jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Simone Perego | Luigi Bonati
Source: ChemRxiv (preprint, 2024-06-06)
Field: Materials Science — Materials Chemistry

Data-efficient modeling of catalytic reactions via enhanced sampling and on-the-fly learning of machine learning potentials Simulating catalytic reactivity under operative conditions poses a significant challenge due to the dynamic nature of the catalysts and the high computational cost of electronic structure calculations. Machine learning potentials offer a promising avenue to simulate dynamics at a fraction of the cost, but they require datasets containing all relevant configurations, particularly reactive ones. Here we present a scheme to construct reactive potentials in a data-efficient manner. This is achieved by combining enhanced sampling methods first with Gaussian processes to discover transition paths and then with graph neural networks to obtain a uniformly accurate description. The necessary configurations are extracted via an active learning procedure based on local environment uncertainty. We validated our approach by studying several reactions related to the decomposition of ammonia on iron-cobalt alloy catalysts. Our scheme proved efficient, requiring only ~1,000 DFT calculations per reaction, and robust, sampling reactive configurations from the different accessible pathways. Using this potential, we calculated free energy profiles and characterized reaction mechanisms, showing the ability to provide microscopic insights into complex processes under dynamic conditions. On the fly ; Sampling (signal processing) ; Computer science ; Catalysis ; Machine learning ; Artificial intelligence ; Chemistry ; Organic chemistry Machine Learning in Materials Science
Authors: Yiming Wang | Yue Fang | Haifan Zhou | Hanyu Gao
Source: Molecules (article, 2024-10-03)
Field: Chemistry — Organic Chemistry

A Machine Learning Model for Predicting the Propagation Rate Coefficient in Free-Radical Polymerization The propagation rate coefficient (<i>k</i><sub>p</sub>) is one of the most crucial kinetic parameters in free-radical polymerization (FRP) as it directly governs the rate of polymerization and the resulting molecular weight distribution. The <i>k</i><sub>p</sub> in FRP can typically be obtained through experimental measurements or quantum chemical calculations, both of which can be time consuming and resource intensive. Herein, we developed a machine learning model based solely on the structural features of monomers involved in FRP, utilizing molecular embedding and a Lasso regression algorithm to predict <i>k</i><sub>p</sub> more efficiently and accurately. The result shows that the model achieves a mean absolute percentage error (MAPE) of only 5.49% in the predictions for four new monomers, which indicates that the model exhibits strong generalization capabilities and provides reliable and robust predictions. In addition, this model can accurately predict the influence of the ester side chain length of (meth)acrylates on <i>k</i><sub>p</sub>, aligning well with established scientific knowledge. This approach offers a straightforward and practical model for other researchers to rapidly obtain accurate <i>k</i><sub>p</sub> values by employing monomer structural information. The model is sufficiently general to apply to a wide range of (meth)acrylate and butadiene FRP monomers, thereby supporting kinetic modeling of polymerization reactions. Polymerization ; Monomer ; Radical polymerization ; Computer science ; Biological system ; Generalization ; Materials science ; Polymer ; Thermodynamics ; Chemistry ; Mathematics ; Physics ; Organic chemistry Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Photopolymerization techniques and applications
Authors: V. A. Milyutin | N. N. Nikulchenkov
Source: The Physics of Metals and Metallography (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Machine Learning Application for Functional Properties Prediction in Magnetic Materials Machine learning (ML) has proven to be a powerful tool, significantly speeding up and simplifying the development of new materials while enhancing their functional characteristics. In recent years, there has been an exponential growth in the number of scientific publications exploring the use of ML in materials science. Using this approach, various materials, including magnetic ones, are being actively developed and studied. This article aims to critically review research that applies ML to predict the functional characteristics of soft and hard magnetic materials. The paper is divided into three sections: the first outlines the basic principles and algorithms of machine learning, highlighting its use in addressing practical materials science challenges; the second discusses recent advances in developing magnetic functional alloys using ML; the last section provides a critical analysis of the use of machine learning methods in this area, analyzes its advantages and disadvantages, and gives recommendations for organizing such research. Computer science ; Materials science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Jakub Kubečka | Daniel Ayoubi | Zeyuan Tang | Yosef Knattrup | Morten Engsvang | Haide Wu | Jonas Elm
Source: Environmental Science Advances (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Accurate modeling of the potential energy surface of atmospheric molecular clusters boosted by neural networks The computational cost of accurate quantum chemistry (QC) calculations of large molecular systems can often be unbearably high. Machine learning offers a lower computational cost compared to QC methods while maintaining their accuracy. In this study, we employ the polarizable atom interaction neural network (PaiNN) architecture to train and model the potential energy surface of molecular clusters relevant to atmospheric new particle formation, such as sulfuric acid-ammonia clusters. We compare the differences between PaiNN and previous kernel ridge regression modeling for the Clusteromics I-V data sets. We showcase three models capable of predicting electronic binding energies and interatomic forces with mean absolute errors of <0.3 kcal mol<sup>-1</sup> and <0.2 kcal mol<sup>-1</sup> Å<sup>-1</sup>, respectively. Furthermore, we demonstrate that the error of the modeled properties remains below the chemical accuracy of 1 kcal mol<sup>-1</sup> even for clusters vastly larger than those in the training database (up to (H<sub>2</sub>SO<sub>4</sub>)<sub>15</sub>(NH<sub>3</sub>)<sub>15</sub> clusters, containing 30 molecules). Consequently, we emphasize the potential applications of these models for faster and more thorough configurational sampling and for boosting molecular dynamics studies of large atmospheric molecular clusters. Artificial neural network ; Energy (signal processing) ; Surface (topology) ; Environmental science ; Computer science ; Artificial intelligence ; Physics ; Mathematics Spectroscopy and Laser Applications ; Advanced Chemical Physics Studies ; Atmospheric chemistry and aerosols
Authors: Davis Thomas Daniel | Souvik Mitra | Rüdiger‐A. Eichel | Diddo Diddens | Josef Granwehr
Source: Journal of Chemical Theory and Computation (article, 2024-03-08)
Field: Materials Science — Materials Chemistry

Machine Learning Isotropic <i>g</i> Values of Radical Polymers Methods for electronic structure computations, such as density functional theory (DFT), are routinely used for the calculation of spectroscopic parameters to establish and validate structure-parameter correlations. DFT calculations, however, are computationally expensive for large systems such as polymers. This work explores the machine learning (ML) of isotropic <i>g</i> values, <i>g</i><sub>iso</sub>, obtained from electron paramagnetic resonance (EPR) experiments of an organic radical polymer. An ML model based on regression trees is trained on DFT-calculated <i>g</i> values of poly(2,2,6,6-tetramethylpiperidinyloxy-4-yl methacrylate) (PTMA) polymer structures extracted from different time frames of a molecular dynamics trajectory. The DFT-derived <i>g</i> values, <i>g</i><sub>iso</sub><sup>calc</sup>, for different radical densities of PTMA, are compared against experimentally derived <i>g</i> values obtained from <i>in operando</i> EPR measurements of a PTMA-based organic radical battery. The ML-predicted <i>g</i><sub>iso</sub> values, <i>g</i><sub>iso</sub><sup>pred</sup>, were compared with <i>g</i><sub>iso</sub><sup>calc</sup> to evaluate the performance of the model. Mean deviations of <i>g</i><sub>iso</sub><sup>pred</sup> from <i>g</i><sub>iso</sub><sup>calc</sup> were found to be on the order of 0.0001. Furthermore, a performance evaluation on test structures from a separate MD trajectory indicated that the model is sensitive to the radical density and efficiently learns to predict <i>g</i><sub>iso</sub> values even for radical densities that were not part of the training data set. Since our trained model can reproduce the changes in <i>g</i><sub>iso</sub> along the MD trajectory and is sensitive to the extent of equilibration of the polymer structure, it is a promising alternative to computationally more expensive DFT methods, particularly for large systems that cannot be easily represented by a smaller model system. Electron paramagnetic resonance ; Density functional theory ; Isotropy ; Polymer ; Computer science ; Computation ; Biological system ; Computational chemistry ; Materials science ; Chemistry ; Statistical physics ; Physics ; Algorithm ; Nuclear magnetic resonance ; Quantum mechanics Machine Learning in Materials Science ; Free Radicals and Antioxidants ; Conducting polymers and applications
Authors: Chi Jiang | Yi Zhang | Yang Liu | Jing Peng
Source: Computational Biology and Chemistry (article, 2024-03-15)
Field: Materials Science — Materials Chemistry

Tensor improve equivariant graph neural network for molecular dynamics prediction Kinematics ; Computer science ; Artificial neural network ; Scalar (mathematics) ; Molecular dynamics ; Position (finance) ; Equivariant map ; Tensor (intrinsic definition) ; Molecular graph ; Algorithm ; Topology (electrical circuits) ; Statistical physics ; Graph ; Computational chemistry ; Artificial intelligence ; Mathematics ; Theoretical computer science ; Physics ; Chemistry ; Classical mechanics ; Geometry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Topic Modeling
Authors: Umberto Michelucci | Francesca Venturini
Source: Scientific Reports (article, 2024-09-27)
Field: Chemistry — Analytical Chemistry

Deep learning domain adaptation to understand physico-chemical processes from fluorescence spectroscopy small datasets and application to the oxidation of olive oil Fluorescence spectroscopy is a fundamental tool in life sciences and chemistry, with applications in environmental monitoring, food quality control, and biomedical diagnostics. However, analysis of spectroscopic data with deep learning, in particular of fluorescence excitation-emission matrices (EEMs), presents significant challenges due to the typically small and sparse datasets available. Furthermore, the analysis of EEMs is difficult due to their high dimensionality and overlapping spectral features. This study proposes a new approach that exploits domain adaptation with pretrained vision models, along with a novel interpretability algorithm to address these challenges. Thanks to specialised feature engineering of the neural networks described in this work, we are now able to provide deeper insights into the physico-chemical processes underlying the data. The proposed approach is demonstrated through the analysis of the oxidation process in extra virgin olive oil (EVOO), showing its effectiveness in predicting quality indicators and identifying the spectral bands and thus the molecules involved in the process. This work describes a significantly innovative approach to deep learning for spectroscopy, transforming it from a black box into a tool for understanding complex biological and chemical processes. Domain adaptation ; Adaptation (eye) ; Olive oil ; Domain (mathematical analysis) ; Computer science ; Fluorescence spectroscopy ; Fluorescence ; Chemistry ; Data science ; Environmental chemistry ; Artificial intelligence ; Biology ; Neuroscience ; Physics ; Mathematics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Hema Rajesh Nadella | Sankha Mukherjee | Abu Anand | Chandra Veer Singh
Source: ACS Materials Letters (article, 2024-01-26)
Field: Materials Science — Materials Chemistry

Machine Learning Enabled Prediction of High Stiffness 2D Materials Persistent exploration of high stiffness two-dimensional (2D) materials is necessary for advancements in applications such as nanocomposites, flexible electronics, and resonant sensors, all of which demand elevated resistance to deformation. However, data-centric material models developed for this purpose remain in their early stages, often due to incomplete stiffness estimation or limited transferability to unseen 2D materials. In this context, we examined stiffness trends among different classes of 2D materials and identified the elastic constants pivotal for estimating the 2D material stiffness irrespective of their crystal symmetry. Subsequently, we developed Gaussian Process Regression machine learning models with the capability of relative stiffness comparison, which are used to predict high stiffness candidates across a broad spectrum of unseen 2D materials during model training. The probability of finding high stiffness 2D materials increased significantly, from a mere 1% in the training data set to a notable 47% in the set of machine learning-predicted 2D materials. We also discussed potential stiffening mechanisms, competing stiffness characteristics, and complementary properties of these predicted high-stiffness 2D materials that are crucial for enhancing the effectiveness of the aforementioned applications. Stiffness ; Materials science ; Computer science ; Artificial intelligence ; Machine learning ; Composite material MXene and MAX Phase Materials ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Д. Р. Байгутлин | V. V. Sokolovskiy | V. D. Buchelnikov | Sergey Taskaev
Source: Journal of Applied Physics (article, 2024-11-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning algorithms for optimization of magnetocaloric effect in all-<i>d</i>-metal Heusler alloys This study examines the application of machine learning algorithms, specifically the Random Forest regression model, to optimize the magnetocaloric effect in all-d-metal Heusler alloys. The model was trained using descriptors related to the mean properties of individual atoms, the properties of simple compounds in their ground state, and measures of chemical disorder. It demonstrated high accuracy in predicting structural properties, while exhibiting moderate accuracy in predicting magnetic properties. To identify optimal alloy compositions, a genetic algorithm was used to find those with the greatest differences in magnetization during martensitic transitions. Using this combined approach, the Ni–Co–Mn–Ti alloy system was thoroughly explored, resulting in the discovery of an alloy with a maximum magnetization difference. These results are consistent with previous research based on density functional theory and highlight the effectiveness of integrating machine learning with genetic algorithms for the discovery of new materials with outstanding magnetocaloric properties. The study emphasizes the need for further refinement of models capable of accurately predicting complex magnetic interactions, which is essential for fully leveraging the potential of all-d-metal Heusler alloys in practical applications. Magnetic refrigeration ; Materials science ; Optimization algorithm ; Algorithm ; Computer science ; Condensed matter physics ; Physics ; Mathematics ; Mathematical optimization ; Magnetization ; Magnetic field ; Quantum mechanics Heusler alloys: electronic and magnetic properties ; Magnetic and transport properties of perovskites and related materials ; Machine Learning in Materials Science
Authors: Lei Song | Aijun Yin | Shuhui Chen | Zhendong Long
Source: Physica Scripta (article, 2024-04-12)
Field: Materials Science — Materials Chemistry

Adaptive residual convolutional neural network for compressive strength prediction of energetic materials using SEM images Abstract The microstructure of materials has a great influence on the macroscopic mechanical properties of materials, and the relationship between them is of great significance to the design of materials. With the development of artificial intelligence and deep learning, many researchers have used convolutional neural networks (CNN) to correlate microscopic images with material properties, and achieved good results. However, for different types of material performance prediction tasks and datasets of different sizes, researchers need to design a special CNN network structure, and the adjustment steps of network structure parameters are cumbersome and time consuming. In this paper, an adaptive residual convolutional neural network with variable depth and width is proposed. We take the prediction error of the test dataset of the specific material performance prediction task as the evaluation index, and adaptively adjusts the network structure based on simulated annealing algorithm to find the best network structure. Finally, it is verified on the dataset composed of scanning electron microscopy (SEM) images and compressive strength of polymer explosive materials. Mean absolute percentage error (MAPE) of our model is 1.3% and the goodness of fit <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi mathvariant="normal">R</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:math> ) is 0.943. Further, we use Grad-CAM to explore the correlation between material microstructure and material properties. Convolutional neural network ; Residual ; Computer science ; Artificial neural network ; Artificial intelligence ; Simulated annealing ; Pattern recognition (psychology) ; Compressive strength ; Algorithm ; Machine learning ; Materials science ; Composite material Machine Learning in Materials Science
Authors: Abhishek Ojha | Satya Bulusu | Arup Banerjee
Source: Artificial Intelligence Chemistry (article, 2024-11-07)
Field: Materials Science — Materials Chemistry

Machine learning approaches for modelling of molecular polarizability in gold nanoclusters The polarizability of molecules describes their response to an external electric field. It quantifies the ability of a system to form an induced dipole moment when subjected to an electric field. In this work, we investigated isotropic polarizability and anisotropy in the polarizability of gold nanoclusters using various machine-learning algorithms. We utilized high-order invariant descriptors based on spherical harmonics, integrated with machine-learning models like artificial neural network, Gaussian process regression, and kernel ridge regression. Our results demonstrate the efficacy of machine-learning in accurately predicting the polarizability of gold nanoclusters. We find that ANN-based model performs better than the other models. • We used different ML models to predict the polarizability of gold nanoclusters. • ANN, GPR, and KRR models were compared, with ANN achieving the best performance. • Bispectrum and power spectrum-based descriptors were used as input to ML models. • Performance of ANN with Kalman filter optimizer was better than Adam optimizer. Nanoclusters ; Polarizability ; Nanotechnology ; Materials science ; Computer science ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Nanocluster Synthesis and Applications
Authors: Akram Ibrahim | Daniel Wines | Can Ataca
Source: The Journal of Physical Chemistry C (article, 2024-01-10)
Field: Materials Science — Materials Chemistry

Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations The chemical exfoliation of non-van der Waals (vdW) materials to ultrathin nanosheets remains a formidable challenge. This difficulty arises from the strong preference of these materials to engage in three-dimensional chemical bonding, resulting in uncontrolled atomic migration into the vdW gaps during cation deintercalation from the bulk structure, ultimately leading to unpredictable structural disorder. Computational models capable of comprehending the widespread nonstoichiometric local environments resulting from disordered atomic migrations during the exfoliation of non-vdW materials are crucial for understanding the underlying mechanisms. Here, we propose a generic framework using neural network potentials (NNPs) to accurately model nonstoichiometric systems over a broad range of vacancy concentrations. We apply our framework to investigate the crystal structures and phase transformations occurring during the exfoliation of non-vdW nonstoichiometric Cr(1–x)S systems, a compelling material category with substantial potential for two-dimensional (2D) magnetic applications. The efficacy of the NNP outperforms conventional cluster expansion, exhibiting superior accuracy and transferability to unexplored crystal structures and compositions. By employing the NNP in simulated annealing optimizations, we predict low-energy Cr(1–x)S structures anticipated to result from experimental synthesis. A notable structural transition is discerned at the Cr0.5S composition, with half of the Cr atoms preferentially migrating to vdW gaps. This aligns with experimental observations in the chemical exfoliation of 2D CrS2 and emphasizes the vital role of excess Cr atoms beyond the Cr/S = 1/2 composition ratio in stabilizing vdW gaps. Additionally, we utilize the NNP in a large-scale vacancy diffusion Monte Carlo simulation to illustrate the impact of lateral compressive strains in catalyzing the formation of vdW gaps within non-vdW CrS2 slabs through Poisson’s axial expansion. This provides a direct pathway for more facile exfoliation of ultrathin nanosheets from non-vdW materials through strain engineering. The implemented methodology, leveraging machine learning potentials, is imperative to bridge the quantum-level accuracy to large scales necessary for modeling the intricate mechanisms underlying the chemical exfoliation of non-vdW materials. van der Waals force ; Vacancy defect ; Chemical physics ; Materials science ; Crystal (programming language) ; Exfoliation joint ; Crystal structure ; Crystallography ; Nanotechnology ; Chemistry ; Molecule ; Computer science ; Graphene Machine Learning in Materials Science ; 2D Materials and Applications ; MXene and MAX Phase Materials