second-order model (R<sup>2</sup> > 0.96) were fitted well to the experimental data and the equilibrium of metal adsorption occurred within 60 min. Nuclear chemistry ; Freundlich equation ; Nanoparticle ; Cadmium ; Copper ; Adsorption ; Metal ; Langmuir adsorption model ; Fourier transform infrared spectroscopy ; Nickel oxide ; Chemistry ; Nickel ; Materials science ; Chemical engineering ; Nanotechnology ; Organic chemistry Nanoparticles: synthesis and applications ; Copper-based nanomaterials and applications ; Nanomaterials for catalytic reactions