jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shidang Xu | Xiaoli Liu | Pengfei Cai | Jiali Li | Xiaonan Wang | Bin Liu
Source: Advanced Science (article, 2021-11-25)
Field: Materials Science — Materials Chemistry

Machine‐Learning‐Assisted Accurate Prediction of Molecular Optical Properties upon Aggregation For practical applications, molecules often exist in an aggregate state. Therefore, it is of great value if one can predict the performance of molecules when forming aggregates, for example, aggregation-induced emission (AIE) or aggregation-caused quenching (ACQ). Herein, a database containing AIE/ACQ molecules reported in the literature is first established. Through training, these machine learning (ML) models can build up the structure-property relationship and thus implement fast prediction of AIE/ACQ properties. To this end, a multi-modal approach is proposed, multiple prediction methods are compared and designed, and thus an ensemble strategy is developed. First, multiple molecular descriptors are considered at the same time, major features are extracted by dimensionality reduction, and multi-modal features are synthesized. Then, several state-of-the-art methods are designed and compared to analyze the advantages of the different methods. Finally, the ensemble strategy combines the advantages of the multiple methods to obtain the final prediction result. The reliability of this approach in an unknown molecular space is further verified by three newly designed molecules. Reasonable consistency between model predictions and experimental outcomes is obtained. The result indicates that ML can be a powerful tool to predict molecular properties in the aggregated state, thus accelerating the development of solid-state optical materials. Computer science ; Consistency (knowledge bases) ; Reliability (semiconductor) ; Molecular descriptor ; Aggregate (composite) ; Curse of dimensionality ; Biological system ; Modal ; Dimensionality reduction ; Artificial intelligence ; Machine learning ; Materials science ; Nanotechnology ; Quantitative structure–activity relationship ; Physics Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Nanoplatforms for cancer theranostics
Authors: Medard Edmund Mswahili | Min‐Jeong Lee | Gati Lother Martin | Jung‐Hyun Kim | Paul Kim | Guang J. Choi | Young-Seob Jeong
Source: Applied Sciences (article, 2021-02-01)
Field: Chemistry — Physical and Theoretical Chemistry

Cocrystal Prediction Using Machine Learning Models and Descriptors Cocrystals are of much interest in industrial application as well as academic research, and screening of suitable coformers for active pharmaceutical ingredients is the most crucial and challenging step in cocrystal development. Recently, machine learning techniques are attracting researchers in many fields including pharmaceutical research such as quantitative structure-activity/property relationship. In this paper, we develop machine learning models to predict cocrystal formation. We extract descriptor values from simplified molecular-input line-entry system (SMILES) of compounds and compare the machine learning models by experiments with our collected data of 1476 instances. As a result, we found that artificial neural network shows great potential as it has the best accuracy, sensitivity, and F1 score. We also found that the model achieved comparable performance with about half of the descriptors chosen by feature selection algorithms. We believe that this will contribute to faster and more accurate cocrystal development. Cocrystal ; Artificial intelligence ; Machine learning ; Computer science ; Artificial neural network ; Chemistry Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Computational Drug Discovery Methods
Authors: Zhiwei Qiao | Yaling Yan | Yaxing Tang | Hong Liang | Jianwen Jiang
Source: The Journal of Physical Chemistry C (article, 2021-04-02)
Field: Chemistry — Inorganic Chemistry

Metal–Organic Frameworks for Xylene Separation: From Computational Screening to Machine Learning Separation of xylene isomers is an important process in the chemical industry and there has been considerable interest in developing advanced materials for xylene separation. In this study, we synergize computational screening and machine learning to explore the selective adsorption of p-xylene over o- and m-xylene in metal–organic frameworks (MOFs). First, a large set (4764) of computation-ready experimental MOFs is screened by geometric analysis and molecular simulation. The relationships between MOF structural descriptors (void fraction, volumetric surface area, and largest cavity diameter) and separation performance metrics (adsorption capacity of p-xylene Np-xylene and selectivity of p-xylene over o- and m-xylene Sp/(m+o)) are established. Then two machine-learning methods (back-propagation neural network and decision tree), as well as particle swarm optimization, are utilized to analyze and optimize Np-xylene and Sp/(m+o). The importance of each descriptor for separation is evaluated in six different MOF data sets. In the 100 top-performing MOFs, the pore limiting diameter (PLD) and largest cavity diameter (LCD) are revealed to be key factors governing separation performance. On the basis of the threshold values of Np-xylene > 0.5 mol/kg and Sp/(m+o) > 5, seven top-performing MOFs are identified. By further incorporating framework flexibility, JIVFUQ is predicted to be the best and superior to many reported MOFs in the literature. Xylene ; Adsorption ; Materials science ; Metal-organic framework ; p-Xylene ; o-Xylene ; Chemical engineering ; Artificial intelligence ; Computer science ; Chemistry ; Organic chemistry ; Toluene ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Membrane Separation and Gas Transport
Authors: Haoshi Gao | Haoyue Jia | Jie Dong | Xinggang Yang | Haifeng Li | Defang Ouyang
Source: Acta Pharmaceutica Sinica B (article, 2021-05-04)
Field: Chemistry — Spectroscopy

Integrated in silico formulation design of self-emulsifying drug delivery systems The drug formulation design of self-emulsifying drug delivery systems (SEDDS) often requires numerous experiments, which are time- and money-consuming. This research aimed to rationally design the SEDDS formulation by the integrated computational and experimental approaches. 4495 SEDDS formulation datasets were collected to predict the pseudo-ternary phase diagram by the machine learning methods. Random forest (RF) showed the best prediction performance with 91.3% for accuracy, 92.0% for sensitivity and 90.7% for specificity in 5-fold cross-validation. The pseudo-ternary phase diagrams of meloxicam SEDDS were experimentally developed to validate the RF prediction model and achieved an excellent prediction accuracy (89.51%). The central composite design (CCD) was used to screen the best ratio of oil-surfactant-cosurfactant. Finally, molecular dynamic (MD) simulation was used to investigate the molecular interaction between excipients and drugs, which revealed the diffusion behavior in water and the role of cosurfactants. In conclusion, this research combined machine learning, central composite design, molecular modeling and experimental approaches for rational SEDDS formulation design. The integrated computer methodology can decrease traditional drug formulation design works and bring new ideas for future drug formulation design. Computer science ; Drug delivery ; Central composite design ; Ternary operation ; In silico ; Machine learning ; Simulation ; Nanotechnology ; Materials science ; Chemistry ; Response surface methodology Analytical Chemistry and Chromatography ; Drug Solubulity and Delivery Systems ; Surfactants and Colloidal Systems
Authors: Pengcheng Xu | Dongping Chang | Tian Lu | Long Li | Minjie Li | Wencong Lu
Source: Journal of Chemical Information and Modeling (review, 2021-08-10)
Field: Materials Science — Materials Chemistry

Search for ABO<sub>3</sub> Type Ferroelectric Perovskites with Targeted Multi-Properties by Machine Learning Strategies Ferroelectric perovskites are one of the most promising functional materials due to the pyroelectric and piezoelectric effect. In the practical applications of ferroelectric perovskites, it is often necessary to meet the requirements of multiple properties. In this work, a multiproperties machine learning strategy was proposed to accelerate the discovery and design of new ferroelectric ABO<sub>3</sub>-type perovskites. First, a classification model was constructed with data collected from publications to distinguish ferroelectric and nonferroelectric perovskites. The classification accuracies of LOOCV and the test set are 87.29% and 86.21%, respectively. Then, two machine learning strategies, Machine-Learning Workflow and SISSO, were used to construct the regression models to predict the specific surface area (SSA), band gap (<i>E</i><sub>g</sub>), Curie temperature (<i>T</i><sub>c</sub>), and dielectric loss (tan δ) of ABO<sub>3</sub>-type perovskites. The correlation coefficients of LOOCV in the optimal models for SSA, <i>E</i><sub>g</sub>, and <i>T</i><sub>c</sub> are 0.935, 0.891, and 0.971, respectively, while the correlation coefficient of the predicted and experimental values of the SISSO model for tan δ prediction could reach 0.913. On the basis of the models, 20 ABO<sub>3</sub> ferroelectric perovskites with three different application prospects were screened out with the required properties, which could be explained by the patterns between the important descriptors and the properties by using SHAP. Furthermore, the constructed models were developed into web servers for the researchers to accelerate the rational design and discovery of ABO<sub>3</sub> ferroelectric perovskites with desired multiple properties. Ferroelectricity ; Machine learning ; Materials science ; Artificial intelligence ; Dielectric ; Curie temperature ; Computer science ; Condensed matter physics ; Physics ; Optoelectronics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Perovskite Materials and Applications
Authors: Wanrun Jiang | Yuzhi Zhang | Linfeng Zhang | Han Wang
Source: Chinese Physics B (article, 2021-03-24)
Field: Materials Science — Materials Chemistry

Accurate Deep Potential model for the Al–Cu–Mg alloy in the full concentration space* Combining first-principles accuracy and empirical-potential efficiency for the description of the potential energy surface (PES) is the philosopher’s stone for unraveling the nature of matter via atomistic simulation. This has been particularly challenging for multi-component alloy systems due to the complex and non-linear nature of the associated PES. In this work, we develop an accurate PES model for the Al–Cu–Mg system by employing deep potential (DP), a neural network based representation of the PES, and DP generator (DP-GEN), a concurrent-learning scheme that generates a compact set of ab initio data for training. The resulting DP model gives predictions consistent with first-principles calculations for various binary and ternary systems on their fundamental energetic and mechanical properties, including formation energy, equilibrium volume, equation of state, interstitial energy, vacancy and surface formation energy, as well as elastic moduli. Extensive benchmark shows that the DP model is ready and will be useful for atomistic modeling of the Al–Cu–Mg system within the full range of concentration. Binary number ; Benchmark (surveying) ; Ternary operation ; Alloy ; Representation (politics) ; Surface (topology) ; Range (aeronautics) ; Generator (circuit theory) ; Set (abstract data type) Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Microstructure and mechanical properties
Authors: Stephen Lam | Qing‐Jie Li | R. G. Ballinger | Charles Forsberg | Ju Li
Source: ACS Applied Materials & Interfaces (article, 2021-05-21)
Field: Materials Science — Materials Chemistry

Modeling LiF and FLiBe Molten Salts with Robust Neural Network Interatomic Potential Lithium-based molten salts have attracted significant attention due to their applications in energy storage, advanced fission reactors, and fusion devices. Lithium fluorides and particularly 66.6%LiF-33.3%BeF<sub>2</sub> (Flibe) are of considerable interest in nuclear systems, as they show an excellent combination of favorable heat transfer, neutron moderation, and transmutation characteristics. For nuclear salts, the range of possible local structures, compositions, and thermodynamic conditions presents significant challenges in atomistic modeling. In this work, we demonstrate that atom-centered neural network interatomic potentials (NNIPs) provide a fast method for performing molecular dynamics of molten salts that is as accurate as ab initio molecular dynamics. For LiF, these potentials are able to accurately reproduce ab initio interactions of dimers, crystalline solids under deformation, crystalline LiF near the melting point, and liquid LiF at high temperatures. For Flibe, NNIPs accurately predict the structures and dynamics at normal operating conditions, high-temperature-pressure conditions, and in the crystalline solid phase. Furthermore, we show that NNIP-based molecular dynamics of molten salts are scalable to reach long time scales (e.g., nanosecond) and large system sizes (e.g., 10<sup>5</sup> atoms) while maintaining ab initio density functional theory accuracy and providing more than 3 orders of magnitude of computational speedup for calculating structure and transport properties. Materials science ; Molecular dynamics ; Lithium (medication) ; Melting point ; Chemical physics ; Ab initio ; Ab initio quantum chemistry methods ; Embedded atom model ; Interatomic potential ; Thermodynamics ; Computational chemistry ; Molecule ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Moorthy Maruthapandi | Arumugam Saravanan | Priyanka Manohar | John H. T. Luong | Aharon Gedanken
Source: Nanomaterials (article, 2021-04-27)
Field: Materials Science — Polymers and Plastics

Photocatalytic Degradation of Organic Dyes and Antimicrobial Activities by Polyaniline–Nitrogen-Doped Carbon Dot Nanocomposite Nitrogen-doped carbon nanodots (N@CDs) were prepared by hydrothermal processing of bovine serum albumin (Mw: 69,324 with 607 amino acids). A polyaniline (PANI-N@CDs) nanocomposite was then synthesized by ultrasonication and used to degrade Congo red (CR), methylene blue (MB), Rhodamine B (RhB), and crystal violet (CV) four common organic dyes. The PANI-N@CD nanocomposite simultaneously adsorbed and concentrated the dye from the bulk solution and degraded the adsorbed dye, resulting in a high rate of dye degradation. The combination of holes (h+), hydroxyl (OH<sup>•</sup>), and O<sub>2</sub><sup>•-</sup> was involved in the N@CD-mediated photocatalytic degradation of the dyes. Under visible light illumination at neutral pH, the PANI-N@CDs were proven as an efficient adsorbent and photocatalyst for the complete degradation of CR within 20 min. MB and RhB were also degraded but required longer treatment times. These findings supported the design of remediation processes for such dyes and predicted their fate in the environment. The nanocomposite also exhibited antimicrobial activities against Gram-negative bacterium <i>E. coli</i> and Gram-positive bacterium <i>S. aureus</i>. Polyaniline ; Nanocomposite ; Degradation (telecommunications) ; Photocatalysis ; Materials science ; Carbon fibers ; Nitrogen ; Antimicrobial ; Doping ; Organic dye ; Chemistry ; Chemical engineering ; Nanotechnology ; Polymer ; Organic chemistry ; Composite material ; Composite number ; Polymerization ; Catalysis Conducting polymers and applications ; Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors
Authors: Чан Лю | Dong-Liang Shan | Zhonghui Shen | Guang‐Kun Ren | Yue Wang | Zhifang Zhou | Jiangyu Li | Di Yi | Jin-Le Lan | Long‐Qing Chen | G. Jeffrey Snyder | Yuanhua Lin | Ce‐Wen Nan
Source: Nano Energy (article, 2021-07-28)
Field: Materials Science — Materials Chemistry

Role of interfaces in organic–inorganic flexible thermoelectrics Materials science ; Thermal conductivity ; Seebeck coefficient ; Thermoelectric effect ; PEDOT:PSS ; Thermoelectric materials ; Phonon scattering ; Nanocomposite ; Carbon nanotube ; Scanning thermal microscopy ; Optoelectronics ; Nanotechnology ; Composite material ; Polymer ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: William Z. Payne | Dmitry Kurouski
Source: Frontiers in Plant Science (review, 2021-01-20)
Field: Chemistry — Analytical Chemistry

Raman-Based Diagnostics of Biotic and Abiotic Stresses in Plants. A Review Digital farming is a novel agricultural philosophy that aims to maximize a crop yield with the minimal environmental impact. Digital farming requires the development of technologies that can work directly in the field providing information about a plant health. Raman spectroscopy (RS) is an emerging analytical technique that can be used for non-invasive, non-destructive, and confirmatory diagnostics of diseases, as well as the nutrient deficiencies in plants. RS is also capable of probing nutritional content of grains, as well as highly accurate identification plant species and their varieties. This allows for Raman-based phenotyping and digital selection of plants. These pieces of evidence suggest that RS can be used for chemical-free surveillance of plant health directly in the field. High selectivity and specificity of this technique show that RS may transform the agriculture in the US. This review critically discusses the most recent research articles that demonstrate the use of RS in diagnostics of abiotic and abiotic stresses in plants, as well as the identification of plant species and their nutritional analysis. Abiotic component ; Identification (biology) ; Agriculture ; Abiotic stress ; Biotechnology ; Biochemical engineering ; Environmental science ; Biology ; Computer science ; Agricultural engineering ; Botany ; Ecology ; Engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Date Palm Research Studies
Authors: Roberta Noberini | Evelyn Oliva Savoia | Stefania Brandini | Francesco Greco | Francesca Marra | Giovanni Bertalot | Giancarlo Pruneri | Liam A. McDonnell | Tiziana Bonaldi
Source: Clinical Epigenetics (article, 2021-07-27)
Field: Chemistry — Spectroscopy

Spatial epi-proteomics enabled by histone post-translational modification analysis from low-abundance clinical samples These results demonstrate that analyzing histone PTMs from very small tissue areas and detecting differences from adjacent tumor regions is technically feasible. Our method opens the way for spatial epi-proteomics, namely the investigation of epigenetic features in the context of tissue and tumor heterogeneity, which will be instrumental for the identification of novel epigenetic biomarkers and aberrant epigenetic mechanisms. Proteomics ; Human genetics ; Posttranslational modification ; Abundance (ecology) ; Histone ; Computational biology ; Biology ; Bioinformatics ; Medicine ; Genetics ; Ecology ; Biochemistry ; Enzyme Advanced Proteomics Techniques and Applications ; Molecular Biology Techniques and Applications ; Cancer Genomics and Diagnostics
Authors: Beatrice Zoanni | Maura Brioschi | Alice Mallia | Erica Gianazza | Sonia Eligini | M. Carini | Giancarlo Aldini | Cristina Banfi
Source: Mass Spectrometry Reviews (review, 2021-11-08)
Field: Chemistry — Spectroscopy

Novel insights about albumin in cardiovascular diseases: Focus on heart failure The Human Plasma Proteome has always been the most investigated compartment in proteomics-based biomarker discovery, and is considered the largest and deepest version of the human proteome, reflecting the state of the body in health and disease. Even if efforts have been always dedicated to the refinement of proteomic approaches to investigate more deeply the plasma proteome, it should not be forgotten that also highly abundant plasma proteins, like human serum albumin (HSA), often neglected in these studies, might provide fundamental physiological functions in plasma, and should be better considered. This review summarizes the important roles of HSA in the context of cardiovascular diseases (CVD), and in particular in heart failure. Notwithstanding much attention has been historically directed toward the association of HSA levels and CVD risk, the advances in the field of mass spectrometry research allow also a better characterization of the effects of oxidative modifications that could alter not only the structure but also the function of HSA. Proteome ; Chemistry ; Context (archaeology) ; Proteomics ; Human plasma ; Computational biology ; Biomarker ; Albumin ; Human serum albumin ; Function (biology) ; Human health ; Biochemistry ; Cell biology ; Medicine ; Biology ; Gene ; Chromatography ; Environmental health Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Protein Interaction Studies and Fluorescence Analysis
Authors: He‐Xiu Xu | Yanzhao Wang | Chaohui Wang | Mingzhao Wang | Shaojie Wang | Fei Ding | Yongjun Huang | Xiaokuan Zhang | Haiwen Liu | Xiaohui Ling | Wei Huang
Source: Research (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deterministic Approach to Achieve Full-Polarization Cloak Achieving full-polarization (<i>σ</i>) invisibility on an arbitrary three-dimensional (3D) platform is a long-held knotty issue yet extremely promising in real-world stealth applications. However, state-of-the-art invisibility cloaks typically work under a specific polarization because the anisotropy and orientation-selective resonant nature of artificial materials made the <i>σ</i>-immune operation elusive and terribly challenging. Here, we report a deterministic approach to engineer a metasurface skin cloak working under an arbitrary polarization state by theoretically synergizing two cloaking phase patterns required, respectively, at spin-up (<i>σ</i>+) and spin-down (<i>σ</i>-) states. Therein, the wavefront of any light impinging on the cloak can be well preserved since it is a superposition of <i>σ</i>+ and <i>σ</i>- wave. To demonstrate the effectiveness and applicability, several proof-of-concept metasurface cloaks are designed to wrap over a 3D triangle platform at microwave frequency. Results show that our cloaks are essentially capable of restoring the amplitude and phase of reflected beams as if light was incident on a flat mirror or an arbitrarily predesigned shape under full polarization states with a desirable bandwidth of ~17.9%, conceiving or deceiving an arbitrary object placed inside. Our approach, deterministic and robust in terms of accurate theoretical design, reconciles the milestone dilemma in stealth discipline and opens up an avenue for the extreme capability of ultrathin 3D cloaking of an arbitrary shape, paving up the road for real-world applications. Cloak ; Polarization (electrochemistry) ; Computer science ; Optics ; Physics ; Metamaterial ; Chemistry Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Zhenwei Li | Zijia Lin | Meisheng Han | Yongbiao Mu | Pengpeng Yu | Yunlong Zhang | Jie Yu
Source: Chemical Engineering Journal (article, 2021-04-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Flexible electrospun carbon nanofibers/silicone composite films for electromagnetic interference shielding, electrothermal and photothermal applications Materials science ; Electromagnetic shielding ; EMI ; Electromagnetic interference ; Composite material ; Composite number ; Photothermal therapy ; Silicone ; Coating ; Optoelectronics ; Carbon nanotube ; Nanotechnology ; Computer science Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies