jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Longhai Zhang | Xu Zhang | Changsheng Chen | Jiaxi Zhang | Weiquan Tan | Zhihang Xu | Ziying Zhong | Li Du | Huiyu Song | Shijun Liao | Ye Zhu | Zhen Zhou | Zhiming Cui
Source: Angewandte Chemie (article, 2024-10-07)
Field: Materials Science — Materials Chemistry

Machine Learning‐Aided Discovery of Low‐Pt High Entropy Intermetallic Compounds for Electrochemical Oxygen Reduction Reaction Abstract Advancing the design of cathode catalysts to significantly maximize platinum utilization and augment the longevity has emerged as a formidable challenge in the field of fuel cells. Herein, we rationally design a high entropy intermetallic compound (HEIC, Pt(FeCoNiCu) 3 ) for catalyzing oxygen reduction reaction (ORR) by an efficient machine learning stategy, where crystal graph convolutional neural networks are employed to expedite the multicomponent design. Based on a dataset generated from first‐principles calculations, the model can achieve a high prediction accuracy with mean absolute errors of 0.003 for surface strain and 0.011 eV atom −1 for formation energy. In addition, we identify two chemical features (atomic size difference and mixing enthalpy) as new descriptors to explore advanced ORR catalysts. The carbon supported Pt(FeCoNiCu) 3 catalyst with small particle size is successfully synthesized by a freeze‐drying‐annealing technology, and exhibits ultrahigh mass activity (4.09 A mg Pt −1 ) and specific activity (7.92 mA cm −2 ). Meanwhile, The catalyst also shows significantly enhanced electrochemical stability which can be ascribed to the sluggish diffussion effect in the HEIC structure. Beyond offering a promising low‐Pt electrocatalysts for fuel cell cathode, this work offers a new paradigm to rationally design advanced catalysts for energy storage and conversion devices. Intermetallic ; Electrochemistry ; Oxygen reduction reaction ; Oxygen reduction ; Oxygen ; Chemistry ; Materials science ; Combinatorial chemistry ; Metallurgy ; Physical chemistry ; Organic chemistry ; Electrode ; Alloy Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Yuwei Mao | Shahriyar Keshavarz | Muhammed Nur Talha Kilic | Kewei Wang | Youjia Li | Andrew Reid | Wei‐keng Liao | Alok Choudhary | Ankit Agrawal
Source: Scripta Materialia (article, 2024-08-26)
Field: Materials Science — Materials Chemistry

A deep learning-based crystal plasticity finite element model Materials science ; Crystal plasticity ; Finite element method ; Plasticity ; Element (criminal law) ; Mechanical engineering ; Composite material ; Structural engineering ; Engineering Machine Learning in Materials Science ; Material Properties and Failure Mechanisms ; Rock Mechanics and Modeling
Authors: Shuxia Ouyang | Xiaobing Hu | Qingfeng Wu | Jeong‐Ah Lee | Jae Heung Lee | Chenjin Zhang | Chunhui Wang | Hyoung Seop Kim | Guangyu Yang | Wanqi Jie
Source: Journal of Magnesium and Alloys (article, 2024-08-01)
Field: Materials Science — Biomaterials

Understanding the creep behaviors and mechanisms of Mg-Gd-Zn alloys via machine learning • A machine learning model was established to accurately predict the minimum creep rate of Mg-Gd-Zn based alloys. • The effects of alloying elements and heat treatment conditions on the creep properties of Mg-Gd-Zn based alloys were revealed. • Variations of creep mechanism in Mg-Gd-Zn based alloys were analyzed based on the predicted creep stress exponents and creep activation energies. • Creep constitutive equations of Mg-Gd-Zn based alloys were established for future design and selection of Mg alloys served at high temperatures. Mg-Gd-Zn based alloys have better creep resistance than other Mg alloys and attract more attention at elevated temperatures. However, the multiple alloying elements and various heat treatment conditions, combined with complex microstructural evolution during creep tests, bring great challenges in understanding and predicting creep behaviors. In this study, we proposed to predict the creep properties and reveal the creep mechanisms of Mg-Gd-Zn based alloys by machine learning. On the one hand, the minimum creep rates were effectively predicted by using a support vector regression model. The complex and nonmonotonic effects of test temperature, test stress, alloying elements, and heat treatment conditions on the creep properties were revealed. On the other hand, the creep stress exponents and creep activation energies were calculated by machine learning to analyze the variation of creep mechanisms, based on which the constitutive equations of Mg-Gd-Zn based alloys were obtained. This study introduces an efficient method to comprehend creep behaviors through machine learning, offering valuable insights for the future design and selection of Mg alloys. Materials science ; Creep ; Metallurgy ; Composite material Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; High Temperature Alloys and Creep
Authors: Wissal Ghabi | Kamel Landolsi | Fraj Echouchene | Abdullah Bajahzar | Moncef Msaddek | Hafedh Belmabrouk
Source: The Canadian Journal of Chemical Engineering (article, 2024-03-13)
Field: Chemistry — Organic Chemistry

Taguchi method and neural network for efficient <scp>β‐ketoenamine</scp> synthesis in deionized water Abstract The optimization of performance parameters, in particular the yield of the synthesis reaction of β‐enaminones in demineralized water, is crucial to improve their efficiency and accuracy. In this report, we investigate the optimization of the synthesis of β‐ketoenamines in deionized water by controlling several parameters such as reaction time, temperature, amine equivalent, acid percentage, and stirring rate. An orthogonal L 16 (4 5 ) network was created using Taguchi's approach, allowing for the best possible parameters. To forecast the contribution of each parameter, analysis of variance (ANOVA) techniques are also used. Multiple linear and nonlinear regression (MLR, MNLR) and multilayer perception artificial neural network (MLP‐ANN) predictive models were developed. Analysis of the results led to optimized design parameters, with time = 6 h, temperature = 25°C, amine equivalent = 1.5, acid percentage = 20%, and stirring rate = 1000 rpm, leading to a maximum yield of 63%. ANOVA analysis revealed that temperature, stirring rate, acid percentage, and time are the parameters with the greatest influence. The least sensitive parameter is the amine equivalent. The two main interactions are temperature * acid % and amine equivalent * rpm. The MLP‐ANN predictions are in good agreement with the experimental values, resulting in a higher R 2 compared to the quadratic regression model and the MLR model. By using molecular docking studies, the produced compounds' biological activity was investigated. Some of the synthesized compounds appear to be interesting and could be used for therapeutic applications. The results of this study give us insight into the gentle, cost‐effective, and biologically active synthesis of β‐enaminones in deionized water. Taguchi methods ; Amine gas treating ; Yield (engineering) ; Artificial neural network ; Biological system ; Linear regression ; Orthogonal array ; Quadratic model ; Design of experiments ; Materials science ; Chemistry ; Mathematics ; Response surface methodology ; Chromatography ; Computer science ; Statistics ; Organic chemistry ; Machine learning ; Composite material Multicomponent Synthesis of Heterocycles ; Synthesis and biological activity ; Synthesis and Characterization of Pyrroles
Authors: Meena Rittiruam | Pisit Khamloet | Sirapat Tiwtusthada | Annop Ektarawong | Tinnakorn Saelee | Chayanon Atthapak | Patcharaporn Khajondetchairit | Björn Alling | Piyasan Praserthdam | Supareak Praserthdam
Source: Applied Surface Science (article, 2024-11-26)
Field: Materials Science — Materials Chemistry

Machine-learning-accelerated density functional theory screening of Cu-based high-entropy alloys for carbon dioxide reduction to ethylene Carbon dioxide ; Density functional theory ; Ethylene ; Reduction (mathematics) ; Materials science ; Entropy (arrow of time) ; Thermodynamics ; Chemistry ; Physics ; Computational chemistry ; Organic chemistry ; Mathematics ; Catalysis Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts
Authors: Grant M. Rotskoff
Source: Current Opinion in Solid State and Materials Science (article, 2024-04-06)
Field: Materials Science — Materials Chemistry

Sampling thermodynamic ensembles of molecular systems with generative neural networks: Will integrating physics-based models close the generalization gap? Generative grammar ; Computer science ; Construct (python library) ; Generative model ; Generalization ; Artificial intelligence ; Artificial neural network ; Machine learning ; Suite ; Data science ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Quantum many-body systems
Authors: Zhiwei Ye | Jialing Li | Wenjun Wang | Fanzhi Qin | Keteng Li | Hao Tan | Chen Zhang
Source: Journal of Cleaner Production (article, 2024-03-06)
Field: Materials Science — Materials Chemistry

Data-driven visualization of the dynamics of machine learning in materials research Visualization ; Dynamics (music) ; Computer science ; Data science ; Industrial engineering ; Artificial intelligence ; Engineering ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Xiangjun Liu | Baolong Wang | Kun Jia | Wang Quan-jie | Di Wang | Yucheng Xiong
Source: Journal of Applied Physics (article, 2024-05-28)
Field: Materials Science — Materials Chemistry

First-principles-based machine learning interatomic potential for molecular dynamics simulations of 2D lateral MoS2/WS2 heterostructures Understanding the mechanical and thermodynamic properties of transition-metal dichalcogenides (TMDs) and their heterostructures is pivotal for advancing the development of flexible semiconductor devices, and molecular dynamics (MD) simulation is widely applied to study these properties. However, current uncertainties persist regarding the efficacy of empirical potentials in MD simulations to accurately describe the intricate performance of complex interfaces within heterostructures. This study addresses these challenges by developing an interatomic potential based on deep neural networks and first-principles calculations. Specifically focusing on MoS2/WS2 heterostructures, our approach aims to predict Young's modulus and thermal conductivities. The potential's effectiveness is demonstrated through the validation of structural features, mechanical properties, and thermodynamic characteristics, revealing close alignment with values derived from first-principles calculations. A noteworthy finding is the substantial influence of the load direction on Young's modulus of heterostructures. Furthermore, our results highlight that the interfacial thermal conductance of the MoS2/WS2 heterostructures is considerably larger than that of graphene-based interfaces. The potential developed in this work facilitates large-scale material simulations, bridging the gap with first-principles calculations. Notably, it outperforms empirical potentials under interface conditions, establishing its significant competitiveness in simulation computations. Our approach not only contributes to a deeper understanding of TMDs and heterostructures but also presents a robust tool for the simulation of their mechanical and thermal behaviors, paving the way for advancements in flexible semiconductor device manufacturing. Molecular dynamics ; Interatomic potential ; Heterojunction ; Dynamics (music) ; Computer science ; Materials science ; Statistical physics ; Physics ; Chemistry ; Computational chemistry ; Optoelectronics 2D Materials and Applications ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Jiaqi Ding | Haonan Gu | Yao Shi | Yi He | Yaqiong Su | Mi Yan | Pengfei Xie
Source: Advanced Functional Materials (article, 2024-11-13)
Field: Materials Science — Materials Chemistry

High‐Throughput Screening of Dual‐Atom Catalysts for Methane Combustion: A Combined Density Functional Theory and Machine‐Learning Study Abstract Ceria‐supported precious metal catalysts have undergone extensive investigation for the catalytic methane combustion. However, it remains a significant challenge to achieve both highly synergistic oxidation activity and efficient atom utilization remains a challenge for commonly used supported nanoparticles and single‐atom catalysts. Dual‐atom catalysts (DACs) emerges as a frontier of advanced catalysts, presenting unique catalytic properties that benefit from the synergy of neighboring metal sites. In this study, 361 ceria‐supported DACs (M 1 M 2 /CeO 2 ) encompassing combinations of 19 transition metals are systematically explored. Using high‐throughput density functional theory calculations, the structures, stability as well as activity of M 1 M 2 /CeO 2 are assessed. Notably, Au 1 Ga 1 /CeO 2 is identified as a promising DAC exhibiting high activity for methane total oxidation, substantiated by comprehensive DFT‐calculated reaction pathways. Furthermore, employing six machine‐learning algorithms, the structure‐properties relationship is explored within ceria‐based DACs and highlight the importance of oxidation states and atomic radii of doped metals as the descriptors. The trained model by computational dataset exhibits high accuracy and predict a more active Mn 1 Au 1 /CeO 2 than those screened using only DFT datasets. The high‐throughput strategy demonstrated in this work not only provides insights into the rational design of methane oxidation catalysts, but also paves the way for exploring DACs for diverse applications. Density functional theory ; Materials science ; Dual (grammatical number) ; Catalysis ; Throughput ; Methane ; Atom (system on chip) ; Combustion ; Nanotechnology ; Chemical engineering ; Physical chemistry ; Computational chemistry ; Organic chemistry ; Computer science ; Parallel computing Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Catalytic Processes in Materials Science
Authors: Hao Tang | Brian Xiao | Wenhao He | Pero Subasic | Avetik R. Harutyunyan | Yao Wang | Fang Liu | Haowei Xu | Ju Li
Source: Nature Computational Science (article, 2024-12-27)
Field: Materials Science — Materials Chemistry

Approaching coupled-cluster accuracy for molecular electronic structures with multi-task learning Density functional theory ; Computer science ; Generalization ; Coupled cluster ; Electronic structure ; Task (project management) ; Excited state ; Cluster (spacecraft) ; Artificial intelligence ; Machine learning ; Molecule ; Algorithm ; Computational chemistry ; Chemistry ; Quantum mechanics ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Praveen Kumar | Ramanpreet Kaur | Shilpi Verma | Seema Singh | Urška Lavrenčić Štangar
Source: Catalysis Today (article, 2024-03-14)
Field: Materials Science — Materials Chemistry

Tuning the catalytic active sites of TiO2/CeO2 interactions to improve oxygen vacancy and lattice parameters for CO2 conversion to green fuel additive In recent years, the conversion of carbon dioxide (CO2) to value-added products has become an area of great interest, both from an industrial and environmental perspective. Here we have successfully synthesized TiO2/CeO2-based catalysts by exo- and endo-template and use for direct conversion of CO2 to dimethyl carbonate (DMC). It was found that the catalytic activity of TiO2/CeO2 catalysts depends on the acidic and basic sites present in the catalyst. The study also investigated the optimization of reaction conditions in a batch reactor. The catalytic activity, compared to that of CeO2, TiO2 and TiO2/CeO2-based catalysts, significantly improves CO2 conversion while exhibiting a high DMC yield of 2.52 mmol g-1 cat under a CO2 pressure of 15 MPa. The reusability of the TiO2/CeO2 catalysts was examined as well. Additionally, the heat of the reaction and Gibbs free energy were calculated using chemical equilibrium models. The experimental response data was validated through the using artificial neural networks (ANN). The experimental response data were validated through the use of artificial neural networks (ANN). The R2 values between the expected and experimental results for DMC yield and CO2 conversion were 0.9953 and 0.9969, respectively. The developed ANN model successfully predicts the percentage of CO2 conversion and DMC yield under the given reaction conditions, with prediction accuracy MSE of 0.0013 and 0.0008, respectively. Catalysis ; Lattice (music) ; Vacancy defect ; Oxygen ; Materials science ; Heterogeneous catalysis ; Chemistry ; Chemical engineering ; Nanotechnology ; Physics ; Crystallography ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Dohyeong Kwon | Duho Kim
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potentials in engineering perspective for developing cathode materials Machine learning interatomic potentials (MLIPs) predict thermodynamic phase stability and structural parameters like density functional theory (DFT) but are much faster, making them valuable for engineering applications. Density functional theory ; Stability (learning theory) ; Interatomic potential ; Perspective (graphical) ; Phase (matter) ; Computer science ; Structural stability ; Cathode ; Materials science ; Statistical physics ; Artificial intelligence ; Machine learning ; Molecular dynamics ; Computational chemistry ; Chemistry ; Physics ; Physical chemistry ; Engineering ; Quantum mechanics ; Structural engineering Machine Learning in Materials Science ; Advancements in Battery Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Sai Govind Hari Kumar | Carlota Bozal‐Ginesta | Ning Wang | Jehad Abed | Chung Hsuan Shan | Zhenpeng Yao | Alán Aspuru‐Guzik
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

From computational screening to the synthesis of a promising OER catalyst The search for new materials can be laborious and expensive. Given the challenges that mankind faces today concerning the climate change crisis, the need to accelerate materials discovery for applications like water-splitting could be very relevant for a renewable economy. In this work, we introduce a computational framework to predict the activity of oxygen evolution reaction (OER) catalysts, in order to accelerate the discovery of materials that can facilitate water splitting. We use this framework to screen 6155 ternary-phase spinel oxides and have isolated 33 candidates which are predicted to have potentially high OER activity. We have also trained a machine learning model to predict the binding energies of the *O, *OH and *OOH intermediates calculated within this workflow to gain a deeper understanding of the relationship between electronic structure descriptors and OER activity. Out of the 33 candidates predicted to have high OER activity, we have synthesized three compounds and characterized them using linear sweep voltammetry to gauge their performance in OER. From these three catalyst materials, we have identified a new material, Co<sub>2.5</sub>Ga<sub>0.5</sub>O<sub>4</sub>, that is competitive with benchmark OER catalysts in the literature with a low overpotential of 220 mV at 10 mA cm<sup>-2</sup> and a Tafel slope at 56.0 mV dec<sup>-1</sup>. Given the vast size of chemical space as well as the success of this technique to date, we believe that further application of this computational framework based on the high-throughput virtual screening of materials can lead to the discovery of additional novel, high-performing OER catalysts. Inverse ; Computer science ; Catalysis ; Biochemical engineering ; Nanotechnology ; Materials science ; Chemistry ; Engineering ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Youheng Liang | Xiaoliu Huangfu | Ruixing Huang | Zhenpeng Han | Sisi Wu | Jingrui Wang | Xinlong Long | Jun Ma | Qiang He
Source: Journal of Hazardous Materials (article, 2024-05-06)
Field: Materials Science — Materials Chemistry

Machine learning for predicting halogen radical reactivity toward aqueous organic chemicals Halogen ; Reactivity (psychology) ; Aqueous solution ; Chemistry ; Organic chemicals ; Organic chemistry ; Environmental chemistry ; Alkyl Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Gabe Guo | Judah Goldfeder | Ling Lan | A. K. Ray | Albert Hanming Yang | Boyuan Chen | Simon J. L. Billinge | Hod Lipson
Source: npj Computational Materials (article, 2024-09-07)
Field: Materials Science — Materials Chemistry

Towards end-to-end structure determination from x-ray diffraction data using deep learning Powder crystallography is the experimental science of determining the structure of molecules provided in crystalline-powder form, by analyzing their x-ray diffraction (XRD) patterns. Since many materials are readily available as crystalline powder, powder crystallography is of growing usefulness to many fields. However, powder crystallography does not have an analytically known solution, and therefore the structural inference typically involves a laborious process of iterative design, structural refinement, and domain knowledge of skilled experts. A key obstacle to fully automating the inference process computationally has been formulating the problem in an end-to-end quantitative form that is suitable for machine learning, while capturing the ambiguities around molecule orientation, symmetries, and reconstruction resolution. Here we present an ML approach for structure determination from powder diffraction data. It works by estimating the electron density in a unit cell using a variational coordinate-based deep neural network. We demonstrate the approach on computed powder x-ray diffraction (PXRD), along with partial chemical composition information, as input. When evaluated on theoretically simulated data for the cubic and trigonal crystal systems, the system achieves up to 93.4% average similarity (as measured by structural similarity index) with the ground truth on unseen materials, both with known and partially-known chemical composition information, showing great promise for successful structure solution even from degraded and incomplete input data. The approach does not presuppose a crystalline structure and the approach are readily extended to other situations such as nanomaterials and textured samples, paving the way to reconstruction of yet unresolved nanostructures. End-to-end principle ; Diffraction ; Computer science ; Artificial intelligence ; Physics ; Optics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Enzyme Structure and Function
Authors: Qinli Ye | Miaomiao Wu | Qian Xu | Shuwen Zeng | Tao Jiang | Wei Xiong | Songyin Fu | Muhammad Danang Birowosuto | Chenjie Gu
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-01-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Porous carbon film/WO3-x nanosheets based SERS substrate combined with deep learning technique for molecule detection Raman scattering ; Materials science ; Raman spectroscopy ; Annealing (glass) ; Detection limit ; Substrate (aquarium) ; Analytical Chemistry (journal) ; Nanotechnology ; Optoelectronics ; Chemistry ; Chromatography ; Optics ; Metallurgy Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Spectroscopy Techniques in Biomedical and Chemical Research