jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kamal Choudhary | Kevin F. Garrity | Andrew C. E. Reid | Brian DeCost | Adam J. Biacchi | Angela R. Hight Walker | Zachary Trautt | Jason Hattrick-Simpers | A. Gilad Kusne | Andrea Centrone | Albert Davydov | Jie Jiang | Ruth Pachter | Gowoon Cheon | Evan Reed | Ankit Agrawal | Xiaofeng Qian | Vinit Sharma | Houlong Zhuang | Sergei V. Kalinin | Bobby G. Sumpter | Ghanshyam Pilania | Pinar Acar | Subhasish Mandal | Kristjan Haule | David Vanderbilt | Karin Rabe | Francesca Tavazza
Source: npj Computational Materials (article, 2020-11-12)
Field: Materials Science — Materials Chemistry

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design Abstract The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov . Workflow ; Joint (building) ; Variety (cybernetics) ; Scripting language ; Toolbox ; Development (topology) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Model Reduction and Neural Networks
Authors: Jan Hermann | Zeno Schätzle | Frank Noé
Source: Nature Chemistry (article, 2020-09-23)
Field: Materials Science — Materials Chemistry

Deep-neural-network solution of the electronic Schrödinger equation Ansatz ; Wave function ; Diatomic molecule ; Quantum Monte Carlo ; Electronic structure ; Monte Carlo method ; Quantum ; Full configuration interaction Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum Computing Algorithms and Architecture
Authors: Anthony Wang | Ryan Murdock | Steven K. Kauwe | Anton O. Oliynyk | Aleksander Gurlo | Jakoah Brgoch | Kristin A. Persson | Taylor D. Sparks
Source: Chemistry of Materials (article, 2020-05-19)
Field: Materials Science — Materials Chemistry

Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices This Methods/Protocols article is intended for materials scientists interested in performing machine learning-centered research. We cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training, validation, evaluation and comparison, popular repositories for materials data and benchmarking data sets, model and architecture sharing, and finally publication. In addition, we include interactive Jupyter notebooks with example Python code to demonstrate some of the concepts, workflows, and best practices discussed. Overall, the data-driven methods and machine learning workflows and considerations are presented in a simple way, allowing interested readers to more intelligently guide their machine learning research using the suggested references, best practices, and their own materials domain expertise. Best practice ; Benchmarking ; Workflow ; Computer science ; Python (programming language) ; Feature engineering ; Data science ; Architecture ; Artificial intelligence ; Software engineering ; Machine learning ; Deep learning ; Programming language ; Database Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Computational Drug Discovery Methods
Authors: Philippe Schwaller | Riccardo Petraglia | Valerio Zullo | Vishnu H Nair | Rico Andreas Haeuselmann | Riccardo Pisoni | Costas Bekas | Anna Iuliano | Teodoro Laino
Source: Chemical Science (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy We present an extension of our Molecular Transformer model combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention. The single-step retrosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts for each retrosynthetic step. We introduce four metrics (coverage, class diversity, round-trip accuracy and Jensen-Shannon divergence) to evaluate the single-step retrosynthetic models, using the forward prediction and a reaction classification model always based on the transformer architecture. The hypergraph is constructed on the fly, and the nodes are filtered and further expanded based on a Bayesian-like probability. We critically assessed the end-to-end framework with several retrosynthesis examples from literature and academic exams. Overall, the frameworks have an excellent performance with few weaknesses related to the training data. The use of the introduced metrics opens up the possibility to optimize entire retrosynthetic frameworks by focusing on the performance of the single-step model only. Retrosynthetic analysis ; Transformer ; Computer science ; Artificial intelligence ; Graph ; Machine learning ; Data mining ; Engineering ; Theoretical computer science Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Asymmetric Hydrogenation and Catalysis
Authors: Paul Geerlings | Eduardo Chamorro | Pratim Kumar Chattaraj | Frank De Proft | José L. Gázquez | Shubin Liu | Christophe Morell | Alejandro Toro‐Labbé | Alberto Vela | Paul W. Ayers
Source: Theoretical Chemistry Accounts (article, 2020-01-31)
Field: Chemistry — Organic Chemistry

Conceptual density functional theory: status, prospects, issues Density functional theory ; Function (biology) ; Conceptual framework ; Computer science ; Exposition (narrative) ; Chemistry ; Computational chemistry ; Epistemology ; Philosophy Free Radicals and Antioxidants ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: David Pfau | James S. Spencer | Alexander G. D. G. Matthews | W. M. C. Foulkes
Source: Physical Review Research (article, 2020-09-16)
Field: Materials Science — Materials Chemistry

<i>Ab initio</i> solution of the many-electron Schrödinger equation with deep neural networks Given access to accurate solutions of the many-electron Schrdinger equation, nearly all chemistry could be derived from first principles. Exact wave functions of interesting chemical systems are out of reach because they are NP-hard to compute in general, but approximations can be found using polynomially scaling algorithms. Scaling ; Artificial neural network ; Representation (politics) ; Exact solutions in general relativity ; Mathematical model ; Function (biology) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Jiashuai Wang | Xiao Zhang | Zhe Li | Yanqing Ma | Lei Ma
Source: Journal of Power Sources (article, 2020-02-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent progress of biomass-derived carbon materials for supercapacitors Supercapacitor ; Biomass (ecology) ; Carbon fibers ; Energy storage ; Materials science ; Nanotechnology ; Environmental science ; Process engineering ; Electrochemistry ; Electrode ; Power (physics) ; Engineering ; Chemistry ; Ecology ; Composite material ; Physics Supercapacitor Materials and Fabrication ; Catalysis for Biomass Conversion ; Electrocatalysts for Energy Conversion
Authors: John E. Blume | William C. Manning | Gregory Troiano | Daniel Hornburg | Michael Figa | Lyndal K. Hesterberg | Theodore L. Platt | Xiaoyan Zhao | Rea A. Cuaresma | Patrick A. Everley | Marwin Ko | Hope Liou | Max Mahoney | Shadi Ferdosi | Eltaher M. Elgierari | Craig Stolarczyk | Behzad Tangeysh | Hongwei Xia | Ryan W. Benz | Asim Siddiqui | Steven A. Carr | Philip Ma | Róbert Langer | Vivek F. Farias | Omid C. Farokhzad
Source: Nature Communications (article, 2020-07-22)
Field: Chemistry — Spectroscopy

Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona Large-scale, unbiased proteomics studies are constrained by the complexity of the plasma proteome. Here we report a highly parallel protein quantitation platform integrating nanoparticle (NP) protein coronas with liquid chromatography-mass spectrometry for efficient proteomic profiling. A protein corona is a protein layer adsorbed onto NPs upon contact with biofluids. Varying the physicochemical properties of engineered NPs translates to distinct protein corona patterns enabling differential and reproducible interrogation of biological samples, including deep sampling of the plasma proteome. Spike experiments confirm a linear signal response. The median coefficient of variation was 22%. We screened 43 NPs and selected a panel of 5, which detect more than 2,000 proteins from 141 plasma samples using a 96-well automated workflow in a pilot non-small cell lung cancer classification study. Our streamlined workflow combines depth of coverage and throughput with precise quantification based on unique interactions between proteins and NPs engineered for deep and scalable quantitative proteomic studies. Proteome ; Proteomics ; Chemistry ; Mass spectrometry ; Nanotechnology ; Chromatography ; Materials science ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Biosensors and Analytical Detection
Authors: Guilherme Migliato Marega | Yanfei Zhao | Ahmet Avşar | Zhenyu Wang | Mukesh Tripathi | Aleksandra Rađenović | András Kis
Source: Nature (article, 2020-11-04)
Field: Materials Science — Materials Chemistry

Logic-in-memory based on an atomically thin semiconductor Von Neumann architecture ; Computer science ; Electronic circuit ; Logic gate ; Programmable logic device ; Transistor ; Electronics ; Pass transistor logic ; Logic family ; Computer architecture ; Embedded system ; Logic synthesis ; Electrical engineering ; Digital electronics ; Engineering ; Voltage ; Algorithm 2D Materials and Applications ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Lichen Liu | Avelino Corma
Source: Nature Reviews Materials (review, 2020-10-23)
Field: Chemistry — Inorganic Chemistry

Confining isolated atoms and clusters in crystalline porous materials for catalysis Catalysis ; Metal ; Materials science ; Nanoparticle ; Porosity ; Nanotechnology ; Polyoxometalate ; Chemical physics ; Chemical engineering ; Characterization (materials science) ; Reactivity (psychology) ; Chemistry ; Composite material ; Organic chemistry ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Covalent Organic Framework Applications
Authors: Dane Morgan | Ryan Jacobs
Source: Annual Review of Materials Research (article, 2020-05-06)
Field: Materials Science — Materials Chemistry

Opportunities and Challenges for Machine Learning in Materials Science Advances in machine learning have impacted myriad areas of materials science, such as the discovery of novel materials and the improvement of molecular simulations, with likely many more important developments to come. Given the rapid changes in this field, it is challenging to understand both the breadth of opportunities and the best practices for their use. In this review, we address aspects of both problems by providing an overview of the areas in which machine learning has recently had significant impact in materials science, and then we provide a more detailed discussion on determining the accuracy and domain of applicability of some common types of machine learning models. Finally, we discuss some opportunities and challenges for the materials community to fully utilize the capabilities of machine learning. Domain (mathematical analysis) ; Field (mathematics) ; Deep learning ; Domain knowledge Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Teng Tan | Xiantao Jiang | Cong Wang | Baicheng Yao | Han Zhang
Source: Advanced Science (review, 2020-04-08)
Field: Materials Science — Materials Chemistry

2D Material Optoelectronics for Information Functional Device Applications: Status and Challenges Graphene and the following derivative 2D materials have been demonstrated to exhibit rich distinct optoelectronic properties, such as broadband optical response, strong and tunable light-mater interactions, and fast relaxations in the flexible nanoscale. Combining with optical platforms like fibers, waveguides, grating, and resonators, these materials has spurred a variety of active and passive applications recently. Herein, the optical and electrical properties of graphene, transition metal dichalcogenides, black phosphorus, MXene, and their derivative van der Waals heterostructures are comprehensively reviewed, followed by the design and fabrication of these 2D material-based optical structures in implementation. Next, distinct devices, ranging from lasers to light emitters, frequency convertors, modulators, detectors, plasmonic generators, and sensors, are introduced. Finally, the state-of-art investigation progress of 2D material-based optoelectronics offers a promising way to realize new conceptual and high-performance applications for information science and nanotechnology. The outlook on the development trends and important research directions are also put forward. Graphene ; Materials science ; Nanotechnology ; Broadband ; Optoelectronics ; Black phosphorus ; Plasmon ; Heterojunction ; Computer science ; Telecommunications 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Dorte B. Bekker‐Jensen | Oliver M. Bernhardt | Alexander Hogrebe | Ana Martínez‐Val | Lynn Verbeke | Tejas Gandhi | Christian D. Kelstrup | Lukas Reiter | Jesper V. Olsen
Source: Nature Communications (article, 2020-02-07)
Field: Chemistry — Spectroscopy

Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries Quantitative phosphoproteomics has transformed investigations of cell signaling, but it remains challenging to scale the technology for high-throughput analyses. Here we report a rapid and reproducible approach to analyze hundreds of phosphoproteomes using data-independent acquisition (DIA) with an accurate site localization score incorporated into Spectronaut. DIA-based phosphoproteomics achieves an order of magnitude broader dynamic range, higher reproducibility of identification, and improved sensitivity and accuracy of quantification compared to state-of-the-art data-dependent acquisition (DDA)-based phosphoproteomics. Notably, direct DIA without the need of spectral libraries performs close to analyses using project-specific libraries, quantifying > 20,000 phosphopeptides in 15 min single-shot LC-MS analysis per condition. Adaptation of a 3D multiple regression model-based algorithm enables global determination of phosphorylation site stoichiometry in DIA. Scalability of the DIA approach is demonstrated by systematically analyzing the effects of thirty kinase inhibitors in context of epidermal growth factor (EGF) signaling showing that specific protein kinases mediate EGF-dependent phospho-regulation. Profiling (computer programming) ; Computer science ; Computational biology ; Information retrieval ; Biology ; Operating system Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Gene expression and cancer classification
Authors: Yue Liu | Biru Guo | Xinxin Zou | Yajie Li | Siqi Shi
Source: Energy storage materials (article, 2020-07-02)
Field: Materials Science — Materials Chemistry

Machine learning assisted materials design and discovery for rechargeable batteries Battery (electricity) ; Machine learning ; Computer science ; Artificial intelligence ; Process (computing) ; Property (philosophy) ; Feature selection ; Component (thermodynamics) ; Power (physics) Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Charles Yang | Youngsoo Kim | Seunghwa Ryu | Grace X. Gu
Source: Materials & Design (article, 2020-01-20)
Field: Materials Science — Materials Chemistry

Prediction of composite microstructure stress-strain curves using convolutional neural networks Stress-strain curves are an important representation of a material's mechanical properties, from which important properties such as elastic modulus, strength, and toughness, are defined. However, generating stress-strain curves from numerical methods such as finite element method (FEM) is computationally intensive, especially when considering the entire failure path for a material. As a result, it is difficult to perform high throughput computational design of materials with large design spaces, especially when considering mechanical responses beyond the elastic limit. In this work, a combination of principal component analysis (PCA) and convolutional neural networks (CNN) are used to predict the entire stress-strain behavior of binary composites evaluated over the entire failure path, motivated by the significantly faster inference speed of empirical models. We show that PCA transforms the stress-strain curves into an effective latent space by visualizing the eigenbasis of PCA. Despite having a dataset of only 10-27% of possible microstructure configurations, the mean absolute error of the prediction is <10% of the range of values in the dataset, when measuring model performance based on derived material descriptors, such as modulus, strength, and toughness. Our study demonstrates the potential to use machine learning to accelerate material design, characterization, and optimization. Materials science ; Convolutional neural network ; Finite element method ; Stress (linguistics) ; Elastic modulus ; Toughness ; Principal component analysis ; Structural engineering ; Fracture toughness ; Stress–strain curve ; Modulus ; Composite material ; Computer science ; Artificial intelligence ; Deformation (meteorology) ; Engineering Machine Learning in Materials Science ; Composite Material Mechanics ; Corrosion Behavior and Inhibition
Authors: Preeti Thakur | Deepika Chahar | Shilpa Taneja | Nikhil Bhalla | Atul Thakur
Source: Ceramics International (review, 2020-04-07)
Field: Materials Science — Materials Chemistry

A review on MnZn ferrites: Synthesis, characterization and applications Materials science ; Ferrite (magnet) ; Inductor ; Laptop ; Coercivity ; Characterization (materials science) ; Doping ; Transformer ; Optoelectronics ; Electrical engineering ; Nanotechnology ; Composite material ; Computer science ; Voltage ; Condensed matter physics Magnetic Properties and Synthesis of Ferrites ; Advancements in Battery Materials ; Copper-based nanomaterials and applications
Authors: Sajjad Abdollahramezani | Omid Hemmatyar | Hossein Taghinejad | Alex Krasnok | Yashar Kiarashinejad | Mohammadreza Zandehshahvar | Andrea Alù | Ali Adibi
Source: Nanophotonics (article, 2020-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tunable nanophotonics enabled by chalcogenide phase‐change materials Abstract Nanophotonics has garnered intensive attention due to its unique capabilities in molding the flow of light in the subwavelength regime. Metasurfaces (MSs) and photonic integrated circuits (PICs) enable the realization of mass‐producible, cost‐effective, and efficient flat optical components for imaging, sensing, and communications. In order to enable nanophotonics with multipurpose functionalities, chalcogenide phase‐change materials (PCMs) have been introduced as a promising platform for tunable and reconfigurable nanophotonic frameworks. Integration of non‐volatile chalcogenide PCMs with unique properties such as drastic optical contrasts, fast switching speeds, and long‐term stability grants substantial reconfiguration to the more conventional static nanophotonic platforms. In this review, we discuss state‐of‐the‐art developments as well as emerging trends in tunable MSs and PICs using chalcogenide PCMs. We outline the unique material properties, structural transformation, and thermo‐optic effects of well‐established classes of chalcogenide PCMs. The emerging deep learning‐based approaches for the optimization of reconfigurable MSs and the analysis of light‐matter interactions are also discussed. The review is concluded by discussing existing challenges in the realization of adjustable nanophotonics and a perspective on the possible developments in this promising area. Nanophotonics ; Chalcogenide ; Photonics ; Control reconfiguration ; Realization (probability) ; Chalcogenide glass ; Optical switch Metamaterials and Metasurfaces Applications ; Phase-change materials and chalcogenides ; Solar-Powered Water Purification Methods
Authors: Venkata S. Mandala | Matthew J. McKay | A. Shcherbakov | Aurelio J. Dregni | Antonios Kolocouris | Mei Hong
Source: Nature Structural & Molecular Biology (article, 2020-11-11)
Field: Chemistry — Spectroscopy

Structure and drug binding of the SARS-CoV-2 envelope protein transmembrane domain in lipid bilayers Transmembrane domain ; Transmembrane protein ; Lipid bilayer ; Protein structure ; Helix bundle ; Chemistry ; Helix (gastropod) ; Biophysics ; Nuclear magnetic resonance spectroscopy ; Protein folding ; Crystallography ; Biology ; Biochemistry ; Stereochemistry ; Amino acid ; Membrane Advanced NMR Techniques and Applications ; Infectious Encephalopathies and Encephalitis ; Lipid Membrane Structure and Behavior