jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chiho Kim | Rohit Batra | Lihua Chen | Tran Doan Huan | Rampi Ramprasad
Source: Computational Materials Science (article, 2020-09-28)
Field: Materials Science — Materials Chemistry

Polymer design using genetic algorithm and machine learning Polymer ; Crossover ; Property (philosophy) ; Algorithm ; Computer science ; Genetic algorithm ; Sequence (biology) ; Material Design ; Materials science ; Biological system ; Artificial intelligence ; Machine learning ; Chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Conducting polymers and applications
Authors: Qian Ma | Tie Jun Cui
Source: PhotoniX (article, 2020-03-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Information Metamaterials: bridging the physical world and digital world Abstract Over the past 5 years, digital coding and programmable metamaterials have been developed rapidly since their first exhibition in 2014. The iconic feature of the digital coding metamaterial is using digital codes like “0” and “1” to represent the distinct electromagnetic (EM) responses. This seemingly trivial progress has successfully reform the design theory from the effective medium to coding patterns, bridging the physical world and digital information world. More interestingly, beyond the simple coding on the parameters or patterns, the digital coding metamaterials are more intend to introduce the concept of direct interactions and operations of digital information within EM fields, to realize information processing, transmission or recognition. To accurately exhibit the informational specialties, we classify the coding metamaterials, digital metamaterials and programmable metamaterials, as well as other information-operating metamaterials, as information metamaterials . In this review article, we firstly introduce the digital coding concept, working mechanism, and related design methods. Then, three important theories including the scattering pattern calculation, convolution operation, and entropy of digital coding metamaterials, are discussed in details. Finally we introduce several system-level works based on the information metamaterials, such as the new-architecture wireless communication systems and reprogrammable imaging systems, to show the powerful manipulation capabilities of information metamaterials. As the next generation of information metamaterials, two proof-of-concept smart metamaterials and their advanced architectures are discussed. In the summary, the development track of information metamaterials and future trends are presented. Metamaterial ; Computer science ; Coding (social sciences) ; Physics ; Optics ; Mathematics Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Wireless Communication Technologies
Authors: Zhuoran Qiao | Matthew Welborn | Animashree Anandkumar | Frederick R. Manby | Thomas F. Miller
Source: The Journal of Chemical Physics (article, 2020-09-25)
Field: Materials Science — Materials Chemistry

OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features We introduce a machine learning method in which energy solutions from the Schrödinger equation are predicted using symmetry adapted atomic orbital features and a graph neural-network architecture. OrbNet is shown to outperform existing methods in terms of learning efficiency and transferability for the prediction of density functional theory results while employing low-cost features that are obtained from semi-empirical electronic structure calculations. For applications to datasets of drug-like molecules, including QM7b-T, QM9, GDB-13-T, DrugBank, and the conformer benchmark dataset of Folmsbee and Hutchison [Int. J. Quantum Chem. (published online) (2020)], OrbNet predicts energies within chemical accuracy of density functional theory at a computational cost that is 1000-fold or more reduced. Density functional theory ; Deep learning ; Benchmark (surveying) ; Quantum chemical ; Quantum chemistry ; Quantum ; Graph ; Graph theory ; Computational learning theory Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Gabriele Scalia | Colin A. Grambow | Barbara Pernici | Yi‐Pei Li | William H. Green
Source: Journal of Chemical Information and Modeling (article, 2020-04-03)
Field: Materials Science — Materials Chemistry

Evaluating Scalable Uncertainty Estimation Methods for Deep Learning-Based Molecular Property Prediction Advances in deep neural network (DNN)-based molecular property prediction have recently led to the development of models of remarkable accuracy and generalization ability, with graph convolutional neural networks (GCNNs) reporting state-of-the-art performance for this task. However, some challenges remain, and one of the most important that needs to be fully addressed concerns uncertainty quantification. DNN performance is affected by the volume and the quality of the training samples. Therefore, establishing when and to what extent a prediction can be considered reliable is just as important as outputting accurate predictions, especially when out-of-domain molecules are targeted. Recently, several methods to account for uncertainty in DNNs have been proposed, most of which are based on approximate Bayesian inference. Among these, only a few scale to the large data sets required in applications. Evaluating and comparing these methods has recently attracted great interest, but results are generally fragmented and absent for molecular property prediction. In this paper, we quantitatively compare scalable techniques for uncertainty estimation in GCNNs. We introduce a set of quantitative criteria to capture different uncertainty aspects and then use these criteria to compare MC-dropout, Deep Ensembles, and bootstrapping, both theoretically in a unified framework that separates aleatoric/epistemic uncertainty and experimentally on public data sets. Our experiments quantify the performance of the different uncertainty estimation methods and their impact on uncertainty-related error reduction. Our findings indicate that Deep Ensembles and bootstrapping consistently outperform MC-dropout, with different context-specific pros and cons. Our analysis leads to a better understanding of the role of aleatoric/epistemic uncertainty, also in relation to the target data set features, and highlights the challenge posed by out-of-domain uncertainty. Computer science ; Uncertainty quantification ; Scalability ; Bootstrapping (finance) ; Inference ; Artificial intelligence ; Machine learning ; Dropout (neural networks) ; Generalization ; Context (archaeology) ; Data mining ; Deep learning ; Convolutional neural network ; Bayesian inference ; Set (abstract data type) ; Bayesian probability ; Econometrics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Weile Jia | Handong Wang | Mohan Chen | Denghui Lu | Lin Lin | Roberto Car | E Weinan | Linfeng Zhang
Source:  (article, 2020-11-01)
Field: Materials Science — Materials Chemistry

Pushing the Limit of Molecular Dynamics with Ab Initio Accuracy to 100 Million Atoms with Machine Learning For 35 years, ab initio molecular dynamics (AIMD) has been the method of choice for modeling complex atomistic phenomena from first principles. However, most AIMD applications are limited by computational cost to systems with thousands of atoms at most. We report that a machine learning based simulation protocol (Deep Potential Molecular Dynamics), while retaining ab initio accuracy, can simulate more than 1 nanosecond-long trajectory of over 100 million atoms per day, using a highly optimized code (GPU DeePMD-kit) on the Summit supercomputer. Our code can efficiently scale up to the entire Summit supercomputer, attaining 91 PFLOPS in double precision (45.5% of the peak) and 162/275 PFLOPS in mixed-single/half precision. The great accomplishment of this work is that it opens the door to simulating unprecedented size and time scales with ab initio accuracy. It also poses new challenges to the next-generation supercomputer for a better integration of machine learning and physical modeling. Supercomputer ; Computer science ; Molecular dynamics ; Ab initio ; Computational science ; Double-precision floating-point format ; Ab initio quantum chemistry methods ; Code (set theory) ; Artificial intelligence ; Parallel computing ; Algorithm ; Computational chemistry ; Chemistry ; Physics ; Floating point ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Rayssa de Sousa Victor | Adillys Marcelo da Cunha Santos | Bianca Viana de Sousa | Gelmires de Araújo Neves | Lisiane Navarro de Lima Santana | Romualdo Rodrigues Menezes
Source: Materials (review, 2020-11-06)
Field: Materials Science — Biomaterials

A Review on Chitosan’s Uses as Biomaterial: Tissue Engineering, Drug Delivery Systems and Cancer Treatment Chitosan, derived from chitin, is a biopolymer consisting of arbitrarily distributed β-(1-4)-linked D-glucosamine and N-acetyl-D-glucosamine that exhibits outstanding properties- biocompatibility, biodegradability, non-toxicity, antibacterial activity, the capacity to form films, and chelating of metal ions. Most of these peculiar properties are attributed to the presence of free protonable amino groups along the chitosan backbone, which also gives it solubility in acidic conditions. Moreover, this biopolymer can also be physically modified, thereby presenting a variety of forms to be developed. Consequently, this polysaccharide is used in various fields, such as tissue engineering, drug delivery systems, and cancer treatment. In this sense, this review aims to gather the state-of-the-art concerning this polysaccharide when used as a biomaterial, providing information about its characteristics, chemical modifications, and applications. We present the most relevant and new information about this polysaccharide-based biomaterial's applications in distinct fields and also the ability of chitosan and its various derivatives to selectively permeate through the cancer cell membranes and exhibit anticancer activity, and the possibility of adding several therapeutic metal ions as a strategy to improve the therapeutic potential of this polymer. Biomaterial ; Chitosan ; Drug delivery ; Cancer ; Tissue engineering ; Nanotechnology ; Biomedical engineering ; Drug ; Materials science ; Medicine ; Pharmacology ; Engineering ; Chemical engineering ; Internal medicine Electrospun Nanofibers in Biomedical Applications ; Graphene and Nanomaterials Applications ; Nanoparticle-Based Drug Delivery
Authors: Mehwish Shafiq | Sumaira Anjum | Christophe Hano | Iram Anjum | Bilal Haider Abbasi
Source: Foods (review, 2020-02-03)
Field: Materials Science — Materials Chemistry

An Overview of the Applications of Nanomaterials and Nanodevices in the Food Industry The efficient progress in nanotechnology has transformed many aspects of food science and the food industry with enhanced investment and market share. Recent advances in nanomaterials and nanodevices such as nanosensors, nano-emulsions, nanopesticides or nanocapsules are intended to bring about innovative applications in the food industry. In this review, the current applications of nanotechnology for packaging, processing, and the enhancement of the nutritional value and shelf life of foods are targeted. In addition, the functionality and applicability of food-related nanotechnologies are also highlighted and critically discussed in order to provide an insight into the development and evaluation of the safety of nanotechnology in the food industry. Nanotechnology ; Food industry ; Applications of nanotechnology ; Nanosensor ; Food packaging ; Impact of nanotechnology ; Nanocapsules ; Food safety ; Risk analysis (engineering) ; Business ; Societal impact of nanotechnology ; Engineering ; Materials science ; Nanoparticle ; Chemistry Nanoparticles: synthesis and applications ; Dye analysis and toxicity ; Nanocomposite Films for Food Packaging
Authors: Zhaxylyk A. Kudyshev | Alexander V. Kildishev | Vladimir M. Shalaev | Alexandra Boltasseva
Source: Applied Physics Reviews (article, 2020-05-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-learning-assisted metasurface design for high-efficiency thermal emitter optimization Nanophotonic devices can provide solutions to challenges in energy conversion, information technologies, chemical or biological sensing, quantum computing, and secure communications. The realization of practical optical structures and devices is a complex problem due to the multitude of constraints on their optical performance, materials, scalability, and experimental tolerances, all of which are requirements implying large optimization spaces. However, despite the complexity of the process, to date, almost all nanophotonic structures are designed either intuitively or based on a priori selected topologies, and by adjusting a limited number of parameters. These intuition-based models are limited to ad hoc needs and have narrow applicability and predictive power, with the exhaustive parameter searches often performed manually. Since the comprehensive search in hyper-dimensional design space is highly resource-heavy, multi-objective optimization has so far been almost impossible. Humans' restrained capacity to think hyper-dimensionally also limits the perception of multivariate optimization models, and, therefore, advanced machinery is needed to manage the multi-domain, hyper-dimensional design parameter space. In this work, we merge the topology optimization method with deep learning algorithms, such as adversarial autoencoders, and show substantial improvement of the optimization process in terms of computational time (4900 times faster) and final devices efficiencies (∼98%) by providing unparalleled control of the compact design space representations. By enabling efficient, global optimization searches within complex landscapes, the proposed compact hyperparametric representations could become crucial for multi-constrained problems. The proposed approach could enable a much broader scope of the optimal designs and data-driven materials synthesis that goes beyond photonic and optoelectronic applications. Optimization problem ; Photonics ; Bayesian optimization ; Topology optimization ; A priori and a posteriori ; Scalability ; Realization (probability) ; Fine-tuning ; Nanomanufacturing Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Topology Optimization in Engineering
Authors: Daniel Insuasty | Juan‐Carlos Castillo | Diana Becerra | Hugo Rojas | Rodrigo Abonı́a
Source: Molecules (review, 2020-01-24)
Field: Chemistry — Organic Chemistry

Synthesis of Biologically Active Molecules through Multicomponent Reactions Focusing on the literature progress since 2002, the present review explores the highly significant role that multicomponent reactions (MCRs) have played as a very important tool for expedite synthesis of a vast number of organic molecules, but also, highlights the fact that many of such molecules are biologically active or at least have been submitted to any biological screen. The selected papers covered in this review must meet two mandatory requirements: (1) the reported products should be obtained via a multicomponent reaction; (2) the reported products should be biologically actives or at least tested for any biological property. Given the diversity of synthetic approaches utilized in MCRs, the highly diverse nature of the biological activities evaluated for the synthesized compounds, and considering their huge structural variability, much of the reported data are organized into concise schemes and tables to facilitate comparison, and to underscore the key points of this review. Biological activity ; Biochemical engineering ; Combinatorial chemistry ; Chemistry ; Nanotechnology ; Computer science ; Computational biology ; Biology ; Materials science ; Biochemistry ; Engineering Multicomponent Synthesis of Heterocycles ; Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Mary J. Donahue | Ana Sanchez‐Sanchez | Sahika Inal | Jing Qu | Róisı́n M. Owens | David Mecerreyes | George G. Malliaras | David C. Martin
Source: Materials Science and Engineering R Reports (article, 2020-02-12)
Field: Materials Science — Polymers and Plastics

Tailoring PEDOT properties for applications in bioelectronics Bioelectronics ; PEDOT:PSS ; Materials science ; Nanotechnology ; Conductive polymer ; Dopant ; Fabrication ; Flexibility (engineering) ; Polymer ; Doping ; Optoelectronics ; Composite material ; Biosensor Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Supercapacitor Materials and Fabrication
Authors: Denghui Lu | Handong Wang | Mohan Chen | Lin Lin | Roberto Car | E Weinan | Weile Jia | Linfeng Zhang
Source: Computer Physics Communications (article, 2020-09-21)
Field: Materials Science — Materials Chemistry

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy Ab initio ; Molecular dynamics ; Supercomputer ; Computer science ; Computational science ; Ab initio quantum chemistry methods ; Nanosecond ; Code (set theory) ; Computational chemistry ; Parallel computing ; Chemistry ; Molecule ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Nuclear Materials and Properties
Authors: Elizabeth A. Holm | Ryan Cohn | Nan Gao | Andrew R. Kitahara | Thomas P. Matson | Bo Lei | Srujana Rao Yarasi
Source: Metallurgical and Materials Transactions A (article, 2020-09-29)
Field: Materials Science — Materials Chemistry

Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis Microstructural characterization and analysis is the foundation of microstructural science, connecting materials structure to composition, process history, and properties. Microstructural quantification traditionally involves a human deciding what to measure and then devising a method for doing so. However, recent advances in computer vision (CV) and machine learning (ML) offer new approaches for extracting information from microstructural images. This overview surveys CV methods for numerically encoding the visual information contained in a microstructural image using either feature-based representations or convolutional neural network (CNN) layers, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships. Convolutional neural network ; Characterization (materials science) ; Process (computing) ; Image processing ; Machine vision ; Deep learning ; Artificial neural network ; Pattern recognition (psychology) Machine Learning in Materials Science ; Advanced Neural Network Applications ; Mineral Processing and Grinding
Authors: Agnieszka Ścibior | Łukasz Pietrzyk | Zbigniew Plewa | Andrzej Skiba
Source: Journal of Trace Elements in Medicine and Biology (review, 2020-04-12)
Field: Chemistry — Inorganic Chemistry

Vanadium: Risks and possible benefits in the light of a comprehensive overview of its pharmacotoxicological mechanisms and multi-applications with a summary of further research trends Vanadium ; Risk analysis (engineering) ; Biochemical engineering ; Nanotechnology ; Engineering ; Chemistry ; Business ; Materials science Vanadium and Halogenation Chemistry ; Medicinal plant effects and applications
Authors: Ruchira Mitra | Tong Xu | Hua Xiang | Jing Han
Source: Microbial Cell Factories (review, 2020-04-07)
Field: Materials Science — Biomaterials

Current developments on polyhydroxyalkanoates synthesis by using halophiles as a promising cell factory Plastic pollution is a severe threat to our environment which necessitates implementation of bioplastics to realize sustainable development for a green world. Polyhydroxyalkanoates (PHA) represent one of the potential candidates for these bioplastics. However, a major challenge faced by PHA is the high production cost which limits its commercial application. Halophiles are considered to be a promising cell factory for PHA synthesis due to its several unique characteristics including high salinity requirement preventing microbial contamination, high intracellular osmotic pressure allowing easy cell lysis for PHA recovery, and capability to utilize wide spectrum of low-cost substrates. Optimization of fermentation parameters has made it plausible to achieve large-scale production at low cost by using halophiles. Further deeper insights into halophiles have revealed the existence of diversified and even novel PHA synthetic pathways within different halophilic species that greatly affects PHA type. Thus, precise metabolic engineering of halophiles with the help of advanced tools and strategies have led to more efficient microbial cell factory for PHA production. This review is an endeavour to summarize the various research achievements in these areas which will help the readers to understand the current developments as well as the future efforts in PHA research. Polyhydroxyalkanoates ; Factory (object-oriented programming) ; Halophile ; Biotechnology ; Biochemical engineering ; Biology ; Engineering ; Computer science ; Bacteria ; Genetics biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis ; Electrospun Nanofibers in Biomedical Applications
Authors: Paraskevi Gkeka | Gabriel Stoltz | Amir Barati Farimani | Zineb Belkacemi | Michele Ceriotti | John D. Chodera | Aaron R. Dinner | Andrew L. Ferguson | Jean‐Bernard Maillet | Hervé Minoux | Christine Peter | Fabio Pietrucci | Ana Silveira | Alexandre Tkatchenko | Zofia Trstanova | Rafal Wiewiora | Tony Lelièvre
Source: arXiv (Cornell University) (review, 2020-04-15)
Field: Materials Science — Materials Chemistry

Machine learning force fields and coarse-grained variables in molecular\n dynamics: application to materials and biological systems Machine learning encompasses a set of tools and algorithms which are now\nbecoming popular in almost all scientific and technological fields. This is\ntrue for molecular dynamics as well, where machine learning offers promises of\nextracting valuable information from the enormous amounts of data generated by\nsimulation of complex systems. We provide here a review of our current\nunderstanding of goals, benefits, and limitations of machine learning\ntechniques for computational studies on atomistic systems, focusing on the\nconstruction of empirical force fields from ab-initio databases and the\ndetermination of reaction coordinates for free energy computation and enhanced\nsampling.\n Computer science ; Field (mathematics) ; Computation ; Machine learning ; Force field (fiction) ; Molecular dynamics ; Artificial intelligence ; Data science ; Complex system ; Computational science ; Algorithm ; Computational chemistry ; Chemistry ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications