jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: А. Н. Филанович | A. V. Lukoyanov | А. А. Повзнер
Source: Physica B Condensed Matter (article, 2023-09-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electronic structure and elastic properties of double half-Heusler thermoelectric materials MgXY2Z2 (X=Zr/Hf, Y=Pd/Pt, Z=Bi/Sb) Materials science ; Thermoelectric effect ; Thermoelectric materials ; Condensed matter physics ; Anisotropy ; Thermal conductivity ; Heusler compound ; Ab initio quantum chemistry methods ; Ab initio ; Electronic structure ; Work (physics) ; Electronic band structure ; Seebeck coefficient ; Band gap ; Crystallography ; Thermodynamics ; Metallurgy ; Composite material ; Metal ; Optoelectronics ; Chemistry ; Optics ; Physics Heusler alloys: electronic and magnetic properties ; Advanced Thermoelectric Materials and Devices ; MXene and MAX Phase Materials
Authors: Jean Flores‐Gómez | Mario Villegas-Ruvalcaba | José Miguel Blancas-Flores | Juan Morales‐Rivera
Source: ChemEngineering (article, 2023-09-11)
Field: Chemistry — Spectroscopy

Chitosan–Resole–Pectin Aerogel in Methylene Blue Removal: Modeling and Optimization Using an Artificial Neuron Network In this study, a novel chitosan–resole–pectin aerogel (CS–R–P) was created from a sol–gel reaction with a solution of Cs and P with resole by a freeze-drying technique, and this adsorbent was proposed for the removal of methylene blue (MB). In addition, with the use of an artificial intelligence technique known as an artificial neural network (ANN), this material was modeled and optimized. Its physical morphology and chemical composition were also characterized with FTIR and XPS, and its adsorption properties were analyzed. For modeling the adsorption process, three main parameters were used: the chitosan–resole–pectin concentration (45–75%), thermal treatment (6–36 h), and known concentrations of methylene blue (25–50 and 100 mg/L), established on the Box–Behnken design. The ANN was coupled with the improved gray wolf optimization (IWGO) metaheuristic algorithm, achieving a correlation coefficient of R2 = 0.99. The characterization indicates that the surface of the aerogels was micro- and mesoporous, the resole gave physical stability, and the polysaccharide base delivered the functional groups necessary for dye adsorption; the aerogels were successful dye adsorbents with a qe of 12.44 mg/g. Finally, the physical and chemical sorption was ascertainable with an adsorption that followed pseudo-second-order kinetics. The MB adsorption was clearly occurring though cation exchange and hydrogen binding as observed in the chemical composition. The ANN with the gray wolf optimizer was used for the prediction of the best operating parameters for MB removal, applying the following conditions—the CS–R–P aerogel concentration (52/30/18), the thermal treatment (9.12 h), and the initial concentration of methylene blue (37 mg/L)—achieving a 94.6% removal. These conclusions suggest that using artificial intelligence such as an ANN can provide an efficient and practical model for maximizing the removal action of new aerogels based on chitosan. Aerogel ; Adsorption ; Methylene blue ; Chitosan ; Chemical engineering ; Thermal stability ; Materials science ; Pectin ; Fourier transform infrared spectroscopy ; Chemistry ; Mesoporous material ; Nuclear chemistry ; Organic chemistry ; Nanotechnology ; Catalysis Aerogels and thermal insulation ; Adsorption and biosorption for pollutant removal ; Advanced Cellulose Research Studies
Authors: Lyuben Borislаvov | Miroslava Nedyalkova | Alia Tadjer | Önder Aydemir | Julia Romanova
Source: The Journal of Physical Chemistry Letters (article, 2023-11-03)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Screening for Potential Singlet Fission Chromophores: The Challenge of Imbalanced Data Sets Excitation with one photon of a singlet fission (SF) material generates two triplet excitons, thus doubling the solar cell efficiency. Therefore, the SF molecules are regarded as new generation organic photovoltaics, but it is hard to identify them. Recently, it was demonstrated that molecules of low-to-intermediate diradical character (DRC) are potential SF chromophores. This prompts a low-cost strategy for finding new SF candidates by computational high-throughput workflows. We propose a machine learning aided screening for SF entrants based on their DRC. Our data set comprises 469 784 compounds extracted from the PubChem database, structurally rich but inherently imbalanced regarding DRC values. We developed well performing classification models that can retrieve potential SF chromophores. The latter (∼4%) were analyzed by K-means clustering to reveal qualitative structure-property relationships and to extract strategies for molecular design. The developed screening procedure and data set can be easily adapted for applications of diradicaloids in photonics and spintronics. Chromophore ; Singlet fission ; Computer science ; Singlet state ; Photovoltaics ; Chemistry ; Artificial intelligence ; Physics ; Excited state ; Nuclear physics ; Engineering ; Photovoltaic system ; Photochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular Junctions and Nanostructures
Authors: Dorián László Galata | Szilveszter Gergely | Rebeka Nagy | János Slezsák | Ferenc Ronkay | Zsombor Kristóf Nagy | Attila Farkas
Source: Pharmaceuticals (article, 2023-09-01)
Field: Chemistry — Analytical Chemistry

Comparing the Performance of Raman and Near-Infrared Imaging in the Prediction of the In Vitro Dissolution Profile of Extended-Release Tablets Based on Artificial Neural Networks In this work, the performance of two fast chemical imaging techniques, Raman and near-infrared (NIR) imaging is compared by utilizing these methods to predict the rate of drug release from sustained-release tablets. Sustained release is provided by adding hydroxypropyl methylcellulose (HPMC), as its concentration and particle size determine the dissolution rate of the drug. The chemical images were processed using classical least squares; afterwards, a convolutional neural network was applied to extract information regarding the particle size of HPMC. The chemical images were reduced to an average HPMC concentration and a predicted particle size value; these were used as inputs in an artificial neural network with a single hidden layer to predict the dissolution profile of the tablets. Both NIR and Raman imaging yielded accurate predictions. As the instrumentation of NIR imaging allows faster measurements than Raman imaging, this technique is a better candidate for implementing a real-time technique. The introduction of chemical imaging in the routine quality control of pharmaceutical products would profoundly change quality assurance in the pharmaceutical industry. Chemical imaging ; Raman spectroscopy ; Dissolution ; Particle size ; Materials science ; Partial least squares regression ; Biomedical engineering ; Artificial neural network ; Particle (ecology) ; Computer science ; Artificial intelligence ; Chemistry ; Machine learning ; Optics ; Medicine Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Drug Solubulity and Delivery Systems
Authors: Sandipp Krishnan Ravi | Anindya Bhaduri | Amer Ahmad | Sayan Ghosh | Liping Wang | Andrew Hoffman | Rajnikant V. Umretiya | Indranil Roy | Raúl B. Rebak | Voramon S. Dheeradhada | Siyeong Ju | Changjie Sun | Patrick Shower | Anteneh Kebbede | Alexander L. Kitt | Lee Kerwin | Luke Mohr | Lang Yuan | Bojun Feng | Subhrajit Roychowdhury
Source: AIAA SCITECH 2023 Forum (article, 2023-01-19)
Field: Materials Science — Materials Chemistry

On Uncertainty Quantification in Materials Modeling and Discovery: Applications of GE's BHM and IDACE View Video Presentation: https://doi.org/10.2514/6.2023-0528.vid The coupling of artificial intelligence and materials characterizations has been a center piece of almost all materials discovery efforts since 1990. Furthermore, with the constant development in probabilistic machine learning tools (i.e. machine learning models that can efficiently propagate uncertainty from inputs to outputs), the synergistic interplay between materials expert knowledge and machine learning model accuracy has been a major driver for state-of-the-art materials research. However, there still exist major challenges facing the machine learning community today when it comes to materials discovery. Be it properly representing uncertainties in the data, or leveraging materials expert knowledge in the learning phase, or actively learning from the usually-sparse, and in many cases non-comprehensive, data sets available to the machine learning expert. These challenges need to be overcome to accelerate research endeavors in the domain of material discovery. To this end, General Electric Research (GER) has been developing and applying highly robust, industry-grade tools that can do tasks like uncertainty quantification (UQ), probabilistic calibration, meta-modelling, active learning, and many more. Application and demonstration of two such tools namely Bayesian Hybrid Modeling (GEBHM) and Intelligent Design and Analysis of Computer Experiments (IDACE) are presented in this paper in the context of materials modeling and discovery. Computer science ; Machine learning ; Artificial intelligence ; Probabilistic logic ; Context (archaeology) ; Knowledge extraction ; Domain (mathematical analysis) ; Bayesian network Machine Learning in Materials Science ; Fault Detection and Control Systems ; Non-Destructive Testing Techniques
Authors: Yufeng Mao | Xiaokang Yao | Ze Yu | Zhongfu An | Huili Ma
Source: Angewandte Chemie (article, 2023-12-23)
Field: Materials Science — Materials Chemistry

Ground‐State Orbital Descriptors for Accelerated Development of Organic Room‐Temperature Phosphorescent Materials Abstract Organic materials with room‐temperature phosphorescence (RTP) are in high demand for optoelectronics and bioelectronics. Developing RTP materials highly relies on expert experience and costly excited‐state calculations. It is a challenge to find a tool for effectively screening RTP materials. Herein we first establish ground‐state orbital descriptors (π FMOs ) derived from the π‐electron component of the frontier molecular orbitals to characterize the RTP lifetime (τ p ), achieving a balance in screening efficiency and accuracy. Using the π FMOs , a data‐driven machine learning model gains a high accuracy in classifying long τ p , filtering out 836 candidates with long‐lived RTP from a virtual library of 19,295 molecules. With the aid of the excited‐state calculations, 287 compounds are predicted with high RTP efficiency. Impressively, experiments further confirm the reliability of this workflow, opening a novel avenue for designing high‐performance RTP materials for potential applications. Phosphorescence ; Excited state ; Workflow ; Materials science ; Nanotechnology ; Molecular orbital ; Atomic orbital ; Bioelectronics ; Computer science ; Chemistry ; Molecule ; Electron ; Fluorescence ; Atomic physics ; Database ; Physics ; Biosensor ; Organic chemistry Luminescence and Fluorescent Materials ; Organoboron and organosilicon chemistry ; Molecular Sensors and Ion Detection
Authors: Xiaolu Yang | Zhenguo Liu | Zhe Zhang | Wei Xiang | Ming‐Yang Geng | Hao Chen | Xiaochun Liu | Weibing Lu
Source: Engineering Applications of Artificial Intelligence (article, 2023-09-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultrathin optically transparent and flexible wideband absorber based on ANN and DGCNN Computer science ; Bandwidth (computing) ; Metamaterial ; Wideband ; Artificial neural network ; Algorithm ; Materials science ; Optics ; Optoelectronics ; Artificial intelligence ; Telecommunications ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Alexey A. Gavrilov | Igor I. Potemkin
Source: The Journal of Physical Chemistry B (review, 2023-02-15)
Field: Materials Science — Materials Chemistry

Copolymers with Nonblocky Sequences as Novel Materials with Finely Tuned Properties The copolymer sequence can be considered as a new tool to shape the resulting system properties on demand. This perspective is devoted to copolymers with "partially segregated" (or nonblocky) sequences. Such copolymers include gradient copolymers and copolymers with random sequences as well as copolymers with precisely controlled sequences. We overview recent developments in the synthesis of these systems as well as new findings regarding their properties, in particular, self-assembly in solutions and in melts. An emphasis is put on how the microscopic behavior of polymer chains is influenced by the chain sequences. In addition to that, a novel class of approaches allowing one to efficiently tackle the problem of copolymer chain sequence design─data driven methods (artificial intelligence and machine learning)─is discussed. Copolymer ; Sequence (biology) ; Polymer ; Chain (unit) ; Materials science ; Class (philosophy) ; Computer science ; Polymer science ; Chemistry ; Artificial intelligence ; Physics Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science
Authors: Samuel Yang | Shutong Li | Subhashini Venugopalan | Vahe Tshitoyan | Muratahan Aykol | Amil Merchant | Ekin D. Cubuk | Gowoon Cheon
Source: arXiv (Cornell University) (preprint, 2023-11-23)
Field: Materials Science — Materials Chemistry

Accurate Prediction of Experimental Band Gaps from Large Language Model-Based Data Extraction Machine learning is transforming materials discovery by providing rapid predictions of material properties, which enables large-scale screening for target materials. However, such models require training data. While automated data extraction from scientific literature has potential, current auto-generated datasets often lack sufficient accuracy and critical structural and processing details of materials that influence the properties. Using band gap as an example, we demonstrate Large language model (LLM)-prompt-based extraction yields an order of magnitude lower error rate. Combined with additional prompts to select a subset of experimentally measured properties from pure, single-crystalline bulk materials, this results in an automatically extracted dataset that's larger and more diverse than the largest existing human-curated database of experimental band gaps. Compared to the existing human-curated database, we show the model trained on our extracted database achieves a 19% reduction in the mean absolute error of predicted band gaps. Finally, we demonstrate that LLMs are able to train models predicting band gap on the extracted data, achieving an automated pipeline of data extraction to materials property prediction. Pipeline (software) ; Computer science ; Property (philosophy) ; Data mining ; Artificial intelligence ; Extraction (chemistry) ; Machine learning ; Data extraction Machine Learning in Materials Science ; Topic Modeling
Authors: Elizabeth Champa-Bujaico | Ana M. Díez‐Pascual | Pilar García-Díaz
Source: Polymers (article, 2023-09-13)
Field: Materials Science — Biomaterials

Poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) Bionanocomposites with Crystalline Nanocellulose and Graphene Oxide: Experimental Results and Support Vector Machine Modeling Poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBHHx) is a biodegradable and biocompatible bacterial copolymer used in the biomedical and food industries. However, it displays low stiffness and strength for certain applications. This issue can be solved via reinforcement with nanofillers. In this work, PHBHHx-based bionanocomposites reinforced with different loadings of crystalline nanocellulose (CNC) and graphene oxide (GO) were developed by a green and straightforward solution casting technique. Their crystalline nature and surface topography were explored via X-ray diffraction (XRD) and field-emission scanning electron microscopy (FE-SEM), respectively, their composition was corroborated via Fourier-transformed infrared spectroscopy (FTIR), and their crystallization and melting behavior were determined via differential scanning calorimetry (DSC). The nanofillers had a nucleating role, raising the crystallization temperature of the polymer, whilst hardly any changes were found in the melting temperature. Further, significant enhancements in the stiffness, strength, and thermal stability of the PHBHHx matrix were observed with the incorporation of both nanofillers, which was attributed to a synergic effect. The mechanical properties for various concentrations of CNC and GO were accurately predicted using a machine learning (ML) model in the form of a support vector machine (SVM). The model performance was evaluated in terms of the mean absolute error (MAE), the mean square error (MSE), and the correlation coefficient (<i>R</i><sup>2</sup>). These bio-based nanocomposites are a valuable alternative to conventional petroleum-based synthetic polymeric materials used nowadays for biomedicine and food packaging applications. Materials science ; Differential scanning calorimetry ; Fourier transform infrared spectroscopy ; Nanocomposite ; Graphene ; Chemical engineering ; Crystallization ; Composite material ; Nanocellulose ; Thermal stability ; Casting ; Oxide ; Dynamic mechanical analysis ; Polymer ; Nanotechnology ; Cellulose biodegradable polymer synthesis and properties ; Advanced Cellulose Research Studies ; Additive Manufacturing and 3D Printing Technologies
Authors: Daming Yan | Yang Yang | Xiangdong Ding | Turab Lookman | Hongxiang Zong | Jun Sun
Source: Computational Materials Science (article, 2023-01-31)
Field: Materials Science — Ceramics and Composites

Effects of chemical complexity on the initial oxidation resistance of HfC1−N ceramics Adsorption ; Ceramic ; Atom (system on chip) ; Alloy ; Oxygen ; Materials science ; Diffusion ; Refractory metals ; Chemical physics ; Refractory (planetary science) ; Oxygen atom ; Chemical engineering ; Physical chemistry ; Chemistry ; Metallurgy ; Molecule ; Thermodynamics ; Computer science ; Physics ; Organic chemistry Advanced ceramic materials synthesis ; Semiconductor materials and devices ; Advanced materials and composites
Authors: Zengyun Gong | Chen Chen | Cheng Chen | Chenxi Li | Xuecong Tian | Zhongcheng Gong | Xiaoyi Lv
Source: Analytica Chimica Acta (article, 2023-08-28)
Field: Chemistry — Analytical Chemistry

RamanCMP: A Raman spectral classification acceleration method based on lightweight model and model compression techniques Artificial intelligence ; Convolutional neural network ; Raman spectroscopy ; Extractor ; Linear discriminant analysis ; Pattern recognition (psychology) ; Chemistry ; Computer science ; Process engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Sam Shepherd | Gareth A. Tribello | David M. Wilkins
Source: The Journal of Chemical Physics (article, 2023-05-23)
Field: Materials Science — Biomaterials

A fully quantum-mechanical treatment for kaolinite Neural network potentials for kaolinite minerals have been fitted to data extracted from density functional theory calculations that were performed using the revPBE + D3 and revPBE + vdW functionals. These potentials have then been used to calculate the static and dynamic properties of the mineral. We show that revPBE + vdW is better at reproducing the static properties. However, revPBE + D3 does a better job of reproducing the experimental IR spectrum. We also consider what happens to these properties when a fully quantum treatment of the nuclei is employed. We find that nuclear quantum effects (NQEs) do not make a substantial difference to the static properties. However, when NQEs are included, the dynamic properties of the material change substantially. Kaolinite ; Quantum ; Density functional theory ; Materials science ; Statistical physics ; Physics ; Mineralogy ; Chemistry ; Quantum mechanics Clay minerals and soil interactions ; High-pressure geophysics and materials ; Geological and Geochemical Analysis
Authors: Tuan Hoang Nguyen | Khang M. Le | Lam Nguyen | Thanh N. Truong
Source: ACS Omega (article, 2023-10-02)
Field: Materials Science — Materials Chemistry

Atom-Based Machine Learning Model for Quantitative Property–Structure Relationship of Electronic Properties of Fusenes and Substituted Fusenes This study presents the development of machine-learning-based quantitative structure-property relationship (QSPR) models for predicting electron affinity, ionization potential, and band gap of fusenes from different chemical classes. Three variants of the atom-based Weisfeiler-Lehman (WL) graph kernel method and the machine learning model Gaussian process regressor (GPR) were used. The data pool comprises polycyclic aromatic hydrocarbons (PAHs), thienoacenes, cyano-substituted PAHs, and nitro-substituted PAHs computed with density functional theory (DFT) at the B3LYP-D3/6-31+G(d) level of theory. The results demonstrate that the GPR/WL kernel methods can accurately predict the electronic properties of PAHs and their derivatives with root-mean-square deviations of 0.15 eV. Additionally, we also demonstrate the effectiveness of the active learning protocol for the GPR/WL kernel methods pipeline, particularly for data sets with greater diversity. The interpretation of the model for contributions of individual atoms to the predicted electronic properties provides reasons for the success of our previous degree of π-orbital overlap model. Density functional theory ; Ionization energy ; Quantitative structure–activity relationship ; Kernel (algebra) ; Atom (system on chip) ; Gaussian ; Electronic structure ; Interpretation (philosophy) ; Chemistry ; Computer science ; Computational chemistry ; Machine learning ; Artificial intelligence ; Biological system ; Statistical physics ; Ionization ; Mathematics ; Physics ; Combinatorics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering