jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thomas E. Gartner | Linfeng Zhang | Pablo M. Piaggi | Roberto Car | Athanassios Z. Panagiotopoulos | Pablo G. Debenedetti
Source: Proceedings of the National Academy of Sciences (article, 2020-10-02)
Field: Materials Science — Materials Chemistry

Signatures of a liquid–liquid transition in an ab initio deep neural network model for water The possible existence of a metastable liquid-liquid transition (LLT) and a corresponding liquid-liquid critical point (LLCP) in supercooled liquid water remains a topic of much debate. An LLT has been rigorously proved in three empirically parametrized molecular models of water, and evidence consistent with an LLT has been reported for several other such models. In contrast, experimental proof of this phenomenon has been elusive due to rapid ice nucleation under deeply supercooled conditions. In this work, we combined density functional theory (DFT), machine learning, and molecular simulations to shed additional light on the possible existence of an LLT in water. We trained a deep neural network (DNN) model to represent the ab initio potential energy surface of water from DFT calculations using the Strongly Constrained and Appropriately Normed (SCAN) functional. We then used advanced sampling simulations in the multithermal-multibaric ensemble to efficiently explore the thermophysical properties of the DNN model. The simulation results are consistent with the existence of an LLCP, although they do not constitute a rigorous proof thereof. We fit the simulation data to a two-state equation of state to provide an estimate of the LLCP's location. These combined results-obtained from a purely first-principles approach with no empirical parameters-are strongly suggestive of the existence of an LLT, bolstering the hypothesis that water can separate into two distinct liquid forms. Metastability ; Supercooling ; Ab initio ; Water model ; Statistical physics ; Nucleation ; Artificial neural network ; Liquid water ; Density functional theory ; Thermodynamics ; Work (physics) ; Potential energy surface ; Molecular dynamics ; Materials science ; Physics ; Computer science ; Artificial intelligence ; Quantum mechanics Material Dynamics and Properties ; Theoretical and Computational Physics ; Phase Equilibria and Thermodynamics
Authors: E. van der Giessen | Peter A. Schultz | Nicolas Bertin | Vasily V. Bulatov | Wei Cai | Gábor Csányi | Stephen M. Foiles | M.G.D. Geers | Carlos González | Markus Hütter | Woo Kyun Kim | Dennis M. Kochmann | Javier LLorca | Ann E. Mattsson | Jörg Rottler | Alexander L. Shluger | Ryan B. Sills | Ingo Steinbach | Alejandro Strachan | Ellad B. Tadmor
Source: Modelling and Simulation in Materials Science and Engineering (article, 2020-03-23)
Field: Materials Science — Materials Chemistry

Roadmap on multiscale materials modeling Abstract Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware. Computer science ; Management science ; Field (mathematics) ; Multiscale modeling ; Scale (ratio) ; Systems engineering ; Data science ; Nanotechnology ; Biochemical engineering ; Materials science ; Engineering ; Physics Machine Learning in Materials Science ; Microstructure and mechanical properties ; Solidification and crystal growth phenomena
Authors: Haiyang Niu | Luigi Bonati | Pablo M. Piaggi | Michele Parrinello
Source: Nature Communications (article, 2020-05-27)
Field: Materials Science — Materials Chemistry

Ab initio phase diagram and nucleation of gallium Elemental gallium possesses several intriguing properties, such as a low melting point, a density anomaly and an electronic structure in which covalent and metallic features coexist. In order to simulate this complex system, we construct an ab initio quality interaction potential by training a neural network on a set of density functional theory calculations performed on configurations generated in multithermal-multibaric simulations. Here we show that the relative equilibrium between liquid gallium, α-Ga, β-Ga, and Ga-II is well described. The resulting phase diagram is in agreement with the experimental findings. The local structure of liquid gallium and its nucleation into α-Ga and β-Ga are studied. We find that the formation of metastable β-Ga is kinetically favored over the thermodinamically stable α-Ga. Finally, we provide insight into the experimental observations of extreme undercooling of liquid Ga. Gallium ; Phase diagram ; Metastability ; Nucleation ; Ab initio ; Materials science ; Supercooling ; Pseudopotential ; Chemical physics ; Ab initio quantum chemistry methods ; Density functional theory ; Phase (matter) ; Thermodynamics ; Computational chemistry ; Condensed matter physics ; Chemistry ; Physics ; Molecule Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Advanced Chemical Physics Studies
Authors: Lukáš Krásný | Paul H. Huang
Source: Molecular Omics (article, 2020-10-09)
Field: Chemistry — Spectroscopy

Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology Data-independent acquisition mass spectrometry (DIA-MS) is a next generation proteomic methodology that generates permanent digital proteome maps offering highly reproducible retrospective analysis of cellular and tissue specimens. The adoption of this technology has ushered a new wave of oncology studies across a wide range of applications including its use in molecular classification, oncogenic pathway analysis, drug and biomarker discovery and unravelling mechanisms of therapy response and resistance. In this review, we provide an overview of the experimental workflows commonly used in DIA-MS, including its current strengths and limitations versus conventional data-dependent acquisition mass spectrometry (DDA-MS). We further summarise a number of key studies to illustrate the power of this technology when applied to different facets of oncology. Finally we offer a perspective of the latest innovations in DIA-MS technology and machine learning-based algorithms necessary for driving the development of high-throughput, in-depth and reproducible proteomic assays that are compatible with clinical diagnostic workflows, which will ultimately enable the delivery of precision cancer medicine to achieve optimal patient outcomes. Mass spectrometry ; Proteomics ; Mass spectrometry imaging ; Accelerator mass spectrometry ; Medicine ; Oncology ; Chemistry ; Chromatography ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Yashar Kiarashinejad | Sajjad Abdollahramezani | Ali Adibi
Source: npj Computational Materials (article, 2020-02-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures Abstract In this paper, we demonstrate a computationally efficient new approach based on deep learning (DL) techniques for analysis, design and optimization of electromagnetic (EM) nanostructures. We use the strong correlation among features of a generic EM problem to considerably reduce the dimensionality of the problem and thus, the computational complexity, without imposing considerable errors. By employing the dimensionality reduction concept using the more recently demonstrated autoencoder technique, we redefine the conventional many-to-one design problem in EM nanostructures into a one-to-one problem plus a much simpler many-to-one problem, which can be simply solved using an analytic formulation. This approach reduces the computational complexity in solving both the forward problem (i.e., analysis) and the inverse problem (i.e., design) by orders of magnitude compared to conventional approaches. In addition, it provides analytic formulations that, despite their complexity, can be used to obtain intuitive understanding of the physics and dynamics of EM wave interaction with nanostructures with minimal computation requirements. As a proof-of-concept, we applied such an efficacious method to design a new class of on-demand reconfigurable optical metasurfaces based on phase-change materials (PCMs). The experimental results of the fabricated devices are in good agreement with those predicted by the proposed approach. We envision that the integration of such a DL-based technique with full-wave commercial software packages offers a powerful toolkit to facilitate the analysis, design, and optimization of the EM nanostructures as well as explaining, understanding, and predicting the observed responses in such structures. It will thus enable to solve complex design problems that could not be solved with existing techniques. Curse of dimensionality ; Autoencoder ; Deep learning ; Dimensionality reduction ; Reduction (mathematics) ; Computation ; Computational complexity theory ; Inverse problem Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Electromagnetic Scattering and Analysis
Authors: Andrew Hennessy | Kenneth Clarke | Megan Lewis
Source: Remote Sensing (review, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral Classification of Plants: A Review of Waveband Selection Generalisability Hyperspectral sensing, measuring reflectance over visible to shortwave infrared wavelengths, has enabled the classification and mapping of vegetation at a range of taxonomic scales, often down to the species level. Classification with hyperspectral measurements, acquired by narrow band spectroradiometers or imaging sensors, has generally required some form of spectral feature selection to reduce the dimensionality of the data to a level suitable for the construction of a classification model. Despite the large number of hyperspectral plant classification studies, an in-depth review of feature selection methods and resultant waveband selections has not yet been performed. Here, we present a review of the last 22 years of hyperspectral vegetation classification literature that evaluates the overall waveband selection frequency, waveband selection frequency variation by taxonomic, structural, or functional group, and the influence of feature selection choice by comparing such methods as stepwise discriminant analysis (SDA), support vector machines (SVM), and random forests (RF). This review determined that all characteristics of hyperspectral plant studies influence the wavebands selected for classification. This includes the taxonomic, structural, and functional groups of the target samples, the methods, and scale at which hyperspectral measurements are recorded, as well as the feature selection method used. Furthermore, these influences do not appear to be consistent. Moreover, the considerable variability in waveband selection caused by the feature selectors effectively masks the analysis of any variability between studies related to plant groupings. Additionally, questions are raised about the suitability of SDA as a feature selection method, with it producing waveband selections at odds with the other feature selectors. Caution is recommended when choosing a feature selector for hyperspectral plant classification: We recommend multiple methods being performed. The resultant sets of selected spectral features can either be evaluated individually by multiple classification models or combined as an ensemble for evaluation by a single classifier. Additionally, we suggest caution when relying upon waveband recommendations from the literature to guide waveband selections or classifications for new plant discrimination applications, as such recommendations appear to be weakly generalizable between studies. Hyperspectral imaging ; Feature selection ; Remote sensing ; Random forest ; Pattern recognition (psychology) ; Support vector machine ; Selection (genetic algorithm) ; Computer science ; Linear discriminant analysis ; Feature (linguistics) ; Artificial intelligence ; Dimensionality reduction ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Parisa Ebrahimi | Anand Kumar | Majeda Khraisheh
Source: Emergent Materials (review, 2020-08-03)
Field: Materials Science — Materials Chemistry

A review of recent advances in water-gas shift catalysis for hydrogen production Abstract The water-gas shift reaction (WGSR) is an intermediate reaction in hydrocarbon reforming processes, considered one of the most important reactions for hydrogen production. Here, water and carbon monoxide molecules react to generate hydrogen and carbon dioxide. From the thermodynamics aspect, pressure does not have an impact, whereas low-temperature conditions are suitable for high hydrogen selectivity because of the exothermic nature of the WGSR reaction. The performance of this reaction can be greatly enhanced in the presence of suitable catalysts. The WGSR has been widely studied due do the industrial significance resulting in a good volume of open literature on reactor design and catalyst development. A number of review articles are also available on the fundamental aspects of the reaction, including thermodynamic analysis, reaction condition optimization, catalyst design, and deactivation studies. Over the past few decades, there has been an exceptional development of the catalyst characterization techniques such as near-ambient x-ray photoelectron spectroscopy (NA-XPS) and in situ transmission electron microscopy (in situ TEM), providing atomic level information in presence of gases at elevated temperatures. These tools have been crucial in providing nanoscale structural details and the dynamic changes during reaction conditions, which were not available before. The present review is an attempt to gather the recent progress, particularly in the past decade, on the catalysts for low-temperature WGSR and their structural properties, leading to new insights that can be used in the future for effective catalyst design. For the ease of reading, the article is divided into subsections based on metals (noble and transition metal), oxide supports, and carbon-based supports. It also aims at providing a brief overview of the reaction conditions by including a table of catalysts with synthesis methods, reaction conditions, and key observations for a quick reference. Based on our study of literature on noble metal catalysts, atomic Pt substituted Mn 3 O 4 shows almost full CO conversion at 260 °C itself with zero methane formation. In the case of transition metals group, the inclusion of Cu in catalytic system seems to influence the CO conversion significantly, and in some cases, with CO conversion improvement by 65% at 280 °C. Moreover, mesoporous ceria as a catalyst support shows great potential with reports of full CO conversion at a low temperature of 175 °C. Water-gas shift reaction ; Catalysis ; Hydrogen production ; Exothermic reaction ; Chemistry ; Hydrogen ; X-ray photoelectron spectroscopy ; Carbon monoxide ; Chemical engineering ; Nanotechnology ; Materials science ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Gaofeng Liang | Haojie Wang | Hao Shi | Haitao Wang | Mengxi Zhu | Aihua Jing | Jinghua Li | Guangda Li
Source: Journal of Nanobiotechnology (review, 2020-10-29)
Field: Materials Science — Materials Chemistry

Recent progress in the development of upconversion nanomaterials in bioimaging and disease treatment Multifunctional lanthanide-based upconversion nanoparticles (UCNPs), which feature efficiently convert low-energy photons into high-energy photons, have attracted considerable attention in the domain of materials science and biomedical applications. Due to their unique photophysical properties, including light-emitting stability, excellent upconversion luminescence efficiency, low autofluorescence, and high detection sensitivity, and high penetration depth in samples, UCNPs have been widely applied in biomedical applications, such as biosensing, imaging and theranostics. In this review, we briefly introduced the major components of UCNPs and the luminescence mechanism. Then, we compared several common design synthesis strategies and presented their advantages and disadvantages. Several examples of the functionalization of UCNPs were given. Next, we detailed their biological applications in bioimaging and disease treatment, particularly drug delivery and photodynamic therapy, including antibacterial photodynamic therapy. Finally, the future practical applications in materials science and biomedical fields, as well as the remaining challenges to UCNPs application, were described. This review provides useful practical information and insights for the research on and application of UCNPs in the field of cancer. Nanotechnology ; Photon upconversion ; Photodynamic therapy ; Nanomaterials ; Materials science ; Cancer therapy ; Cancer treatment ; Computer science ; Luminescence ; Chemistry ; Cancer ; Medicine ; Optoelectronics Luminescence Properties of Advanced Materials ; Radiation Detection and Scintillator Technologies ; Nanoplatforms for cancer theranostics
Authors: Donglin Gan | Shuai Tao | Xiaoping Wang | Ziqiang Huang | Fuzeng Ren | Liming Fang | Kefeng Wang | Chaoming Xie | Xiong Lu
Source: Nano-Micro Letters (article, 2020-08-18)
Field: Materials Science — Polymers and Plastics

Mussel-Inspired Redox-Active and Hydrophilic Conductive Polymer Nanoparticles for Adhesive Hydrogel Bioelectronics Conductive polymers (CPs) are generally insoluble, and developing hydrophilic CPs is significant to broaden the applications of CPs. In this work, a mussel-inspired strategy was proposed to construct hydrophilic CP nanoparticles (CP NPs), while endowing the CP NPs with redox activity and biocompatibility. This is a universal strategy applicable for a series of CPs, including polyaniline, polypyrrole, and poly(3,4-ethylenedioxythiophene). The catechol/quinone contained sulfonated lignin (LS) was doped into various CPs to form CP/LS NPs with hydrophilicity, conductivity, and redox activity. These CP/LS NPs were used as versatile nanofillers to prepare the conductive hydrogels with long-term adhesiveness. The CP/LS NPs-incorporated hydrogels have a good conductivity because of the uniform distribution of the hydrophilic NPs in the hydrogel network, forming a well-connected electric path. The hydrogel exhibits long-term adhesiveness, which is attributed to the mussel-inspired dynamic redox balance of catechol/quinone groups on the CP/LS NPs. This conductive and adhesive hydrogel shows good electroactivity and biocompatibility and therefore has broad applications in electrostimulation of tissue regeneration and implantable bioelectronics. Bioelectronics ; Self-healing hydrogels ; Biocompatibility ; Polyaniline ; Polypyrrole ; Adhesive ; Materials science ; Conductive polymer ; Redox ; Nanoparticle ; Polymer ; Nanotechnology ; Chemical engineering ; Quinone ; Smart polymer ; Polymer chemistry ; Chemistry ; Biosensor ; Organic chemistry ; Composite material ; Polymerization Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Supercapacitor Materials and Fabrication
Authors: M. Ge | Fei Su | Zhicheng Zhao | Dong Su
Source: Materials Today Nano (article, 2020-06-04)
Field: Materials Science — Materials Chemistry

Deep learning analysis on microscopic imaging in materials science Workflow ; Deep learning ; Artificial intelligence ; Computer science ; Perspective (graphical) ; Data science ; Nanotechnology ; Pattern recognition (psychology) ; Materials science Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Rohit Batra | Carmen Chen | Tania G. Evans | Krista S. Walton | Rampi Ramprasad
Source: Nature Machine Intelligence (article, 2020-11-09)
Field: Chemistry — Inorganic Chemistry

Prediction of water stability of metal–organic frameworks using machine learning Stability (learning theory) ; Metal-organic framework ; Adsorption ; Materials science ; Computer science ; Nanotechnology ; Process engineering ; Machine learning ; Chemistry ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Sebastian Dick | Mariví Fernández-Serra
Source: Nature Communications (article, 2020-07-14)
Field: Materials Science — Materials Chemistry

Machine learning accurate exchange and correlation functionals of the electronic density Density functional theory (DFT) is the standard formalism to study the electronic structure of matter at the atomic scale. In Kohn-Sham DFT simulations, the balance between accuracy and computational cost depends on the choice of exchange and correlation functional, which only exists in approximate form. Here, we propose a framework to create density functionals using supervised machine learning, termed NeuralXC. These machine-learned functionals are designed to lift the accuracy of baseline functionals towards that provided by more accurate methods while maintaining their efficiency. We show that the functionals learn a meaningful representation of the physical information contained in the training data, making them transferable across systems. A NeuralXC functional optimized for water outperforms other methods characterizing bond breaking and excels when comparing against experimental results. This work demonstrates that NeuralXC is a first step towards the design of a universal, highly accurate functional valid for both molecules and solids. Density functional theory ; Lift (data mining) ; Formalism (music) ; Computer science ; Correlation ; Hybrid functional ; Representation (politics) ; Machine learning ; Artificial intelligence ; Statistical physics ; Theoretical computer science ; Algorithm ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies