jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kai Yuan | Shuai Zhou | Ning Li | Tianyan Li | Bowen Ding | Danhuai Guo | Yingjin Ma
Source: Journal of Computational Chemistry (article, 2024-07-29)
Field: Materials Science — Materials Chemistry

Fault‐tolerant quantum chemical calculations with improved machine‐learning models Easy and effective usage of computational resources is crucial for scientific calculations. Following our recent work of machine-learning (ML) assisted scheduling optimization [J. Comput. Chem. 2023, 44, 1174], we further propose (1) the improved ML models for the better predictions of computational loads, and as such, more elaborate load-balancing calculations can be expected; (2) the idea of coded computation, that is, the integration of gradient coding, in order to introduce fault tolerance during the distributed calculations; and (3) their applications together with re-normalized exciton model with time-dependent density functional theory (REM-TDDFT) for calculating the excited states. Illustrated benchmark calculations include P38 protein, and solvent model with one or several excitable centers. The results show that the improved ML-assisted coded calculations can further improve the load-balancing and cluster utilization, owing primarily profit in fault tolerance that aims at the automated quantum chemical calculations for both ground and excited states. Quantum chemical ; Computer science ; Quantum ; Fault tolerance ; Distributed computing ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: A Mahalekshmi | Heren Chellam
Source:  (article, 2024-12-30)
Field: Materials Science — Materials Chemistry

A NOVEL CRYSTAL STRUCTURE PREDICTION USING HYBRID METHOD Crystal structure prediction is the most challenging problem for material science and the chemical field.Nowadays, there are many methods have been developed in machine learning and deep learning methods to predict the crystal structure with less computational time.In this paper, we combine the machine learning and deep learning methods to predict the crystal structure.We propose the Deep-Machine Learning methods to classify the crystal structure.In this work, to identify the four crystal structures.They are orthorhombic, cubic, tetragonal, and rhombohedral with the best accuracy.To harness the deep-machine learning methods to classify the crystal structure, it gives the best accuracy and less time complexity.Corr-ANB model gives the 87% accuracy of predict the crystal structure in 10-fold cross validation.Our method works very fast, and it is inexpensive. Crystal structure prediction ; Materials science ; Crystal structure ; Crystal (programming language) ; Computer science ; Artificial intelligence ; Crystallography ; Chemistry ; Programming language Crystallization and Solubility Studies
Authors: Shaoting Zeng | Renshui Zhang | Yifei Cai
Source: Research Square (preprint, 2024-02-13)
Field: Materials Science — Biomaterials

Research on Digital Morphogenesis and Sustainability of 3D Printing Bionic Materials Based on Convolutional Neural Networks <title>Abstract</title> This study applies convolutional neural networks (CNNs) and digital morphogenesis research methods to perform biomimetic design of the morphology of 3D printed materials, furthering structural innovation based on the lightweight sustainability of biomimetic materials. Natural two-dimensional forms such as leaf veins, spider webs, and dragonfly wings are selected for digital reconstruction into three-dimensional biomimetic forms. This process involves transferring the material properties and structural advantages of natural two-dimensional biological forms to three-dimensional models. Hence, digital methods are employed to create three-dimensional representations of leaf veins, spider webs, and dragonfly wings while preserving their structural performance advantages observed in nature. CNNs style transfer technologies are utilized, employing 53 cross-sectional images of 3D models as content images for the style transfer algorithm, alongside natural two-dimensional form images as style images. This allows for the parametric reconstruction of three-dimensional biomimetic models. Finally, a series of mechanical and material performance tests are conducted to validate the mechanical and structural performance of 3D printed biomimetic structural morphologies. This study presents a research methodology for the digital reconstruction of natural two-dimensional forms into three-dimensional representations and innovatively applies digital technologies such as CNNs to material morphology research. Through the application of digital morphogenesis research methods, this study explores the sustainability and innovation of 3D printed materials. Convolutional neural network ; Sustainability ; Computer science ; Morphogenesis ; Business ; Artificial intelligence ; Process management ; Ecology ; Chemistry ; Biology Supramolecular Self-Assembly in Materials ; Silk-based biomaterials and applications ; Polymer composites and self-healing
Authors: Angelina Vypritskaia
Source: Acta Crystallographica Section A Foundations and Advances (article, 2024-08-26)
Field: Materials Science — Materials Chemistry

Neural network-assisted crystal detection for automated electron diffraction data collection Electron diffraction acts as a powerful alternative for crystals and samples that are challenging for X-ray diffraction [1].It can be performed in-house on a transmission electron microscope, and is also easily accessible.Automated data collection is always profitable, and the use of crystal picking algorithms can significantly reduce both the data collection time and radiation damage to the sample.Current crystal detection methods based on threshold [2, 3] are effective on TEM grids with uniform background, such as continuous or holey grids.However, it is problematic when the methods are applied on commonly used lacey carbon grids due to their randomly distributed pore size and shapes and varying density.Therefore, this variability requires advanced crystal identification method.We propose an improvement to the existing approach for crystal identification on lacey carbon TEM grids by subtracting the holeycarbon background from images before thresholding.Traditional image processing techniques struggle with this task, but it can be effectively addressed using neural networks.By training a neural network to recognize the lacey carbon grid background and eliminate it later, we aim to improve the accuracy of crystal identification (Fig. 1).This improvement will enable the collection of more useful data and minimize the inclusion of empty data from the grid background.Figure 1.Crystal identification on a lacey carbon grid covered with ice by thresholding method without (left) and with (right) preliminary grid removal.Yellow lines represent the identified borders of the crystals, red dots show center of the crystals, blue areas mark found carbon grid. Artificial neural network ; Computer science ; Diffraction ; Data collection ; Electron diffraction ; Materials science ; Artificial intelligence ; Physics ; Optics ; Statistics ; Mathematics X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies ; Electron and X-Ray Spectroscopy Techniques
Authors: Oleksii Veretiuk | Nazariy Andrushchak
Source: Computer Design Systems Theory and Practice (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Methods and Models of Machine Learning in Chemistry and Material Science Using Solute Diffusion Experiment Machine learning is a logical extension of automation using computer systems. While a large number of different areas of human activity have been improved by algorithmic software, a large number of other problems remain unsolved because creating an algorithm for them is almost impossible. One of these fields is science. The empirical approach is still main approach in achieving results, because for many studies there is still no clear mathematical apparatus. Machine learning is the solution that allows to save resources and speed up the research process. Conducting experiments always leads to collecting data about the results. Machine learning algorithms allow to use this information to build a model capable of predicting the results of experiments or the properties of new compounds. Within the scope of this article, the effectiveness of different algorithms, both standard and ensemble algorithms, is tested on the data obtained from experiments with solute diffusion. As a result, the effectiveness data of various algorithms were calculated using the formulas of root mean square error, as well as mean absolute percentage error. An example and description of the process of building different types of machine learning models are given. Computer science ; Machine learning ; Artificial intelligence ; Process (computing) ; Scope (computer science) ; Algorithm ; Extension (predicate logic) ; Automation ; Diffusion ; Computational learning theory ; Experimental data ; Active learning (machine learning) ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Thorben Fröhlking | Luigi Bonati | Valerio Rizzi | Francesco Luigi Gervasio
Source: arXiv (Cornell University) (preprint, 2024-02-02)
Field: Materials Science — Materials Chemistry

Deep learning path-like collective variable for enhanced sampling molecular dynamics Several enhanced sampling techniques rely on the definition of collective variables to effectively explore free energy landscapes. Existing variables that describe the progression along a reactive pathway offer an elegant solution but face a number of limitations. In this paper, we address these challenges by introducing a new path-like collective variable called the `Deep-locally-non-linear-embedding', which is inspired by principles of the locally linear embedding technique and is trained on a reactive trajectory. The variable mimics the ideal reaction coordinate by automatically generating a non-linear combination of features through a differentiable generalized autoencoder that combines a neural network with a continuous k-nearest-neighbor selection. Among the key advantages of this method is its capability to automatically choose the metric for searching neighbors and to learn the path from state A to state B without the need to handpick landmarks a priori. We demonstrate the effectiveness of DeepLNE by showing that the progression along the path variable closely approximates the ideal reaction coordinate in toy models such as the Müller-Brown-potential and alanine dipeptide. We then use it in molecular dynamics simulations of an RNA tetraloop, where we highlight its capability to accelerate transitions and converge the free energy of folding. Path (computing) ; Variable (mathematics) ; Sampling (signal processing) ; Dynamics (music) ; Statistical physics ; Computer science ; Artificial intelligence ; Mathematics ; Psychology ; Physics ; Telecommunications ; Mathematical analysis Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; Metabolomics and Mass Spectrometry Studies
Authors: A. D. Terets | Tymofii Yu. Nikolaienko
Source: Low Temperature Physics (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Which molecules can challenge density-functional tight-binding methods in evaluating the energies of conformers? investigation with machine-learning toolset Large organic molecules and biomolecules can adopt multiple conformations, with the occurrences determined by their relative energies. Identifying the energetically most favorable conformations is crucial, especially when interpreting spectroscopic experiments conducted under cryogenic conditions. When the effects of irregular surrounding medium, such as noble gas matrices, on the vibrational properties of molecules become important, semi-empirical (SE) quantum-chemical methods are often employed for computational simulations. Although SE methods are computationally more efficient than first-principle quantum-chemical methods, they can be inaccurate in determining the energies of conformers in some molecules while displaying good accuracy in others. In this study, we employ a combination of advanced machine learning techniques, such as graph neural networks, to identify molecules with the highest errors in the relative energies of conformers computed by the semi-empirical tight-binding method GFN1-xTB. The performance of three different machine learning models is assessed by comparing their predicted errors with the actual errors in conformer energies obtained via the GFN1-xTB method. We further applied the ensemble machine-learning model to a larger collection of molecules from the ChEMBL database and identified a set of molecules as being challenging for the GFN1-xTB method. These molecules hold potential for further improvement of the GFN1-xTB method, showcasing the capability of machine learning models in identifying molecules that can challenge its physical model. chEMBL ; Conformational isomerism ; Molecule ; Density functional theory ; Computer science ; Quantum chemical ; Quantum ; Set (abstract data type) ; Artificial intelligence ; Biomolecule ; Quantum chemistry ; Machine learning ; Computational chemistry ; Statistical physics ; Chemistry ; Materials science ; Physics ; Nanotechnology ; Quantum mechanics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Md. Azharul Islam | Dwyer Deighan | Danial Faghihi
Source:  (article, 2024-11-17)
Field: Chemistry — Spectroscopy

Predicting Microstructure-Property of Silica Aerogel Materials via Bayesian Convolutional Neural Networks Surrogate Model Abstract Deep neural networks have become essential for developing data-driven surrogate models of complex multiscale and multi-physics simulations. Trained with high-fidelity simulation data, these surrogate models enable computational predictions with significantly reduced time and resources compared to physics-based simulations. Surrogate models based on Convolutional Neural Networks (CNNs) are emerging as powerful tools for learning the complex relationships between microstructural images and the corresponding macroscopic properties of materials, facilitating tasks once computationally prohibitive, such as optimal design and synthesizing materials with target properties. However, the common neural network training method, relying on maximum likelihood parameter estimation, limits CNNs’ ability to handle uncertainty due to sparse and limited high-fidelity data generated by physics-based simulations. This often leads to overfitting and overly confident predictions, compromising the reliability of CNNs, especially in high-consequence tasks such as model-based material design. This contribution proposes a Bayesian CNN for surrogate modeling by treating the network’s training as statistical inference to overcome the formidable challenge of uncertainty assessment in predictions provided by neural network-based models. We employ Variational Inference to introduce probability distributions over the CNN’s weights, ensuring accurate uncertainty estimation. The proposed Bayesian CNN surrogate model is applied to learn microstructure image-mechanical property relations in silica aerogel porous materials, known for its superior insulation properties but suffer from low mechanical strength. Training data is obtained from elastic deformation simulations of the solid phase in the porous materials governed by stochastic partial differential equations. Results demonstrate the effectiveness of the Bayesian CNN in predicting the strain energy corresponding to a given microstructure image while considering confidence levels in predictions. The impact of training data points on prediction accuracy and reliability is also investigated using Bayesian CNN. Aerogel ; Convolutional neural network ; Microstructure ; Bayesian probability ; Property (philosophy) ; Computer science ; Materials science ; Surrogate model ; Surrogate endpoint ; Artificial intelligence ; Composite material ; Machine learning ; Medicine ; Internal medicine Aerogels and thermal insulation
Authors: Haoxuan Liu | Xiang Zhao | Haile Yan
Source:  (article, 2024-11-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predicting and Enhancing Ductility in X<sub>2</sub>YZ-type Heusler Alloys Using Machine Learning and Abinitio Calculations X<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>YZ-type Heusler alloys, such as Ni<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>MnGa, exhibit promising properties like magnetic shape memory, thermoelectric effects, and superconductivity, making them ideal for smart sensors and refrigeration. However, their inherent brittleness limits their mechanical performance, especially in applications requiring elastic or shape memory effects. This study utilizes machine learning (ML) to predict the ductility of these alloys, applying a random forest model combined with advanced feature selection techniques. By training the model on a dataset Heusler alloys from Materials-Project, the study identifies key factors influencing ductility. First-principles calculations validate the predictions, revealing that alloys like Pt<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>TaAl show exceptional ductility, surpassing other known Heusler alloys. Ductility (Earth science) ; Materials science ; Type (biology) ; Computer science ; Crystallography ; Metallurgy ; Chemistry ; Geology Heusler alloys: electronic and magnetic properties ; Advanced Welding Techniques Analysis ; Intermetallics and Advanced Alloy Properties
Authors: Murilo Moreira | E. Cottancin | M. Pellarin | Lucian Roiban | Karine Massenelli-Varlot | D. Ugarte | Varlei Rodrigues | Matthias Hillenkamp
Source: BIO Web of Conferences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Quantitative chemical analysis by STEM-EDS and machine learning: Are AgAu alloyed at the nanoscale? Nanoscopic scale ; Psychology ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Stephen J. Harris | Xin Zhang
Source: ECS Meeting Abstracts (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

(Invited) Multiscale Contact Mechanics and Electrochemistry of Li-SE Interfaces with a SE-Reinforced Polymer Interfacial Coating, Using Machine Learning to Optimize the Coating and Stack Pressure Conditions We describe a multi-scale three-dimensional time-dependent contact model for the interface between Li and a solid electrolyte, with and without a SE-reinforced polymer interface coating. Our simulation without an interface coating considers the surface roughness of the Li and SEs, Li elastoplasticity, Li creep, and the Li metal plating/stripping process to predict the time-evolution during plating. When we also have an interface coating, we consider its viscoelasticity as well as the effect of the reinforcement on the polymer’s modulus and on its conductivity. Machine learning allows us to optimize values for thickness, stack pressure, and coating material properties (modulus, conductivity, etc). We describe the technique used to solve the multiscale problem. Coating ; Materials science ; Stack (abstract data type) ; Polymer ; Electrochemistry ; Composite material ; Contact mechanics ; Computer science ; Structural engineering ; Electrode ; Finite element method ; Engineering ; Chemistry ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Lubricants and Their Additives
Authors: Joshua Ojih | Mohammed Fahdi | Yagang Yao | Jianjun Hu | Ming Hu
Source: Scholar Commons (University of South Carolina) (article, 2024-03-06)
Field: Materials Science — Materials Chemistry

Graph Theory and Graph Neural Network Assisted High-Throughput Crystal Structure Prediction and Screening for Energy Conversion and Storage Prediction of crystal structures with desirable material properties is a grand challenge in materials research, due to the enormous search space of possible combinations of elements and their countless arrangements in 3D space. Despite the recent progress of a few crystal structure prediction algorithms, most of those methods only target a few specific material families or are restricted to simple systems with limited element diversity. Moreover, these algorithms are usually coupled with first principles calculations and thus are computationally expensive and very time consuming. Therefore, establishing a workflow that can generate a large number of hypothetical structures with diverse elements and quickly optimize and screen out stable structures is urgently needed for the crystal structure prediction field. In this study, we take 17 277 compositions involving 63 elements across the periodic table from the open quantum materials database (OQMD) and use a graph theory assisted universal structure searcher (MAGUS) to generate more than 3.4 million hypothetical structures. We employ a pre-trained universal interatomic potential named Crystal Hamiltonian Graph neural Network (CHGNet) to rapidly optimize this large number of hypothetical structures. Subsequently, we validate these optimized structures using density functional theory (DFT) and find 4145 structures are successfully optimized and 2368 structures have energy lower than those from the original OQMD. 647 structures are further identified to be dynamically stable using CHGNet. Moreover, the stability of 123 out of 200 randomly chosen structures are validated by DFT, corresponding to a high success rate of 61.5%. We further use 4706 DFT data points to train 3 graph neural network models to predict lattice thermal conductivity (LTC) and heat capacity. Numerous structures with ultralow LTC and high heat capacity, which are promising for advanced energy conversion and energy storage, have been identified. The success of our workflow demonstrates that combining graph theory with pre-trained universal interatomic potential is highly expected to accelerate the search for new both thermodynamically and dynamically stable structures with target material properties. Crystal structure prediction ; Data structure ; Artificial neural network ; Crystal structure ; Graph theory ; Graph ; Workflow ; Periodic table ; Hamiltonian (control theory) Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; Advanced Thermoelectric Materials and Devices