jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andreas Singraber | Tobias Morawietz | Jörg Behler | Christoph Dellago
Source: Journal of Chemical Theory and Computation (article, 2019-04-17)
Field: Materials Science — Materials Chemistry

Parallel Multistream Training of High-Dimensional Neural Network Potentials Over the past years high-dimensional neural network potentials (HDNNPs), fitted to accurately reproduce ab initio potential energy surfaces, have become a powerful tool in chemistry, physics and materials science. Here, we focus on the training of the neural networks that lies at the heart of the HDNNP method. We present an efficient approach for optimizing the weight parameters of the neural network via multistream Kalman filtering, using potential energies and forces as reference data. In this procedure, the choice of the free parameters of the Kalman filter can have a significant impact on the fit quality. Carrying out a large parameter study, we determine optimal settings and demonstrate how to optimize training results of HDNNPs. Moreover, we illustrate our HDNNP training approach by revisiting previously presented fits for water and developing a new potential for copper sulfide. This material, accessible in computer simulations so far only via first-principles methods, forms a particularly complex solid structure at low temperatures and undergoes a phase transition to a superionic state upon heating. Analyzing MD simulations carried out with the Cu<sub>2</sub>S HDNNP, we confirm that the underlying ab initio reference method indeed reproduces this behavior. Artificial neural network ; Computer science ; Kalman filter ; Ab initio ; Training set ; Training (meteorology) ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Pumidech Puthongkham | B. Jill Venton
Source: The Analyst (review, 2019-12-27)
Field: Chemistry — Electrochemistry

Recent advances in fast-scan cyclic voltammetry Fast-scan cyclic voltammetry (FSCV) at carbon-fiber microelectrodes (CFMEs) is a versatile electrochemical technique to probe neurochemical dynamics in vivo. Progress in FSCV methodology continues to address analytical challenges arising from biological needs to measure low concentrations of neurotransmitters at specific sites. This review summarizes recent advances in FSCV method development in three areas: (1) waveform optimization, (2) electrode development, and (3) data analysis. First, FSCV waveform parameters such as holding potential, switching potential, and scan rate have been optimized to monitor new neurochemicals. The new waveform shapes introduce better selectivity toward specific molecules such as serotonin, histamine, hydrogen peroxide, octopamine, adenosine, guanosine, and neuropeptides. Second, CFMEs have been modified with nanomaterials such as carbon nanotubes or replaced with conducting polymers to enhance sensitivity, selectivity, and antifouling properties. Different geometries can be obtained by 3D-printing, manufacturing arrays, or fabricating carbon nanopipettes. Third, data analysis is important to sort through the thousands of CVs obtained. Recent developments in data analysis include preprocessing by digital filtering, principal components analysis for distinguishing analytes, and developing automated algorithms to detect peaks. Future challenges include multisite measurements, machine learning, and integration with other techniques. Advances in FSCV will accelerate research in neurochemistry to answer new biological questions about dynamics of signaling in the brain. Cyclic voltammetry ; Voltammetry ; Computer science ; Chemistry ; Electrochemistry ; Electrode ; Physical chemistry Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Aikaterini Geladaki | Nina Kočevar Britovšek | Lisa M. Breckels | Tom Smith | Owen L. Vennard | Claire M. Mulvey | Oliver M. Crook | Laurent Gatto | Kathryn S. Lilley
Source: Nature Communications (article, 2019-01-18)
Field: Chemistry — Spectroscopy

Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics The study of protein localisation has greatly benefited from high-throughput methods utilising cellular fractionation and proteomic profiling. Hyperplexed Localisation of Organelle Proteins by Isotope Tagging (hyperLOPIT) is a well-established method in this area. It achieves high-resolution separation of organelles and subcellular compartments but is relatively time- and resource-intensive. As a simpler alternative, we here develop Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation (LOPIT-DC) and compare this method to the density gradient-based hyperLOPIT approach. We confirm that high-resolution maps can be obtained using differential centrifugation down to the suborganellar and protein complex level. HyperLOPIT and LOPIT-DC yield highly similar results, facilitating the identification of isoform-specific localisations and high-confidence localisation assignment for proteins in suborganellar structures, protein complexes and signalling pathways. By combining both approaches, we present a comprehensive high-resolution dataset of human protein localisations and deliver a flexible set of protocols for subcellular proteomics. Analytical Ultracentrifugation ; Proteomics ; Differential (mechanical device) ; Ultracentrifuge ; High resolution ; Computational biology ; Computer science ; Chemistry ; Chromatography ; Biology ; Physics ; Biochemistry ; Geography ; Remote sensing Advanced Proteomics Techniques and Applications ; Glycosylation and Glycoproteins Research ; Microbial Community Ecology and Physiology
Authors: Liang Lv | Fuwei Zhuge | Fengjun Xie | Xujing Xiong | Qingfu Zhang | Nan Zhang | Yu Huang | Tianyou Zhai
Source: Nature Communications (article, 2019-07-26)
Field: Materials Science — Materials Chemistry

Reconfigurable two-dimensional optoelectronic devices enabled by local ferroelectric polarization Ferroelectric engineered pn doping in two-dimensional (2D) semiconductors hold essential promise in realizing customized functional devices in a reconfigurable manner. Here, we report the successful pn doping in molybdenum disulfide (MoS<sub>2</sub>) optoelectronic device by local patterned ferroelectric polarization, and its configuration into lateral diode and npn bipolar phototransistors for photodetection from such a versatile playground. The lateral pn diode formed in this way manifests efficient self-powered detection by separating ~12% photo-generated electrons and holes. When polarized as bipolar phototransistor, the device is customized with a gain ~1000 by its transistor action, reaching the responsivity ~12 A W<sup>-1</sup> and detectivity over 10<sup>13</sup> Jones while keeping a fast response speed within 20 μs. A promising pathway toward high performance optoelectronics is thus opened up based on local ferroelectric polarization coupled 2D semiconductors. Photodetection ; Optoelectronics ; Responsivity ; Materials science ; Ferroelectricity ; Photodiode ; Diode ; Semiconductor ; Polarization (electrochemistry) ; Doping ; Transistor ; Photodetector ; Voltage ; Physics ; Chemistry ; Dielectric 2D Materials and Applications ; Perovskite Materials and Applications ; MXene and MAX Phase Materials
Authors: Prakash Pitchappa | Abhishek Kumar | Saurav Prakash | Hariom Jani | T. Venkatesan | Ranjan Singh
Source: Advanced Materials (article, 2019-01-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Chalcogenide Phase Change Material for Active Terahertz Photonics The strikingly contrasting optical properties of various phases of chalcogenide phase change materials (PCM) has recently led to the development of novel photonic devices such as all-optical non-von Neumann memory, nanopixel displays, color rendering, and reconfigurable nanoplasmonics. However, the exploration of chalcogenide photonics is currently limited to optical and infrared frequencies. Here, a phase change material integrated terahertz metamaterial for multilevel nonvolatile resonance switching with spatial and temporal selectivity is demonstrated. By controlling the crystalline proportion of the PCM film, multilevel, non-volatile, terahertz resonance switching states with long retention time at zero hold power are realized. Spatially selective reconfiguration at sub-metamaterial scale is shown by delivering electrical stimulus locally through designer interconnect architecture. The PCM metamaterial also features ultrafast optical modulation of terahertz resonances with tunable switching speed based on the crystalline order of the PCM film. The multilevel nonvolatile, spatially selective, and temporally tunable PCM metamaterial will provide a pathway toward development of novel and disruptive terahertz technologies including spatio-temporal terahertz modulators for high speed wireless communication, neuromorphic photonics, and machine-learning metamaterials. Terahertz radiation ; Materials science ; Metamaterial ; Chalcogenide ; Optoelectronics ; Photonics ; Neuromorphic engineering ; Control reconfiguration ; Optical switch ; Ultrashort pulse ; Photodetection ; Optics ; Photodetector ; Computer science ; Laser ; Physics Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Phase-change materials and chalcogenides
Authors: Jon Paul Janet | Chenru Duan | Tzuhsiung Yang | Aditya Nandy | Heather J. Kulik
Source: Chemical Science (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

A quantitative uncertainty metric controls error in neural network-driven chemical discovery Machine learning (ML) models, such as artificial neural networks, have emerged as a complement to high-throughput screening, enabling characterization of new compounds in seconds instead of hours. The promise of ML models to enable large-scale chemical space exploration can only be realized if it is straightforward to identify when molecules and materials are outside the model's domain of applicability. Established uncertainty metrics for neural network models are either costly to obtain (<i>e.g.</i>, ensemble models) or rely on feature engineering (<i>e.g.</i>, feature space distances), and each has limitations in estimating prediction errors for chemical space exploration. We introduce the distance to available data in the latent space of a neural network ML model as a low-cost, quantitative uncertainty metric that works for both inorganic and organic chemistry. The calibrated performance of this approach exceeds widely used uncertainty metrics and is readily applied to models of increasing complexity at no additional cost. Tightening latent distance cutoffs systematically drives down predicted model errors below training errors, thus enabling predictive error control in chemical discovery or identification of useful data points for active learning. Metric (unit) ; Artificial neural network ; Computer science ; Machine learning ; Drug discovery ; Artificial intelligence ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Mattias Ångqvist | William A. Muñoz | J. Magnus Rahm | Erik Fransson | Céline Durniak | Piotr Rozyczko | Thomas H. Rod | Paul Erhart
Source: Advanced Theory and Simulations (article, 2019-05-02)
Field: Materials Science — Materials Chemistry

ICET – A Python Library for Constructing and Sampling Alloy Cluster Expansions Abstract Alloy cluster expansions (CEs) provide an accurate and computationally efficient mapping of the potential energy surface of multi‐component systems that enables comprehensive sampling of the many‐dimensional configuration space. Here, integrated cluster expansion toolkit (ICET), a flexible, extensible, and computationally efficient software package, is introduced for the construction and sampling of CEs. ICET is largely written in Python for easy integration in comprehensive workflows, including first‐principles calculations for the generation of reference data and machine learning libraries for training and validation. The package enables training using a variety of linear regression algorithms with and without regularization, Bayesian regression, feature selection, and cross‐validation. It also provides complementary functionality for structure enumeration and mapping as well as data management and analysis. Potential applications are illustrated by two examples, including the computation of the phase diagram of a prototypical metallic alloy and the analysis of chemical ordering in an inorganic semiconductor. Python (programming language) ; Computation ; Enumeration ; Software ; Cluster (spacecraft) ; Bayesian probability ; Sampling (signal processing) ; Data structure Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Jino Im | Sungwon Lee | Tae-Wook Ko | Hyun Woo Kim | YunKyong Hyon | Hyunju Chang
Source: npj Computational Materials (article, 2019-03-26)
Field: Materials Science — Materials Chemistry

Identifying Pb-free perovskites for solar cells by machine learning Abstract Recent advances in computing power have enabled the generation of large datasets for materials, enabling data-driven approaches to problem-solving in materials science, including materials discovery. Machine learning is a primary tool for manipulating such large datasets, predicting unknown material properties and uncovering relationships between structure and property. Among state-of-the-art machine learning algorithms, gradient-boosted regression trees (GBRT) are known to provide highly accurate predictions, as well as interpretable analysis based on the importance of features. Here, in a search for lead-free perovskites for use in solar cells, we applied the GBRT algorithm to a dataset of electronic structures for candidate halide double perovskites to predict heat of formation and bandgap. Statistical analysis of the selected features identifies design guidelines for the discovery of new lead-free perovskites. Computer science ; Artificial intelligence ; Machine learning ; Statistical learning ; Property (philosophy) ; Band gap ; Materials science ; Optoelectronics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Changsheng Wang | Huadong Fu | Lei Jiang | Dezhen Xue | Jianxin Xie
Source: npj Computational Materials (article, 2019-08-27)
Field: Materials Science — Materials Chemistry

A property-oriented design strategy for high performance copper alloys via machine learning Abstract Traditional strategies for designing new materials with targeted property including methods such as trial and error, and experiences of domain experts, are time and cost consuming. In the present study, we propose a machine learning design system involving three features of machine learning modeling, compositional design and property prediction, which can accelerate the discovery of new materials. We demonstrate better efficiency of on a rapid compositional design of high-performance copper alloys with a targeted ultimate tensile strength of 600–950 MPa and an electrical conductivity of 50.0% international annealed copper standard. There exists a good consistency between the predicted and measured values for three alloys from literatures and two newly made alloys with designed compositions. Our results provide a new recipe to realize the property-oriented compositional design for high-performance complex alloys via machine learning. Property (philosophy) ; Consistency (knowledge bases) ; Copper ; Computer science ; Materials science ; Machine learning ; Domain (mathematical analysis) ; Ultimate tensile strength ; Artificial intelligence ; Metallurgy ; Mathematics Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Microstructure and mechanical properties
Authors: Noam Bernstein | Gábor Cśanyi | Volker L. Deringer
Source: npj Computational Materials (article, 2019-10-11)
Field: Materials Science — Materials Chemistry

De novo exploration and self-guided learning of potential-energy surfaces Abstract Interatomic potential models based on machine learning (ML) are rapidly developing as tools for material simulations. However, because of their flexibility, they require large fitting databases that are normally created with substantial manual selection and tuning of reference configurations. Here, we show that ML potentials can be built in a largely automated fashion, exploring and fitting potential-energy surfaces from the beginning (de novo) within one and the same protocol. The key enabling step is the use of a configuration-averaged kernel metric that allows one to select the few most relevant and diverse structures at each step. The resulting potentials are accurate and robust for the wide range of configurations that occur during structure searching, despite only requiring a relatively small number of single-point DFT calculations on small unit cells. We apply the method to materials with diverse chemical nature and coordination environments, marking an important step toward the more routine application of ML potentials in physics, chemistry, and materials science. Flexibility (engineering) ; Computer science ; Metric (unit) ; Kernel (algebra) ; Interatomic potential ; Range (aeronautics) ; Point (geometry) ; Potential energy ; Energy (signal processing) ; Selection (genetic algorithm) ; Statistical physics ; Theoretical computer science ; Computational science ; Molecular dynamics ; Artificial intelligence ; Physics ; Computational chemistry ; Chemistry ; Mathematics ; Geometry ; Engineering ; Aerospace engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Xiao-Ye Wang | Xuelin Yao | Kläus Müllen
Source: Science China Chemistry (article, 2019-06-25)
Field: Materials Science — Materials Chemistry

Polycyclic aromatic hydrocarbons in the graphene era Polycyclic aromatic hydrocarbons (PAHs) have been the subject of interdisciplinary research in the fields of chemistry, physics, materials science, and biology. Notably, PAHs have drawn increasing attention since the discovery of graphene, which has been regarded as the "wonder" material in the 21st century. Different from semimetallic graphene, nanoscale graphenes, such as graphene nanoribbons and graphene quantum dots, exhibit finite band gaps owing to the quantum confinement, making them attractive semiconductors for next-generation electronic applications. Researches based on PAHs and graphenes have expanded rapidly over the past decade, thereby posing a challenge in conducting a comprehensive review. This study aims to interconnect the fields of PAHs and graphenes, which have mainly been discussed separately. In particular, by selecting representative examples, we explain how these two domains can stimulate each other. We hope that this integrated approach can offer new opportunities and further promote synergistic developments in these fields. Graphene ; Nanotechnology ; Nanoscopic scale ; Materials science Graphene research and applications ; Fullerene Chemistry and Applications ; Carbon and Quantum Dots Applications
Authors: Xueyang Li | Christian C. Roth | Dirk Mohr
Source: International Journal of Plasticity (article, 2019-02-18)
Field: Materials Science — Materials Chemistry

Machine-learning based temperature- and rate-dependent plasticity model: Application to analysis of fracture experiments on DP steel Materials science ; Plasticity ; Fracture (geology) ; Composite material High-Velocity Impact and Material Behavior ; Material Properties and Failure Mechanisms ; Metallurgy and Material Forming
Authors: Manoj Ravi | Vitaly L. Sushkevich | Amy J. Knorpp | Mark A. Newton | Dennis Palagin | Ana B. Pinar | Marco Ranocchiari | Jeroen A. van Bokhoven
Source: Nature Catalysis (article, 2019-05-20)
Field: Materials Science — Materials Chemistry

Misconceptions and challenges in methane-to-methanol over transition-metal-exchanged zeolites Methane ; Methanol ; Biochemical engineering ; Anaerobic oxidation of methane ; Process (computing) ; Catalysis ; Chemistry ; Nanotechnology ; Process engineering ; Environmental science ; Computer science ; Materials science ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Mesoporous Materials and Catalysis
Authors: Jessica Freeze | H. Ray Kelly | Víctor S. Batista
Source: Chemical Reviews (review, 2019-05-06)
Field: Materials Science — Materials Chemistry

Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists In silico catalyst design is a grand challenge of chemistry. Traditional computational approaches have been limited by the need to compute properties for an intractably large number of possible catalysts. Recently, inverse design methods have emerged, starting from a desired property and optimizing a corresponding chemical structure. Techniques used for exploring chemical space include gradient-based optimization, alchemical transformations, and machine learning. Though the application of these methods to catalysis is in its early stages, further development will allow for robust computational catalyst design. This review provides an overview of the evolution of inverse design approaches and their relevance to catalysis. The strengths and limitations of existing techniques are highlighted, and suggestions for future research are provided. Inverse ; Chemistry ; Biochemical engineering ; Relevance (law) ; Chemical space ; Catalysis ; Alchemy ; Artificial intelligence ; Management science ; Computer science ; Organic chemistry ; Engineering ; Drug discovery ; Mathematics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Sida Huang | Cheng Shang | Pei‐Lin Kang | Zhang Xiao-jie | Zhi‐Pan Liu
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2019-03-10)
Field: Materials Science — Materials Chemistry

LASP: Fast global potential energy surface exploration Here we introduce the LASP code, which is designed for large‐scale atomistic simulation of complex materials with neural network (NN) potential. The software architecture and functionalities of LASP will be overviewed. LASP features with the global neural network (G‐NN) potential that is generated by learning the first principles dataset of global PES from stochastic surface walking (SSW) global optimization. The combination of the SSW method with global NN potential facilitates greatly the PES exploration for a wide range of complex materials. Not limited to SSW‐NN global optimization, the software implements standard interfaces to dock with other energy/force evaluation packages and can also perform common tasks for computing PES properties, such as single‐ended and double‐ended transition state search, the molecular dynamics simulation with and without restraints. A few examples are given to illustrate the efficiency and capabilities of LASP code. Our ongoing efforts for code developing and G‐NN potential library building are also presented. This article is categorized under: Software &gt; Simulation Methods Computer science ; Global optimization ; Code (set theory) ; Software ; Artificial neural network ; Range (aeronautics) ; Potential energy surface ; Distributed computing ; Source code ; Computational science ; Artificial intelligence ; Algorithm ; Engineering ; Programming language ; Aerospace engineering ; Chemistry ; Set (abstract data type) Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Electron and X-Ray Spectroscopy Techniques