jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Angelika Plota-Pietrzak | Anna Masek
Source: Materials (review, 2020-10-12)
Field: Materials Science — Materials Chemistry

Lifetime Prediction Methods for Degradable Polymeric Materials—A Short Review The determination of the secure working life of polymeric materials is essential for their successful application in the packaging, medicine, engineering and consumer goods industries. An understanding of the chemical and physical changes in the structure of different polymers when exposed to long-term external factors (e.g., heat, ozone, oxygen, UV radiation, light radiation, chemical substances, water vapour) has provided a model for examining their ultimate lifetime by not only stabilization of the polymer, but also accelerating the degradation reactions. This paper presents an overview of the latest accounts on the impact of the most common environmental factors on the degradation processes of polymeric materials, and some examples of shelf life of rubber products are given. Additionally, the methods of lifetime prediction of degradable polymers using accelerated ageing tests and methods for extrapolation of data from induced thermal degradation are described: the Arrhenius model, time-temperature superposition (TTSP), the Williams-Landel-Ferry (WLF) model and 5 isoconversional approaches: Friedman's, Ozawa-Flynn-Wall (OFW), the OFW method corrected by N. Sbirrazzuoli et al., the Kissinger-Akahira-Sunose (KAS) algorithm, and the advanced isoconversional method by S. Vyazovkin. Examples of applications in recent years are given. Polymer ; Degradation (telecommunications) ; Extrapolation ; Materials science ; Arrhenius equation ; Natural rubber ; Polymer science ; Chemical engineering ; Computer science ; Composite material ; Process engineering ; Chemistry ; Activation energy ; Physical chemistry ; Mathematics ; Engineering ; Telecommunications Thermal and Kinetic Analysis ; Polymer Nanocomposites and Properties ; Polymer crystallization and properties
Authors: Chengxiong Zou | Jinshan Li | William Yi Wang | Ying Zhang | De-Ye Lin | Ruihao Yuan | Xiaodan Wang | Bin Tang | Jun Wang | Xingyu Gao | Hongchao Kou | Xidong Hui | Xiaoqin Zeng | Ma Qian | Haifeng Song | Zi‐Kui Liu | Dongsheng Xu
Source: Acta Materialia (article, 2020-10-27)
Field: Materials Science — Materials Chemistry

Integrating data mining and machine learning to discover high-strength ductile titanium alloys Materials science ; Ductility (Earth science) ; Engineering physics ; Machine learning ; Computer science ; Artificial intelligence ; Metallurgy Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Metal and Thin Film Mechanics
Authors: Kasra Momeni | Yanzhou Ji | Yuanxi Wang | Shiddartha Paul | Sara Neshani | Dündar E. Yılmaz | Yun Kyung Shin | Difan Zhang | Jin-Wu Jiang | Harold S. Park | Susan B. Sinnott | Adri C. T. van Duin | Vincent H. Crespi | Long‐Qing Chen
Source: npj Computational Materials (review, 2020-03-19)
Field: Materials Science — Materials Chemistry

Multiscale computational understanding and growth of 2D materials: a review Abstract The successful discovery and isolation of graphene in 2004, and the subsequent synthesis of layered semiconductors and heterostructures beyond graphene have led to the exploding field of two-dimensional (2D) materials that explore their growth, new atomic-scale physics, and potential device applications. This review aims to provide an overview of theoretical, computational, and machine learning methods and tools at multiple length and time scales, and discuss how they can be utilized to assist/guide the design and synthesis of 2D materials beyond graphene. We focus on three methods at different length and time scales as follows: (i) nanoscale atomistic simulations including density functional theory (DFT) calculations and molecular dynamics simulations employing empirical and reactive interatomic potentials; (ii) mesoscale methods such as phase-field method; and (iii) macroscale continuum approaches by coupling thermal and chemical transport equations. We discuss how machine learning can be combined with computation and experiments to understand the correlations between structures and properties of 2D materials, and to guide the discovery of new 2D materials. We will also provide an outlook for the applications of computational approaches to 2D materials synthesis and growth in general. Graphene ; Computation ; Nanotechnology ; Molecular dynamics ; Multiscale modeling ; Field (mathematics) ; Density functional theory ; Materials science ; Heterojunction ; Computer science ; Statistical physics ; Physics ; Chemistry ; Computational chemistry ; Algorithm ; Mathematics ; Quantum mechanics Graphene research and applications ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Thomas A. A. Batchelor | Tobias Löffler | Bin Xiao | Olga A. Krysiak | Valerie Strotkötter | Jack K. Pedersen | Christian M. Clausen | Alan Savan | Yujiao Li | Wolfgang Schuhmann | Jan Rossmeisl | Alfred Ludwig
Source: Angewandte Chemie International Edition (article, 2020-12-29)
Field: Materials Science — Materials Chemistry

Complex‐Solid‐Solution Electrocatalyst Discovery by Computational Prediction and High‐Throughput Experimentation** Complex solid solutions ("high entropy alloys"), comprising five or more principal elements, promise a paradigm change in electrocatalysis due to the availability of millions of different active sites with unique arrangements of multiple elements directly neighbouring a binding site. Thus, strong electronic and geometric effects are induced, which are known as effective tools to tune activity. With the example of the oxygen reduction reaction, we show that by utilising a data-driven discovery cycle, the multidimensionality challenge raised by this catalyst class can be mastered. Iteratively refined computational models predict activity trends around which continuous composition-spread thin-film libraries are synthesised. High-throughput characterisation datasets are then used as input for refinement of the model. The refined model correctly predicts activity maxima of the exemplary model system Ag-Ir-Pd-Pt-Ru. The method can identify optimal complex-solid-solution materials for electrocatalytic reactions in an unprecedented manner. Electrocatalyst ; Computational model ; Entropy (arrow of time) ; Oxygen reduction ; Reduction (mathematics) ; Class (philosophy) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; High Entropy Alloys Studies
Authors: Wenbo Li | Zhaoning Li | Changfeng Si | Michael Y. Wong | Kazuya Jinnai | Abhishek Kumar Gupta | Ryota Kabe | Chihaya Adachi | Wei Huang | Eli Zysman‐Colman | Ifor D. W. Samuel
Source: Advanced Materials (article, 2020-10-07)
Field: Materials Science — Materials Chemistry

Organic Long‐Persistent Luminescence from a Thermally Activated Delayed Fluorescence Compound Organic long-persistent luminescence (OLPL) is one of the most promising methods for long-lived-emission applications. However, present room-temperature OLPL emitters are mainly based on a bimolecular exciplex system which usually needs an expensive small molecule such as 2,8-bis(diphenyl-phosphoryl)dibenzo[b,d]thiophene (PPT) as the acceptor. In this study, a new thermally activated delayed fluorescence (TADF) compound, 3-(4-(9H-carbazol-9-yl)phenyl)acenaphtho[1,2-b]pyrazine-8,9-dicarbonitrile (CzPhAP), is designed, which also shows OLPL in many well-known hosts such as PPT, 2,2',2″-(1,3,5-benzinetriyl)-tris(1-phenyl-1-H-benzimidazole) (TPBi), and poly(methyl methacrylate) (PMMA), without any exciplex formation, and its OLPL duration reaches more than 1 h at room temperature. Combining the low cost of PMMA manufacture and flexible designs of TADF molecules, pure organic, large-scale, color tunable, and low-cost room-temperature OLPL applications become possible. Moreover, it is found that the onset of the 77 K afterglow spectra from a TADF-emitter-doped film is not necessarily reliable for determining the lowest triplet state energy level. This is because in some TADF-emitter-doped films, optical excitation can generate charges (electron and holes) that can later recombine to form singlet excitons during the phosphorescence spectrum measurement. The spectrum taken in the phosphorescence time window at low temperature may consequently consist of both singlet and triplet emission. Phosphorescence ; Materials science ; Photochemistry ; Luminescence ; Singlet state ; Fluorescence ; Acceptor ; Excimer ; Afterglow ; OLED ; Exciton ; Doping ; Intersystem crossing ; Optoelectronics ; Nanotechnology ; Excited state ; Atomic physics ; Chemistry ; Optics Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Lanthanide and Transition Metal Complexes
Authors: Brooke E. Husic | Nicholas E. Charron | Dominik Lemm | Jiang Wang | Adrià Pérez | Maciej Majewski | Andreas Krämer | Yaoyi Chen | Simon Olsson | Gianni De Fabritiis | Frank Noé | Cecilia Clementi
Source: Refubium (Universitätsbibliothek der Freien Universität Berlin) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Coarse graining molecular dynamics with graph neural networks Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at an atomic resolution. However, a coarse graining model must be formulated such that the conclusions we draw from it are consistent with the conclusions we would draw from a model at a finer level of detail. It has been proved that a force matching scheme defines a thermodynamically consistent coarse-grained model for an atomistic system in the variational limit. Wang et al. [ACS Cent. Sci. 5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space. Their framework, however, requires the manual input of molecular features to machine learn the force field. In the present contribution, we build upon the advance of Wang et al. and introduce a hybrid architecture for the machine learning of coarse-grained force fields that learn their own features via a subnetwork that leverages continuous filter convolutions on a graph neural network architecture. We demonstrate that this framework succeeds at reproducing the thermodynamics for small biomolecular systems. Since the learned molecular representations are inherently transferable, the architecture presented here sets the stage for the development of machine-learned, coarse-grained force fields that are transferable across molecular systems. Granularity ; Computer science ; Statistical physics ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Junxiao Zhou | Haoliang Qian | Junxiang Zhao | Min Tang | Qianyi Wu | Ming Lei | Hailu Luo | Shuangchun Wen | Shaochen Chen | Zhaowei Liu
Source: National Science Review (article, 2020-08-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Two-dimensional optical spatial differentiation and high-contrast imaging Optical analog signal processing technology has been widely studied and applied in a variety of science and engineering fields, with the advantages of overcoming the low-speed and high-power consumption associated with its digital counterparts. Much attention has been given to emerging metasurface technology in the field of optical imaging and processing systems. Here, we demonstrate, for the first time, broadband two-dimensional spatial differentiation and high-contrast edge imaging based on a dielectric metasurface across the whole visible spectrum. This edge detection method works for both intensity and phase objects simply by inserting the metasurface into a commercial optical microscope. This highly efficient metasurface performing a basic optical differentiation operation opens up new opportunities in applications of fast, compactible and power-efficient ultrathin devices for data processing and biological imaging. Broadband ; Dielectric ; Optics ; Computer science ; Signal processing ; Enhanced Data Rates for GSM Evolution ; Contrast (vision) ; SIGNAL (programming language) ; Image processing ; Power consumption ; Optical imaging ; Digital signal processing ; Power (physics) ; Optoelectronics ; Telecommunications ; Materials science ; Physics ; Computer hardware ; Artificial intelligence ; Image (mathematics) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Hao Lyu | Zhe Ji | Stefan Wuttke | Omar M. Yaghi
Source: Chem (article, 2020-09-01)
Field: Materials Science — Materials Chemistry

Digital Reticular Chemistry Reticular connective tissue ; Chemistry ; Polymer science ; Nanotechnology ; Environmental chemistry ; Materials science ; Anatomy ; Biology Luminescence and Fluorescent Materials ; Covalent Organic Framework Applications ; Photoreceptor and optogenetics research
Authors: Feliciano Giustino | Jin Hong Lee | Felix Trier | Manuel Bibes | Stephen M Winter | Roser Valentí | Young-Woo Son | Louis Taillefer | Christoph Heil | Adriana I Figueroa | Bernard Plaçais | QuanSheng Wu | Oleg V Yazyev | Erik P A M Bakkers | Jesper Nygård | Pol Forn-Díaz | Silvano De Franceschi | J W McIver | L E F Foa Torres | Tony Low | Anshuman Kumar | Regina Galceran | Sergio O Valenzuela | Marius V Costache | Aurélien Manchon | Eun-Ah Kim | Gabriel R Schleder | Adalberto Fazzio | Stephan Roche
Source: Journal of Physics Materials (article, 2020-09-10)
Field: Materials Science — Materials Chemistry

The 2021 quantum materials roadmap Abstract In recent years, the notion of ‘Quantum Materials’ has emerged as a powerful unifying concept across diverse fields of science and engineering, from condensed-matter and coldatom physics to materials science and quantum computing. Beyond traditional quantum materials such as unconventional superconductors, heavy fermions, and multiferroics, the field has significantly expanded to encompass topological quantum matter, two-dimensional materials and their van der Waals heterostructures, Moiré materials, Floquet time crystals, as well as materials and devices for quantum computation with Majorana fermions. In this Roadmap collection we aim to capture a snapshot of the most recent developments in the field, and to identify outstanding challenges and emerging opportunities. The format of the Roadmap, whereby experts in each discipline share their viewpoint and articulate their vision for quantum materials, reflects the dynamic and multifaceted nature of this research area, and is meant to encourage exchanges and discussions across traditional disciplinary boundaries. It is our hope that this collective vision will contribute to sparking new fascinating questions and activities at the intersection of materials science, condensed matter physics, device engineering, and quantum information, and to shaping a clearer landscape of quantum materials science as a new frontier of interdisciplinary scientific inquiry. We stress that this article is not meant to be a fully comprehensive review but rather an up-to-date snapshot of different areas of research on quantum materials with a minimal number of references focusing on the latest developments. Quantum ; Quantum technology ; Quantum information science ; Field (mathematics) ; Topological insulator ; Macroscopic quantum phenomena ; Quantum computer ; Discipline Electronic and Structural Properties of Oxides ; Topological Materials and Phenomena ; 2D Materials and Applications
Authors: You‐Song Ding | Tian Han | Yuan‐Qi Zhai | Daniel Reta | Nicholas F. Chilton | Richard E. P. Winpenny | Yan‐Zhen Zheng
Source: Chemistry - A European Journal (article, 2020-02-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Study of Magnetic Relaxation in Dysprosium(III) Single‐Molecule Magnets Although the development of single-molecule magnets (SMMs) is rapid, there are only two families of high energy barrier (U<sub>eff</sub> ) dysprosium(III) SMMs known so far: the cyclopentadienyl (Cp) family with a sandwich structure and the pentagonal-bipyramidal (PB) family with D<sub>5h</sub> symmetry. These high-barrier SMMs, which usually possess U<sub>eff</sub> >500 cm<sup>-1</sup> allow the separate study of the four magnetic relaxation paths, namely, direct, quantum tunnelling, Raman and Orbach processes, in detail. Whereas the first family is chemically more challenging to modify the Cp rings, it is shown herein that the latter family, with the common formulae [DyX<sup>1</sup> X<sup>2</sup> (L<sub>eq</sub> )<sub>5</sub> ]<sup>+</sup> , such as X<sup>1</sup> /X<sup>2</sup> =<sup>-</sup> OCMe<sub>3</sub> , <sup>-</sup> OSiMe<sub>3</sub> , <sup>-</sup> OPh, Cl<sup>-</sup> or Br<sup>-</sup> ; L<sub>eq</sub> =THF/pyridine/4-methylpyridine, can be readily fine-tuned with a range of axial and equatorial ligands by simple substitution reactions. This allows unambiguous confirmation that the U<sub>eff</sub> mainly depends on the identity of X<sup>1</sup> and X<sup>2</sup> , rather than on L<sub>eq</sub> . More importantly, the fitted parameters are barrier dependent. If X<sup>1</sup> is an O donor and X<sup>2</sup> is a halide, 500<U<sub>eff</sub> <600 cm<sup>-1</sup> , log τ<sub>0avg</sub> (s)=-10.66, log C<sub>avg</sub> (s<sup>-1</sup> K<sup>-n</sup> )= -5.05, n<sub>avg</sub> =4.1 and T<sub>H</sub> =9 K (in which τ<sub>0</sub> is the pre-exponential factor for the Orbach relaxation process, C and n are parameters used to describe Raman relaxation, and T<sub>H</sub> is the highest temperature at which magnetic hysteresis is observed). For cases in which both X<sup>1</sup> and X<sup>2</sup> are O donors, 900<U<sub>eff</sub> <1300 cm<sup>-1</sup> , log τ<sub>0avg</sub> (s)=-11.63, log C<sub>avg</sub> (s<sup>-1</sup> K<sup>-n</sup> )= -6.03, n<sub>avg</sub> =4.1 and 18<T<sub>H</sub> <25 K. Based on these results, it can be further concluded that U<sub>eff</sub> not only has a linear correlation to the axial Dy-X bond lengths, but also to T<sub>H</sub> for these PB SMMs. This represents the first systematic study of a family of lanthanide SMMs and derives the first magneto-structural correlation in Dy SMMs. Dysprosium ; Single-molecule magnet ; Relaxation (psychology) ; Chemistry ; Raman spectroscopy ; Crystallography ; Cyclopentadienyl complex ; Activation energy ; Magnetic relaxation ; Magnetization ; Physical chemistry ; Physics ; Magnetic field ; Inorganic chemistry Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Organic and Molecular Conductors Research
Authors: Steven B. Torrisi | Matthew R. Carbone | Brian A. Rohr | Joseph H. Montoya | Yang Ha | Junko Yano | Santosh K. Suram | Linda Hung
Source: npj Computational Materials (article, 2020-07-29)
Field: Materials Science — Materials Chemistry

Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships Abstract X-ray absorption spectroscopy (XAS) produces a wealth of information about the local structure of materials, but interpretation of spectra often relies on easily accessible trends and prior assumptions about the structure. Recently, researchers have demonstrated that machine learning models can automate this process to predict the coordinating environments of absorbing atoms from their XAS spectra. However, machine learning models are often difficult to interpret, making it challenging to determine when they are valid and whether they are consistent with physical theories. In this work, we present three main advances to the data-driven analysis of XAS spectra: we demonstrate the efficacy of random forests in solving two new property determination tasks (predicting Bader charge and mean nearest neighbor distance), we address how choices in data representation affect model interpretability and accuracy, and we show that multiscale featurization can elucidate the regions and trends in spectra that encode various local properties. The multiscale featurization transforms the spectrum into a vector of polynomial-fit features, and is contrasted with the commonly-used “pointwise” featurization that directly uses the entire spectrum as input. We find that across thousands of transition metal oxide spectra, the relative importance of features describing the curvature of the spectrum can be localized to individual energy ranges, and we can separate the importance of constant, linear, quadratic, and cubic trends, as well as the white line energy. This work has the potential to assist rigorous theoretical interpretations, expedite experimental data collection, and automate analysis of XAS spectra, thus accelerating the discovery of new functional materials. Interpretability ; Pointwise ; X-ray absorption spectroscopy ; Random forest ; Computer science ; Support vector machine ; Spectral line ; Artificial intelligence ; Machine learning ; Absorption spectroscopy ; Statistical physics ; Algorithm ; Mathematics ; Physics ; Optics ; Mathematical analysis Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Katherine B. McCullough | Travis Williams | Kathleen Mingle | Pooyan Jamshidi | Jochen Lauterbach
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery High throughput experimentation in heterogeneous catalysis provides an efficient solution to the generation of large datasets under reproducible conditions. Knowledge extraction from these datasets has mostly been performed using statistical methods, targeting the optimization of catalyst formulations. The combination of advanced machine learning methodologies with high-throughput experimentation has enormous potential to accelerate the predictive discovery of novel catalyst formulations that do not exist with current statistical design of experiments. This perspective describes selective examples ranging from statistical design of experiments for catalyst synthesis to genetic algorithms applied to catalyst optimization, and finally random forest machine learning using experimental data for the discovery of novel catalysts. Lastly, this perspective also provides an outlook on advanced machine learning methodologies as applied to experimental data for materials discovery. Throughput ; High-throughput screening ; Artificial intelligence ; Computer science ; Nanotechnology ; Biology ; Materials science ; Bioinformatics ; Telecommunications Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Zi‐Kui Liu
Source: Acta Materialia (article, 2020-08-18)
Field: Materials Science — Materials Chemistry

Computational thermodynamics and its applications Non-equilibrium thermodynamics ; Thermodynamics ; Statistical physics ; Extended irreversible thermodynamics ; Laws of thermodynamics ; Metastability ; Equilibrium thermodynamics ; Function (biology) ; Internal energy ; Second law of thermodynamics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Thermal properties of materials ; Force Microscopy Techniques and Applications