jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Diego M. Nascimento | Si Li | Bruno Focassio | Gabriel R. Schleder | Carlos Alberto Rodrigues Costa | Cleyton Alexandre Biffe | Liu Ling | Rubia F. Gouveia | Mathias Strauss | George J. M. Rocha | Edson R. Leite | A. Fazzio | Rodrigo B. Capaz | Carlos Driemeier | Juliana S. Bernardes
Source: Nanoscale (article, 2022-01-01)
Field: Materials Science — Biomaterials

How lignin sticks to cellulose—insights from atomic force microscopy enhanced by machine-learning analysis and molecular dynamics simulations Elucidating cellulose-lignin interactions at the molecular and nanometric scales is an important research topic with impacts on several pathways of biomass valorization. Here, the interaction forces between a cellulosic substrate and lignin are investigated. Atomic force microscopy with lignin-coated tips is employed to probe the site-specific adhesion to a cellulose film in liquid water. Over seven thousand force-curves are analyzed by a machine-learning approach to cluster the experimental data into types of cellulose-tip interactions. The molecular mechanisms for distinct types of cellulose-lignin interactions are revealed by molecular dynamics simulations of lignin globules interacting with different cellulose Iβ crystal facets. This unique combination of experimental force-curves, data-driven analysis, and molecular simulations opens a new approach of investigation and updates the understanding of cellulose-lignin interactions at the nanoscale. Cellulose ; Lignin ; Molecular dynamics ; Atomic force microscopy ; Nanoscopic scale ; Materials science ; Chemical physics ; Substrate (aquarium) ; Nanotechnology ; Cellulosic ethanol ; Chemical engineering ; Chemistry ; Computational chemistry ; Organic chemistry Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Power Transformer Diagnostics and Insulation
Authors: Xiaoyu Wu | Yu Che | Linjiang Chen | Eric Amigues | Ruiyao Wang | Jinghui He | Huilong Dong | Lifeng Ding
Source: ACS Applied Materials & Interfaces (article, 2022-10-05)
Field: Chemistry — Inorganic Chemistry

Mapping the Porous and Chemical Structure–Function Relationships of Trace CH<sub>3</sub>I Capture by Metal–Organic Frameworks using Machine Learning Large-scale computational screening has become an indispensable tool for functional materials discovery. It, however, remains a challenge to adequately interrogate the large amount of data generated by a screening study. Here, we computationally screened 1087 metal-organic frameworks (MOFs), from the CoRE MOF 2014 database, for capturing trace amounts (300 ppmv) of methyl iodide (CH<sub>3</sub>I); as a primary representative of organic iodides, CH<sub>3</sub><sup>129</sup>I is one of the most difficult radioactive contaminants to separate. Furthermore, we demonstrate a simple and general approach for mapping and interrogating the high-dimensional structure-function data obtained by high-throughput screening; this involves learning two-dimensional embeddings of the high-dimensional data by applying unsupervised learning to encoded structural and chemical features of MOFs. The resulting various porous and chemical structure-function maps are human-interpretable, revealing not only top-performing MOFs but also complex structure-function correlations that are hidden when inspecting individual MOF features. These maps also alleviate the need of laborious visual inspection of a large number of MOFs by clustering similar MOFs, per the encoding features, into defined regions on the map. We also show that these structure-function maps are amenable to supervised classification of the performances of MOFs for trace CH<sub>3</sub>I capture. We further show that the machine-learning models trained on the 1087 CoRE MOFs can be used to predict an unseen set of 250 MOFs randomly selected from a different MOF database, achieving high prediction accuracies. TRACE (psycholinguistics) ; Metal-organic framework ; Cluster analysis ; Function (biology) ; Computer science ; Unsupervised learning ; Artificial intelligence ; Machine learning ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Radioactive element chemistry and processing
Authors: Chongcan Li | Yong Cong | Weihua Deng
Source: Magnetic Resonance in Chemistry (article, 2022-06-08)
Field: Chemistry — Spectroscopy

Identifying molecular functional groups of organic compounds by deep learning of NMR data We preprocess the raw nuclear magnetic resonance (NMR) spectrum and extract key features by using two different methodologies, called equidistant sampling and peak sampling for subsequent substructure pattern recognition. We also provide a strategy to address the imbalance issue frequently encountered in statistical modeling of NMR data set and establish two conventional support vector machine (SVM) and K-nearest neighbor (KNN) models to assess the capability of two feature selections, respectively. Our results in this study show that the models using the selected features of peak sampling outperform those using equidistant sampling. Then we build the recurrent neural network (RNN) model trained by data collected from peak sampling. Furthermore, we illustrate the easier optimization of hyperparameters and the better generalization ability of the RNN deep learning model by detailed comparison with traditional machine learning SVM and KNN models. Artificial intelligence ; Sampling (signal processing) ; Support vector machine ; Pattern recognition (psychology) ; Hyperparameter ; Feature (linguistics) ; Generalization ; Artificial neural network ; Machine learning ; Data set ; Raw data ; Chemistry ; Computer science ; Mathematics Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Afshin Saadat | Asma Dehghani Varniab | Seyyed Mohammad Madani
Source: Journal of Nanomaterials (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Prediction of the Antibacterial Activity of the Green Synthesized Silver Nanoparticles against Gram Negative and Positive Bacteria by using Machine Learning Algorithms With the appearance and growth of microbial organisms resistant to various antibiotics, as well as the need to reduce the cost of care of health, the production of antimicrobials at lower costs has become an inescapable necessity for today’s human societies. Recently, the interdisciplinary field of nanotechnology has developed widely. One of the applications of nanobiotechnology is the use of silver nanoparticles (AgNPs) for new solutions in the treatment of microbial infections. AgNPs have unique properties which help in molecular diagnostics, therapies, and also in devices that are used in several medical procedures. In this field, machine learning algorithms have been used with hopeful results. One of the branches of artificial intelligence (AI) is machine learning (ML) that focuses on data and shows the power of the data. Machine learning techniques are taking considerable attention because of their obvious successes in a broad range of predictive tasks. In this work, we studied machine learning technique to predict the antibacterial activity of AgNPs against Escherichia coli , Pseudomonas aeruginosa , Staphylococcus aureus , and Klebsiella pneumoniae . Here, we reviewed 100 articles for completing the data, highlighting the recently used different plants for the synthesis of highly efficient antimicrobial green AgNPs, which consist of key experimental conditions (amount of plant extract, volume of plant extract, volume of solvent, volume of AgNO 3 solution, reaction temperature, reaction time, concentration of precursors, and nanoparticle size). The results showed that nanoparticles size and concentration of AgNPs are key factors in predicting the antibacterial effect of AgNPs. Materials science ; Gram ; Antibacterial activity ; Silver nanoparticle ; Bacteria ; Nanoparticle ; Gram-negative bacteria ; Nanotechnology ; Algorithm ; Computer science ; Escherichia coli ; Biology ; Biochemistry Nanoparticles: synthesis and applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Diverse Scientific Research Studies
Authors: Yanwei Cao | Peng Yao | Danyang Cheng | Lin Chen | Maolin Wang | Cheng Shang | Lirong Zheng | Ding Ma | Zhi‐Pan Liu | Lin He
Source: ACS Catalysis (article, 2022-12-23)
Field: Materials Science — Materials Chemistry

Room-Temperature CO Oxidative Coupling for Oxamide Production over Interfacial Au/ZnO Catalysts The conversion of carbon monoxide (CO) into high-value organic molecules has long been pursued by both industry and academia. One of the central challenges is to realize carbon–carbon coupling with high selectivity for CO insertion. We report here a highly selective Au/ZnO composite catalyst for one-step oxidative coupling of CO with secondary amines, producing oxamides. The reaction is >99% selective under ambient conditions, which exhibits a subtle change of catalyst activity over ∼120 h in a fixed-bed reactor. A combination of microscopic and spectroscopic studies (including X-ray photoemission spectroscopy, X-ray absorption spectroscopy, CO-Fourier transform infrared spectroscopy, and aberration-corrected high-angle annular dark-field scanning transmission electron microscopy) and machine learning reveal that the active site is the Au–ZnO interface with a Au nanocluster attached to the ZnO support via nonstoichiometric ZnOx (x > 1) linkages. The reaction mechanism and key reaction intermediates were confirmed by density functional theory calculations and in situ diffused reflectance infrared Fourier transform spectroscopy signals. Our results open an avenue in heterogeneous catalysis to achieve the ambient-condition CO transformation by exploiting the interfacial synergistic effects. Catalysis ; Diffuse reflectance infrared fourier transform ; Fourier transform infrared spectroscopy ; Oxidative coupling of methane ; Carbon monoxide ; Chemistry ; Heterogeneous catalysis ; Spectroscopy ; Attenuated total reflection ; Photochemistry ; Scanning transmission electron microscopy ; Infrared spectroscopy ; Selectivity ; Materials science ; Chemical engineering ; Transmission electron microscopy ; Nanotechnology ; Organic chemistry ; Photocatalysis Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Carbon dioxide utilization in catalysis
Authors: Zhanpeng Gong | Zhixuan Xu | Jinlong Hu | Biaojie Yan | Xiangdong Ding | Jun Sun | Pengcheng Zhang | Junkai Deng
Source: Acta Materialia (article, 2022-09-10)
Field: Materials Science — Materials Chemistry

Thermal conductivity prediction of UO2-BeO composite fuels and related decisive features discovery via convolutional neural network Materials science ; Convolutional neural network ; Microstructure ; Composite number ; Thermal conductivity ; Transfer of learning ; Feature (linguistics) ; Artificial intelligence ; Deep learning ; Nuclear engineering ; Composite material ; Pattern recognition (psychology) ; Computer science Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: Efthymia Ramou | Susana I. C. J. Palma | Ana Cecília A. Roque
Source: ACS Applied Materials & Interfaces (article, 2022-01-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nanoscale Events on Cyanobiphenyl-Based Self-Assembled Droplets Triggered by Gas Analytes Liquid crystals (LCs) are prime examples of dynamic supramolecular soft materials. Their autonomous self-assembly at the nanoscale level and the further nanoscale events that give rise to unique stimuli-responsive properties have been exploited for sensing purposes. One of the key features to employ LCs as sensing materials derives from the fine-tuning between stability and dynamics. This challenging task was addressed in this work by studying the effect of the alkyl chain length of cyanobiphenyl LCs on the molecular self-assembled compartments organized in the presence of ionic liquid molecules and gelatin. The resulting multicompartment nematic and smectic gels were further used as volatile organic compound chemical sensors. The LC structures undergo a dynamic sequence of phase transitions, depending on the nature of the LC component, yielding a variety of optical signals, which serve as optical fingerprints. In particular, the materials incorporating smectic compartments resulted in unexpected and rich optical textures that have not been reported previously. Their sensing capability was tested in an in-house-assembled electronic nose and further assessed via signal collection and machine-learning algorithms based on support vector machines, which classified 12 different gas analytes with high accuracy scores. Our work expands the knowledge on controlling LC self-assembly to yield fast and autonomous accurate chemical-sensing systems based on the combination of complex nanoscale sensing events with artificial intelligence tools. Nanoscopic scale ; Liquid crystal ; Supramolecular chemistry ; Alkyl ; Analyte ; Molecule ; Phase (matter) ; Ionic liquid Liquid Crystal Research Advancements ; Polydiacetylene-based materials and applications ; Pickering emulsions and particle stabilization
Authors: Nathan M. Wilson | Daniel Willhelm | Xiaoning Qian | Raymundo Arróyave | Xiaofeng Qian
Source: Computational Materials Science (article, 2022-03-17)
Field: Materials Science — Materials Chemistry

Batch active learning for accelerating the development of interatomic potentials Interatomic potential ; Chemical physics ; Materials science ; Chemistry ; Computer science ; Nanotechnology ; Molecular dynamics ; Engineering physics ; Computational chemistry ; Physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Takeshi Aoyagi
Source: Computational Materials Science (article, 2022-02-28)
Field: Materials Science — Materials Chemistry

Optimization of the elastic properties of block copolymers using coarse-grained simulation and an artificial neural network Block copolymers consisting of immiscible glassy and rubbery blocks have microphase-separated structures that result in various elastic properties depending on the polymer structures. However, because the complete simulation approach to surveying a wide variety of polymer and microphase-separated structures is time-consuming, a more efficient approach is required for the design of materials with desired properties. In the present study, we used coarse-grained molecular dynamics (CGMD) simulations and an artificial neural network (ANN) to design polymer structures with the desired stress–strain properties. CGMD simulations were conducted to obtain stress–strain curves of linear diblock and triblock copolymers of various chain lengths, block volume fractions, and asymmetricities. We trained the ANN for regression between the polymer structures and the stress–strain curves. Then, using the trained ANN, we performed Bayesian optimization to obtain a polymer structure with an arbitrary target stress–strain curve. CGMD simulations of the optimized polymer structure produced a stress–strain curve that agreed with the curve predicted by the ANN. Therefore, simulation and use of an ANN are potentially useful strategies for the design of polymer structures with desired properties. Copolymer ; Materials science ; Polymer ; Artificial neural network ; Molecular dynamics ; Stress–strain curve ; Stress (linguistics) ; Biological system ; Composite material ; Computer science ; Computational chemistry ; Chemistry ; Deformation (meteorology) ; Artificial intelligence Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Model Reduction and Neural Networks
Authors: Sergey N. Pozdnyakov | Michele Ceriotti
Source: Machine Learning Science and Technology (article, 2022-11-10)
Field: Materials Science — Materials Chemistry

Incompleteness of graph neural networks for points clouds in three dimensions Abstract Graph neural networks (GNN) are very popular methods in machine learning and have been applied very successfully to the prediction of the properties of molecules and materials. First-order GNNs are well known to be incomplete, i.e. there exist graphs that are distinct but appear identical when seen through the lens of the GNN. More complicated schemes have thus been designed to increase their resolving power. Applications to molecules (and more generally, point clouds), however, add a geometric dimension to the problem. The most straightforward and prevalent approach to construct graph representation for molecules regards atoms as vertices in a graph and draws a bond between each pair of atoms within a chosen cutoff. Bonds can be decorated with the distance between atoms, and the resulting ‘distance graph NNs’ (dGNN) have empirically demonstrated excellent resolving power and are widely used in chemical ML, with all known indistinguishable configurations being resolved in the fully-connected limit, which is equivalent to infinite or sufficiently large cutoff. Here we present a counterexample that proves that dGNNs are not complete even for the restricted case of fully-connected graphs induced by 3D atom clouds. We construct pairs of distinct point clouds whose associated graphs are, for any cutoff radius, equivalent based on a first-order Weisfeiler-Lehman (WL) test. This class of degenerate structures includes chemically-plausible configurations, both for isolated structures and for infinite structures that are periodic in 1, 2, and 3 dimensions. The existence of indistinguishable configurations sets an ultimate limit to the expressive power of some of the well-established GNN architectures for atomistic machine learning. Models that explicitly use angular or directional information in the description of atomic environments can resolve this class of degeneracies. Cutoff ; Counterexample ; Degenerate energy levels ; Vertex (graph theory) ; Artificial neural network ; Point cloud ; Mathematics ; Graph ; Combinatorics ; Computer science ; Discrete mathematics ; Physics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Abdul Gani Abdul Jameel | Ali Al-Muslem | Nabeel Ahmad | Awad B.S. Alquaity | Umer Zahid | Usama Ahmed
Source: Processes (article, 2022-11-14)
Field: Chemistry — Organic Chemistry

Predicting Enthalpy of Combustion Using Machine Learning The present work discusses the development and application of a machine-learning-based model to predict the enthalpy of combustion of various oxygenated fuels of interest. A detailed dataset containing 207 pure compounds and 38 surrogate fuels has been prepared, representing various chemical classes, namely paraffins, olefins, naphthenes, aromatics, alcohols, ethers, ketones, and aldehydes. The dataset was subsequently used for constructing an artificial neural network (ANN) model with 14 input layers, 26 hidden layers, and 1 output layer for predicting the enthalpy of combustion for various oxygenated fuels. The ANN model was trained using the collected dataset, validated, and finally tested to verify its accuracy in predicting the enthalpy of combustion. The results for various oxygenated fuels are discussed, especially in terms of the influence of different functional groups in shaping the enthalpy of combustion values. In predicting the enthalpy of combustion, 96.3% accuracy was achieved using the ANN model. The developed model can be successfully employed to predict the enthalpies of neat compounds and mixtures as the obtained percentage error of 4.2 is within the vicinity of experimental uncertainty. Combustion ; Enthalpy ; Artificial neural network ; Standard enthalpy change of formation ; Work (physics) ; Chemistry ; Thermodynamics ; Machine learning ; Computer science ; Organic chemistry ; Physics Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics ; Thermal and Kinetic Analysis
Authors: Yijing Gao | Shijie Zhang | Xiang Sun | Wei Zhao | Han Zhuo | Gui‐Lin Zhuang | Shibin Wang | Zihao Yao | Shengwei Deng | Xing Zhong | Zhongzhe Wei | Jianguo Wang
Source: CHINESE JOURNAL OF CATALYSIS (CHINESE VERSION) (article, 2022-05-21)
Field: Materials Science — Materials Chemistry

Computational screening of O-functional MXenes for electrocatalytic ammonia synthesis MXenes ; Catalysis ; Density functional theory ; Limiting ; Rational design ; Lasso (programming language) ; Chemistry ; Combinatorial chemistry ; Biochemical engineering ; Computer science ; Materials science ; Computational chemistry ; Nanotechnology ; Organic chemistry MXene and MAX Phase Materials ; Ammonia Synthesis and Nitrogen Reduction ; Advanced Photocatalysis Techniques
Authors: Alberto Fabrizio | Ksenia R. Briling | Clémence Corminbœuf
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

SPA<sup>H</sup>M: the spectrum of approximated Hamiltonian matrices representations Physics-inspired molecular representations are the cornerstone of similarity-based learning applied to solve chemical problems. Despite their conceptual and mathematical diversity, this class of descriptors shares a common underlying philosophy: they all rely on the molecular information that determines the form of the electronic Schrödinger equation. Existing representations take the most varied forms, from non-linear functions of atom types and positions to atom densities and potential, up to complex quantum chemical objects directly injected into the ML architecture. In this work, we present the spectrum of approximated Hamiltonian matrices (SPA<sup>H</sup>M) as an alternative pathway to construct quantum machine learning representations through leveraging the foundation of the electronic Schrödinger equation itself: the electronic Hamiltonian. As the Hamiltonian encodes all quantum chemical information at once, SPA<sup>H</sup>M representations not only distinguish different molecules and conformations, but also different spin, charge, and electronic states. As a proof of concept, we focus here on efficient SPA<sup>H</sup>M representations built from the eigenvalues of a hierarchy of well-established and readily-evaluated "guess" Hamiltonians. These SPA<sup>H</sup>M representations are particularly compact and efficient for kernel evaluation and their complexity is independent of the number of different atom types in the database. Hamiltonian (control theory) ; Mathematical physics ; Spectrum (functional analysis) ; Quantum ; Mathematics ; Physics ; Pure mathematics ; Quantum mechanics ; Algebra over a field Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems