jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tomer Weiss | Alexandra Wahab | Alex Bronstein | Renana Gershoni‐Poranne
Source: The Journal of Organic Chemistry (article, 2023-01-25)
Field: Materials Science — Materials Chemistry

Interpretable Deep-Learning Unveils Structure–Property Relationships in Polybenzenoid Hydrocarbons In this work, interpretable deep learning was used to identify structure-property relationships governing the HOMO-LUMO gap and the relative stability of polybenzenoid hydrocarbons (PBHs) using a ring-based graph representation. This representation was combined with a subunit-based perception of PBHs, allowing chemical insights to be presented in terms of intuitive and simple structural motifs. The resulting insights agree with conventional organic chemistry knowledge and electronic structure-based analyses and also reveal new behaviors and identify influential structural motifs. In particular, we evaluated and compared the effects of linear, angular, and branching motifs on these two molecular properties and explored the role of dispersion in mitigating the torsional strain inherent in nonplanar PBHs. Hence, the observed regularities and the proposed analysis contribute to a deeper understanding of the behavior of PBHs and form the foundation for design strategies for new functional PBHs. Deep learning ; Branching (polymer chemistry) ; Representation (politics) ; Chemical physics ; Biological system ; Perception ; Computational chemistry ; Computer science ; Artificial intelligence ; Chemistry ; Biology ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Hanxun Jin | Horacio D. Espinosa
Source: Journal of Applied Mechanics (article, 2023-11-30)
Field: Chemistry — Organic Chemistry

Mechanical Metamaterials Fabricated From Self-Assembly: A Perspective Abstract Mechanical metamaterials, whose unique mechanical properties stem from their structural design rather than material constituents, are gaining popularity in engineering applications. In particular, recent advances in self-assembly techniques offer the potential to fabricate load-bearing mechanical metamaterials with unparalleled feature size control and scalability compared to those produced by additive manufacturing (AM). Yet, the field is still in its early stages. In this perspective, we first provide an overview of the state-of-the-art self-assembly techniques, with a focus on the copolymer and colloid crystal self-assembly processes. We then discuss current challenges and future opportunities in this research area, focusing on novel fabrication approaches, the need for high-throughput characterization methods, and the integration of Machine Learning (ML) and lab automation for inverse design. Given recent progress in all these areas, we foresee mechanical metamaterials fabricated from self-assembly techniques impacting a variety of applications relying on lightweight, strong, and tough materials. Metamaterial ; Nanotechnology ; Computer science ; Materials science ; Fabrication ; Scalability ; Mechanical engineering ; Engineering Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly ; Polymer composites and self-healing
Authors: Silvan Käser | Markus Meuwly
Source: The Journal of Chemical Physics (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Transfer-learned potential energy surfaces: Toward microsecond-scale molecular dynamics simulations in the gas phase at CCSD(T) quality The rise of machine learning has greatly influenced the field of computational chemistry and atomistic molecular dynamics simulations in particular. One of its most exciting prospects is the development of accurate, full-dimensional potential energy surfaces (PESs) for molecules and clusters, which, however, often require thousands to tens of thousands of ab initio data points restricting the community to medium sized molecules and/or lower levels of theory (e.g., density functional theory). Transfer learning, which improves a global PES from a lower to a higher level of theory, offers a data efficient alternative requiring only a fraction of the high-level data (on the order of 100 are found to be sufficient for malonaldehyde). This work demonstrates that even with Hartree-Fock theory and a double-zeta basis set as the lower level model, transfer learning yields coupled-cluster single double triple [CCSD(T)]-level quality for H-transfer barrier energies, harmonic frequencies, and H-transfer tunneling splittings. Most importantly, finite-temperature molecular dynamics simulations on the sub-μs time scale in the gas phase are possible and the infrared spectra determined from the transfer-learned PESs are in good agreement with the experiment. It is concluded that routine, long-time atomistic simulations on PESs fulfilling CCSD(T)-standards become possible. Coupled cluster ; Molecular dynamics ; Potential energy ; Ab initio ; Chemistry ; Statistical physics ; Basis set ; Quantum tunnelling ; Density functional theory ; Atomic physics ; Work (physics) ; Potential energy surface ; Quality (philosophy) ; Physics ; Molecular physics ; Chemical physics ; Computational chemistry ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Tamás K. Stenczel | Zakariya El‐Machachi | Guoda Liepuoniute | Joe D. Morrow | Albert P. Bartók | Matt Probert | Gábor Cśanyi | Volker L. Deringer
Source: The Journal of Chemical Physics (article, 2023-07-27)
Field: Materials Science — Materials Chemistry

Machine-learned acceleration for molecular dynamics in CASTEP Machine learning (ML) methods are of rapidly growing interest for materials modeling, and yet, the use of ML interatomic potentials for new systems is often more demanding than that of established density-functional theory (DFT) packages. Here, we describe computational methodology to combine the CASTEP first-principles simulation software with the on-the-fly fitting and evaluation of ML interatomic potential models. Our approach is based on regular checking against DFT reference data, which provides a direct measure of the accuracy of the evolving ML model. We discuss the general framework and the specific solutions implemented, and we present an example application to high-temperature molecular-dynamics simulations of carbon nanostructures. The code is freely available for academic research. CASTEP ; Computer science ; Molecular dynamics ; Density functional theory ; Acceleration ; Computational science ; Measure (data warehouse) ; Code (set theory) ; Software ; Interatomic potential ; Theoretical computer science ; Statistical physics ; Data mining ; Computational chemistry ; Programming language ; Physics ; Chemistry ; Classical mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Xinshui Lei | Jiangen Wang | Teng Wang | Xinjie Wang | Xiaowen Xie | Haocheng Huang | Didi Li | Zhimin Ao
Source: Journal of Hazardous Materials (article, 2023-05-22)
Field: Materials Science — Materials Chemistry

Toluene decomposition by non-thermal plasma assisted CoOx − γ-Al2O3: The relative contributions of specific energy input of plasma, Co3+ and oxygen vacancy Catalysis ; Oxygen ; Toluene ; Calcination ; Decomposition ; Chemistry ; Selectivity ; Vacancy defect ; Inorganic chemistry ; Chemical engineering ; Materials science ; Organic chemistry Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Plasma Applications and Diagnostics
Authors: Pavel V. Kolesnichenko | Axl Eriksson | Linnea Lindh | Donatas Zigmantas | Jens Uhlig
Source: Journal of Chemical Education (article, 2023-02-09)
Field: Chemistry — Physical and Theoretical Chemistry

Viking Spectrophotometer: A Home-Built, Simple, and Cost-Efficient Absorption and Fluorescence Spectrophotometer for Education in Chemistry Investigating the optical properties of various chemical compounds using UV–vis spectrophotometers is an essential part of education in chemistry. However, commercial spectrophotometers are usually treated as “magic black boxes”, where the dominant majority of optical elements are hidden “under the hood”. This often limits understanding of the mechanisms behind the generation of spectral curves, which in turn may impede the ability to understand the limitations of the applied method and, in some cases, interpret the acquired data. In addition, the study of optical emission phenomena using fluorescence spectrophotometers is seldom implemented in educational laboratories due to the practical challenges and costs of the devices, which severely limit pedagogic access to this topic. For students to be more confident with these two basic spectroscopy techniques, we have developed a laboratory kit that provides a multifaceted learning experience. Starting with a basic exploration of an instrument assembly, it teaches, for example, such technical concepts as spectral resolution and detection sensitivity. More fundamentally, it enables deeper learning of the Beer–Lambert law and the notion of Stokes shift. The spectrophotometer is built from cost-efficient materials and is easily scalable, making it affordable for many educational laboratories. Due to a modular design, it is adaptable to various levels of education and has been successfully applied during high school-, undergraduate-, and graduate-level classes. Modular design ; Computer science ; Chemistry ; Nanotechnology ; Process engineering ; Engineering ; Materials science Various Chemistry Research Topics ; Analytical Chemistry and Chromatography ; Chemistry and Chemical Engineering
Authors: Yuchen Liu | Dane Morgan | Takuya Yamamoto | G.R. Odette
Source: Acta Materialia (article, 2023-07-05)
Field: Materials Science — Materials Chemistry

Characterizing the flux effect on the irradiation embrittlement of reactor pressure vessel steels using machine learning Embrittlement ; Materials science ; Fluence ; Neutron flux ; Reactor pressure vessel ; Irradiation ; Pressure vessel ; Flux (metallurgy) ; Metallurgy ; Nuclear engineering ; Composite material ; Nuclear physics ; Neutron ; Physics Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Fusion materials and technologies
Authors: Seunghee Han | Jihan Kim
Source: ACS Omega (article, 2023-01-18)
Field: Chemistry — Inorganic Chemistry

Design and Screening of Metal–Organic Frameworks for Ethane/Ethylene Separation Separation of ethane and ethylene is considered to be industrially important for various chemical processes, but their similarities make the process expensive. In this study, we integrated computational screening with machine learning to find optimal metal-organic frameworks (MOFs) with high ethane/ethylene selectivity. Using our algorithm, a hypothetical MOF structure with an ideal adsorption solution theory selectivity of 3.6 at 298 K and 1 bar was discovered. Furthermore, structural analysis was implemented, and the full adsorption isotherm of some of the top structures was obtained. Ethylene ; Selectivity ; Adsorption ; Metal-organic framework ; Separation process ; Bar (unit) ; Selective adsorption ; Materials science ; Process (computing) ; Metal ; Chemistry ; Organic chemistry ; Computer science ; Chromatography ; Catalysis ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Catalytic Processes in Materials Science
Authors: Giuseppe Bonifazi | Giuseppe Capobianco | Paola Cucuzza | Silvia Serranti | Valeria Spizzichino
Source: Waste and Biomass Valorization (article, 2023-05-10)
Field: Chemistry — Analytical Chemistry

Black Plastic Waste Classification by Laser-Induced Fluorescence Technique Combined with Machine Learning Approaches Abstract Sensor-based sorting devices commonly used in plastic recycling plants, mainly working in the near infrared range (NIR), are unable to identify black plastics, due to their low spectral reflectance. The aim of this work was to investigate the potentialities offered by laser-induced fluorescence (LIF) technique (spectral range 270–750 nm) for the identification of black polymers inside a plastic waste stream, thus allowing the possibility to build efficient sorting strategies to be applied in recycling plants. Representative samples of black plastics collected among the most utilized in household packaging were selected, constituted by four different types of polymers, i.e., expanded polystyrene (EPS), polystyrene (PS), polypropylene (PP) and high-density polyethylene (HDPE). The acquired LIF spectra were processed using multivariate approaches in order to optimize polymer classification. The developed hierarchical—partial least square-discriminant analysis (Hi-PLS-DA) classification model, showed excellent performances, confirmed by the values of sensitivity and specificity values in prediction, being equal to 1. The correctness of classification obtained by LIF was confirmed by the application of Fourier Transform Infrared spectroscopy (FTIR) on the same samples. The achieved results demonstrated the potential of LIF technique combined with a machine learning approach as sorting/quality control tool of black polymers in recycling plants. Graphical Abstract Polypropylene ; Polystyrene ; Materials science ; Polymer ; Biological system ; Fourier transform infrared spectroscopy ; High-density polyethylene ; Polyethylene ; Linear discriminant analysis ; Sorting ; Analytical Chemistry (journal) ; Process engineering ; Artificial intelligence ; Computer science ; Chemical engineering ; Composite material ; Chemistry ; Chromatography ; Algorithm ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Zeeshan Haider Jaffari | Ather Abbas | Muhammad Umer | Eun-Sik Kim | Kyung Hwa Cho
Source: Journal of Materials Chemistry A (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb<sub>2</sub>CT<sub><i>x</i></sub> towards Pb(<scp>ii</scp>) and Cd(<scp>ii</scp>) ions Precisely measuring the adsorption capability of materials towards toxic heavy metal ions in aqueous solution is essential for the synthesis of effective novel adsorbents. Convolution (computer science) ; Graph ; Adsorption ; Artificial neural network ; Crystal (programming language) ; Convolutional neural network ; Computer science ; Materials science ; Mineralogy ; Chemistry ; Crystallography ; Artificial intelligence ; Physical chemistry ; Theoretical computer science MXene and MAX Phase Materials ; Chemical Synthesis and Characterization ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Claire E. J. Armstrong | Jun Niimi | Paul K. Boss | Vinay Pagay | David W. Jeffery
Source: Foods (article, 2023-02-09)
Field: Chemistry — Analytical Chemistry

Use of Machine Learning with Fused Spectral Data for Prediction of Product Sensory Characteristics: The Case of Grape to Wine Generations of sensors have been developed for predicting food sensory profiles to circumvent the use of a human sensory panel, but a technology that can rapidly predict a suite of sensory attributes from one spectral measurement remains unavailable. Using spectra from grape extracts, this novel study aimed to address this challenge by exploring the use of a machine learning algorithm, extreme gradient boosting (XGBoost), to predict twenty-two wine sensory attribute scores from five sensory stimuli: aroma, colour, taste, flavour, and mouthfeel. Two datasets were obtained from absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) spectroscopy with different fusion methods: variable-level data fusion of absorbance and fluorescence spectral fingerprints, and feature-level data fusion of A-TEEM and CIELAB datasets. The results for externally validated models showed slightly better performance using only A-TEEM data, predicting five out of twenty-two wine sensory attributes with R<sup>2</sup> values above 0.7 and fifteen with R<sup>2</sup> values above 0.5. Considering the complex biotransformation involved in processing grapes to wine, the ability to predict sensory properties based on underlying chemical composition in this way suggests that the approach could be more broadly applicable to the agri-food sector and other transformed foodstuffs to predict a product's sensory characteristics from raw material spectral attributes. Wine ; Sensory system ; Product (mathematics) ; Artificial intelligence ; Grape wine ; Machine learning ; Computer science ; Food science ; Mathematics ; Pattern recognition (psychology) ; Chemistry ; Psychology ; Cognitive psychology Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Advanced Chemical Sensor Technologies
Authors: Bing Huang | O. Anatole von Lilienfeld | Jaron T. Krogel | Anouar Benali
Source: Journal of Chemical Theory and Computation (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

Toward DMC Accuracy Across Chemical Space with Scalable Δ-QML In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated to successfully predict the energetics and properties of a wide range of molecules and solids by numerically solving the electronic many-body Schrödinger equation. With <i>O</i>(<i>N</i><sup>3</sup>) scaling with the number of electrons <i>N</i>, DMC has the potential to be a reference method for larger systems that are not accessible to more traditional methods such as CCSD(T). Assessing the accuracy of DMC for smaller molecules becomes the stepping stone in making the method a reference for larger systems. We show that when coupled with quantum machine learning (QML)-based surrogate methods, the computational burden can be alleviated such that quantum Monte Carlo (QMC) shows clear potential to undergird the formation of high-quality descriptions across chemical space. We discuss three crucial approximations necessary to accomplish this: the fixed-node approximation, universal and accurate references for chemical bond dissociation energies, and scalable minimal amons-set-based QML (AQML) models. Numerical evidence presented includes converged DMC results for over 1000 small organic molecules with up to five heavy atoms used as amons and 50 medium-sized organic molecules with nine heavy atoms to validate the AQML predictions. Numerical evidence collected for Δ-AQML models suggests that already modestly sized QMC training data sets of amons suffice to predict total energies with near chemical accuracy throughout chemical space. Chemical space ; Scalability ; Quantum Monte Carlo ; Scaling ; Monte Carlo method ; Statistical physics ; Computer science ; Quantum chemical ; Dissociation (chemistry) ; Quantum ; Diffusion Monte Carlo ; Basis set ; Quantum chemistry ; Molecule ; Bond-dissociation energy ; Physics ; Chemistry ; Quantum mechanics ; Mathematics ; Physical chemistry ; Hybrid Monte Carlo ; Statistics ; Drug discovery Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Matteo Cioni | Daniela Polino | Daniele Rapetti | Luca Pesce | Massimo Delle Piane | Giovanni M. Pavan
Source: The Journal of Chemical Physics (article, 2023-03-06)
Field: Materials Science — Materials Chemistry

Innate dynamics and identity crisis of a metal surface unveiled by machine learning of atomic environments Metals are traditionally considered hard matter. However, it is well known that their atomic lattices may become dynamic and undergo reconfigurations even well below the melting temperature. The innate atomic dynamics of metals is directly related to their bulk and surface properties. Understanding their complex structural dynamics is, thus, important for many applications but is not easy. Here, we report deep-potential molecular dynamics simulations allowing to resolve at an atomic resolution the complex dynamics of various types of copper (Cu) surfaces, used as an example, near the Hüttig (∼1/3 of melting) temperature. The development of deep neural network potential trained on density functional theory calculations provides a dynamically accurate force field that we use to simulate large atomistic models of different Cu surface types. A combination of high-dimensional structural descriptors and unsupervized machine learning allows identifying and tracking all the atomic environments (AEs) emerging in the surfaces at finite temperatures. We can directly observe how AEs that are non-native in a specific (ideal) surface, but that are, instead, typical of other surface types, continuously emerge/disappear in that surface in relevant regimes in dynamic equilibrium with the native ones. Our analyses allow estimating the lifetime of all the AEs populating these Cu surfaces and to reconstruct their dynamic interconversions networks. This reveals the elusive identity of these metal surfaces, which preserve their identity only in part and in part transform into something else under relevant conditions. This also proposes a concept of "statistical identity" for metal surfaces, which is key to understanding their behaviors and properties. Identity (music) ; Molecular dynamics ; Surface (topology) ; Chemical physics ; Nanotechnology ; Computer science ; Statistical physics ; Artificial intelligence ; Biological system ; Materials science ; Physics ; Chemistry ; Computational chemistry ; Mathematics ; Geometry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Theoretical and Computational Physics
Authors: Razieh Pourdarbani | Sajad Sabzi | Mohsen Dehghankar | Mohammad Hossein Rohban | Juan Ignacio Arribas
Source: Algorithms (article, 2023-02-14)
Field: Chemistry — Analytical Chemistry

Examination of Lemon Bruising Using Different CNN-Based Classifiers and Local Spectral-Spatial Hyperspectral Imaging The presence of bruises on fruits often indicates cell damage, which can lead to a decrease in the ability of the peel to keep oxygen away from the fruits, and as a result, oxygen breaks down cell walls and membranes damaging fruit content. When chemicals in the fruit are oxidized by enzymes such as polyphenol oxidase, the chemical reaction produces an undesirable and apparent brown color effect, among others. Early detection of bruising prevents low-quality fruit from entering the consumer market. Hereupon, the present paper aims at early identification of bruised lemon fruits using 3D-convolutional neural networks (3D-CNN) via a local spectral-spatial hyperspectral imaging technique, which takes into account adjacent image pixel information in both the frequency (wavelength) and spatial domains of a 3D-tensor hyperspectral image of input lemon fruits. A total of 70 sound lemons were picked up from orchards. First, all fruits were labeled and the hyperspectral images (wavelength range 400–1100 nm) were captured as belonging to the healthy (unbruised) class (class label 0). Next, bruising was applied to each lemon by freefall. Then, the hyperspectral images of all bruised samples were captured in a time gap of 8 (class label 1) and 16 h (class label 2) after bruising was induced, thus resulting in a 3-class ternary classification problem. Four well-known 3D-CNN model namely ResNet, ShuffleNet, DenseNet, and MobileNet were used to classify bruised lemons in Python. Results revealed that the highest classification accuracy (90.47%) was obtained by the ResNet model, followed by DenseNet (85.71%), ShuffleNet (80.95%) and MobileNet (73.80%); all over the test set. ResNet model had larger parameter sizes, but it was proven to be trained faster than other models with fewer number of free parameters. ShuffleNet and MobileNet were easier to train and they needed less storage, but they could not achieve a classification error as low as the other two counterparts. Hyperspectral imaging ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Convolutional neural network ; Pixel ; Mathematics ; Computer vision Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Essential Oils and Antimicrobial Activity