jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haitao Yang | Ruiqi Hu | Heng Wu | Xiaolong He | Yan Zhou | Yizhe Xue | Kexin He | Wenshuai Hu | Haosen Chen | Mingming Gong | Xin Zhang | Ping‐Heng Tan | Eduardo Hernández | Yong Xie
Source: Nano Letters (article, 2024-02-26)
Field: Materials Science — Materials Chemistry

Identification and Structural Characterization of Twisted Atomically Thin Bilayer Materials by Deep Learning Two-dimensional materials are expected to play an important role in next-generation electronics and optoelectronic devices. Recently, twisted bilayer graphene and transition metal dichalcogenides have attracted significant attention due to their unique physical properties and potential applications. In this study, we describe the use of optical microscopy to collect the color space of chemical vapor deposition (CVD) of molybdenum disulfide (MoS<sub>2</sub>) and the application of a semantic segmentation convolutional neural network (CNN) to accurately and rapidly identify thicknesses of MoS<sub>2</sub> flakes. A second CNN model is trained to provide precise predictions on the twist angle of CVD-grown bilayer flakes. This model harnessed a data set comprising over 10,000 synthetic images, encompassing geometries spanning from hexagonal to triangular shapes. Subsequent validation of the deep learning predictions on twist angles was executed through the second harmonic generation and Raman spectroscopy. Our results introduce a scalable methodology for automated inspection of twisted atomically thin CVD-grown bilayers. Characterization (materials science) ; Bilayer ; Nanotechnology ; Materials science ; Identification (biology) ; Chemistry ; Membrane Electronic and Structural Properties of Oxides ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Vivin Vinod | Ulrich Kleinekathöfer | Peter Zaspel
Source: Machine Learning Science and Technology (article, 2024-02-26)
Field: Materials Science — Materials Chemistry

Optimized multifidelity machine learning for quantum chemistry Abstract Machine learning (ML) provides access to fast and accurate quantum chemistry (QC) calculations for various properties of interest such as excitation energies. It is often the case that high accuracy in prediction using a ML model, demands a large and costly training set. Various solutions and procedures have been presented to reduce this cost. These include methods such as Δ-ML, hierarchical-ML, and multifidelity machine learning (MFML). MFML combines various Δ-ML like sub-models for various fidelities according to a fixed scheme derived from the sparse grid combination technique. In this work we implement an optimization procedure to combine multifidelity models in a flexible scheme resulting in optimized MFML (o-MFML) that provides superior prediction capabilities. This hyperparameter optimization is carried out on a holdout validation set of the property of interest. This work benchmarks the o-MFML method in predicting the atomization energies on the QM7b dataset, and again in the prediction of excitation energies for three molecules of growing size. The results indicate that o-MFML is a strong methodological improvement over MFML and provides lower error of prediction. Even in cases of poor data distributions and lack of clear hierarchies among the fidelities, which were previously identified as issues for multifidelity methods, the o-MFML is advantageous for the prediction of quantum chemical properties. Quantum machine learning ; Computer science ; Quantum chemistry ; Quantum ; Artificial intelligence ; Chemistry ; Machine learning ; Physics ; Quantum computer ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Luhan Dai | Yulong Fu | Mengran Wei | Fangyuan Wang | Bailin Tian | Guoqiang Wang | Shuhua Li | Mengning Ding
Source: Journal of the American Chemical Society (article, 2024-07-04)
Field: Chemistry — Electrochemistry

Harnessing Electro-Descriptors for Mechanistic and Machine Learning Analysis of Photocatalytic Organic Reactions Photocatalysis has emerged as an effective tool for addressing the contemporary challenges in organic synthesis. However, the trial-and-error-based screening of feasible substrates and optimal reaction conditions remains time-consuming and potentially expensive in industrial practice. Here, we demonstrate an electrochemical-based data-acquisition approach that derives a simple set of redox-relevant electro-descriptors for effective mechanistic analysis and performance evaluation through machine learning (ML) in photocatalytic synthesis. These electro-descriptors correlate to the quantification of shifted charge transfer processes in response to the photoirradiation and enabled construction of reactivity diagram where high-yield reactive "hot zones" can reflect subtle changes of the reaction system. For the model reaction of photocatalytic deoxygenation reaction, the influence of varying carboxylic acids (substrate A, oxidation-intended) and alkenes (substrate B, reduction-intended) and varying reaction conditions on the reaction yield can be visualized, while mathematical analysis of the electro-descriptor patterns further revealed distinct mechanistic/kinetic impacts from different substrates and conditions. Additionally, in the application of ML algorithms, the experimentally derived electro-descriptors reflect an overall redox kinetic outcome contributed from vast reaction parameters, serving as a capable means to reduce the dimensionality in the case of complex multiparameter chemical space. As a result, utilization of electro-descriptors enabled efficient and robust quantitative evaluation of chemical reactivity, demonstrating promising potential of introducing operando-relevant experimental insights in the data-driven chemistry. Chemistry ; Photocatalysis ; Reactivity (psychology) ; Redox ; Yield (engineering) ; Biological system ; Substrate (aquarium) ; Biochemical engineering ; Combinatorial chemistry ; Catalysis ; Organic chemistry ; Thermodynamics Electrochemical Analysis and Applications ; Radical Photochemical Reactions ; Advanced Chemical Sensor Technologies
Authors: Teng Long | Li Jia | Chenlu Wang | Hua Wang | Xiao Cheng | Haifeng Lu | Ying Zhang | Chuanjian Zhou
Source: Polymer (article, 2024-07-17)
Field: Materials Science — Materials Chemistry

Polymers simulation using machine learning interatomic potentials Polymer ; Interatomic potential ; Computer science ; Materials science ; Statistical physics ; Chemical physics ; Polymer science ; Molecular dynamics ; Chemistry ; Computational chemistry ; Physics ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Yang Hu | Roland Sandt | Robert Spatschek
Source: Scientific Reports (article, 2024-09-03)
Field: Materials Science — Materials Chemistry

Practical feature filter strategy to machine learning for small datasets in chemistry Many potential use cases for machine learning in chemistry and materials science suffer from small dataset sizes, which demands special care for the model design in order to deliver reliable predictions. Hence, feature selection as the key determinant for dataset design is essential here. We propose a practical and efficient feature filter strategy to determine the best input feature candidates. We illustrate this strategy for the prediction of adsorption energies based on a public dataset and sublimation enthalpies using an in-house training dataset. The input of adsorption energies reduces the feature space from 12 dimensions to two and still delivers accurate results. For the sublimation enthalpies, three input configurations are filtered from 14 possible configurations with different dimensions for further productive predictions as being most relevant by using our feature filter strategy. The best extreme gradient boosting regression model possesses a good performance and is evaluated from statistical and theoretical perspectives, reaching a level of accuracy comparable to density functional theory computations and allowing for physical interpretations of the predictions. Overall, the results indicate that the feature filter strategy can help interdisciplinary scientists without rich professional AI knowledge and limited computational resources to establish a reliable small training dataset first, which may make the final machine learning model training easier and more accurate, avoiding time-consuming hyperparameter explorations and improper feature selection. Feature (linguistics) ; Computer science ; Filter (signal processing) ; Artificial intelligence ; Machine learning ; Data science ; Computer vision Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics ; Mineral Processing and Grinding
Authors: Yuming Wen | Shule Wang | Libo Wu | Emmerson Hondo | Chuchu Tang | Jianchun Jiang | Ghim Wei Ho | Sibudjing Kawi | Chi‐Hwa Wang
Source: International Journal of Hydrogen Energy (article, 2024-05-31)
Field: Materials Science — Materials Chemistry

Exploring the role of process control and catalyst design in methane catalytic decomposition: A machine learning perspective Catalysis ; Decomposition ; Perspective (graphical) ; Methane ; Process (computing) ; Control (management) ; Process engineering ; Computer science ; Chemical engineering ; Chemistry ; Engineering ; Artificial intelligence ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Polina A. Leger | A. Ramesh | Talianna Ulloa | Yingying Wu
Source: Scientific Reports (article, 2024-11-13)
Field: Materials Science — Materials Chemistry

Machine learning enabled fast optical identification and characterization of 2D materials Two-dimensional materials are a class of atomically thin materials with assorted electronic and quantum properties. Accurate identification of layer thickness, especially for a single monolayer, is crucial for their characterization. This characterization process, however, is often time-consuming, requiring highly skilled researchers and expensive equipment like atomic force microscopy. This project aims to streamline the identification process by using machine learning to analyze optical images and quickly determine layer thickness. In this paper, we evaluate the performance of three machine learning models - SegNet, 1D U-Net, and 2D U-Net- in accurately identifying monolayers in microscopic images. Additionally, we explore labeling and image processing techniques to determine the most effective and accessible method for identifying layer thickness in this class of materials. Identification (biology) ; Computer science ; Characterization (materials science) ; Data science ; Artificial intelligence ; Computational biology ; Nanotechnology ; Materials science ; Biology 2D Materials and Applications ; Graphene research and applications ; Photonic and Optical Devices
Authors: Dongxu Jiao | Xinyi Li | Mingzi Sun | Lin Liu | Jinchang Fan | Jingxiang Zhao | Bolong Huang | Xiaoqiang Cui
Source: Nano Research (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Machine learning driven rational design of dual atom catalysts on graphene for carbon dioxide electroreduction The development of high-performance atomic catalysts for the carbon dioxide reduction reaction (CO<sub>2</sub>RR) is a time-consuming process due to the complexity of the reaction mechanism and the uncertainty of the active site. Herein, we have proposed combining density functional theory (DFT) and machine learning (ML) to investigate the potential of topological graphene-based dual-atom catalysts (DACs) as CO<sub>2</sub>RR electrocatalysts. By analyzing the ML results, we identify the number of d-orbital electrons in the active site as a key factor influencing the CO<sub>2</sub>RR catalytic activity. Additionally, we propose a simple descriptor to measure the CO<sub>2</sub>RR activity of these DACs. Our findings provide plausible explanations for the synergistic interactions between bimetallic atoms in CO<sub>2</sub>RR and allow us to screen the homogeneous Ni-Ni pair as the most promising dual-atom catalysts. This work offers a fast ML approach based on limited DFT calculations to predict the most electroactive and stable DACs on carbon support for CO<sub>2</sub>RR, facilitating rapid screening of high-performance dual-atom catalysts. Graphene ; Dual (grammatical number) ; Rational design ; Carbon dioxide ; Catalysis ; Atom (system on chip) ; Materials science ; Nanotechnology ; Carbon atom ; Chemical engineering ; Combinatorial chemistry ; Chemistry ; Organic chemistry ; Computer science ; Engineering ; Ring (chemistry) Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalytic Processes in Materials Science
Authors: Timothy Jinsoo Yoo | Eitan Hershkovitz | Yang Yang | Flávia da Cruz Gallo | Michele V. Manuel | Honggyu Kim
Source: npj Computational Materials (article, 2024-09-18)
Field: Materials Science — Materials Chemistry

Unsupervised machine learning and cepstral analysis with 4D-STEM for characterizing complex microstructures of metallic alloys Four-dimensional scanning transmission electron microscopy, coupled with a wide array of data analytics, has unveiled new insights into complex materials. Here, we introduce a straightforward unsupervised machine learning approach that entails dimensionality reduction and clustering with minimal hyperparameter tuning to semi-automatically identify unique coexisting structures in metallic alloys. Applying cepstral transformation to the original diffraction dataset improves this process by effectively isolating phase information from potential signal ambiguity caused by sample tilt and thickness variations, commonly observed in electron diffraction patterns. In a case study of a NiTiHfAl shape memory alloy, conventional scanning transmission electron microscopy imaging struggles to accurately identify a low-contrast precipitate at lower magnifications, posing challenges for microscale analyses. We find that our method efficiently separates multiple coherent structures while using objective means of determining hyperparameters. Furthermore, we demonstrate how the clustering result facilitates more robust strain mapping to provide immediate and quantitative structural insights. Microstructure ; Artificial intelligence ; Materials science ; Metal ; Computer science ; Unsupervised learning ; Metallurgy Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Microstructure and mechanical properties
Authors: Reham E. Kannouma | Ahmed M. Gab Allah | Amira H. Kamal | Mohamed A. Hammad | Fotouh R. Mansour
Source: Microchemical Journal (article, 2024-12-17)
Field: Materials Science — Materials Chemistry

Machine learning and genetic algorithm prediction of high quantum yield carbon quantum dots for chemical analysis Quantum dot ; Carbon fibers ; Yield (engineering) ; Carbon quantum dots ; Quantum yield ; Quantum chemical ; Quantum ; Algorithm ; Computer science ; Materials science ; Artificial intelligence ; Nanotechnology ; Physics ; Quantum mechanics ; Composite material ; Molecule Carbon and Quantum Dots Applications ; Advanced biosensing and bioanalysis techniques ; Nanocluster Synthesis and Applications
Authors: Yujiang Meng | Yaling Lin | Anqiang Zhang
Source: Macromolecules (article, 2024-11-26)
Field: Materials Science — Polymers and Plastics

Prediction and Explanation of Properties in Multicomponent Polyurethane Elastomers: Integrating Molecular Dynamics and Machine Learning Establishing quantitative connections among the chemical composition, molecular structure, and macroscopic properties of multicomponent polyurethane elastomers remains a challenging task. Molecular dynamics (MD) has been extensively utilized in the study of various materials and serves as a crucial tool for exploring the relationship between structure and properties. However, the intricate modeling process and lengthy computation times associated with the MD method complicate the attainment of complex combinatorial results for the various components of polyurethane elastomers. Machine learning (ML) offers a solution by integrating and analyzing existing data, along with the capability to predict new outcomes. Consequently, we combine MD and ML methods to conduct a comprehensive investigation of multicomponent polyurethane elastomers. MD simulations indicate the presence of various types of hydrogen bonds within the elastic matrix of polyurethane, and the strong hydrogen bonds formed in the hard segments significantly affect the tensile properties of material. While the incorporation of long molecular chains in the soft segments enhances the material’s flexibility, it simultaneously diminishes its tensile strength. Feature engineering techniques, including parametric representation and feature screening of the MD model, were employed to create a data set suitable for ML applications. The application of the interpretable ML method has demonstrated that the number of hydrogen bonds in the hard segment is regulated by the hydrogen bond donor and acceptor, while the rotatable bonds in the soft segment are the primary characteristics contributing to the material’s flexibility and are also key factors that regulate the number of free hydrogen bonds. This integration of MD and ML methods not only enhances predictive capabilities for novel polyurethane elastomers but also facilitates quantitative analysis of how microstructural characteristics affect macroscopic properties. Elastomer ; Molecular dynamics ; Polyurethane ; Polymer science ; Materials science ; Polymer chemistry ; Computer science ; Chemistry ; Composite material ; Computational chemistry Polymer composites and self-healing ; Machine Learning in Materials Science ; Polymer Nanocomposites and Properties
Authors: Tao Fan | Artem R. Oganov
Source: Journal of Materials Chemistry C (article, 2024-11-05)
Field: Materials Science — Materials Chemistry

Combining machine-learning models with first-principles high-throughput calculations to accelerate the search for promising thermoelectric materials From high-throughput screening of high-power-factor materials, through first-principles calculation of transport properties, to training machine-learning models for identifying good thermoelectric materials. Materials science ; Throughput ; Thermoelectric effect ; Nanotechnology ; Computer science ; Thermodynamics Machine Learning in Materials Science
Authors: Youjia Li | Vishu Gupta | Muhammed Nur Talha Kilic | Kamal Choudhary | Daniel Wines | Wei‐keng Liao | Alok Choudhary | Ankit Agrawal
Source: Digital Discovery (article, 2024-12-18)
Field: Materials Science — Materials Chemistry

Hybrid-LLM-GNN: integrating large language models and graph neural networks for enhanced materials property prediction This study combines Graph Neural Networks (GNNs) and Large Language Models (LLMs) to improve material property predictions. By leveraging both embeddings, this hybrid approach achieves up to a 25% improvement over GNN-only model in accuracy. Computer science ; Property (philosophy) ; Artificial neural network ; Graph ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Computational Drug Discovery Methods
Authors: Sukriti Singh | José Miguel Hernández-Lobato
Source: Communications Chemistry (article, 2024-06-14)
Field: Materials Science — Materials Chemistry

Deep Kernel learning for reaction outcome prediction and optimization Recent years have seen a rapid growth in the application of various machine learning methods for reaction outcome prediction. Deep learning models have gained popularity due to their ability to learn representations directly from the molecular structure. Gaussian processes (GPs), on the other hand, provide reliable uncertainty estimates but are unable to learn representations from the data. We combine the feature learning ability of neural networks (NNs) with uncertainty quantification of GPs in a deep kernel learning (DKL) framework to predict the reaction outcome. The DKL model is observed to obtain very good predictive performance across different input representations. It significantly outperforms standard GPs and provides comparable performance to graph neural networks, but with uncertainty estimation. Additionally, the uncertainty estimates on predictions provided by the DKL model facilitated its incorporation as a surrogate model for Bayesian optimization (BO). The proposed method, therefore, has a great potential towards accelerating reaction discovery by integrating accurate predictive models that provide reliable uncertainty estimates with BO. Artificial intelligence ; Machine learning ; Computer science ; Deep learning ; Bayesian optimization ; Artificial neural network ; Surrogate model ; Gaussian process ; Outcome (game theory) ; Global Positioning System ; Deep neural networks ; Kernel (algebra) ; Gaussian ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Kabiru Haruna | Tawfik A. Saleh | Abdulmajid Lawal
Source: Surfaces and Interfaces (article, 2024-09-06)
Field: Materials Science — Materials Chemistry

Acrylic acid modified indapamide-based polymer as an effective inhibitor against carbon steel corrosion in CO2-saturated NaCl with variable H2S levels: An electrochemical, weight loss and machine learning study Indapamide ; Materials science ; Corrosion ; Electrochemistry ; Carbon steel ; Polymer ; Corrosion inhibitor ; Acrylic acid ; Chemical engineering ; Metallurgy ; Nuclear chemistry ; Composite material ; Electrode ; Copolymer ; Blood pressure ; Internal medicine Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Gas Sensing Nanomaterials and Sensors
Authors: Zhuo Li | Changquan Zhao | Hai‐Kun Wang | Yingfei Ding | Yechao Chen | Philippe Schwaller | Ke Yang | Hua Cheng | Yulian He
Source: Proceedings of the National Academy of Sciences (article, 2024-03-13)
Field: Materials Science — Materials Chemistry

Interpreting chemisorption strength with AutoML-based feature deletion experiments The chemisorption energy of reactants on a catalyst surface, [Formula: see text], is among the most informative characteristics of understanding and pinpointing the optimal catalyst. The intrinsic complexity of catalyst surfaces and chemisorption reactions presents significant difficulties in identifying the pivotal physical quantities determining [Formula: see text]. In response to this, the study proposes a methodology, the feature deletion experiment, based on Automatic Machine Learning (AutoML) for knowledge extraction from a high-throughput density functional theory (DFT) database. The study reveals that, for binary alloy surfaces, the local adsorption site geometric information is the primary physical quantity determining [Formula: see text], compared to the electronic and physiochemical properties of the catalyst alloys. By integrating the feature deletion experiment with instance-wise variable selection (INVASE), a neural network-based explainable AI (XAI) tool, we established the best-performing feature set containing 21 intrinsic, non-DFT computed properties, achieving an MAE of 0.23 eV across a periodic table-wide chemical space involving more than 1,600 types of alloys surfaces and 8,400 chemisorption reactions. This study demonstrates the stability, consistency, and potential of AutoML-based feature deletion experiment in developing concise, predictive, and theoretically meaningful models for complex chemical problems with minimal human intervention. Algorithm ; Artificial intelligence ; Computer science ; Chemisorption ; Machine learning ; Feature (linguistics) ; Database ; Adsorption ; Chemistry ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science