jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qin Li | Nihang Fu | Sadman Sadeed Omee | Jianjun Hu
Source: npj Computational Materials (article, 2024-10-18)
Field: Materials Science — Materials Chemistry

MD-HIT: Machine learning for material property prediction with dataset redundancy control Abstract Materials datasets usually contain many redundant (highly similar) materials due to the tinkering approach historically used in material design. This redundancy skews the performance evaluation of machine learning (ML) models when using random splitting, leading to overestimated predictive performance and poor performance on out-of-distribution samples. This issue is well-known in bioinformatics for protein function prediction, where tools like CD-HIT are used to reduce redundancy by ensuring sequence similarity among samples greater than a given threshold. In this paper, we survey the overestimated ML performance in materials science for material property prediction and propose MD-HIT, a redundancy reduction algorithm for material datasets. Applying MD-HIT to composition- and structure-based formation energy and band gap prediction problems, we demonstrate that with redundancy control, the prediction performances of the ML models on test sets tend to have relatively lower performance compared to the model with high redundancy, but better reflect models’ true prediction capability. Redundancy (engineering) ; Property (philosophy) ; Computer science ; Machine learning ; Artificial intelligence ; Control (management) ; Operating system Machine Learning in Materials Science ; Mineral Processing and Grinding ; X-ray Diffraction in Crystallography
Authors: Rohit Unni | Mingyuan Zhou | Peter R. Wiecha | Yuebing Zheng
Source: Current Opinion in Solid State and Materials Science (article, 2024-04-03)
Field: Materials Science — Materials Chemistry

Advancing materials science through next-generation machine learning Computer science ; Generative grammar ; Field (mathematics) ; Artificial intelligence ; Representation (politics) ; Variety (cybernetics) ; Crowdsourcing ; Data science ; Scope (computer science) ; Generative model ; Component (thermodynamics) ; Machine learning ; Key (lock) ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Electron Microscopy Techniques and Applications
Authors: Qiming Fu | Tao Xu | Chenggong He | Daomiao Wang | Meiling Liu | Chao Liu
Source: Langmuir (article, 2024-05-08)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Study of REN<sub><i>x</i></sub>C<sub>6–<i>x</i></sub>-Doped Graphene as Potential Electrocatalysts for Oxygen Electrode Reactions In the application of renewable energy, the oxidation-reduction reaction (ORR) and oxygen evolution reaction (OER) are two crucial reactions. Single-atom catalysts (SACs) based on metal-doped graphene have been widely employed due to their high activity and high atom utilization efficiency. However, the catalytic activity is significantly influenced by different metals and local coordination, making it challenging to efficiently screen through either experimental or density functional theory (DFT) calculations. To address this issue, this study employed a combination of DFT calculations and machine learning (DFT-ML) to investigate rare earth-modified carbon-based (REN<sub><i>x</i></sub>C<sub>6-<i>x</i></sub>) electrocatalysts. Based on computational data from 75 catalysts, we trained two ML models to capture the underlying patterns of physical properties and overpotential. Subsequently, the candidate catalysts were screened, leading to the discovery of four ORR catalysts, nine OER catalysts, and five bifunctional electrocatalysts, all of which were thoroughly validated for their stability. Lastly, by integrating the ML models with the SHAP analysis framework, we revealed the influence of atomic radius, Pauling electronegativity, and other features on the catalytic activity. Additionally, we analyzed the physicochemical properties of potential catalysts through DFT calculations. The revolutionary DFT-ML approach provides a crucial driving force for the design and synthesis of potential catalysts in subsequent studies. Graphene ; Doping ; Oxygen reduction reaction ; Oxygen ; Electrode ; Electrocatalyst ; Materials science ; Fuel cells ; Nanotechnology ; Electrochemistry ; Chemistry ; Chemical engineering ; Physical chemistry ; Optoelectronics ; Organic chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Qingchun Yang | Yingjie Fan | Dongwen Rong | Ruijie Bao | Dawei Zhang
Source: AIChE Journal (article, 2024-04-17)
Field: Materials Science — Materials Chemistry

An auto‐configurable machine learning framework to optimize and predict catalysts for <scp>CO<sub>2</sub></scp> to light olefins process Abstract This study proposed an auto‐configurable machine learning framework based on the differential evolution algorithm (AutoML‐DE) driven by hybrid data for the screening and discovery of promising CO 2 to light olefins (CO 2 TLO) catalysts candidates. The hybrid dataset comprises 532 experimental data from the literature and 296 simulation data. Results show that the AutoML‐DE model with extreme gradient boosting algorithms demonstrated superior performance for predicting the conversion ratio of CO 2 and selectivity of light olefins (average R 2 &gt; 0.86). After identifying the input feature with the most significant impact on the output feature, the optimal AutoML‐DE model integrated with the genetic algorithm is applied to multiobjective optimization, sensitivity analysis, and prediction of new CO 2 TLO catalysts. The optimized Cu‐Zn‐Al/SAPO‐34 catalyst has the highest catalytic performance among the reported CO 2 TLO catalysts. Moreover, five new CO 2 TLO catalysts with higher yields are successfully predicted. However, the performance of these catalysts should be further verified by experiment. Catalysis ; Selectivity ; Boosting (machine learning) ; Feature (linguistics) ; Process (computing) ; Computer science ; Differential evolution ; Gradient boosting ; Chemistry ; Reaction conditions ; Sensitivity (control systems) ; Biological system ; Artificial intelligence ; Algorithm ; Combinatorial chemistry ; Engineering ; Organic chemistry ; Electronic engineering Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis ; Catalytic Processes in Materials Science
Authors: Donglin He | Yibin Jiang | Melanie Guillén‐Soler | Zack Geary | Lucía Vizcaíno‐Anaya | Daniel Salley | M.C. Gimenez-Lopez | De‐Liang Long | Leroy Cronin
Source: Journal of the American Chemical Society (article, 2024-10-09)
Field: Materials Science — Materials Chemistry

Algorithm-Driven Robotic Discovery of Polyoxometalate-Scaffolding Metal–Organic Frameworks The experimental exploration of the chemical space of crystalline materials, especially metal-organic frameworks (MOFs), requires multiparameter control of a large set of reactions, which is unavoidably time-consuming and labor-intensive when performed manually. To accelerate the rate of material discovery while maintaining high reproducibility, we developed a machine learning algorithm integrated with a robotic synthesis platform for closed-loop exploration of the chemical space for polyoxometalate-scaffolding metal-organic frameworks (POMOFs). The eXtreme Gradient Boosting (XGBoost) model was optimized by using updating data obtained from the uncertainty feedback experiments and a multiclass classification extension based on the POMOF classification from their chemical constitution. The digital signatures for the robotic synthesis of POMOFs were represented by the universal chemical description language (χDL) to precisely record the synthetic steps and enhance the reproducibility. Nine novel POMOFs including one with mixed ligands derived from individual ligands through the imidization reaction of POM amine derivatives with various aldehydes have been discovered with a good repeatability. In addition, chemical space maps were plotted based on the XGBoost models whose F1 scores are above 0.8. Furthermore, the electrochemical properties of the synthesized POMOFs indicate superior electron transfer compared to the molecular POMs and the direct effect of the ratio of Zn, the type of ligands used, and the topology structures in POMOFs for modulating electron transfer abilities. Chemistry ; Polyoxometalate ; Scaffold ; Metal-organic framework ; Combinatorial chemistry ; Nanotechnology ; Organic chemistry ; Catalysis Polyoxometalates: Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Wojciech G. Stark | Cas van der Oord | Ilyes Batatia | Yaolong Zhang | Bin Jiang | Gábor Cśanyi | Reinhard J. Maurer
Source: Machine Learning Science and Technology (article, 2024-07-03)
Field: Materials Science — Materials Chemistry

Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces Abstract Simulations of chemical reaction probabilities in gas surface dynamics require the calculation of ensemble averages over many tens of thousands of reaction events to predict dynamical observables that can be compared to experiments. At the same time, the energy landscapes need to be accurately mapped, as small errors in barriers can lead to large deviations in reaction probabilities. This brings a particularly interesting challenge for machine learning interatomic potentials, which are becoming well-established tools to accelerate molecular dynamics simulations. We compare state-of-the-art machine learning interatomic potentials with a particular focus on their inference performance on CPUs and suitability for high throughput simulation of reactive chemistry at surfaces. The considered models include polarizable atom interaction neural networks (PaiNN), recursively embedded atom neural networks (REANN), the MACE equivariant graph neural network, and atomic cluster expansion potentials (ACE). The models are applied to a dataset on reactive molecular hydrogen scattering on low-index surface facets of copper. All models are assessed for their accuracy, time-to-solution, and ability to simulate reactive sticking probabilities as a function of the rovibrational initial state and kinetic incidence energy of the molecule. REANN and MACE models provide the best balance between accuracy and time-to-solution and can be considered the current state-of-the-art in gas-surface dynamics. PaiNN models require many features for the best accuracy, which causes significant losses in computational efficiency. ACE models provide the fastest time-to-solution, however, models trained on the existing dataset were not able to achieve sufficiently accurate predictions in all cases. Computer science ; Molecular dynamics ; Artificial neural network ; Statistical physics ; Artificial intelligence ; Machine learning ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electrocatalysts for Energy Conversion
Authors: M. Ghorbani | Mario Boley | Philip N. H. Nakashima | N. Birbilis
Source: Scientific Reports (article, 2024-04-09)
Field: Materials Science — Materials Chemistry

An active machine learning approach for optimal design of magnesium alloys using Bayesian optimisation In the pursuit of magnesium (Mg) alloys with targeted mechanical properties, a multi-objective Bayesian optimisation workflow is presented to enable optimal Mg-alloy design. A probabilistic Gaussian process regressor model was trained through an active learning loop, while balancing the exploration and exploitation trade-off via an acquisition function of the upper confidence bound. New candidate alloys suggested by the optimiser within each iteration were appended to the training data, and the performance of this sequential strategy was validated via a regret analysis. Using the proposed approach, the dependency of the prediction error on the training data was overcome by considering both the predictions and their associated uncertainties. The method developed here, has been packaged into a web tool with a graphical user-interactive interface (GUI) that allows the proposed optimal Mg-alloy design strategy to be deployed. Bayesian optimization ; Regret ; Computer science ; Workflow ; Gaussian process ; Probabilistic logic ; Machine learning ; Process (computing) ; Bayesian probability ; Kriging ; Graphical user interface ; Artificial intelligence ; Data mining ; Gaussian ; Database Machine Learning in Materials Science ; Machine Learning and Algorithms ; Metal and Thin Film Mechanics
Authors: Abdulrazak Jinadu Otaru | Zaid Abdulhamid Alhulaybi | Ibrahim Dubdub
Source: Polymers (article, 2024-02-05)
Field: Materials Science — Polymers and Plastics

Machine Learning Backpropagation Prediction and Analysis of the Thermal Degradation of Poly (Vinyl Alcohol) Thermogravimetric analysis (TGA) is crucial for describing polymer materials' thermal behavior as a result of temperature changes. While available TGA data substantiated in the literature significantly focus attention on TGA performed at higher heating rates, this study focuses on the machine learning backpropagation analysis of the thermal degradation of poly (vinyl alcohol), or PVA, at low heating rates, typically 2, 5 and 10 K/min, at temperatures between 25 and 600 °C. Initial TGA analysis showed that a consistent increase in heating rate resulted in an increase in degradation temperature as the resulting thermograms shifted toward a temperature maxima. At degradation temperatures between 205 and 405 °C, significant depths in the characterization of weight losses were reached, which may be attributed to the decomposition and loss of material content. Artificial neural network backpropagation of machine learning algorithms were used for developing mathematical descriptions of the percentage weight loss (output) by these PVA materials as a function of the heating rate (input 1) and degradation temperature (input 2) used in TGA analysis. For all low heating rates, modelling predictions were observably correlated with experiments with a 99.2% correlation coefficient and were used to interpolate TGA data at 3.5 and 7.5 K/min, indicating trends strongly supported by experimental TGA data as well as literature research. Thus, this approach could provide a useful tool for predicting the thermograms of PVA materials at low heating rates and contribute to the development of more advanced PVA/polymer materials for home and industrial applications. Thermogravimetric analysis ; Vinyl alcohol ; Degradation (telecommunications) ; Backpropagation ; Materials science ; Polymer ; Thermal stability ; Thermal analysis ; Composite material ; Thermal ; Chemical engineering ; Artificial neural network ; Thermodynamics ; Machine learning ; Computer science Polymer Science and PVC ; Polymer crystallization and properties ; Thermal and Kinetic Analysis
Authors: Mohsen Asle Zaeem | Siby Thomas | Sepideh Kavousi | Ning Zhang | T. Mukhopadhyay | Avik Mahata
Source: 2D Materials (article, 2024-09-09)
Field: Materials Science — Materials Chemistry

Multiscale computational modeling techniques in study and design of 2D materials: recent advances, challenges, and opportunities Abstract This article provides an overview of recent advances, challenges, and opportunities in multiscale computational modeling techniques for study and design of two-dimensional (2D) materials. We discuss the role of computational modeling in understanding the structures and properties of 2D materials, followed by a review of various length-scale models aiding in their synthesis. We present an integration of multiscale computational techniques for study and design of 2D materials, including density functional theory, molecular dynamics, phase-field modeling, continuum-based molecular mechanics, and machine learning. The study focuses on recent advancements, challenges, and future prospects in modeling techniques tailored for emerging 2D materials. Key challenges include accurately capturing intricate behaviors across various scales and environments. Conversely, opportunities lie in enhancing predictive capabilities to accelerate materials discovery for applications spanning from electronics, photonics, energy storage, catalysis, and nanomechanical devices. Through this comprehensive review, our aim is to provide a roadmap for future research in multiscale computational modeling and simulation of 2D materials. Multiscale modeling ; Computer science ; Management science ; Data science ; Systems engineering ; Nanotechnology ; Engineering ; Materials science ; Chemistry ; Computational chemistry Graphene research and applications ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Abhilash Ravichandran | Valliappan Raman | Yogapriya Selvaraj | M. Prabhavathy | Hemalatha Kuzhandaivel
Source: ACS Omega (article, 2024-07-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning-Based Prediction of Cyclic Voltammetry Behavior of Substitution of Zinc and Cobalt in BiFeO<sub>3</sub>/Bi<sub>25</sub>FeO<sub>40</sub> for Supercapacitor Applications Artificial intelligence and machine learning have become indispensable tools across various disciplines in the present century. In that way, the role of artificial intelligence and machine learning in energy storage devices was investigated. As a preliminary study, the data derived from electrochemical studies were used for the prediction. The prediction of current from cyclic voltammetry (CV) studies was undertaken for bismuth ferrite (BFO), substitution of zinc in BFO (BFZO), and substitution of cobalt in BFO composite (BFCO). CV is a vital electrochemical technique used for studying the electrochemical behavior of any material. The electrochemical study provides insights into the energy storage behavior of the material through the specific capacitance. The machine learning models, such as Artificial Neural Network (ANN), Random Forest (RF), and XGBoost (XGB), are trained and implemented to predict current at different scan rates. These models are trained and validated using the data collected from a CHI 600E electrochemical workstation. Multiple trials of experiments were performed to build the most optimum model for the material. The predicted values provide promising results and align well with the experimental data. The XGBoost, ANN and RF models perform well for the CV data set with an average testing accuracy >97%. Also, a meta-model was created using stacking of the above three machine learning models which further improved the predictive performance, achieving a slightly higher average testing accuracy of over 97.73%. The outcomes from the models can promote the development of machine learning applications in the field of electrochemistry and provide insights into optimizing supercapacitor performance and design through data-driven approaches. Machine learning ; Artificial neural network ; Artificial intelligence ; Materials science ; Supercapacitor ; Computer science ; Random forest ; Electrochemistry ; Electrode ; Chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Electrocatalysts for Energy Conversion
Authors: Wansun Kim | Jisang Han | Yoo Jin Kim | Hyerin Lee | Tae Gi Kim | Jae‐Ho Shin | Dong‐Ho Kim | Ho Sang Jung | Sang Woong Moon | Samjin Choi
Source: ACS Nano (article, 2024-06-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Molybdenum Disulfide-Assisted Spontaneous Formation of Multistacked Gold Nanoparticles for Deep Learning-Integrated Surface-Enhanced Raman Scattering Several fabrication methods have been developed for label-free detection in various fields. However, fabricating high-density and highly ordered nanoscale architectures by using soluble processes remains a challenge. Herein, we report a biosensing platform that integrates deep learning with surface-enhanced Raman scattering (SERS), featuring large-area, close-packed three-dimensional (3D) architectures of molybdenum disulfide (MoS<sub>2</sub>)-assisted gold nanoparticles (AuNPs) for the on-site screening of coronavirus disease (COVID-19) using human tears. Some AuNPs are spontaneously synthesized without a reducing agent because the electrons induced on the semiconductor surface reduce gold ions when the Fermi level of MoS<sub>2</sub> and the gold electrolyte reach equilibrium. With the addition of polyvinylpyrrolidone, a two-dimensional large-area MoS<sub>2</sub> layer assisted in the formation of close-packed 3D multistacked AuNP structures, resembling electroless plating. This platform, with a convolutional neural network-based deep learning model, achieved outstanding SERS performance at subterascale levels despite the microlevel irradiation power and millisecond-level acquisition time and accurately assessed susceptibility to COVID-19. These results suggest that our platform has the potential for rapid, low-damage, and high-throughput label-free detection of exceedingly low analyte concentrations. Materials science ; Raman scattering ; Molybdenum disulfide ; Nanotechnology ; Colloidal gold ; Nanoparticle ; Polyvinylpyrrolidone ; Nanoclusters ; Raman spectroscopy Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; SARS-CoV-2 detection and testing
Authors: Ata Madanchi | Emna Azek | Karim Zongo | Laurent Karim Béland | Normand Mousseau | Lena Simine
Source: ACS Physical Chemistry Au (review, 2024-12-31)
Field: Materials Science — Materials Chemistry

Is the Future of Materials Amorphous? Challenges and Opportunities in Simulations of Amorphous Materials Amorphous solids form an enormous and underutilized class of materials. In order to drive the discovery of new useful amorphous materials further we need to achieve a closer convergence between computational and experimental methods. In this review, we highlight some of the important gaps between computational simulations and experiments, discuss popular state-of-the-art computational techniques such as the Activation Relaxation Technique <i>nouveau</i> (ARTn) and Reverse Monte Carlo (RMC), and introduce more recent advances: machine learning interatomic potentials (MLIPs) and generative machine learning for simulations of amorphous matter (e.g., MAP). Examples are drawn from amorphous silicon and silica literature as well as from molecular glasses. Our outlook stresses the need for new computational methods to extend the time- and length-scales accessible through numerical simulations. Amorphous solid ; Materials science ; Nanotechnology ; Engineering physics ; Engineering ; Chemistry ; Crystallography Material Dynamics and Properties ; Metallic Glasses and Amorphous Alloys ; Phase-change materials and chalcogenides