jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhecheng Fang | Sifan Wang | Haoan Fan | Xuezhi Zhao | Huiping Ji | Bolong Li | Zhenyu Zhang | Jianghao Wang | Kaige Wang | Weiyu Song | Reinout Meijboom | Jie Fu
Source: Applied Catalysis A General (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

Machine-learning-assisted catalytic performance predictions of binary alloy catalysts for glucose hydrogenation Sorbitol production involves glucose hydrogenation, which requires co-adsorption of glucose and hydrogen on the catalyst surface for high catalytic performance. Binary alloy catalysts can modulate substrate adsorption to achieve better catalytic performance than Raney Ni. Herein, we established a pioneering DFT/ML approach to investigate the adsorption energies of glucose (ΔE GCHO ) and H atoms (ΔE H ) on 1155 binary alloy catalysts. The Light Gradient Boosting Machine (LGBM) algorithm proved the most effective ML model, predicting ΔE GCHO and ΔE H with R² values of 0.785 and 0.636, respectively. Microkinetic simulation demonstrated a correlation between catalytic activity and adsorption energy, revealing high-performance catalyst screening criteria as ΔE GCHO = −1.45 to −0.65 eV and ΔE H = −0.55–0.00 eV. Nine possible binary alloy catalysts with high predicted activity were identified, with Pd 3 Mg performing best. The present study highlights the potential of the DFT/ML-assisted approach in the development of efficient glucose hydrogenation binary alloy catalysts. • A DFT-based machine learning approach was established to investigate ΔE ads of glucose and H atom on bimetallic catalysts. • Microkinetic simulation was used to identify screening criteria and 9 high-performance bimetallic catalysts were found. • Electronic analysis showed a positive correlation between Pd’s D-band center and glucose's ΔE ads on Pd-based catalysts. Chemistry ; Catalysis ; Alloy ; Binary number ; Chemical engineering ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Asymmetric Hydrogenation and Catalysis
Authors: Russlan Jaafreh | Santiago Pereznieto | Seonghun Jeong | I Putu Widiantara | Jeong Moo Oh | Jee-Hyun Kang | Junyoung Mun | Young Gun Ko | Jung‐Gu Kim | Kotiba Hamad
Source: International Journal of Energy Research (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Phonon DOS‐Based Machine Learning Model for Designing High‐Performance Solid Electrolytes in Li‐Ion Batteries In the present work, a machine learning (ML) model was built to design solid electrolytes with improved ionic conductivity for Li‐ion batteries (LIBs), and the model was based on the phonon density of states (PhDOS). Compounds with PhDOS calculations were collected from the Materials Project (MP) and processed to obtain frequencies and PhDOS to calculate the total phonon band center, a proxy for ionic conductivity. Total phonon band centers were involved in a learning process using four ML algorithms (extra random trees (XT), gradient boosting (GB), extreme gradient boosting (XGB), and decision trees (DT)). The cross‐validation results from the algorithms showed that the performance of the XT‐model was superior and confirmed through density functional theory‐ (DFT‐) based phonon calculations conducted on LiYO 3 , Li 4 CO 4 , LiNiO 3 , LiGeO 3 , and LiSiO 3 . The XT‐model was then used to predict the total phonon band centers of new compounds where these had no phonon calculations beforehand. Experimental validation of the XT‐model involved electrochemical impedance spectroscopy (EIS) measurements on two compounds: Li 2 CO 3 from the high range and Li 6 PS 5 Cl from the low range. Additionally, LiBiO 2 that is predicted to have low total phonon band center, according to the XT‐model, was considered further to estimate its potential as solid electrolyte in LIBs. Phonon ; Electrolyte ; Ion ; Materials science ; Fast ion conductor ; Engineering physics ; Computer science ; Engineering ; Physics ; Condensed matter physics ; Chemistry ; Physical chemistry ; Electrode Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Jonathan Schmidt | Tiago F. T. Cerqueira | A. Romero | Antoine Loew | Fabian Jäger | Haichen Wang | Silvana Botti | Miguel A. L. Marques
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Improving Machine-Learning Models in Materials Science Through Large Datasets Computer science ; Machine learning ; Artificial intelligence ; Data science Machine Learning in Materials Science
Authors: Liang Sun | Mohan Chen
Source: Electronic Structure (preprint, 2024-10-25)
Field: Materials Science — Materials Chemistry

Multi-channel machine learning based nonlocal kinetic energy density functional for semiconductors Abstract The recently proposed machine learning-based physically-constrained nonlocal (MPN) kinetic energy density functional (KEDF) can be used for simple metals and their alloys (Sun and Chen 2024 Phys. Rev. B 109 115135). However, the MPN KEDF does not perform well for semiconductors. Here we propose a multi-channel MPN (CPN) KEDF, which extends the MPN KEDF to semiconductors by integrating information collected from multiple channels, with each channel featuring a specific length scale in real space. The CPN KEDF is systematically tested on silicon and binary semiconductors. We find that the multi-channel design for KEDF is beneficial for machine-learning-based models in capturing the characteristics of semiconductors, particularly in handling covalent bonds. In particular, the CPN 5 KEDF, which utilizes five channels, demonstrates excellent accuracy across all tested systems. These results offer a new path for generating KEDFs for semiconductors. Kinetic energy ; Semiconductor ; Channel (broadcasting) ; Energy (signal processing) ; Computer science ; Statistical physics ; Physics ; Classical mechanics ; Quantum mechanics ; Telecommunications Machine Learning in Materials Science
Authors: Erencan Oranli | Chenbin Ma | Nahsan Gungoren | Asghar Heydari Astaraee | Sara Bagherifard | Mario Guagliano
Source: Applied Surface Science Advances (article, 2024-08-28)
Field: Materials Science — Surfaces, Coatings and Films

Sand blasting for hydrophobic surface generation in polymers: Experimental and machine learning approaches Wettability is a crucial surface feature of polymers due to their numerous interaction-destined applications. This study focuses on the application of sand blasting process for investigating the wettability of polymeric materials to produce hydrophobic behavior. Four different polymeric materials, Acrylonitrile Butadiene Styrene (ABS), Poly(methyl methacrylate) (PMMA), Polypropylene (PP), and Polycarbonate (PC) underwent sand blasting with varying process parameters, following a comprehensive plan for the design of experiments. Subsequent analyses included surface roughness measurement and wettability tests, supplemented by scanning electron and confocal microscopy observations to gain deeper insights into the blasted surfaces. A predictive model based on a machine learning algorithm was developed using the backpropagation technique to correlate the surface treatment parameters to surface roughness and wettability indexes. From the experimental results sand blasting proved to be efficient in creating hydrophobic surfaces on all the tested materials. The developed neural network demonstrated high fitting degrees between the predicted and measured values. ABS exhibited the most hydrophobic behavior and emerged as a strong candidate for further investigations. Rock blasting ; Polymer ; Surface (topology) ; Materials science ; Composite material ; Engineering ; Geotechnical engineering ; Mathematics ; Geometry Surface Modification and Superhydrophobicity ; Fluid Dynamics and Heat Transfer ; Adhesion, Friction, and Surface Interactions
Authors: Yuanbin Liu | Xin Liu | Bing Cao
Source: Journal of Physics Condensed Matter (article, 2024-02-02)
Field: Materials Science — Materials Chemistry

Graph attention neural networks for mapping materials and molecules beyond short-range interatomic correlations Bringing advances in machine learning to chemical science is leading to a revolutionary change in the way of accelerating materials discovery and atomic-scale simulations. Currently, most successful machine learning schemes can be largely traced to the use of localized atomic environments in the structural representation of materials and molecules. However, this may undermine the reliability of machine learning models for mapping complex systems and describing long-range physical effects because of the lack of non-local correlations between atoms. To overcome such limitations, here we report a graph attention neural network as a unified framework to map materials and molecules into a generalizable and interpretable representation that combines local and non-local information of atomic environments from multiple scales. As an exemplary study, our model is applied to predict the electronic structure properties of metal-organic frameworks (MOFs) which have notable diversity in compositions and structures. The results show that our model achieves the state-of-the-art performance. The clustering analysis further demonstrates that our model enables high-level identification of MOFs with spatial and chemical resolution, which would facilitate the rational design of promising reticular materials. Furthermore, the application of our model in predicting the heat capacity of complex nanoporous materials, a critical property in a carbon capture process, showcases its versatility and accuracy in handling diverse physical properties beyond electronic structures. Computer science ; Artificial neural network ; Nanoporous ; Representation (politics) ; Graph ; Artificial intelligence ; Cluster analysis ; Machine learning ; Theoretical computer science ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Xiang He
Source: Langmuir (article, 2024-07-13)
Field: Materials Science — Materials Chemistry

Correlating Nitrate Adsorption with the Local Environments of FeCoNiCuZn High-Entropy Alloy Catalysts Using Machine Learning The electrocatalytic nitrate reduction to ammonia holds significant values for water remediations and energy applications, which quests for the development of highly effective catalysts with considerable stability and selectivity. Recently, high-entropy alloys (HEAs) are attracting growing attention for electrocatalytic processes. Nonetheless, studies of HEA-based nitrate reduction to ammonia are still at the early stage, and it remains unclear how the HEA compositions affect the adsorption and activation of the reaction intermediates. Herein, high-throughput density functional theory (DFT) calculations were integrated with machine learning to investigate the dependence of nitrate adsorption on the FeCoNiCuZn HEA structures. In particular, a total of 1268 different structures were sampled and constructed from the multidimensional configuration space, followed by the DFT calculations to investigate the Gibbs free energy of nitrate adsorption (i.e., Δ<i>G</i><sub>NO<sub>3</sub></sub>) on different surface microstructures. Four regression models were successfully developed, which can accurately predict Δ<i>G</i><sub>NO<sub>3</sub></sub> using the HEA structures as the input features. Through the analysis of the feature importance, it was found that the active sites are crucial for nitrate adsorption; meanwhile, the local environments also play a considerable role. The dependence of the Δ<i>G</i><sub>NO<sub>3</sub></sub> and adsorption geometries on the HEA compositions demonstrates that the compositional modulation of the HEA catalysts could be a promising avenue for facile adsorption and activation of reaction intermediates. Overall, this work will contribute to the probabilistic optimization of the HEA microstructures for enhanced electrochemical nitrate reduction. Adsorption ; Alloy ; Catalysis ; Nitrate ; Entropy (arrow of time) ; Materials science ; Chemical engineering ; Chemistry ; Metallurgy ; Thermodynamics ; Physical chemistry ; Engineering ; Organic chemistry ; Physics Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Electrocatalysts for Energy Conversion
Authors: Ariel Yi Hui Chua | Eunice W. Q. Yeap | David M. Walker | Joel M. Hawkins | Saif A. Khan
Source: Crystal Growth & Design (article, 2024-02-26)
Field: Materials Science — Materials Chemistry

Harnessing Birefringence for Real-Time Classification of Molecular Crystals Using Dynamic Polarized Light Microscopy, Microfluidics, and Machine Learning Molecular crystals are ubiquitous in a variety of industrial contexts, from foods to chemicals and pharmaceuticals. The timely identification of different molecular crystal forms (and transformations between forms) is critical in both manufacturing and chemical/pharmaceutical product design, as they possess different physicochemical properties (e.g., solubility, melting and boiling point, etc.) that could affect product attributes such as stability and dissolution rate. Current characterization methods typically involve a time delay between sampling and analysis and are unable to directly quantify forms/transformations in crystal ensembles at a single crystal level in real time. Here, we introduce a new methodology to accomplish such measurements, which utilizes a combination of microfluidic flow cells, machine learning, and a rotating polarizer–analyzer pair with orthogonally aligned polarization axes for imaging and automated access to interference colors of birefringent molecular crystals that are characteristic of the polymorphic form. Since the polarized light microscopy images of the crystal ensembles captured represent their instantaneous states at the time of acquisition, the methodology uniquely enables real-time, in situ quantification of polymorphically mixed pharmaceutical crystals in both static (polymorph or pseudopolymorph mixtures) and dynamic crystallization systems (e.g., solution mediated phase transformations). The classification of crystal ensembles (∼3000 crystals classified in under 10 s) at a single crystal level can be achieved with an accuracy of ∼86% (azithromycin dihydrate and azithromycin sesquihydrate) to 94% (α-glycine and β-glycine). This sheds quantitative insights into the dominant crystallization phenomena such as nucleation, growth, or dissolution, potentially enabling both process monitoring as well as extraction of crucial kinetics data needed for crystallization process modeling and control. We envision the applicability of this methodology in accelerating the exploration of storage, process condition, or additive dependent polymorphic form outcomes that are of interest during early stage research and development when limited quantities of materials are available. Crystallization ; Microfluidics ; Birefringence ; Dissolution ; Crystal (programming language) ; Polarized light microscopy ; Materials science ; Process analytical technology ; Nanotechnology ; Biological system ; Chemistry ; Computer science ; Optics ; Chemical engineering ; Organic chemistry ; Physics Crystallization and Solubility Studies ; Crystallography and molecular interactions ; Enzyme Structure and Function
Authors: Harikrishnan Sibi | Jovita Biju | Chandra Chowdhury
Source: RSC Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Advancing 2D material predictions: superior work function estimation with atomistic line graph neural networks Despite the increased research and scholarly attention on two-dimensional (2D) materials, there is still a limited range of practical applications for these materials. This is because it is challenging to acquire properties that are usually obtained by experiments or first-principles predictions, which require substantial time and resources. Descriptor-based machine learning models frequently require further density functional theory (DFT) calculations to enhance prediction accuracy due to the intricate nature of the systems and the constraints of the descriptors employed. Unlike these models, research has demonstrated that graph neural networks (GNNs), which solely rely on the systems' coordinates for model description, greatly improve the ability to represent and simulate atomistic materials. Within this framework, we employed the Atomistic Line Graph Neural Network (ALIGNN) to predict the work function, a crucial material characteristic, for a diverse array of 2D materials sourced from the Computational 2D Materials Database (C2DB). We found that the ALIGNN algorithm shows superior performance compared to standard feature-based approaches. It attained a mean absolute error of 0.20 eV, whereas random forest models achieved 0.27 eV. Artificial neural network ; Graph ; Computer science ; Work (physics) ; Line (geometry) ; Function (biology) ; Biological system ; Materials science ; Theoretical computer science ; Artificial intelligence ; Mathematics ; Mechanical engineering ; Engineering Graphene research and applications ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Kajjana Boonpalit | Jiramet Kinchagawat | Supawadee Namuangruk‬
Source: ChemElectroChem (article, 2024-02-09)
Field: Materials Science — Materials Chemistry

Expanding the Applicability Domain of Machine Learning Model for Advancements in Electrochemical Material Discovery Abstract Machine learning has gained considerable attention in the material science domain and helped discover advanced materials for electrochemical applications. Numerous studies have demonstrated its potential to reduce the resources required for material screening. However, a significant proportion of these studies have adopted a supervised learning approach, which entails the laborious task of constructing random training databases and does not always ensure the model‘s reliability while screening unseen materials. Herein, we evaluate the limitations of supervised machine learning from the perspective of the applicability domain. The applicability domain of a model is the region in chemical space where the structure‐property relationship is covered by the training set so that the model can give reliable predictions. We review methods that have been developed to overcome such limitations, such as the active learning framework and self‐supervised learning. Domain (mathematical analysis) ; Computer science ; Data science ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Tongzhao Gong | Weiye Hao | Weiqi Fan | Yun Chen | Xing‐Qiu Chen | Dianzhong Li
Source: Computational Materials Science (article, 2024-12-06)
Field: Materials Science — Materials Chemistry

Accelerating phase-field simulation of multi-component alloy solidification by shallow artificial neural network Component (thermodynamics) ; Artificial neural network ; Alloy ; Phase (matter) ; Field (mathematics) ; Materials science ; Computer science ; Artificial intelligence ; Metallurgy ; Thermodynamics ; Chemistry ; Physics ; Mathematics Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Metallurgical Processes and Thermodynamics
Authors: Jiale Ma | Wenchao Zhang | Zhiqiang Han | Qingyan Xu | Haidong Zhao
Source: Integrating materials and manufacturing innovation (article, 2024-08-12)
Field: Materials Science — Materials Chemistry

An Explainable Deep Learning Model Based on Multi-scale Microstructure Information for Establishing Composition–Microstructure–Property Relationship of Aluminum Alloys Microstructure ; Deep learning ; Artificial intelligence ; Computer science ; Machine learning ; Scale (ratio) ; Property (philosophy) ; Materials science ; Metallurgy Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition
Authors: Jovana Z. Jelić | Aleksa Denčevski | Mihailo D. Rabasović | Janez Križan | Svetlana Savić-Šević | Marko G. Nikolić | Myriam H. Aguirre | Dragutin Sevic | M.S. Rabasović
Source: Photonics (article, 2024-07-06)
Field: Materials Science — Materials Chemistry

Improving the Two-Color Temperature Sensing Using Machine Learning Approach: GdVO4:Sm3+ Prepared by Solution Combustion Synthesis (SCS) The gadolinium vanadate doped with samarium (GdVO4:Sm3+) nanopowder was prepared by the solution combustion synthesis (SCS) method. After synthesis, in order to achieve full crystallinity, the material was annealed in air atmosphere at 900 °C. Phase identification in the post-annealed powder samples was performed by X-ray diffraction, and morphology was investigated by high-resolution scanning electron microscope (SEM) and transmission electron microscope (TEM). Photoluminescence characterization of emission spectrum and time resolved analysis was performed using tunable laser optical parametric oscillator excitation and streak camera. In addition to samarium emission bands, a weak broad luminescence emission band of host VO43− was also observed by the detection system. In our earlier work, we analyzed the possibility of using the host luminescence for two-color temperature sensing, improving the method by introducing the temporal dependence in line intensity ratio measurements. Here, we showed that further improvements are possible by using the machine learning approach. To facilitate the initial data assessment, we incorporated Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) clustering of GdVO4:Sm3+ spectra at various temperatures. Good predictions of temperature were obtained using deep neural networks. Performance of the deep learning network was enhanced by data augmentation technique. Materials science ; Photoluminescence ; Luminescence ; Samarium ; Analytical Chemistry (journal) ; Scanning electron microscope ; Crystallinity ; Transmission electron microscopy ; Optics ; Optoelectronics ; Nanotechnology ; Physics ; Chemistry ; Composite material Luminescence Properties of Advanced Materials ; Gas Sensing Nanomaterials and Sensors ; Spectroscopy and Laser Applications
Authors: Xinyang Dong | Emanuel Gull | Lei Wang
Source: Physical review. B./Physical review. B (article, 2024-02-06)
Field: Materials Science — Materials Chemistry

Equivariant neural network for Green's functions of molecules and materials The many-body Green's function provides access to electronic properties beyond density-functional theory level in ab inito calculations. In this manuscript, we propose a deep learning framework for predicting the finite-temperature Green's function in atomic orbital space, aiming to achieve a balance between accuracy and efficiency. By predicting the self-energy matrices in Lehmann representation using an equivariant message-passing neural network, our method respects its analytical property and the $E(3)$ equivariance. The Green's function is obtained from the predicted self-energy through the Dyson equation with target total number of electrons. We present proof-of-concept benchmark results for both molecules and simple periodic systems, showing that our method is able to provide an accurate estimate of physical observables such as energy and density of states based on the predicted Green's function. Equivariant map ; Green S ; Artificial neural network ; Physics ; Theoretical physics ; Pure mathematics ; Mathematics ; Computer science ; Mathematical analysis ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration