jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Valerio Rizzi | Luigi Bonati | Narjes Ansari | Michele Parrinello
Source: Nature Communications (article, 2021-01-04)
Field: Materials Science — Materials Chemistry

The role of water in host-guest interaction One of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape. Very often this difficulty is circumvented by enhanced sampling techniques. Typically, these techniques depend on the introduction of appropriate collective variables that are meant to capture the system's degrees of freedom. In ligand binding, water has long been known to play a key role, but its complex behaviour has proven difficult to fully capture. In this paper we combine machine learning with physical intuition to build a non-local and highly efficient water-describing collective variable. We use it to study a set of host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding free energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail. Intuition ; Key (lock) ; Process (computing) ; Obstacle ; Sampling (signal processing) ; Set (abstract data type) ; Field (mathematics) ; Molecular dynamics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Protein Structure and Dynamics
Authors: Um e Kalsoom | Rongxing Yi | Junle Qu | Liwei Liu
Source: Frontiers in Physics (article, 2021-04-20)
Field: Materials Science — Materials Chemistry

Nonlinear Optical Properties of CdSe and CdTe Core-Shell Quantum Dots and Their Applications The strong nonlinear optical behavior of low-dimensional materials, such as quantum dots and core-shell quantum dots, has been a topic of intense research in recent years. As quantum dots have tunable emission via changes in their sizes, they are potentially useful in photo-electronics, photovoltaic nonlinear optics, light-emitting diode fabrication, and laser protections. Variation among core and shell shape and size, along with the chemical composition of quantum dots, define their enhanced nonlinear optical properties. Some specific nonlinear optical properties, such as nonlinear refraction, optical limiting, saturable absorption, reverse saturable absorption of CdTe and CdSe quantum dots (QDs), as well as core-shell QDs and their applications, were assessed in this paper. Quantum dot ; Optoelectronics ; Materials science ; Saturable absorption ; Nonlinear optics ; Core (optical fiber) ; Cadmium telluride photovoltaics ; Laser ; Absorption (acoustics) ; Shell (structure) ; Optics ; Physics Quantum Dots Synthesis And Properties ; Nonlinear Optical Materials Studies ; Near-Field Optical Microscopy
Authors: Adzra Zahra Ziva | Yuni Kartika Suryana | Yusrianti Sabrina Kurniadianti | Asep Bayu Dani Nandiyanto | Tedi Kurniawan
Source: Mechanical Engineering for Society and Industry (review, 2021-09-08)
Field: Materials Science — Materials Chemistry

Recent Progress on the Production of Aluminum Oxide (Al2O3) Nanoparticles: A Review This study aims at discussing several methods to produce aluminum oxide (Al2O3) synthesis methods along with the advantages and disadvantages of each method used. In general, several methods are available: (1) precipitation, (2) combustion, (3) sol-gel, (4) wet chemical, (5) synthesis in supercritical water conditions, (6) microwave, (7) mechanochemical, and (8) hydrolysis, and the most efficient method for synthesizing Al2O3 is precipitation because it is facile and the simplest method (compared to other methods), can be proceeded using inexpensive raw materials, produces less pollution, and has several advantages: high purity product, high thermal stability, nearly homogeneous nanoparticle in size, and control desired particle size. The results of the study help to provide comparisons in producing various Al2O3 synthesis methods. Raw material ; Materials science ; Nanoparticle ; Particle size ; Precipitation ; Process engineering ; Supercritical fluid ; Aluminum oxide ; Chemical engineering ; Nanotechnology ; Aluminium ; Metallurgy ; Chemistry ; Organic chemistry ; Engineering ; Meteorology Catalytic Processes in Materials Science ; Semiconductor materials and devices ; Magnesium Oxide Properties and Applications
Authors: M. K. Ravindra | G.P. Darshan | D.R. Lavanya | K. M. Mahadevan | H.B. Premkumar | S.C. Sharma | H. Adarsha | H. Nagabhushana
Source: Scientific Reports (article, 2021-08-18)
Field: Materials Science — Materials Chemistry

Aggregation induced emission based active conjugated imidazole luminogens for visualization of latent fingerprints and multiple anticounterfeiting applications Aggregation-induced emission based organic heterocyclic luminogens bearing conjugated electronic structures showed much attention due to its excellent fluorescence in aggregation state. In this communication, a novel conjugated blue light emitting imidazole molecule is synthesized by one pot multicomponent reaction route is reported for the first time. The prepared molecule exhibits a strong fluorescence in aggregation state with exceptional properties, such as high purity, inexpensive, eco-friendly, large scale production, high photostability, etc. By considering these advantages, a new fluorescence based platform has been setup for in-situ visualization of latent fingerprints and its preservation by spray method followed by Poly(vinyl alcohol) masking. A clear and well defined fluorescence fingerprint images are noticed on variety of surfaces by revealing level 1-3 ridge features upon ultraviolet 365 nm light exposure. The dual nature of binding specificity as well as excellent fluorescence properties permits the visualization of latent fingerprints for longer durations (up to 365 days) with superior contrast, high sensitivity, efficiency, selectivity and minimal background hindrance. We further fabricated unclonable invisible security ink for various printing modes on valuable goods for protection against forging. The developed labels are displaying uniform distribution of ink and exceptional stability under various atmospheric environments. The development of long preservative information using aggregation-induced emission based luminogen opens up a new avenue in advanced forensic and data security applications. Aggregation-induced emission ; Conjugated system ; Imidazole ; Visualization ; Chemistry ; Computer science ; Information retrieval ; Data mining ; Biochemistry ; Physics ; Organic chemistry ; Polymer Luminescence and Fluorescent Materials ; Forensic Fingerprint Detection Methods ; Nicotinic Acetylcholine Receptors Study
Authors: Jianyu Zhang | Xueqian Zhao | Hanchen Shen | Jacky W. Y. Lam | Haoke Zhang | Ben Zhong Tang
Source: Advanced Photonics (review, 2021-12-30)
Field: Materials Science — Materials Chemistry

White-light emission from organic aggregates: a review White light, which contains polychromic visible components, affects the rhythm of organisms and has the potential for advanced applications of lighting, display, and communication. Compared with traditional incandescent bulbs and inorganic diodes, pure organic materials are superior in terms of better compatibility, flexibility, structural diversity, and environmental friendliness. In the past few years, polychromic emission has been obtained based on organic aggregates, which provides a platform to achieve white-light emission. Several white-light emitters are sporadically reported, but the underlying mechanistic picture is still not fully established. Based on these considerations, we will focus on the single-component and multicomponent strategies to achieve efficient white-light emission from pure organic aggregates. Thereinto, single-component strategy is introduced from four parts: dual fluorescence, fluorescence and phosphorescence, dual phosphorescence with anti-Kasha's behavior, and clusteroluminescence. Meanwhile, doping, supramolecular assembly, and cocrystallization are summarized as strategies for multicomponent systems. Beyond the construction strategies of white-light emitters, their advanced representative applications, such as organic light-emitting diodes, white luminescent dyes, circularly polarized luminescence, and encryption, are also prospected. It is expected that this review will draw a comprehensive picture of white-light emission from organic aggregates as well as their emerging applications. Phosphorescence ; Incandescent light bulb ; Materials science ; Nanotechnology ; Luminescence ; White light ; Fluorescence ; Optoelectronics ; OLED ; Light emission ; Optics ; Physics Luminescence and Fluorescent Materials ; Covalent Organic Framework Applications ; Molecular Sensors and Ion Detection
Authors: Katie Dunphy | Paul Dowling | Despina Bazou | Peter O’Gorman
Source: Cancers (review, 2021-04-16)
Field: Chemistry — Spectroscopy

Current Methods of Post-Translational Modification Analysis and Their Applications in Blood Cancers Post-translational modifications (PTMs) add a layer of complexity to the proteome through the addition of biochemical moieties to specific residues of proteins, altering their structure, function and/or localization. Mass spectrometry (MS)-based techniques are at the forefront of PTM analysis due to their ability to detect large numbers of modified proteins with a high level of sensitivity and specificity. The low stoichiometry of modified peptides means fractionation and enrichment techniques are often performed prior to MS to improve detection yields. Immuno-based techniques remain popular, with improvements in the quality of commercially available modification-specific antibodies facilitating the detection of modified proteins with high affinity. PTM-focused studies on blood cancers have provided information on altered cellular processes, including cell signaling, apoptosis and transcriptional regulation, that contribute to the malignant phenotype. Furthermore, the mechanism of action of many blood cancer therapies, such as kinase inhibitors, involves inhibiting or modulating protein modifications. Continued optimization of protocols and techniques for PTM analysis in blood cancer will undoubtedly lead to novel insights into mechanisms of malignant transformation, proliferation, and survival, in addition to the identification of novel biomarkers and therapeutic targets. This review discusses techniques used for PTM analysis and their applications in blood cancer research. Computational biology ; Proteome ; Cancer ; Posttranslational modification ; Cancer cell ; Cancer research ; Biology ; Chemistry ; Bioinformatics ; Biochemistry ; Enzyme Advanced Proteomics Techniques and Applications ; Ubiquitin and proteasome pathways ; Peptidase Inhibition and Analysis
Authors: Shicheng Ren | Bochao Ye | Siying Li | Liping Pang | Ying‐Ming Pan | Haitao Tang
Source: Nano Research (article, 2021-08-10)
Field: Materials Science — Materials Chemistry

Well-defined coordination environment breaks the bottleneck of organic synthesis: Single-atom palladium catalyzed hydrosilylation of internal alkynes Single-atom site (SAS) catalysts have attracted considerable attention due to their excellent performance. However, most of the current research models of SAS catalysts are based on inorganic catalysts, where “metal and coordination atom interaction” cannot simulate the fine-tuning effect of organic ligands on metal catalytic centers in homogeneous catalysts. Therefore, certain chemical transformations in homogeneous catalysis cannot be perfectly replicated. Here, we used porous organic ligand polymers as the carrier, which effectively changes the charge regulation of nanoparticles and monoatomic metal catalysts. Drawing lessons from traditional homogeneous metal/ligand catalysis, we introduced various functional groups into the ligand polymers to adjust the electronic properties, and successfully realized the hydrosilylation of internal alkynes with high catalytic performance. The selectivity and catalytic efficiency under the Pd@POL-1 catalyst system were improved compared with previous studies. The internal alkynes with various structures can complete this reaction, and the ratio of E/Z can reach up to 100:1. Catalysis ; Hydrosilylation ; Palladium ; Ligand (biochemistry) ; Homogeneous catalysis ; Chemistry ; Metal ; Polymer ; Atom (system on chip) ; Selectivity ; Metal-organic framework ; Combinatorial chemistry ; Materials science ; Chemical engineering ; Photochemistry ; Organic chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Alain C. Vaucher | Philippe Schwaller | Joppe Geluykens | Vishnu H Nair | Anna Iuliano | Teodoro Laino
Source: Nature Communications (article, 2021-05-06)
Field: Materials Science — Materials Chemistry

Inferring experimental procedures from text-based representations of chemical reactions The experimental execution of chemical reactions is a context-dependent and time-consuming process, often solved using the experience collected over multiple decades of laboratory work or searching similar, already executed, experimental protocols. Although data-driven schemes, such as retrosynthetic models, are becoming established technologies in synthetic organic chemistry, the conversion of proposed synthetic routes to experimental procedures remains a burden on the shoulder of domain experts. In this work, we present data-driven models for predicting the entire sequence of synthesis steps starting from a textual representation of a chemical equation, for application in batch organic chemistry. We generated a data set of 693,517 chemical equations and associated action sequences by extracting and processing experimental procedure text from patents, using state-of-the-art natural language models. We used the attained data set to train three different models: a nearest-neighbor model based on recently-introduced reaction fingerprints, and two deep-learning sequence-to-sequence models based on the Transformer and BART architectures. An analysis by a trained chemist revealed that the predicted action sequences are adequate for execution without human intervention in more than 50% of the cases. Computer science ; Retrosynthetic analysis ; Representation (politics) ; Context (archaeology) ; Sequence (biology) ; Set (abstract data type) ; Transformer ; Synthetic data ; Artificial intelligence ; Chemical database ; Process (computing) ; Theoretical computer science ; Data mining ; Machine learning ; Chemistry ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Ali. A. Salem | Rahisham Abd Rahman | Wan Rahiman | Samir A. Al-Gailani | Salem Mgammal Al-Ameri | Mohd Taufiq Ishak | Usman Ullah Sheikh
Source: IEEE Access (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Pollution Flashover Under Different Contamination Profiles on High Voltage Insulator: Numerical and Experiment Investigation This work aimed to study the influence of contamination profiles and humidity on flashover electrical characteristics of polluted insulators. Firstly, the flashover tests on cap and pin glass insulators under four pollution levels represented by salinity were conducted. Eight artificial contamination profiles based on the solid layer method have been modeled for the selected insulators. The numerical analysis has been used to determine the insulator electrical characteristics such as potential, electric field, and power dissipation under proposed contamination profiles using finite element methods (FEM). Next, the power dissipation has been simulated with consideration of thermal stress propagation in locations with high power. Finally, flashover voltage gradient tests have been conducted under various humidity and contamination profiles. The values of the flashover voltage gradient due to pollution were determined as the percentage of the value of the flashover voltage gradient in the clean condition which was identified as the reference point. The numerical model indicated that the initiation of arc generally occurs at area in which the electric field and power dissipation is maximum. It was also observed from experimental results that the flashover voltage gradient under different contamination profiles has different values depends on the location and dimension of the pollution region. Arc flash ; Insulator (electricity) ; Contamination ; Environmental science ; Humidity ; Dissipation ; Pollution ; Voltage ; Materials science ; Finite element method ; Composite material ; Electrical engineering ; Meteorology ; Structural engineering ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Thermal Analysis in Power Transmission
Authors: Yun Zhang | Ling Wang | Xinqiao Wang | Chengyun Zhang | Jiamin Ge | Jing Tang | An Su | Hongliang Duan
Source: Organic Chemistry Frontiers (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Data augmentation and transfer learning strategies for reaction prediction in low chemical data regimes An effective and rapid deep learning method to predict chemical reactions contributes to the research and development of organic chemistry and drug discovery. Chemistry ; Drug discovery ; Transfer of learning ; Biochemical engineering ; Data science ; Artificial intelligence ; Computer science ; Biochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Gyoung S. Na | Seunghun Jang | Hyunju Chang
Source: npj Computational Materials (article, 2021-07-14)
Field: Materials Science — Materials Chemistry

Predicting thermoelectric properties from chemical formula with explicitly identifying dopant effects Abstract Dopants play an important role in synthesizing materials to improve target materials properties or stabilize the materials. In particular, the dopants are essential to improve thermoelectic performances of the materials. However, existing machine learning methods cannot accurately predict the materials properties of doped materials due to severely nonlinear relations with their materials properties. Here, we propose a unified architecture of neural networks, called DopNet, to accurately predict the materials properties of the doped materials. DopNet identifies the effects of the dopants by explicitly and independently embedding the host materials and the dopants. In our evaluations, DopNet outperformed existing machine learning methods in predicting experimentally measured thermoelectric properties, and the error of DopNet in predicting a figure of merit (ZT) was 0.06 in mean absolute error. In particular, DopNet was significantly effective in an extrapolation problem that predicts ZTs of unknown materials, which is a key task to discover novel thermoelectric materials. Dopant ; Extrapolation ; Materials science ; Materials informatics ; Doping ; Thermoelectric effect ; Material properties ; Thermoelectric materials ; Characterization (materials science) ; Machine learning ; Computer science ; Figure of merit ; Biological system ; Nanotechnology ; Thermodynamics ; Mathematics ; Optoelectronics ; Physics ; Composite material ; Mathematical analysis Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Yulou Ouyang | Cuiqian Yu | Gang Yan | Jie Chen
Source: Frontiers of Physics (article, 2021-02-11)
Field: Materials Science — Materials Chemistry

Machine learning approach for the prediction and optimization of thermal transport properties Traditional simulation methods have made prominent progress in aiding experiments for understanding thermal transport properties of materials, and in predicting thermal conductivity of novel materials. However, huge challenges are also encountered when exploring complex material systems, such as formidable computational costs. As a rising computational method, machine learning has a lot to offer in this regard, not only in speeding up the searching and optimization process, but also in providing novel perspectives. In this work, we review the state-of-the-art studies on material’s thermal properties based on machine learning technique. First, the basic principles of machine learning method are introduced. We then review applications of machine learning technique in the prediction and optimization of material’s thermal properties, including thermal conductivity and interfacial thermal resistance. Finally, an outlook is provided for the future studies. Thermal conductivity ; Computer science ; Machine learning ; Process (computing) ; Thermal ; Artificial intelligence ; Thermal resistance ; Work (physics) ; Industrial engineering ; Mechanical engineering ; Materials science ; Engineering ; Thermodynamics Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Linbo Tian | Jiachen Sun | Jun Chang | Jinbao Xia | Zhifeng Zhang | A.A. Kolomenskii | H. A. Schuessler | Sasa Zhang
Source: Measurement (article, 2021-06-15)
Field: Chemistry — Spectroscopy

Retrieval of gas concentrations in optical spectroscopy with deep learning Spectroscopy ; Robustness (evolution) ; Absorption spectroscopy ; Perceptron ; Artificial neural network ; Absorption (acoustics) ; Convolutional neural network ; Materials science ; Computer science ; Artificial intelligence ; Optics ; Chemistry ; Physics Spectroscopy and Laser Applications ; Water Quality Monitoring and Analysis ; Analytical Chemistry and Sensors
Authors: Piotr Koniusz | Hongguang Zhang
Source: IEEE Transactions on Pattern Analysis and Machine Intelligence (article, 2021-08-24)
Field: Materials Science — Materials Chemistry

Power Normalizations in Fine-Grained Image, Few-Shot Image and Graph Classification Power Normalizations (PN) are useful non-linear operators which tackle feature imbalances in classification problems. We study PNs in the deep learning setup via a novel PN layer pooling feature maps. Our layer combines the feature vectors and their respective spatial locations in the feature maps produced by the last convolutional layer of CNN into a positive definite matrix with second-order statistics to which PN operators are applied, forming so-called Second-order Pooling (SOP). As the main goal of this paper is to study Power Normalizations, we investigate the role and meaning of MaxExp and Gamma, two popular PN functions. To this end, we provide probabilistic interpretations of such element-wise operators and discover surrogates with well-behaved derivatives for end-to-end training. Furthermore, we look at the spectral applicability of MaxExp and Gamma by studying Spectral Power Normalizations (SPN). We show that SPN on the autocorrelation/covariance matrix and the Heat Diffusion Process (HDP) on a graph Laplacian matrix are closely related, thus sharing their properties. Such a finding leads us to the culmination of our work, a fast spectral MaxExp which is a variant of HDP for covariances/autocorrelation matrices. We evaluate our ideas on fine-grained recognition, scene recognition, and material classification, as well as in few-shot learning and graph classification. Pattern recognition (psychology) ; Artificial intelligence ; Pooling ; Computer science ; Discriminative model ; Feature (linguistics) ; Laplacian matrix ; Autocorrelation ; Covariance matrix ; Graph ; Algorithm ; Mathematics ; Theoretical computer science ; Statistics Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning ; Advanced Graph Neural Networks