jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andi M. N. F. Syamsul | Abdul‐Muizz Pradipto | Agoes Soehianie
Source: Journal of Physics Conference Series (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Optimizing Neural Networks for Enhanced Material Property Predictions: Insights from Bulk Modulus Analysis Abstract This research presents a deep learning model designed to accurately compute material properties, with a specific focus on the bulk modulus. This study places significant emphasis on hyperparameter optimization, involving adjustments to batch size, learning rate, hidden layer, and neuron count. The dataset, comprising 7107 diverse materials, undergoes thorough preprocessing, which includes outlier removal and the extraction of elemental property descriptors using the matminer library and the Magpie dataset. The core model utilized in this research is an Artificial Neural Network (ANN), with the descriptors serving as crucial input features. Model performance assessment is conducted by using the Mean Absolute Error (MAE) as a quantitative metric, providing insights into predictive accuracy. This research also employs sensitivity analysis to scrutinize the significance of 132 features in predicting the bulk modulus property, contributing to an understanding of material behavior dynamics and facilitating model optimization. The results highlight the impact of neuron count, layer depth, learning rate, and batch size on prediction accuracy. Furthermore, feature importance analysis underscores the critical role of specific material properties, with mean covalent radius emerging as the most influential factor in predicting the bulk modulus. These discoveries provide guidelines for optimizing neural network configurations and material property descriptors for predicting material elasticity. Property (philosophy) ; Artificial neural network ; Materials science ; Modulus ; Biological system ; Composite material ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Advanced machining processes and optimization ; Welding Techniques and Residual Stresses
Authors: Parola Athulya | D. Durgalakshmi | R. Ajay Rakkesh
Source: Analysis & Sensing (article, 2024-11-29)
Field: Materials Science — Materials Chemistry

Titanium Mxene: A Promising Material for Next‐Generation Optical Biosensors and Machine Learning Integration Abstract Nano biosensors based on MXenes have been emerging as a promising tool in the detection of biomarkers, for the discrimination of diseases and in the detection of environmental pollutants. Their potential in sensing applications has also drawn a lot of attention to their unique qualities such as their high conductivity, huge surface area, outstanding hydrophilicity, biocompatibility, and simplicity of surface functionalization. The development of scalable synthesis techniques is essential to the large‐scale manufacturing and broad application of MXene‐based sensors. Furthermore, the stability of the MXene layers in diverse environmental circumstances continues to be a difficulty for their practical application. To increase the dependability and precision of MXene‐based sensors, their selectivity must be increased through functionalization and tuning. With innovative technologies like machine learning, MXene biosensor is now taken advantage of new opportunities. Personalized healthcare solutions, remote data analysis, and real‐time monitoring are all possible when MXene sensors and AI algorithms work together. Herein, the optical properties, synthesis approaches, role of MXene biosensors in machine learning, its significant challenges and future prospects of MXene‐based nano(bio)sensors are deliberated. Nanotechnology ; Materials science ; Biosensor ; Titanium ; Computer science ; Metallurgy MXene and MAX Phase Materials ; Advanced biosensing and bioanalysis techniques ; 2D Materials and Applications
Authors: Yanlong Ran | Jiaqi An | Liangshun Zhang
Source: Macromolecular Chemistry and Physics (article, 2024-07-24)
Field: Materials Science — Materials Chemistry

Inference of Constitutive Relation of Phase‐Separated Polymers by Integrating Physics‐Informed Neural Networks and Symbolic Regression Abstract Harnessing data to discover the underlying constitutive relation of phase‐separated polymers can significantly advance the fabrication of high‐performance materials. This work introduces a novel data‐driven method to learn the constitutive equation of diffusional transport of polymers from spatiotemporal density field. In particular, the data‐driven method seamlessly integrated physics‐informed neural networks for inference of approximate solution of diffusivity, and symbolic regression that form explicit expressions of diffusivity. The efficacy and robustness of this method are demonstrated by learning the distinct forms of diffusivity for the phase separation of homopolymer blends with various compositions. In addition, the data‐driven method is generalized to extract the constitutive relation of homogenous chemical potential in the phase separation of homopolymer blends. The data‐driven framework shows the potential for model discovery of nonlinear dynamic system from the spatiotemporal state variables. Thermal diffusivity ; Inference ; Robustness (evolution) ; Symbolic regression ; Artificial neural network ; Biological system ; Constitutive equation ; Nonlinear system ; Statistical physics ; Nonlinear regression ; Computer science ; Materials science ; Artificial intelligence ; Regression analysis ; Algorithm ; Machine learning ; Thermodynamics ; Physics ; Chemistry Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Fuel Cells and Related Materials
Authors: Zhen-Xuan Fan | Sheng D. Chao
Source: Bioengineering (article, 2024-01-03)
Field: Materials Science — Materials Chemistry

A Machine Learning Force Field for Bio-Macromolecular Modeling Based on Quantum Chemistry-Calculated Interaction Energy Datasets Accurate energy data from noncovalent interactions are essential for constructing force fields for molecular dynamics simulations of bio-macromolecular systems. There are two important practical issues in the construction of a reliable force field with the hope of balancing the desired chemical accuracy and working efficiency. One is to determine a suitable quantum chemistry level of theory for calculating interaction energies. The other is to use a suitable continuous energy function to model the quantum chemical energy data. For the first issue, we have recently calculated the intermolecular interaction energies using the SAPT0 level of theory, and we have systematically organized these energies into the ab initio SOFG-31 (homodimer) and SOFG-31-heterodimer datasets. In this work, we re-calculate these interaction energies by using the more advanced SAPT2 level of theory with a wider series of basis sets. Our purpose is to determine the SAPT level of theory proper for interaction energies with respect to the CCSD(T)/CBS benchmark chemical accuracy. Next, to utilize these energy datasets, we employ one of the well-developed machine learning techniques, called the CLIFF scheme, to construct a general-purpose force field for biomolecular dynamics simulations. Here we use the SOFG-31 dataset and the SOFG-31-heterodimer dataset as the training and test sets, respectively. Our results demonstrate that using the CLIFF scheme can reproduce a diverse range of dimeric interaction energy patterns with only a small training set. The overall errors for each SAPT energy component, as well as the SAPT total energy, are all well below the desired chemical accuracy of ~1 kcal/mol. Interaction energy ; Force field (fiction) ; Computer science ; Field (mathematics) ; Benchmark (surveying) ; Ab initio ; Work (physics) ; Energy (signal processing) ; Quantum ; Molecular dynamics ; Intermolecular force ; Statistical physics ; Chemistry ; Artificial intelligence ; Computational chemistry ; Physics ; Molecule ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Taohong Zhang | Chenglong Xia | Huguang Yang | Xuxu Guo | Han Zheng | Aziguli Wulamu
Source: Computational Materials Science (article, 2024-09-10)
Field: Materials Science — Materials Chemistry

SGNN-T: Space graph neural network coupled transformer for molecular property prediction Transformer ; Artificial neural network ; Property (philosophy) ; Computer science ; Biological system ; Topology (electrical circuits) ; Artificial intelligence ; Materials science ; Physics ; Mathematics ; Voltage ; Combinatorics ; Biology ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Rodayna Hmede | Frédéric Chapelle | Yuri Lapusta | Juan Antonio Corrales Ramón
Source: Actuators (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Controlling the Deformation of the Antagonistic Shape Memory Alloy System by LSTM Deep Learning The antagonistic system of two shape memory alloy wires is a great inspiration for the robotics field where it is applied as a linear actuator due to its shape memory effect. However, its control is still a challenge due to its hysteresis behavior. For that reason, a new controller is proposed in this paper for the displacement of the system’s effector. It is based on a Long Short-Term Memory neural network model. The aim is achieved by combining temperature-deformation data from an analytical model with voltage-temperature-deformation data from real experiments. Hence, these datasets are studied to overcome the nonlinearity obstacle of this system in order to be able to integrate it into robotic applications. Shape-memory alloy ; Nonlinear system ; Deformation (meteorology) ; Artificial intelligence ; Actuator ; Artificial neural network ; Controller (irrigation) ; Computer science ; Voltage ; Hysteresis ; Displacement (psychology) ; Control engineering ; Control theory (sociology) ; Field (mathematics) ; Materials science ; Computer vision ; Engineering ; Control (management) ; Physics ; Mathematics ; Electrical engineering Shape Memory Alloy Transformations ; Advanced Memory and Neural Computing ; Piezoelectric Actuators and Control
Authors: Amina Diop | L. Ilsedore Cleeves | D. E. Anderson | Jamila Pegues | Adele Plunkett
Source: The Astrophysical Journal (article, 2024-02-01)
Field: Chemistry — Spectroscopy

Disentangling CO Chemistry in a Protoplanetary Disk Using Explanatory Machine-learning Techniques Abstract Molecular abundances in protoplanetary disks are highly sensitive to the local physical conditions, including gas temperature, gas density, radiation field, and dust properties. Often multiple factors are intertwined, impacting the abundances of both simple and complex species. We present a new approach to understanding these chemical and physical interdependencies using machine learning. Specifically, we explore the case of CO modeled under the conditions of a generic disk and build an explanatory regression model to study the dependence of CO spatial density on the gas density, gas temperature, cosmic-ray ionization rate, X-ray ionization rate, and UV flux. Our findings indicate that combinations of parameters play a surprisingly powerful role in regulating CO abundance compared to any singular physical parameter. Moreover, in general we find the conditions in the disk are destructive toward CO. CO depletion is further enhanced in an increased cosmic-ray environment and in disks with higher initial C/O ratios. These dependencies uncovered by our new approach are consistent with previous studies, which are more modeling intensive and computationally expensive. Our work thus shows that machine learning can be a powerful tool not only for creating efficient predictive models, but also for enabling a deeper understanding of complex chemical processes. Physics ; Protoplanetary disk ; Astrophysics ; Astrochemistry ; Accretion disc ; Astrobiology ; Astronomy ; Stars ; Interstellar medium ; Galaxy Molecular Spectroscopy and Structure ; Astrophysics and Star Formation Studies ; Astro and Planetary Science
Authors: Yulian T. Manchev | Paul L. A. Popelier
Source: ChemRxiv (preprint, 2024-06-10)
Field: Materials Science — Materials Chemistry

ichor: A Python library for computational chemistry data management and machine learning force field development We present ichor, an open-source Python library that simplifies data management in computational chemistry and streamlines machine learning force field development. Ichor implements many easily extendable file management tools, in addition to a lazy file reading system, allowing efficient management of hundreds of thousands of computational chemistry files. Data from calculations can be readily stored into databases for easy sharing and post-processing. Raw data can be directly processed by ichor to create machine learning-ready datasets. In addition to powerful data-related capabilities, ichor provides interfaces to popular workload management software employed by High Performance Computing clusters, making for effortless submission of thousands of separate calculations with only a single line of Python code. Furthermore, a simple-to-use command line interface has been implemented through a series of menu systems to further increase accessibility and efficiency of common important ichor tasks. Finally, ichor implements general tools for visualization and analysis of datasets and tools for measuring machine-learning model quality both on test set data and in simulations. With the current functionalities, ichor can serve as an end-to-end data procurement, data management, and analysis solution for machine-learning force-field development. Python (programming language) ; Computer science ; Software ; Data management ; Scripting language ; Visualization ; Software engineering ; Artificial intelligence ; Programming language ; Machine learning ; Data mining Machine Learning in Materials Science ; Scientific Computing and Data Management ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Ramkumar Muthukrishnan | Yakubu Balogun | Vinooth Rajendran | Anil Prathuru | Mamdud Hossain | Nadimul Haque Faisal
Source: High Temperature Corrosion of Materials (article, 2024-09-22)
Field: Materials Science — Materials Chemistry

Machine Learning Approach to Investigate High Temperature Corrosion of Critical Infrastructure Materials Abstract Degradation of coatings and structural materials due to high temperature corrosion in the presence of molten salt environment is a major concern for critical infrastructure applications to meet its commercial viability. The choice of high value coatings and structural (construction parts) materials comes with challenges, and therefore data centric approach may accelerate change in discovery and data practices. This research aims to use machine learning (ML) approach to estimate corrosion rates of materials when operated at high temperatures conditions (e.g., nuclear, geothermal, oxidation (dry/wet), solar applications) but geared towards nuclear thermochemical cycles. Published data related to materials (structural and coatings materials), their composition and manufacturing, including corrosion environment were gathered and analysed. Analysis demonstrated that random forest regression model is highly precise compared to other models. Assessment indicates that very limited sets of materials are likely to survive high temperature corrosive environment for extended period of exposure. While a higher quality and larger dataset are required to accurately predict the corrosion rate, the findings demonstrated the value of ML’s regression and data mining capabilities for corrosion data analysis. With the research gap in material selection strategies, proposed research will be critical to advancing data analytics approach exploiting their properties for high temperature corrosion applications. Graphical Abstract Corrosion ; Critical infrastructure ; Materials science ; High-temperature corrosion ; Metallurgy ; Computer science ; Computer security Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition
Authors: Zhihao Xing | Rodolfo S. M. Freitas | Xi Jiang
Source: Energy and AI (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

Neural network potential-based molecular investigation of thermal decomposition mechanisms of ethylene and ammonia • NNP-based molecular investigation of ethylene and ethylene-ammonia thermal decomposition was conducted. • The NNP reproduced key results consistent with prior experiments and chemical kinetics modelling. • Novel atomic-scale insights into soot suppression by ammonia addition were revealed. • Six-membered carbon rings are easier to open in the blended system under high temperature. • Six-membered carbon rings transform into seven-membered rings at low temperature and three- or five-membered rings at high temperature. This study developed neural network potentials (NNPs) specifically tailored for pure ethylene and ethylene-ammonia blended systems for the first time. The NNPs were trained on a dataset generated from density functional theory (DFT) calculations, combining the computational accuracy of DFT with a calculation speed comparable to reactive force field methods. The NNPs are employed in reactive molecular dynamics simulations to explore the thermal decomposition reaction mechanisms of ethylene and ammonia. The simulation results revealed that adding ammonia reduces the activation energy for ethylene decomposition, thereby accelerating ethylene consumption. Furthermore, the addition of ammonia uncovers a new reaction pathway for hydrogen radical consumption, which reduces the occurrence of H-abstraction reactions from ethylene by hydrogen radicals. The inhibition effect of ammonia addition on soot formation mainly acts in two aspects: on the one hand, ammonia decomposition products react with carbon-containing species, ultimately producing C 1 N products, thereby decreasing the carbon numbers involved in soot formation. This significantly reduces the concentrations of C 5 C 9 molecules and key polycyclic aromatic hydrocarbons (PAHs) precursors like C 2 H 2 and C 3 H 3 . On the other hand, ammonia promotes the ring-opening reactions of six-membered carbon rings at high-temperature conditions, thereby reducing the formation of PAHs precursors. The results show that with the addition of ammonia, six-membered carbon rings tend to convert into seven-membered carbon rings at lower temperatures, while at higher temperatures, they are more likely to transform into three- and five-membered carbon rings. These variations in the transformation of six-membered carbon rings may also affect soot formation. The insights gained from understanding these fundamental chemical reaction mechanisms can guide the development of ethylene-ammonia co-firing systems. Ammonia ; Ethylene ; Thermal decomposition ; Artificial neural network ; Decomposition ; Biological system ; Chemistry ; Computer science ; Artificial intelligence ; Biology ; Biochemistry ; Organic chemistry Thermal and Kinetic Analysis ; Energetic Materials and Combustion ; Machine Learning in Materials Science
Authors: Susmita Tripathy | Surajit Das | Shweta Jindal | Raghunathan Ramakrishnan
Source: Machine Learning Science and Technology (article, 2024-10-15)
Field: Materials Science — Materials Chemistry

Chemical space-informed machine learning models for rapid predictions of x-ray photoelectron spectra of organic molecules Abstract We present machine learning models based on kernel-ridge regression for predicting x-ray photoelectron spectra of organic molecules originating from the K -shell ionization energies of carbon (C), nitrogen (N), oxygen (O), and fluorine (F) atoms. We constructed the training dataset through high-throughput calculations of K -shell core-electron binding energies (CEBEs) for 12 880 small organic molecules in the bigQM7 ω dataset, employing the Δ-SCF formalism coupled with meta-GGA-DFT and a variationally converged basis set. The models are cost-effective, as they require the atomic coordinates of a molecule generated using universal force fields while estimating the target-level CEBEs corresponding to DFT-level equilibrium geometry. We explore transfer learning by utilizing the atomic environment feature vectors learned using a graph neural network framework in kernel-ridge regression. Additionally, we enhance accuracy within the Δ-machine learning framework by leveraging inexpensive baseline spectra derived from Kohn–Sham eigenvalues. When applied to 208 combinatorially substituted uracil molecules larger than those in the training set, our analyses suggest that the models may not provide quantitatively accurate predictions of CEBEs but offer a strong linear correlation relevant for virtual high-throughput screening. We present the dataset and models as the Python module, cebeconf , to facilitate further explorations. Chemical space ; Spectral line ; X-ray photoelectron spectroscopy ; Molecule ; Space (punctuation) ; Organic molecules ; X-ray ; Materials science ; Chemical physics ; Chemistry ; Computer science ; Physics ; Nuclear magnetic resonance ; Optics ; Organic chemistry ; Quantum mechanics ; Drug discovery Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Various Chemistry Research Topics
Authors: Yi Je Cho | Harrison Chaney | Kathy Lu
Source: Nano Research (article, 2024-12-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning approach for predicting optical and photothermal properties of gold nanoparticle/polymer hybrid films: Effect of synthetic data Emerging machine learning (ML) approaches have been adopted in various material systems to predict novel properties with the assistance of the corresponding large datasets. For new materials, however, collecting sufficient data points for model training is not feasible, which is the case for gold nanoparticle/polymer hybrid films. In this study, an ML approach coupled with finite element modeling was proposed for predicting the optical and photothermal properties of gold nanoparticle/polymer hybrid films. Experimental datasets of the optical and photothermal properties were built using results from the literature. Then, finite element analyses were conducted to generate synthetic data to satisfy the quality and quantity of the data required for training models. Correlation analysis and model training were performed using the datasets with and without synthetic data to evaluate their effects on predicting the performance of the ML models. The relative importance of features to targets (properties) was evaluated by correlation analysis. ML models with high accuracy were obtained by training various models from conventional to newly developed algorithms. Advantages, weaknesses, and improvement of the synthetic data addition were discussed. The proposed workflow and framework offer reliable prediction of optical and photothermal properties over different combinations of gold nanoparticles and polymer matrices, which can be extended to include more features related to processing parameters and microstructures. Photothermal therapy ; Nanoparticle ; Materials science ; Nanotechnology ; Colloidal gold ; Polymer ; 3D optical data storage ; Optoelectronics ; Composite material Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science