jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zineb Laabid | Abdelghani Lakhdar | Khalifa Mansouri | Ali Siadat
Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (article, 2024-10-03)
Field: Materials Science — Polymers and Plastics

Implementation of artificial intelligence in the prediction of the elastic characteristics of bio-loaded polypropylene with bamboo fibers Artificial intelligence is the current trend in the world, which has taken the opportunity to advance in all its fields, particularly in scientific research. In materials engineering, the results obtained from classic methods such as experimentation, homogenization methods, or finite element methods have become input and validation elements for intelligent models to obtain more effective results in an optimal time frame. In this article, we discuss the use of artificial neural networks to determine the mechanical properties of biocomposites, which are the subject of much research due to the advantages they represent. The properties of these complex materials depend on various parameters, such as the behavior of the constituent materials, the percentage of the mixture, and the manufacturing process. In this work, our goal is to predict how polypropylene behaves elastically when reinforced with 15% various natural fillers. and we will study the impact of bamboo on polypropylene to test and validate our model. By exploiting the results of the Mori-Tanaka model, we were able to generate our dataset, with which we feed our feedforward backpropagation neural network and demonstrate that our biocomposite gained in terms of stiffness, marked by an increase in Young's modulus to 550.3 MPa, with better performance validation and a very good regression coefficient. Artificial neural network ; Biocomposite ; Polypropylene ; Backpropagation ; Materials science ; Stiffness ; Bamboo ; Homogenization (climate) ; Computer science ; Composite material ; Biological system ; Artificial intelligence ; Process engineering ; Composite number ; Engineering Natural Fiber Reinforced Composites ; Bamboo properties and applications
Authors: Yuliang Shi | Farnaz A. Shakib
Source: ChemRxiv (preprint, 2024-11-07)
Field: Materials Science — Materials Chemistry

Machine Learning Committee Neural Network Potential Energy Surfaces for Two-Dimensional Metal-Organic Frameworks Two-dimensional (2D) layered metal-organic frameworks (MOFs) are gaining attention due to their unique structural and electronic properties with promising applications in compact electronic device fabrication. Long-time and large-scale molecular dynamics simulation of these materials can enhance and expedite the mapping out of their structure-property-function relationships for these applications. To make such simulations more feasible, herein, we construct a high-dimensional committee neural network potential (CNNP) for archetypal 2D MOFs Ni3(HIB)2 and Ni3(HITP)2 where HIB=hexaiminobenzene and HITP=hexaiminotriphenylene. We harness the power of active learning and committee neural networks to obtain a CNNP model using only hundreds of snapshots from ab initio molecular dynamics (AIMD) trajectories. The developed CNNP models allow for simulations of thousands of atoms over extended timescales, which is typically unfeasible with AIMD simulations, while maintaining the accuracy of the reference data. Our stable MD simulations based on the developed CNNP model reveal the flexible nature of the studied 2D MOFs at room temperature, including puckered layers, as opposed to the planar ones from 0 K electronic structure calculations. Furthermore, our model demonstrates transferability between bulk and monolayers, as well as different organic linkers. As the first model of its kind, we show that the high-dimensional CNNP models could be a reliable and effective approach for future studies on 2D MOFs. Artificial neural network ; Artificial intelligence ; Energy (signal processing) ; Computer science ; Materials science ; Physics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Y. Yuan | Mustafa Mısır
Source:  (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

Enhancing Molecular Docking Performance with a GNN-Based Algorithm Selection Model Molecular docking is a critical process in drug discovery and design with various algorithms to predict how small molecules, or ligands, interact with proteins.The present study introduces a novel use of using Graph Neural Network (GNN) to build effective Algorithm Selection (AS) models for automatically choosing suitable molecular docking algorithms concerning specific scenarios.This method addresses the limitations highlighted by the no free lunch theorem, which states that no single algorithm can outperform all the others universally.We build graphs for molecules by their 3D structures with atoms to be node and bonds to be edges, and set the attributes of nodes and bonds with the atomic numbers and bond types.By using an existing, complex GNN network with a combination of GCN, GAT and GIN, we successfully train an Algorithm Selection (AS) model to predict the (near-)optimal docking algorithm for different docking situations.The trained model shows improvement for accuracy in terms of RMSD.Also, it reaches 25% accuracy for predicting the best algorithm and 50% for predicitng near best, within a 1.5 margin. Computer science ; Docking (animal) ; Selection (genetic algorithm) ; Algorithm ; Artificial intelligence Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Jiangsan Zhao | Krzysztof Kuśnierek
Source: Coatings (article, 2024-09-23)
Field: Chemistry — Analytical Chemistry

A Fully Connected Network (FCN) Trained on a Custom Library of Raman Spectra for Simultaneous Identification and Quantification of Components in Multi-Component Mixtures Raman spectroscopy provides detailed information about the molecular composition of a sample. The classical identification of components in a multi-component sample typically involves comparing the preprocessed spectrum with a known reference stored in a database using various spectral matching or machine-learning techniques or relies on universal models based on a two-step analysis including first, the component identification, and then the decomposition of the mixed signal. However, although large databases and universal models cover a wide range of target materials, they may be not optimized to the variability required in a specific application. In this study, we propose a single-step method using deep learning (DL) modeling to decompose a simulated mixture of real measurements of Raman scattering into relevant individual components regardless of noise, baseline and the number of components involved and quantify their ratios. We hypothesize that training a custom DL model for applications with a fixed set of expected components may yield better results than applying a universal quantification model. To test this hypothesis, we simulated 12,000 Raman spectra by assigning random ratios to each component spectrum within a library containing 13 measured spectra of organic solvent samples. One of the DL methods, a fully connected network (FCN), was designed to work on the raw spectra directly and output the contribution of each component of the library to the input spectrum in form of a component ratio. The developed model was evaluated on 3600 testing spectra, which were simulated similarly to the training dataset. The average component identification accuracy of the FCN was 99.7%, which was significantly higher than that of the universal custom trained DeepRaman model, which was 83.1%. The average mean absolute error for component ratio quantification was 0.000562, over one order of magnitude smaller than that of a well-established non-negative elastic net (NN-EN), which was 0.00677. The predicted non-zero ratio values were further used for component identification. Under the assumption that the components of a mixture are from a fixed library, the proposed method preprocesses and decomposes the raw data in a single step, quantifying every component in a multicomponent mixture, accurately. Notably, the single-step FCN approach has not been implemented in the previously reported DL studies. Component (thermodynamics) ; Identification (biology) ; Raman spectroscopy ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Christopher Broyles | W.D. Charles | Sheng Ran
Source: Physical Review Materials (article, 2024-11-04)
Field: Materials Science — Materials Chemistry

Structure-driven prediction of magnetic order in uranium compounds The advancement of machine learning technologies has revolutionized the search and optimization of material properties. These algorithms often rely on theoretical calculations, such as density functional theory (DFT), for data inputs and validation, which are not always effective for uranium-based materials due to their strong electron correlations. This study presents a computationally inexpensive machine learning approach, specifically a random forest classifier, to predict the magnetic ground states of uranium compounds using only structural inputs. Our model, trained on a curated dataset of experimentally verified magnetic orders, achieves a mean accuracy of $60.2%$, significantly outperforming random chance. Through a purely structural descriptor, our method offers a reliable alternative for discovering new materials with desirable magnetic properties, addressing the challenges posed by strong correlations in quantum materials. Random forest ; Uranium ; Density functional theory ; Materials science ; Classifier (UML) ; Computer science ; Artificial intelligence ; Statistical physics ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Rare-earth and actinide compounds
Authors: Shunsuke Mieda
Source: Journal of Computer Chemistry Japan (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Research on Simulation Methods for Depolymerization of Polystyrene To verify the possibility to simulate depolymerization, simulations of the depolymerization of polystyrene were performed. Molecular Dynamics simulations (MD) using neural network potentials were found to be similar in accuracy to MD using density functional theory calculations. It was also found that long-time simulations using neural network potential-MD predicted styrene monomer yields close to those obtained experimentally, and that the monomer yields tended to decrease with increasing pressure. Depolymerization ; Polystyrene ; Expanded polystyrene ; Chemistry ; Materials science ; Computer science ; Polymer chemistry ; Organic chemistry ; Polymer ; Composite material Advanced Polymer Synthesis and Characterization
Authors: Fangning Ren | Xu Chen | Fang Liu
Source: ChemRxiv (preprint, 2024-08-15)
Field: Materials Science — Materials Chemistry

Size-transferable prediction of excited state properties for molecular assemblies with machine-learned exciton model Computational modeling of the excited states of molecular aggregates faces significant computational challenges and size heterogeneity. Current machine learning (ML) models, typically trained on specific-sized aggregates, struggle with scalability. We found that the exciton model Hamiltonian of large aggregates can be decomposed into dimer pairs, allowing an ML model trained on dimers to reconstruct Hamiltonians for aggregates of any size. We trained an ML model on perylene dimer exciton Hamiltonians using quantum chemistry descriptors to resolve phase correction issues. Our model accurately predicted excitation energies and oscillator strengths in perylene trimers and tetramers, and analyzed the size-dependent optical bandgap in aggregates with up to 50 monomers, revealing quantum dot-like confinement effects. Future work will explore transferability across different monomers to predict optical properties in heterogeneous assemblies. Exciton ; Excited state ; Perylene ; Hamiltonian (control theory) ; Dimer ; Statistical physics ; Excitation ; Monomer ; Quantum ; Chemical physics ; Molecular physics ; Materials science ; Physics ; Computer science ; Quantum mechanics ; Molecule ; Polymer ; Mathematics ; Mathematical optimization Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Perovskite Materials and Applications
Authors: Zhiyu Shi | Aditya Lele | Ahren W. Jasper | Stephen J. Klippenstein | Yiguang Ju
Source:  (article, 2024-01-04)
Field: Materials Science — Materials Chemistry

Quasi-classical Trajectory Calculation of Rate Constants Using Ab-initio Trained Machine Learning Force Field (aML-MD) Machine learning (ML) provides a great opportunity for the construction of molecular dynamics (MD) potentials with almost as high accuracy as quantum mechanical methods and as high efficiency as classical molecular dynamics. In this work, two ab-initio trained ML based MD (aML-MD) potentials or models are developed for hydrogen combustion using two different sets of DFT data (system-wide data and reaction-specific data) within the Deep Potential MD (DPMD) framework. Both aML-MD models exhibit excellent accuracy in capturing the potential energy surface from training data. The accuracy of the aML-MD models in predicting the reaction dynamics is demonstrated by calculating rate constants for the singlet reaction H + HO2 -> OH + OH using quasi classical trajectories (QCT). We show that both models underpredict the rate constants compared to the existing state-of-the-art QCT predictions. It is shown that the system-wide data trained aML-MD model significantly underpredicts the rate constants whereas the reaction specific data trained model improves the rate constant prediction. We show that an accurate and comprehensive dataset with wide-ranging energy levels is critical for an aML-MD model to capture diverse reaction dynamics, which can encompass multiple energy barriers and intermediates. Future work will be focused on using transfer learning to improve the accuracy, efficiency, and generalization of aML-MD models. Ab initio ; Trajectory ; Force field (fiction) ; Field (mathematics) ; Computer science ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Fault Detection and Control Systems ; Gaussian Processes and Bayesian Inference
Authors: Lukas Glänzer | Lennart Göpfert | Thomas Schmitz‐Rode | Ioana Slabu
Source: Journal of Materials Chemistry B (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Navigating predictions at nanoscale: a comprehensive study of regression models in magnetic nanoparticle synthesis The applicability of magnetic nanoparticles (MNP) highly depends on their physical properties, especially their size. Synthesizing MNP with a specific size is challenging due to the large number of interdepend parameters during the synthesis that control their properties. In general, synthesis control cannot be described by white box approaches (empirical, simulation or physics based). To handle synthesis control, this study presents machine learning based approaches for predicting the size of MNP during their synthesis. A dataset comprising 17 synthesis parameters and the corresponding MNP sizes were analyzed. Eight regression algorithms (ridge, lasso, elastic net, decision trees, random forest, gradient boosting, support vectors and multilayer perceptron) were evaluated. The model performance was assessed <i>via</i> root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and standard deviation of residuals. Support vector regression (SVR) exhibited the lowest RMSE values of 3.44 and a standard deviation for the residuals of 5.13. SVR demonstrated a favorable balance between accuracy and consistency among these methods. Qualitative factors like adaptability to online learning and robustness against outliers were additionally considered. Altogether, SVR emerged as the most suitable approach to predict MNP sizes due to its ability to continuously learn from new data and resilience to noise, making it well-suited for real-time applications with varying data quality. In this way, a feasible optimization framework for automated and self-regulated MNP synthesis was implemented. Key challenges included the limited dataset size, potential violations of modeling assumptions, and sensitivity to hyperparameters. Strategies like data regularization, correlation analysis, and grid search for model hyperparameters were employed to mitigate these issues. Nanoscopic scale ; Materials science ; Nanoparticle ; Nanotechnology ; Magnetic nanoparticles Machine Learning in Materials Science ; Grey System Theory Applications ; Statistical and Computational Modeling
Authors: Amisha Bisht | Disha Tewari | Kalpana Rawat | Shilpi Rawat | Mohammad Ali Abdullah Almoyad | Shadma Wahab | Sanjay Kumar | Subhash Chandra
Source: Theoretical Chemistry Accounts (article, 2024-06-01)
Field: Chemistry — Analytical Chemistry

Computational screening of matrix metalloproteinase 3 inhibitors to counteract skin aging from phytochemicals of Nelumbo nucifera Gaertn Nelumbo nucifera ; Matrix metalloproteinase ; Chemistry ; Traditional medicine ; Matrix metalloproteinase inhibitor ; Biochemistry ; Biology ; Medicine ; Botany ; Lotus Chromatography in Natural Products ; Phytochemicals and Antioxidant Activities ; Plant biochemistry and biosynthesis
Authors: Jiangwen Liao | Jiajing Pei | Guikai Zhang | Pengfei An | Shengqi Chu | Yuanyuan Ji | Huan Huang | Jing Zhang | Juncai Dong
Source: Journal of Physics Condensed Matter (article, 2024-02-02)
Field: Materials Science — Materials Chemistry

Artificial neural network for deciphering the structural transformation of condensed ZnO by extended x-ray absorption fine structure spectroscopy Pressure-induced structural phase transitions play a pivotal role in unlocking novel material functionalities and facilitating innovations in materials science. Nonetheless, unveiling the mechanisms of densification, which relies heavily on precise and comprehensive structural analysis, remains a challenge. Herein, we investigated the archetypal<i>B</i>4 →<i>B</i>1 phase transition pathway in ZnO by combining x-ray absorption fine structure (XAFS) spectroscopy with machine learning. Specifically, we developed an artificial neural network (NN) to decipher the extended-XAFS spectra by reconstructing the partial radial distribution functions of Zn-O/Zn pairs. This provided us with access to the evolution of the structural statistics for all the coordination shells in condensed ZnO, enabling us to accurately track the changes in the internal structural parameter<i>u</i>and the anharmonic effect. We observed a clear decrease in<i>u</i>and an increased anharmonicity near the onset of the<i>B</i>4 →<i>B</i>1 phase transition, indicating a preference for the iT phase as the intermediate state to initiate the phase transition that can arise from the softening of shear phonon modes. This study suggests that NN-based approach can facilitate a more comprehensive and efficient interpretation of XAFS under complex<i>in-situ</i>conditions, which paves the way for highly automated data processing pipelines for high-throughput and real-time characterizations in next-generation synchrotron photon sources. X-ray absorption fine structure ; Anharmonicity ; Phase transition ; X-ray absorption spectroscopy ; Spectroscopy ; Synchrotron ; Chemical physics ; Chemistry ; Phase (matter) ; Absorption (acoustics) ; Phonon ; Absorption spectroscopy ; Materials science ; Crystallography ; Physics ; Condensed matter physics ; Optics ZnO doping and properties ; Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Brianne R. King | Kiera H. Sumida | Jessica L. Caruso | David Baker | Jesse G. Zalatan
Source: Angewandte Chemie (article, 2024-10-12)
Field: Chemistry — Inorganic Chemistry

Computational Stabilization of a Non‐Heme Iron Enzyme Enables Efficient Evolution of New Function Abstract Deep learning tools for enzyme design are rapidly emerging, and there is a critical need to evaluate their effectiveness in engineering workflows. Here we show that the deep learning‐based tool ProteinMPNN can be used to redesign Fe(II)/αKG superfamily enzymes for greater stability, solubility, and expression while retaining both native activity and industrially relevant non‐native functions. This superfamily has diverse catalytic functions and could provide a rich new source of biocatalysts for synthesis and industrial processes. Through systematic comparisons of directed evolution trajectories for a non‐native, remote C( sp 3 )−H hydroxylation reaction, we demonstrate that the stabilized redesign can be evolved more efficiently than the wild‐type enzyme. After three rounds of directed evolution, we obtained a 6‐fold activity increase from the wild‐type parent and an 80‐fold increase from the stabilized variant. To generate the initial stabilized variant, we identified multiple structural and sequence constraints to preserve catalytic function. We applied these criteria to produce stabilized, catalytically active variants of a second Fe(II)/αKG enzyme, suggesting that the approach is generalizable to additional members of the Fe(II)/αKG superfamily. ProteinMPNN is user‐friendly and widely accessible, and our results provide a framework for the routine implementation of deep learning‐based protein stabilization tools in directed evolution workflows for novel biocatalysts. Heme ; Chemistry ; Function (biology) ; Enzyme ; Biochemistry ; Biology ; Cell biology Metal-Catalyzed Oxygenation Mechanisms ; Porphyrin Metabolism and Disorders ; Metalloenzymes and iron-sulfur proteins
Authors: Ramil Nugmanov
Source: Journal of Chemical Information and Modeling (article, 2024-03-30)
Field: Materials Science — Materials Chemistry

PaCh (Packed Chemicals): Computationally Effective Binary Format for Chemical Structure Encoding In this work, we propose a versatile molecule and reaction encoding binary data format that aims to bridge the gap between the advantages of SMILES, like local stereo- and implicit hydrogen encoding, and block-structured MDL MOL with a 2D layout and explicit bond encoding, while addressing their respective limitations. Our new format introduces a balance between size efficiency, processing speed, and comprehensive representation, making it well-suited for various applications in cheminformatics, including deep learning, data storage, and searching. By offering an explicit approach to store atom connectivity (including implicit hydrogens), electronic state, stereochemistry, and other crucial molecular attributes, our proposal seeks to enhance data storage efficiency and promote interoperability among different software tools. Cheminformatics ; Encoding (memory) ; Computer science ; Interoperability ; Binary number ; Block (permutation group theory) ; Theoretical computer science ; Binary data ; Representation (politics) ; Data mining ; Artificial intelligence ; Chemistry ; Arithmetic ; World Wide Web ; Mathematics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Oxidation Reactions