jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vadim Sotskov | Evgeny V. Podryabinkin | Alexander V. Shapeev
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2023-10-02)
Field: Materials Science — Materials Chemistry

A machine-learning potential-based generative algorithm for on-lattice crystal structure prediction Crystal structure ; Convex hull ; Ternary operation ; Crystal structure prediction ; Algorithm ; Lattice (music) ; Interatomic potential ; Binary number ; Computer science ; Materials science ; Machine learning ; Artificial intelligence ; Regular polygon ; Computational chemistry ; Crystallography ; Molecular dynamics ; Mathematics ; Physics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Hieu Huynh | Thomas J. Kelly | Linh Vu | Hoàng Thanh Tùng | Phuc An Nguyen | Tu C. Le | Emily Jarvis | Hung Phan
Source: ACS Omega (article, 2023-05-19)
Field: Materials Science — Materials Chemistry

Quantum Chemistry–Machine Learning Approach for Predicting Properties of Lewis Acid–Lewis Base Adducts Synthetic design allowing predictive control of charge transfer and other optoelectronic properties of Lewis acid adducts remains elusive. This challenge must be addressed through complementary methods combining experimental with computational insights from first principles. <i>Ab initio</i> calculations for optoelectronic properties can be computationally expensive and less straightforward than those sufficient for simple ground-state properties, especially for adducts of large conjugated molecules and Lewis acids. In this contribution, we show that machine learning (ML) can accurately predict density functional theory (DFT)-calculated charge transfer and even properties associated with excited states of adducts from readily obtained molecular descriptors. Seven ML models, built from a dataset of over 1000 adducts, show exceptional performance in predicting charge transfer and other optoelectronic properties with a Pearson correlation coefficient of up to 0.99. More importantly, the influence of each molecular descriptor on predicted properties can be quantitatively evaluated from ML models. This contributes to the optimization of a priori design of Lewis adducts for future applications, especially in organic electronics. Lewis acids and bases ; Adduct ; Charge (physics) ; Ab initio ; Chemistry ; Density functional theory ; Excited state ; Computational chemistry ; A priori and a posteriori ; Physics ; Organic chemistry ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Conducting polymers and applications ; Perovskite Materials and Applications
Authors: Murilo Moreira | Matthias Hillenkamp | Varlei Rodrigues | D. Ugarte
Source: ACS Applied Nano Materials (article, 2023-12-19)
Field: Materials Science — Materials Chemistry

Ag Surface Segregation in Sub-10-nm Bimetallic AuAg Nanoparticles Quantified by STEM-EDS and Machine Learning: Implications for Fine-Tuning Physicochemical Properties for Plasmonics and Catalysis Applications Mono- and multimetallic nanoparticles have been extensively studied in various fields due to their tunable physicochemical properties and potential for replacing expensive metals with more abundant and affordable ones. The chemical structure, i.e., the spatial distribution of elements inside nanoparticles, plays a crucial role in defining their properties, particularly in catalytic processes. However, accurately determining the spatial chemical distribution within sub-10-nm bimetallic nanoparticles remains a challenge. In this study, we have used scanning transmission electron microscopy associated with energy-dispersive spectroscopy to acquire hyperspectral images of gold–silver alloy nanoparticles in the 3–10-nm size range. We have quantified the chemical composition as a function of radial position; Ag enrichment toward the nanoparticle surface is robustly confirmed by statistical analysis, error bars, and nonoverlapping 3-sigma uncertainty intervals at the nanoparticle center and surface. Two complementary machine learning analyses (principal component analysis and non-negative matrix factorization) reveal that our experiments contain latent information on subtle composition variations inside the particles. The proposed data analysis procedures have also been validated by simulated data sets. These findings pave the way for more precise structural and chemical investigations of alloys on the nanoscale. Bimetallic strip ; Nanoparticle ; Materials science ; Scanning transmission electron microscopy ; Nanotechnology ; Nanoscopic scale ; Plasmon ; Transmission electron microscopy ; Metal ; Optoelectronics ; Metallurgy Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Electrochemical Analysis and Applications
Authors: Qiang Zou | Basu Dev Oli | Huimin Zhang | Joseph Benigno | Xin Li | Lian Li
Source: ACS Applied Materials & Interfaces (article, 2023-05-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deciphering Alloy Composition in Superconducting Single-Layer FeSe<sub>1–<i>x</i></sub>S<i><sub>x</sub></i> on SrTiO<sub>3</sub>(001) Substrates by Machine Learning of STM/S Data Scanning tunneling microscopy (STM) is a powerful technique for imaging atomic structure and inferring information on local elemental composition, chemical bonding, and electronic excitations. However, a plain visual analysis of STM images can be challenging for such determination in multicomponent alloys, particularly beyond the diluted limit due to chemical disorder and electronic inhomogeneity. One viable solution is to use machine learning to analyze STM data and identify hidden patterns and correlations. Here, we apply this approach to determine the Se/S concentration in superconducting single-layer FeSe<sub>1-<i>x</i></sub>S<i><sub>x</sub></i> alloys epitaxially grown on SrTiO<sub>3</sub>(001) substrates via molecular beam epitaxy. First, the K-means clustering method is applied to identify defect-related d<i>I</i>/d<i>V</i> tunneling spectra taken by current imaging tunneling spectroscopy. Then, the Se/S ratio is calculated by analyzing the remaining spectra based on the singular value decomposition method. Such analysis provides an efficient and reliable determination of alloy composition and further reveals the correlations of nanoscale chemical inhomogeneity to superconductivity in single-layer iron chalcogenide films. Scanning tunneling microscope ; Materials science ; Superconductivity ; Epitaxy ; Alloy ; Chalcogenide ; Molecular beam epitaxy ; Condensed matter physics ; Spectral line ; Electronic structure ; Analytical Chemistry (journal) ; Layer (electronics) ; Nanotechnology ; Optoelectronics ; Physics ; Metallurgy ; Chemistry Iron-based superconductors research ; Physics of Superconductivity and Magnetism ; Electronic and Structural Properties of Oxides
Authors: Iori Shimada | Mitsumasa Osada | Hiroshi Fukunaga | Michihisa Koyama
Source: Industrial & Engineering Chemistry Research (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning with Physics-Based Feature Engineering: Application to the Catalytic Cracking Reaction of Polycyclic Aromatic Hydrocarbons Machine learning has been widely used to predict heterogeneous catalytic reactions, but the trade-off between prediction accuracy and interpretability remains an open issue. This study attempts to develop an interpretable machine learning model for predicting the products of heterogeneous catalytic reactions with complex feedstock compositions. The target reaction was the catalytic cracking reaction of polycyclic aromatic hydrocarbons, and the compositions of the reaction products (monocyclic aromatic hydrocarbons, condensed two-ring aromatic hydrocarbons, and heavy components) were predicted. The combination of the least absolute shrinkage and selection operator (LASSO) and physics-based feature engineering significantly improved the prediction accuracy compared with that of the regression model with only basic descriptors (reaction temperature, contact time, and feedstock composition). The prediction accuracy of the constructed model exceeded that of the black-box nonlinear regression models. Furthermore, it was confirmed that information about important reactions could be extracted from the magnitude of the standard regression coefficients. These findings imply that a combination of LASSO and physics-based feature engineering can be used to construct machine learning models with high prediction accuracy and interpretability for heterogeneous catalytic reactions. Interpretability ; Lasso (programming language) ; Machine learning ; Catalysis ; Feature (linguistics) ; Linear regression ; Fluid catalytic cracking ; Regression ; Artificial intelligence ; Computer science ; Chemistry ; Biological system ; Organic chemistry ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Petroleum Processing and Analysis
Authors: Mohammed A. Jabed | Yeonjoon Kim | Clark Yarbrough | Anne E. Harman‐Ware | Jessica Olstad | Reinhard Seiser | Cheyenne Paeper | Anne K. Starace | Seonah Kim
Source: ACS Sustainable Chemistry & Engineering (article, 2023-08-03)
Field: Chemistry — Analytical Chemistry

A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra Demand for the development of an automated and integrated refining process for biofuels has increased in recent years due to the lack of generalized process inspection tools. In bio-oil upgrading processes, all process variables are maintained based on the offline specification of intermediates and products. A lack of real-time product specifications in batch-wise monitoring can cause process failure and wasted resources. Therefore, there is a need for a fast and accurate intermediates/product specification tool that can be used for real-time specification to reduce waste and mitigate the risk of process failure. To address this gap, we developed a machine learning (ML) model for predicting speciated bio-oil composition, including paraffin, iso-paraffins, olefins, naphthene, and aromatics. The model is trained using the mass spectra from upgraded products collected in the vapor phase before condensation and predicts the composition of the condensed product. Training ML models using raw mass spectra is challenging due to numerous overlapped peaks originating from different parent compounds. With this in mind, we propose a protocol that (i) transforms raw mass spectra to chemistry-inspired predefined features and (ii) trains decision tree-based models using these features. Our results show that the random forest model was robust against overfitting and had the highest accuracy compared to other models. Moreover, a stochastic ablation method determined the eight most significant features while maximizing the accuracy. Our protocol facilitates real-time compositional analysis of upgraded bio-oils and thus real-time process monitoring. Additionally, this protocol enables the rational design of efficient catalysts and the determination of optimal process conditions. Overfitting ; Computer science ; Process (computing) ; Process engineering ; Mass spectrum ; Machine learning ; Artificial intelligence ; Mass spectrometry ; Chemistry ; Engineering ; Chromatography Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Chemical Sensor Technologies
Authors: Haoyang Xu | Boyuan Zhang | Yuanda Tao | Xu Weijia | Bo Hu | Feng Yan | Jin Wen
Source: The Journal of Physical Chemistry A (article, 2023-09-06)
Field: Materials Science — Materials Chemistry

Ultrafast Photocontrolled Rotation in a Molecular Motor Investigated by Machine Learning-Based Nonadiabatic Dynamics Simulations The thermal helix inversion (THI) of the overcrowded alkene-based molecular motors determines the speed of the unidirectional rotation due to the high reaction barrier in the ground state, in comparison with the ultrafast photoreaction process. Recently, a phosphine-based motor has achieved all-photochemical rotation experimentally, promising to be controlled without a thermal step. However, the mechanism of this photochemical reaction has not yet been fully revealed. The comprehensive computational studies on photoisomerization still resort to nonadiabatic molecular dynamics (NAMD) simulations based on electronic structure calculations, which remains a high computational cost for large systems such as molecular motors. Machine learning (ML) has become an accelerating tool in NAMD simulations recently, where excited-state potential energy surfaces (PESs) are constructed analytically with high accuracy, providing an efficient approach for simulations in photochemistry. Herein the reaction pathway is explored by a spin-flip time-dependent density functional theory (SF-TDDFT) approach in combination with ML-based NAMD simulations. According to our computational simulations, we notice that one of the key factors of fulfilling all-photochemical rotation in the phosphine-based motor is that the excitation energies of four isomers are similar. Additionally, a shortcut photoinduced transformation between unstable isomers replaces the THI step, which shares the conical intersection (CI) with photoisomerization. In this study, we provide a practical approach to speed up the NAMD simulations in photochemical reactions for a large system that could be extended to other complex systems. Conical intersection ; Photoisomerization ; Time-dependent density functional theory ; Molecular dynamics ; Chemistry ; QM/MM ; Density functional theory ; Surface hopping ; Potential energy ; Isomerization ; Computational chemistry ; Chemical physics ; Photochemistry ; Molecular physics ; Physics ; Atomic physics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Jianwei Wang | Dipta B. Ghosh | Zelong Zhang
Source: Materials (review, 2023-07-13)
Field: Materials Science — Materials Chemistry

Computational Materials Design for Ceramic Nuclear Waste Forms Using Machine Learning, First-Principles Calculations, and Kinetics Rate Theory Ceramic waste forms are designed to immobilize radionuclides for permanent disposal in geological repositories. One of the principal criteria for the effective incorporation of waste elements is their compatibility with the host material. In terms of performance under environmental conditions, the resistance of the waste forms to degradation over long periods of time is a critical concern when they are exposed to natural environments. Due to their unique crystallographic features and behavior in nature environment as exemplified by their natural analogues, ceramic waste forms are capable of incorporating problematic nuclear waste elements while showing promising chemical durability in aqueous environments. Recent studies of apatite- and hollandite-structured waste forms demonstrated an approach that can predict the compositions of ceramic waste forms and their long-term dissolution rate by a combination of computational techniques including machine learning, first-principles thermodynamics calculations, and modeling using kinetic rate equations based on critical laboratory experiments. By integrating the predictions of elemental incorporation and degradation kinetics in a holistic framework, the approach could be promising for the design of advanced ceramic waste forms with optimized incorporation capacity and environmental degradation performance. Such an approach could provide a path for accelerated ceramic waste form development and performance prediction for problematic nuclear waste elements. Ceramic ; Radioactive waste ; Dissolution ; Process engineering ; Durability ; Waste management ; Materials science ; Municipal solid waste ; Computer science ; Environmental science ; Chemical engineering ; Engineering ; Composite material Nuclear materials and radiation effects ; Nuclear Materials and Properties ; Radioactive element chemistry and processing
Authors: Joziel Aparecido da Cruz | Heitor Luiz Ornaghi | Sandro Campos Amico | Otávio Bianchi
Source: Polymer Bulletin (article, 2023-12-24)
Field: Materials Science — Polymers and Plastics

Predicting viscosity in polyurethane polymerization for liquid composite molding using neural networks and surface methodology Polyurethane ; Materials science ; Polymerization ; Rheometry ; Viscosity ; Response surface methodology ; Composite number ; Rheology ; Chemical engineering ; Polymer ; Molding (decorative) ; Composite material ; Polymer chemistry ; Chromatography ; Chemistry Polymer composites and self-healing ; Epoxy Resin Curing Processes ; Polymer Nanocomposites and Properties
Authors: Jun Yang | Zhitao Chen | Hong Sun | Amit Samanta
Source: Journal of Chemical Theory and Computation (article, 2023-08-15)
Field: Materials Science — Materials Chemistry

Graph-EAM: An Interpretable and Efficient Graph Neural Network Potential Framework The development of deep learning interatomic potentials has enabled efficient and accurate computations in quantum chemistry and materials science, circumventing computationally expensive <i>ab initio</i> calculations. However, the huge number of learnable parameters in deep learning models and their complex architectures hinder physical interpretability and affect the robustness of the derived potential. In this work, we propose graph-EAM, a lightweight graph neural network (GNN) inspired by the empirical embedded atom method to model the interatomic potential of single-element structures. Four material systems: platinum, niobium, silicon, and amorphous-carbon, for which quantum simulation data sets are publicly available, are examined to demonstrate that graph-EAM can achieve high energy and force prediction accuracy─comparable or better than existing state-of-the-art machine learning models─with much fewer parameters. It is also shown that the explicit inclusion of the angular information via three-body atomic density increases the prediction accuracy. The accuracy and efficiency of potentials obtained from graph-EAM can help accelerate the molecular dynamics simulation. Computer science ; Interpretability ; Graph ; Interatomic potential ; Quantum computer ; Artificial neural network ; Robustness (evolution) ; Deep learning ; Quantum ; Computation ; Molecular dynamics ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Algorithm ; Computational chemistry ; Chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Zachary Gariepy | Zhiwen Chen | Isaac Tamblyn | Chandra Veer Singh | Conrard Giresse Tetsassi Feugmo
Source: APL Machine Learning (article, 2023-07-05)
Field: Materials Science — Materials Chemistry

Automatic graph representation algorithm for heterogeneous catalysis One of the most appealing aspects of machine learning for material design is its high throughput exploration of chemical spaces, but to reach the ceiling of machine learning-aided exploration, more than current model architectures and processing algorithms are required. New architectures such as graph neural networks have seen significant research investments recently. For heterogeneous catalysis, defining substrate intramolecular bonds and adsorbate/substrate intermolecular bonds is a time-consuming and challenging process. Before applying a model, dataset pre-processing, node/bond descriptor design, and specific model constraints have to be considered. In this work, a framework designed to solve these issues is presented in the form of an automatic graph representation algorithm (AGRA) tool to extract the local chemical environment of metallic surface adsorption sites. This tool is able to gather multiple adsorption geometry datasets composed of different systems and combine them into a single model. To show AGRA’s excellent transferability and reduced computational cost compared to other graph representation methods, it was applied to five different catalytic reaction datasets and benchmarked against the Open Catalyst Projects graph representation method. The two oxygen reduction reaction (ORR) datasets with O/OH adsorbates obtained 0.053 eV root-mean-square deviation (RMSD) when combined together, whereas the three carbon dioxide reduction reaction datasets with CHO/CO/COOH obtained an average performance of 0.088 eV RMSD. To further display the algorithm’s versatility and extrapolation ability, a model was trained on a subset combination of all five datasets with an RMSD of 0.105 eV. This universal model was then used to predict a wide range of adsorption energies and an entirely new ORR catalyst system, which was then verified through density functional theory calculations. Computer science ; Algorithm ; Planarity testing ; Graph ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: A. Kedharnath | Rajeev Kapoor | Apu Sarkar
Source: Journal of Materials Science (article, 2023-12-21)
Field: Materials Science — Materials Chemistry

Dislocation–grain boundary interactions in Ta: numerical, molecular dynamics, and machine learning approaches Dislocation ; Slip (aerodynamics) ; Materials science ; Critical resolved shear stress ; Peierls stress ; Shear stress ; Glide plane ; Grain boundary ; Shear (geology) ; Molecular dynamics ; Crystal twinning ; Condensed matter physics ; Crystallography ; Geometry ; Dislocation creep ; Physics ; Composite material ; Mathematics ; Thermodynamics ; Microstructure ; Shear rate Microstructure and mechanical properties ; Fusion materials and technologies ; Metal and Thin Film Mechanics
Authors: Yimu Lu | Yongbo Shi | Junyuan Wang | Haikuan Dong | Jie Yu
Source: Journal of Applied Physics (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Reduction of thermal conductivity in carbon nanotubes by fullerene encapsulation from machine-learning molecular dynamics simulations The carbon nano-peapod is a representative structure with interlayer van der Waals (vdW) interactions, in which encapsulated fullerene molecules play a critical role in modulating the transport properties of the carbon nanotubes (CNTs). In particular, their influence on the thermal transport characteristics has been the focal point of considerable attention. In this study, we trained an accurate machine learning potential for fullerene-encapsulated CNTs based on the efficient NEP model to investigate their thermal properties. Using equilibrium molecular dynamics simulation along with the spectral decomposition method for thermal conductivity, we find that the thermal conductivity of fullerene-encapsulated CNTs is roughly 55% lower than that of empty CNTs, aligning with experimental observations for CNT bundles with fullerene encapsulation [Kodama et al., Nat. Mater. 16, 892 (2017)]. The research suggests that weak vdW interactions between both the fullerene and CNTs, as well as between fullerene molecules themselves, hinder phonon propagation. The encapsulated fullerene contributes to an increase in phonon scattering within the CNTs, ultimately leading to a reduction in thermal conductivity. We utilized machine learning potential to investigate the structure of fullerene-encapsulated CNTs and their heat transport property. This approach provides valuable insights for performance research of complex systems featuring interlayer vdW interactions. Fullerene ; Carbon nanotube ; van der Waals force ; Thermal conductivity ; Materials science ; Molecular dynamics ; Phonon ; Chemical physics ; Nanotechnology ; Molecule ; Computational chemistry ; Composite material ; Chemistry ; Condensed matter physics ; Organic chemistry Thermal properties of materials ; Carbon Nanotubes in Composites ; Fullerene Chemistry and Applications