jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yan Liu | Yiming Mo | Youwei Cheng
Source: Journal of Chemical Information and Modeling (article, 2024-10-23)
Field: Materials Science — Materials Chemistry

Uncertainty Qualification for Deep Learning-Based Elementary Reaction Property Prediction The prediction of the thermodynamic and kinetic properties of elementary reactions has shown rapid improvement due to the implementation of deep learning (DL) methods. While various studies have reported the success in predicting reaction properties, the quantification of prediction uncertainty has seldom been investigated, thus compromising the confidence in using these predicted properties in practical applications. Here, we integrated graph convolutional neural networks (GCNN) with three uncertainty prediction techniques, including deep ensemble, Monte Carlo (MC)-dropout, and evidential learning, to provide insights into the uncertainty quantification and utility. The deep ensemble model outperforms others in accuracy and shows the highest reliability in estimating prediction uncertainty across all elementary reaction property data sets. We also verified that the deep ensemble model showed a satisfactory capability in recognizing epistemic and aleatoric uncertainties. Additionally, we adopted a Monte Carlo Tree Search method for extracting the explainable reaction substructures, providing a chemical explanation for DL predicted properties and corresponding uncertainties. Finally, to demonstrate the utility of uncertainty qualification in practical applications, we performed an uncertainty-guided calibration of the DL-constructed kinetic model, which achieved a 25% higher hit ratio in identifying dominant reaction pathways compared to that of the calibration without uncertainty guidance. Property (philosophy) ; Artificial intelligence ; Computer science ; Machine learning ; Mathematics education ; Econometrics ; Mathematics ; Epistemology ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Mohammed Mudabbiruddin | Amir Mosavi | Felde Imre
Source:  (article, 2024-04-04)
Field: Materials Science — Materials Chemistry

From Deep Learning to ChatGPT for Materials Design Large language models (LLMs) provide competitive advantages to various fields of research. This survey explores the journey from using deep learning to adopting ChatGPT in materials design. It covers how methodologies have shifted, moving away from traditional deep learning models to embracing LLMs such as ChatGPT, in advancing materials design research. The focus is on methods and applications, highlighting how LLMs are shaping the landscape of materials design. The survey aims to provide insights into the essential role of ChatGPT in this domain, offering a comprehensive view of its methods and diverse applications. Computer science ; Artificial intelligence Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies
Authors: Jian-Gang Kong | Ke-Lin Zhao | Jian Li | Qingxu Li | Yu Liu | Rui Zhang | Jia-Ji Zhu | Kai Chang
Source: Machine Learning Science and Technology (article, 2024-07-09)
Field: Materials Science — Materials Chemistry

Self-supervised representations and node embedding graph neural networks for accurate and multi-scale analysis of materials Abstract Supervised machine learning algorithms, such as graph neural networks (GNN), have successfully predicted material properties. However, the superior performance of GNN usually relies on end-to-end learning on large material datasets, which may lose the physical insight of multi-scale information about materials. And the process of labeling data consumes many resources and inevitably introduces errors, which constrains the accuracy of prediction. We propose to train the GNN model by self-supervised learning on the node and edge information of the crystal graph. Compared with the popular manually constructed material descriptors, the self-supervised atomic representation can reach better prediction performance on material properties. Furthermore, it may provide physical insights by tuning the range information. Applying the self-supervised atomic representation on the magnetic moment datasets, we show how they can extract rules and information from the magnetic materials. To incorporate rich physical information into the GNN model, we develop the node embedding graph neural networks (NEGNN) framework and show significant improvements in the prediction performance. The self-supervised material representation and the NEGNN framework may investigate in-depth information from materials and can be applied to small datasets with increased prediction accuracy. Computer science ; Embedding ; Graph ; Scale (ratio) ; Node (physics) ; Graph embedding ; Artificial neural network ; Artificial intelligence ; Theoretical computer science ; Engineering ; Geography ; Cartography Machine Learning in Materials Science
Authors: Gianmarco Ricci | M. D’Andrea | Mario Di Castro | Eloise Matheson | Daniele Mirarchi | A. Mostacci | Stefano Redaelli
Source: Physical Review Accelerators and Beams (article, 2024-09-24)
Field: Materials Science — Materials Chemistry

Machine learning based crystal collimator alignment optimization In the CERN Large Hadron Collider (LHC), bent crystals are used to efficiently deflect beam halo particles toward secondary collimators used as absorbers. In this crystal collimation scheme, a crystal with a length of a few millimeters can produce a deflection equivalent to a magnetic field of hundreds of Tesla at LHC top energies, improving the cleaning performance of the machine. However, crystals must be in optimal alignment with respect to the circulating beam to maximize the efficiency of the channeling process. A newly developed machine learning model automatically classifies the channeling condition of crystals using beam loss monitor signals during slow rotation of the crystal. This advancement represents a crucial step toward refining the process of identifying the optimal channeling orientation. The algorithm has been tested for the fist time in operation with Pb ion beams at the record energy of 6.8 Z TeV demonstrating its reliability. Published by the American Physical Society 2024 Collimator ; Computer science ; Crystal (programming language) ; Artificial intelligence ; Optics ; Computer vision ; Physics ; Programming language X-ray Diffraction in Crystallography ; Advanced X-ray and CT Imaging ; Geochemistry and Geologic Mapping
Authors: Koki Nozawa | Takamitsu Ishiyama | Takashi Suemasu | Kaoru Toko
Source: Advanced Electronic Materials (article, 2024-04-09)
Field: Materials Science — Materials Chemistry

Prediction and Elucidation of Physical Properties of Polycrystalline Materials Using Multichannel Machine Learning of Electron Backscattering Diffraction Abstract The application of machine learning in materials science has yielded several benefits, including the prediction of physical properties and the improvement of experimental efficiency. However, with complex models, such as convolutional neural networks (CNN), learning has become a black box, from which no universal physical knowledge can be obtained. In this study, a highly accurate prediction of the electrical properties of polycrystalline semiconductor thin films is achieved by learning multichannel CNN models from electron backscattering diffraction (EBSD) data, that is band contrasts, grain boundaries, and inverse pole figures. In addition, it examines how the CNN model learned the correlation between the crystallinity, grain boundaries, crystallographic orientation, and carrier mobility by polarizing certain EBSD data and checking the predicted changes in carrier mobility. Physical parameters affecting carrier mobility can be extracted, which is challenging via human image recognition. The methods proposed in this study will not only enable the prediction of electrical properties from EBSD data for all materials but also will contribute to the discovery of complex physical phenomena beyond the limits of human analysis. Materials science ; Crystallite ; Diffraction ; Electron ; Electron backscatter diffraction ; Electron diffraction ; Engineering physics ; Nanotechnology ; Composite material ; Optics ; Metallurgy ; Microstructure ; Engineering ; Physics ; Nuclear physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Ion-surface interactions and analysis
Authors: Haoyu Dong | Zhiqiang Li | Baole Sun | Yanguang Zhou | Linhua Liu | Jia‐Yue Yang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Thermal Transport in Disordered Wurtzite Scaln Alloys Using Machine Learning Interatomic Potentials Wurtzite crystal structure ; Materials science ; Interatomic potential ; Thermal ; Chemical physics ; Condensed matter physics ; Chemistry ; Physics ; Computational chemistry ; Metallurgy ; Molecular dynamics ; Thermodynamics ; Zinc Thermal properties of materials ; Thermography and Photoacoustic Techniques ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Makito Takagi | Tomomi Shimazaki | Osamu Kobayashi | Takayoshi Ishimoto | Masanori Tachikawa
Source: Physical Chemistry Chemical Physics (article, 2024-12-27)
Field: Materials Science — Materials Chemistry

Theoretical and machine learning models for reaction-barrier predictions: acrylate and methacrylate radical reactions We propose density functional theory (DFT)- and random forest (RF)-based theoretical and machine learning (ML) models, respectively, for predicting reaction barriers (Δ<i>E</i><sub>TS</sub>) using acrylate and methacrylate radical reactions as representatives. DFT is used to determine 100 transition state (TS) structures of both radicals, after which the obtained data are used to determine theoretical relationships (explained with Bell-Evans-Polanyi or Brønsted-Evans-Polanyi (BEP) and Marcus-like models) between Δ<i>E</i><sub>TS</sub> and stabilization energy of the product. Next, we construct several theoretical regression models for predicting Δ<i>E</i><sub>TS</sub> of the representative reactions based on our theoretical analyses, presenting an RF-based ML model that eases Δ<i>E</i><sub>TS</sub> predictions by circumventing time-consuming DFT calculations. These theoretical and RF-based ML approaches will accelerate the advancement of material development. Methacrylate ; Acrylate ; Chemistry ; Polymer chemistry ; Polymer science ; Photochemistry ; Organic chemistry ; Polymer ; Polymerization ; Copolymer Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Zaid Abdulhamid Alhulaybi | Abdulrazak Jinadu Otaru
Source: Journal of Thermal Analysis and Calorimetry (article, 2024-11-18)
Field: Materials Science — Biomaterials

Mathematical formulation of the machine learning backpropagation network and regression modelling of the chemical stability and thermal properties of PLA/HKUST-1 fabricated porous membranes Porosity ; Backpropagation ; Stability (learning theory) ; Thermal stability ; Membrane ; Chemical stability ; Materials science ; Chemical engineering ; Artificial intelligence ; Biological system ; Composite material ; Artificial neural network ; Computer science ; Machine learning ; Chemistry ; Engineering biodegradable polymer synthesis and properties ; Recycling and Waste Management Techniques ; Extraction and Separation Processes
Authors: Lauren Way | Catalin D. Spataru | Reese Jones | Dallas R. Trinkle | Andrew J. E. Rowberg | Joel B. Varley | Robert B. Wexler | Christopher M. Smyth | Tyra Douglas | Sean R. Bishop | Elliot J. Fuller | Anthony H. McDaniel | Stephan Lany | Matthew Witman
Source: ChemRxiv (preprint, 2024-08-28)
Field: Materials Science — Materials Chemistry

Defect diffusion graph neural networks for materials discovery in high-temperature, clean energy applications The migration of crystallographic defects dictates material properties and performance for a plethora of technological applications. Density functional theory (DFT)-based nudged elastic band (NEB) calculations are a powerful computational technique for predicting defect migration activation energy barriers, yet they become prohibitively expensive for high-throughput screening of defect diffusivities. Without introducing hand-crafted (i.e., chemistry- or structure-specific) descriptors, we propose a generalized deep learning approach to train surrogate models for NEB energies of vacancy migration by hybridizing graph neural networks with transformer encoders and simply using pristine host structures as input. With sufficient training data, computationally efficient and simultaneous inference of vacancy defect thermodynamics and migration activation energies can be obtained to compute temperature-dependent vacancy diffusivities and to down-select candidates for more thorough DFT analysis or experiments. Thus, as we specifically demonstrate for potential water-splitting materials, candidates with desired defect thermodynamics, kinetics, and host stability properties can be more rapidly targeted from open-source databases of experimentally validated or hypothetical materials. Vacancy defect ; Inference ; Density functional theory ; Computer science ; Statistical physics ; Graph ; Artificial neural network ; Materials science ; Diffusion ; Biological system ; Algorithm ; Theoretical computer science ; Physics ; Thermodynamics ; Artificial intelligence ; Chemistry ; Computational chemistry ; Condensed matter physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Yiyu Liu | Zhizhen Mu | Pengyu Hong | Yun Yang | Changxu Lin
Source: Nanoscale (article, 2024-11-29)
Field: Materials Science — Materials Chemistry

Feature mining for thermoelectric materials based on interpretable machine learning In previous laboratory preparation processes, the selection and proportioning of specific experimental parameters often stemmed from the empirical experience of predecessors, necessitating the derivation of the optimal scheme for the target experiment through extensive trial and error. This process typically required the consumption of substantial resources and time. Thanks to the advancement of machine learning technologies, these have gradually become a powerful tool for addressing complex functional problems in material optimization. This study is based on a collected database of thermoelectric materials, aiming to clarify the correspondence between physical characteristics and the thermoelectric figure of merit, <i>zT</i>. The key features that can directly affect the experimental results are identified, and the features that indirectly affect the experimental results are also analyzed. By employing interpretable machine learning methods, this research analyzes critical molecular features in the characterized data, utilizing feature engineering techniques to construct and optimize machine learning models for the selected key features. Furthermore, in the subsequent model fitting and analysis phase, by comparing the efficiency of different feature combinations, the optimal feature descriptors for the current experimental system were determined. The adoption of the aforementioned improved methods endowed the related models with the potential for high-throughput screening, thereby further enhancing the efficiency of experimental optimization. Scheme (mathematics) ; Feature selection ; Feature (linguistics) ; Selection (genetic algorithm) ; Computer science ; Artificial intelligence ; Machine learning ; Thermoelectric effect ; Data mining ; Mathematics ; Physics Machine Learning in Materials Science
Authors: Jia Zhao | Taixi Feng | Guimin Lu
Source: Journal of Materials Science (article, 2024-07-16)
Field: Materials Science — Materials Chemistry

Atomistic simulations on liquid Mg–Sr alloys assisted with deep learning potential Materials science ; Solid mechanics ; Mineralogy ; Nanotechnology ; Thermodynamics ; Chemical engineering ; Composite material Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Fatemeh Shahbazi | Mohammad Nasr Esfahani | Amir Keshmiri | Masoud Jabbari
Source: Scientific Reports (article, 2024-09-03)
Field: Materials Science — Materials Chemistry

Assessment of machine learning models trained by molecular dynamics simulations results for inferring ethanol adsorption on an aluminium surface Molecular dynamics (MD) simulations can reduce our need for experimental tests and provide detailed insight into the chemical reactions and binding kinetics. There are two challenges while dealing with MD simulations: one is the time and length scale limitations, and the latter is efficiently processing the massive amount of data resulting from the MD simulations and generating the proper reaction rates. In this work, we evaluated the use of regression machine learning (ML) methods to solve these two challenges by developing a framework for ethanol adsorption on an Aluminium (Al) slab. This framework comprises three main stages: first, an all-atom molecular dynamics model; second, ML regression models; and third, validation and testing. In stage one, the adsorption of ethanol molecules on the Al surface for various temperatures, velocities and concentrations is simulated using the large-scale atomic/molecular massively parallel simulator (LAMMPS) and ReaxFF. The outcome of stage one is utilised for training, testing, and validating the predictive models in stages two and three. We developed and evaluated 28 different ML models for predicting the number of adsorbed molecules over time, including linear regression, support vector machine (SVM), decision trees, ensemble, Gaussian process regression (GPR), neural network (NN) and Bayesian hyper-parameter optimisation models. Based on the results, the Bayesian-based GPR showed the highest accuracy and the lowest training time. The developed model can predict the number of adsorbed molecules for new cases within seconds, while MD simulations take a few weeks. This adsorption rate can then be used in macroscale simulations to tackle the time and length scale limitations. The proposed numerical framework has the potential to be generalised and, therefore, contribute to future low-cost binding reaction estimations, providing a valuable tool for industry and experimentalists. Molecular dynamics ; Kriging ; Support vector machine ; Computer science ; Adsorption ; Machine learning ; Artificial intelligence ; Artificial neural network ; Molecular descriptor ; Biological system ; Chemistry ; Computational chemistry ; Quantitative structure–activity relationship ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Crystallization and Solubility Studies
Authors: Patthranit Wongpromrat | Rathachai Chawuthai | Teeratat Promchan | Jularak Rojsanga | Somrerk Chandra-ambhorn | Thanasak Nilsonthi | Amata Anantpinijwatna
Source: Research Square (preprint, 2024-03-20)
Field: Materials Science — Materials Chemistry

Novel method for predicting the cracks of oxide scales during high temperature oxidation of metals and alloys by using machine learning <title>Abstract</title> In high temperature processes, the degradation of the materials is the major problem that may lead to higher maintenance costs and accidents. One form of the failure is the crack and spallation of the protective oxide film due to either the mechanical stress developing during the oxidation process or the thermal stress resulting from the mismatch of thermal expansions of oxide formed and alloy. Conventionally, the Pilling-Bedworth ratio (PBR) was used for predicting the crack and spallation of oxide by determining the volume changes of oxide and alloy. Although, the PBR is simple but it gives the inaccurate predictions. Therefore, in this work, the machine learning was used instead of PBR for predicting the formed of oxide produced and its spallation in the temperature range of 600–1200 <sup>o</sup>C. The alloy compositions, the oxide formed during oxidation, oxidation conditions and periods were inserted in the model as the inputs. From the results, the random forest with 15 estimators was the best machine learning model providing more than twice accuracy for predicting oxide spallation than using PBR. Oxide ; Materials science ; Metallurgy Nuclear Materials and Properties ; Metallurgical Processes and Thermodynamics ; High-Temperature Coating Behaviors
Authors: Zhengda Li | Xuyang Chen | Yang Wang
Source: Physical Chemistry Chemical Physics (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Deep learning-driven prediction of chemical addition patterns for carboncones and fullerenes Carboncones and fullerenes are exemplary π-conjugated carbon nanomaterials with unsaturated, positively curved surfaces, enabling the attachment of atoms or functional groups to enhance their physicochemical properties. However, predicting and understanding the addition patterns in functionalized carboncones and fullerenes are extremely challenging due to the formidable complexity of the regioselectivity exhibited in the adducts. Existing predictive models fall short in systems where the carbon molecular framework undergoes severe distortion upon high degrees of addition. Here, we propose an incremental deep learning approach to predict regioselectivity in the hydrogenation of carboncones and chlorination of fullerenes. Utilizing exclusively graph-based features, our deep neural network (DNN) models rely solely on atomic connectivity, without requiring 3D molecular coordinates as input or their iterative optimization. This advantage inherently avoids the risk of obtaining chemically unreasonable optimized structures, enabling the handling of highly distorted adducts. The DNN models allow us to study regioselectivity in hydrogenated carboncones of C<sub>70</sub>H<sub>20</sub> and C<sub>62</sub>H<sub>16</sub>, accommodating up to at least 40 and 30 additional H atoms, respectively. Our approach also correctly predicts experimental addition patterns in C<sub>50</sub>Cl<sub>10</sub> and C<sub>76</sub>Cl<sub><i>n</i></sub> (<i>n</i> = 18, 24, and 28), whereas in the latter cases all other known methods have been proven unsuccessful. Compared to our previously developed topology-based models, the DNN's superior predictive power and generalization ability make it a promising tool for investigating complex addition patterns in similar chemical systems. Fullerene ; Nanomaterials ; Conjugated system ; Carbon fibers ; Nanotechnology ; Materials science ; Chemical physics ; Computational chemistry ; Chemistry ; Organic chemistry ; Composite number ; Polymer ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fullerene Chemistry and Applications