jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chaochao Yan | Peilin Zhao | Chan Lu | Yang Yu | Junzhou Huang
Source: Biomolecules (article, 2022-09-19)
Field: Materials Science — Materials Chemistry

RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction The main target of retrosynthesis is to recursively decompose desired molecules into available building blocks. Existing template-based retrosynthesis methods follow a template selection stereotype and suffer from limited training templates, which prevents them from discovering novel reactions. To overcome this limitation, we propose an innovative retrosynthesis prediction framework that can compose novel templates beyond training templates. As far as we know, this is the first method that uses machine learning to compose reaction templates for retrosynthesis prediction. Besides, we propose an effective reactant candidate scoring model that can capture atom-level transformations, which helps our method outperform previous methods on the USPTO-50K dataset. Experimental results show that our method can produce novel templates for 15 USPTO-50K test reactions that are not covered by training templates. We have released our source implementation. Template ; Retrosynthetic analysis ; Computer science ; Artificial intelligence ; Machine learning ; Data mining ; Programming language Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Computational Drug Discovery Methods
Authors: Mikkel L. Bødker | Mathieu Bauchy | Tao Du | John C. Mauro | Morten M. Smedskjær
Source: npj Computational Materials (article, 2022-09-09)
Field: Materials Science — Ceramics and Composites

Predicting glass structure by physics-informed machine learning Abstract Machine learning (ML) is emerging as a powerful tool to predict the properties of materials, including glasses. Informing ML models with knowledge of how glass composition affects short-range atomic structure has the potential to enhance the ability of composition-property models to extrapolate accurately outside of their training sets. Here, we introduce an approach wherein statistical mechanics informs a ML model that can predict the non-linear composition-structure relations in oxide glasses. This combined model offers an improved prediction compared to models relying solely on statistical physics or machine learning individually. Specifically, we show that the combined model accurately both interpolates and extrapolates the structure of Na 2 O–SiO 2 glasses. Importantly, the model is able to extrapolate predictions outside its training set, which is evidenced by the fact that it is able to predict the structure of a glass series that was kept fully hidden from the model during its training. Machine learning ; Set (abstract data type) ; Artificial intelligence ; Property (philosophy) ; Range (aeronautics) ; Statistical model ; Computer science ; Composition (language) ; Materials science Glass properties and applications ; Material Dynamics and Properties ; Theoretical and Computational Physics
Authors: Nagoor Basha Shaik | Muddasser Inayat | Watit Benjapolakul | Balaji Bakthavatchalam | Surendra D. Barewar | Widhyakorn Asdornwised | Surachai Chaitusaney
Source: Thermal Science and Engineering Progress (article, 2022-06-28)
Field: Materials Science — Materials Chemistry

Artificial neural network modeling and optimization of thermophysical behavior of MXene Ionanofluids for hybrid solar photovoltaic and thermal systems Response surface methodology ; Nanofluid ; Materials science ; Thermal conductivity ; Artificial neural network ; Thermal ; Parametric statistics ; Experimental data ; Viscosity ; Heat capacity ; Thermodynamics ; Computer science ; Machine learning ; Mathematics ; Composite material ; Nanoparticle ; Nanotechnology Thermal properties of materials ; Solar-Powered Water Purification Methods ; Electrohydrodynamics and Fluid Dynamics
Authors: Yannick T. Boni | Ryan C. Cammarota | Kuangbiao Liao | Matthew S. Sigman | Huw M. L. Davies
Source: Journal of the American Chemical Society (article, 2022-08-17)
Field: Chemistry — Organic Chemistry

Leveraging Regio- and Stereoselective C(sp<sup>3</sup>)–H Functionalization of Silyl Ethers to Train a Logistic Regression Classification Model for Predicting Site-Selectivity Bias The C-H functionalization of silyl ethers via carbene-induced C-H insertion represents an efficient synthetic disconnection strategy. In this work, site- and stereoselective C(sp<sup>3</sup>)-H functionalization at α, γ, δ, and even more distal positions to the siloxy group has been achieved using donor/acceptor carbene intermediates. By exploiting the predilections of Rh<sub>2</sub>(<i>R</i>-TCPTAD)<sub>4</sub> and Rh<sub>2</sub>(<i>S</i>-2-Cl-5-BrTPCP)<sub>4</sub> catalysts to target either more electronically activated or more spatially accessible C-H sites, respectively, divergent desired products can be formed with good diastereocontrol and enantiocontrol. Notably, the reaction can also be extended to enable desymmetrization of meso silyl ethers. Leveraging the broad substrate scope examined in this study, we have trained a machine learning classification model using logistic regression to predict the major C-H functionalization site based on intrinsic substrate reactivity and catalyst propensity for overriding it. This model enables prediction of the major product when applying these C-H functionalization methods to a new substrate of interest. Applying this model broadly, we have demonstrated its utility for guiding late-stage functionalization in complex settings and developed an intuitive visualization tool to assist synthetic chemists in such endeavors. Chemistry ; Surface modification ; Silylation ; Carbene ; Stereoselectivity ; Substrate (aquarium) ; Combinatorial chemistry ; Selectivity ; Reactivity (psychology) ; Catalysis ; Stereochemistry ; Organic chemistry Catalytic C–H Functionalization Methods ; Cyclopropane Reaction Mechanisms ; Catalytic Cross-Coupling Reactions
Authors: Xiaohao Cao | Zhengqing Zhang | Yanjing He | Wenjuan Xue | Hongliang Huang | Chongli Zhong
Source: Industrial & Engineering Chemistry Research (article, 2022-07-25)
Field: Chemistry — Inorganic Chemistry

Machine-Learning-Aided Computational Study of Covalent Organic Frameworks for Reversed C<sub>2</sub>H<sub>6</sub>/C<sub>2</sub>H<sub>4</sub> Separation The efficient separation of ethane/ethene (C2H6/C2H4) is imperative yet challenging in industrial processes. We herein combine machine learning (ML) and molecular simulation to predict optimal covalent organic frameworks (COFs) for reversed C2H6/C2H4 separation before experimental efforts. Using molecular simulations, two out of 601 CoRE COFs were identified with excellent separation performance, and eight CoRE COFs exhibit high C2H6/C2H4 selectivity surpassing all of the reported values, although these COFs have a relatively low working capacity. As for ML, we found that the random forest (RF) algorithm displays the highest accuracy (R2 = 0.97) among the four different models, and the density (ρ) of COFs was identified as the key factor that influences the C2H6/C2H4 selectivity. Moreover, the 10 best hypothetical COFs (hCOFs) with excellent selectivity were further predicted. Ultimately, the competitive adsorption behaviors of guests in COF-303 were disclosed, and the adsorption selectivity of COF-303 was enhanced by introducing the fluorine group. Results of this work could provide molecular-level insights for future design and synthesis of novel COFs that can directly remove low-concentration ethane from the C2H4/C2H6 mixture. Selectivity ; Covalent bond ; Adsorption ; Separation (statistics) ; Work (physics) ; Core (optical fiber) ; Covalent organic framework ; Molecular descriptor ; Chemistry ; Materials science ; Computer science ; Thermodynamics ; Organic chemistry ; Machine learning ; Physics ; Stereochemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Jie Sun | Ming Hu | Cunzhi Zhang | Ling Bai | Chenxin Zhang | Qian Wang
Source: Advanced Functional Materials (article, 2022-09-14)
Field: Materials Science — Materials Chemistry

Ultralow Thermal Conductivity of Layered Bi<sub>2</sub>O<sub>2</sub>Se Induced by Twisting Abstract Although the twisting strategy has provided great opportunities to tune the electronic and optical properties of materials, little research has been done on how twisting affects phonon properties. Using machine‐learning‐based interatomic potentials within DFT‐level quality and the perturbation theory to the fourth‐order anharmonicity, the phonon transport properties are studied and the phonon behaviors of layered material Bi 2 O 2 Se when twisting is applied. It is found that the phonons of Bi 2 O 2 Se exhibit hardening effects at finite temperature, and the intrinsic lattice thermal conductivity along the out‐of‐plane (in‐plane) direction is reduced to 3.21 (3.42) W/mK from 3.69 (4.55) W/mK at 300 K by including the four‐phonon scattering. When introducing the twisting between the layers, the out‐of‐plane thermal conductivity can be further reduced by 83% as compared to that of the twist‐free configuration. Such huge reduction of the thermal conductivity arises from the nearly flat acoustic phonon branches and the enhanced third‐ and fourth‐order phonon anharmonicity due to the strong coupling between the twisted layers. These findings unravel that twisting is an effective strategy for tuning phonon band structure and phonon‐phonon interactions, leading to ultralow lattice thermal conductivity of materials. Phonon ; Anharmonicity ; Thermal conductivity ; Materials science ; Condensed matter physics ; Lattice (music) ; Scattering ; Phonon scattering ; Physics ; Optics ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Yuzhen Lu | Sierra Young | Eric Linder | Brian E. Whipker | David H. Suchoff
Source: Frontiers in Plant Science (article, 2022-02-02)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging With Machine Learning to Differentiate Cultivars, Growth Stages, Flowers, and Leaves of Industrial Hemp (Cannabis sativa L.) As an emerging cash crop, industrial hemp (<i>Cannabis sativa</i> L.) grown for cannabidiol (CBD) has spurred a surge of interest in the United States. Cultivar selection and harvest timing are important to produce CBD hemp profitably and avoid economic loss resulting from the tetrahydrocannabinol (THC) concentration in the crop exceeding regulatory limits. Hence there is a need for differentiating CBD hemp cultivars and growth stages to aid in cultivar and genotype selection and optimization of harvest timing. Current methods that rely on visual assessment of plant phenotypes and chemical procedures are limited because of its subjective and destructive nature. In this study, hyperspectral imaging was proposed as a novel, objective, and non-destructive method for differentiating hemp cultivars, growth stages as well as plant organs (leaves and flowers). Five cultivars of CBD hemp were grown greenhouse conditions and leaves and flowers were sampled at five growth stages 2-10 weeks in 2-week intervals after flower initiation and scanned by a benchtop hyperspectral imaging system in the spectral range of 400-1000 nm. The acquired images were subjected to image processing procedures to extract the spectra of hemp samples. The spectral profiles and scatter plots of principal component analysis of the spectral data revealed a certain degree of separation between hemp cultivars, growth stages, and plant organs. Machine learning based on regularized linear discriminant analysis achieved the accuracy of up to 99.6% in differentiating the five hemp cultivars. Plant organ and growth stage need to be factored into model development for hemp cultivar classification. The classification models achieved 100% accuracy in differentiating the five growth stages and two plant organs. This study demonstrates the effectiveness of hyperspectral imaging for differentiating cultivars, growth stages and plant organs of CBD hemp, which is a potentially useful tool for growers and breeders of CBD hemp. Cultivar ; Hyperspectral imaging ; Cannabis sativa ; Crop ; Greenhouse ; Biology ; Horticulture ; Linear discriminant analysis ; Agronomy ; Mathematics ; Artificial intelligence ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Seed and Plant Biochemistry ; Essential Oils and Antimicrobial Activity
Authors: Massimiliano Cavallini | Denis Gentili
Source: ChemPlusChem (review, 2022-03-01)
Field: Materials Science — Materials Chemistry

Atomic Vacancies in Transition Metal Dichalcogenides: Properties, Fabrication, and Limits Structural defects, such as heteroatoms or atomic vacancies, are always present in materials and significantly affect their physical properties, in both positive or unwanted ways. Interestingly, defects generate an impressive range of functionalities in many materials, such as catalysis, electrical and thermal conductivity tuning, thermoelectricity, enhanced ion storage, magnetism, and others. These properties enable the use of defective materials in a great variety of technological applications. Here we review the principal properties generated by atomic vacancies in 2D compounds and thin films of transition metal dichalcogenides and the most consolidated methods for their formation and engineering. Eventually, we critically analysed the most important advantages, the limits and the current open challenges. Nanotechnology ; Materials science ; Transition metal ; Heteroatom ; Thermoelectric effect ; Fabrication ; Magnetism ; Catalysis ; Chemistry ; Condensed matter physics ; Physics 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques
Authors: Heidi Jahandideh | Jun‐Ray Macairan | Aram Bahmani | Mathieu Lapointe | Nathalie Tufenkji
Source: Chemical Science (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

Fabrication of graphene-based porous materials: traditional and emerging approaches The anisotropic nature of 'graphenic' nanosheets enables them to form stable three-dimensional porous materials. The use of these porous structures has been explored in several applications including electronics and batteries, environmental remediation, energy storage, sensors, catalysis, tissue engineering, and many more. As method of fabrication greatly influences the final pore architecture, and chemical and mechanical characteristics and performance of these porous materials, it is essential to identify and address the correlation between property and function. In this review, we report detailed analyses of the different methods of fabricating porous graphene-based structures - with a focus on graphene oxide as the base material - and relate these with the resultant morphologies, mechanical responses, and common applications of use. We discuss the feasibility of the synthesis approaches and relate the GO concentrations used in each methodology against their corresponding pore sizes to identify the areas not explored to date. Fabrication ; Graphene ; Nanotechnology ; Materials science ; Porosity ; Porous medium ; Anisotropy ; Composite material ; Physics Graphene research and applications ; Supercapacitor Materials and Fabrication ; Nanomaterials for catalytic reactions
Authors: Mukesh Singh | Alok Shukla | Brahmananda Chakraborty
Source: Sustainable Energy & Fuels (article, 2022-12-31)
Field: Materials Science — Materials Chemistry

Highly efficient hydrogen storage of a Sc decorated biphenylene monolayer near ambient temperature: <i>ab initio</i> simulations The energy demands for the growing development of society need to be met with alternative and green fuels like hydrogen energy for a lasting and sustainable culture. Hydrogen storage ; Chemistry ; Biphenylene ; Gravimetric analysis ; Ab initio ; Binding energy ; Density functional theory ; Desorption ; Crystallography ; Physical chemistry ; Hydrogen ; Computational chemistry ; Atomic physics ; Physics ; Adsorption ; Organic chemistry Hydrogen Storage and Materials ; Advanced Chemical Physics Studies ; Ammonia Synthesis and Nitrogen Reduction
Authors: Hongying He | Li Guan | Hortense Le Ferrand
Source: Journal of Materials Chemistry A (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Controlled local orientation of 2D nanomaterials in 3D devices: methods and prospects for multifunctional designs and enhanced performance The review provides the recent progress in the processing of functional devices using oriented 2D nanomaterials and highlights the alignment strategies that contributed to the enhancement of device performance. Nanomaterials ; Nanotechnology ; Orientation (vector space) ; Materials science ; Computer science 2D Materials and Applications ; MXene and MAX Phase Materials ; Gas Sensing Nanomaterials and Sensors
Authors: Michael P. Campos | Jonathan De Roo | Matthew W. Greenberg | Brandon M. McMurtry | Mark P. Hendricks | Ellie Bennett | Natalie Saenz | Matthew Y. Sfeir | Benjamin Abécassis | Sanjit Ghose | Jonathan S. Owen
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Growth kinetics determine the polydispersity and size of PbS and PbSe nanocrystals A library of thio- and selenourea derivatives is used to adjust the kinetics of PbE (E = S, Se) nanocrystal formation across a 1000-fold range (<i>k</i> <sub>r</sub> = 10<sup>-1</sup> to 10<sup>-4</sup> s<sup>-1</sup>), at several temperatures (80-120 °C), under a standard set of conditions (Pb : E = 1.2 : 1, [Pb(oleate)<sub>2</sub>] = 10.8 mM, [chalcogenourea] = 9.0 mM). An induction delay (<i>t</i> <sub>ind</sub>) is observed prior to the onset of nanocrystal absorption during which PbE solute is observed using <i>in situ</i> X-ray total scattering. Density functional theory models fit to the X-ray pair distribution function (PDF) support a Pb<sub>2</sub>(μ<sub>2</sub>-S)<sub>2</sub>(Pb(O<sub>2</sub>CR)<sub>2</sub>)<sub>2</sub> structure. Absorption spectra of aliquots reveal a continuous increase in the number of nanocrystals over more than half of the total reaction time at low temperatures. A strong correlation between the width of the nucleation phase and reaction temperature is observed that does not correlate with the polydispersity. These findings are antithetical to the critical concentration dependence of nucleation that underpins the La Mer hypothesis and demonstrates that the duration of the nucleation period has a minor influence on the size distribution. The results can be explained by growth kinetics that are size dependent, more rapid at high temperature, and self limiting at low temperatures. Nanocrystal ; Dispersity ; Kinetics ; Materials science ; Chemical engineering ; Nanotechnology ; Chemistry ; Polymer chemistry ; Physics ; Engineering Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Copper-based nanomaterials and applications
Authors: Guillermo Restrepo
Source: Digital Discovery (article, 2022-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Chemical space: limits, evolution and modelling of an object bigger than our universal library Chemical space as a directed hypergraph network of chemical reactions and its upper bounds (equations). Inside the world different anthropogenic, social and technological factors influencing it. Space (punctuation) ; Object (grammar) ; Hypergraph ; Chemical space ; Chemical evolution ; Theoretical computer science ; Computer science ; Mathematics ; Combinatorics ; Biology ; Artificial intelligence ; Bioinformatics History and advancements in chemistry ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Ian Chesser | Y. Mishin
Source: Communications Materials (article, 2022-11-12)
Field: Materials Science — Materials Chemistry

Point-defect avalanches mediate grain boundary diffusion Abstract Grain boundary diffusion in polycrystalline materials is a physical phenomenon of great fundamental interest and practical significance. Although accelerated atomic transport along grain boundaries has been known for decades, atomic-level understanding of diffusion mechanisms remains poor. Previous atomistic simulations focused on low temperatures where the grain boundary structure is ordered or high temperatures where it is highly disordered. Here, we conduct molecular dynamics simulations of grain boundary diffusion at intermediate temperatures most relevant to applications. A surprising result of this work is the observation of intermittent GB diffusion behavior and its strong system-size dependence unseen in previous work. Both effects are found to originate from thermally activated point-defect avalanches. We identify the length and time scales of the avalanches and link their formation to dynamic heterogeneity in partially disordered systems. Our findings have implications for future computer modeling of grain boundary diffusion and mass transport in nano-scale materials. Grain boundary ; Grain boundary diffusion coefficient ; Diffusion ; Effective diffusion coefficient ; Molecular dynamics ; Work (physics) ; Materials science ; Crystallographic defect ; Statistical physics ; Chemical physics ; Crystallite ; Atomic diffusion ; Atomic units ; Condensed matter physics ; Boundary (topology) ; Physics ; Chemistry ; Thermodynamics ; Metallurgy ; Computational chemistry ; Microstructure ; Mathematics Microstructure and mechanical properties ; Force Microscopy Techniques and Applications ; Thermal properties of materials
Authors: Chatura Samarakoon | Hyung Woo Choi | Sanghyo Lee | Xiang‐Bing Fan | Dong‐Wook Shin | Sang Yun Bang | Jeong‐Wan Jo | Limeng Ni | Jiajie Yang | Yoonwoo Kim | Sung‐Min Jung | Luigi G. Occhipinti | G.A.J. Amaratunga | Jong Min Kim
Source: Nature Communications (article, 2022-08-03)
Field: Materials Science — Materials Chemistry

Optoelectronic system and device integration for quantum-dot light-emitting diode white lighting with computational design framework We propose a computational design framework to design the architecture of a white lighting system having multiple pixelated patterns of electric-field-driven quantum dot light-emitting diodes. The quantum dot of the white lighting system has been optimised by a system-level combinatorial colour optimisation process with the Nelder-Mead algorithm used for machine learning. The layout of quantum dot patterns is designed precisely using rigorous device-level charge transport simulation with an electric-field dependent charge injection model. A theoretical maximum of 97% colour rendering index has been achieved with red, green, cyan, and blue quantum dot light-emitting diodes as primary colours. The white lighting system has been fabricated using the transfer printing technique to validate the computational design framework. It exhibits excellent lighting performance of 92% colour rendering index and wide colour temperature variation from 1612 K to 8903 K with only the four pixelated quantum dots as primary. Quantum dot ; Color rendering index ; Light-emitting diode ; Optoelectronics ; Rendering (computer graphics) ; Diode ; Computer science ; Cyan ; Electric field ; Physics ; Materials science ; Optics ; Artificial intelligence Quantum Dots Synthesis And Properties ; Organic Light-Emitting Diodes Research ; Thin-Film Transistor Technologies