jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tom Wollschläger | Nicholas Gao | Bertrand Charpentier | Mohamed Amine Ketata | Stephan Günnemann
Source: arXiv (Cornell University) (preprint, 2023-06-20)
Field: Materials Science — Materials Chemistry

Uncertainty Estimation for Molecules: Desiderata and Methods Graph Neural Networks (GNNs) are promising surrogates for quantum mechanical calculations as they establish unprecedented low errors on collections of molecular dynamics (MD) trajectories. Thanks to their fast inference times they promise to accelerate computational chemistry applications. Unfortunately, despite low in-distribution (ID) errors, such GNNs might be horribly wrong for out-of-distribution (OOD) samples. Uncertainty estimation (UE) may aid in such situations by communicating the model's certainty about its prediction. Here, we take a closer look at the problem and identify six key desiderata for UE in molecular force fields, three 'physics-informed' and three 'application-focused' ones. To overview the field, we survey existing methods from the field of UE and analyze how they fit to the set desiderata. By our analysis, we conclude that none of the previous works satisfies all criteria. To fill this gap, we propose Localized Neural Kernel (LNK) a Gaussian Process (GP)-based extension to existing GNNs satisfying the desiderata. In our extensive experimental evaluation, we test four different UE with three different backbones and two datasets. In out-of-equilibrium detection, we find LNK yielding up to 2.5 and 2.1 times lower errors in terms of AUC-ROC score than dropout or evidential regression-based methods while maintaining high predictive performance. Computer science ; Inference ; Field (mathematics) ; Machine learning ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Sayanjit Singha Roy | Ashish Paramane | Jiwanjot Singh | Arup Kumar Das | Soumya Chatterjee | Xiangrong Chen
Source: IEEE Transactions on Instrumentation and Measurement (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Automated Space Charge Classification Inside ±500-kV HVDC Cable Insulation Using Fusion of Superpixel and Deep Features for Remote Condition Assessment The accumulated space charges cause electrical field distortion, which is fatal to the safe and reliable operation of polymeric high-voltage direct current (HVDC) cables. Hence, this paper aims to detect and classify the space charges to ensure reliability and a longer operating life of HVDC cables. To achieve this, experiments were carried out on cross-linked polyethylene (XLPE) insulation samples and space charge distributions were recorded under altering electric fields (10–50 kV/mm) and at different temperatures (30–70 °C). Subsequently, super-pixel color features were extracted from the space charge images using the simple linear iterative clustering (SLIC) algorithm. In addition, deep features were extracted using the AlexNet convolutional neural network (CNN) model. The fusion of the handcrafted and deep features was fed to three benchmark machine-learning classifiers for the recognition of different space charge accumulation categories. The method delivered high recognition performance in spite of altering electric fields and varying temperatures. As a result, the proposed framework can detect space charges in HVDC cable insulation in real time. High-voltage direct current ; Convolutional neural network ; Artificial intelligence ; Space charge ; Pixel ; Pattern recognition (psychology) ; Computer science ; Benchmark (surveying) ; High voltage ; Deep learning ; Voltage ; Artificial neural network ; Cluster analysis ; Electric field ; Electronic engineering ; Engineering ; Electrical engineering ; Physics ; Direct current High voltage insulation and dielectric phenomena ; Image Enhancement Techniques ; Advanced Data and IoT Technologies
Authors: Biaojie Yan | Jiaqing Yin | Yi Wang | Ming‐Xing Li | Tao Fa | Bin Bai | Bin Su | Pengcheng Zhang
Source: Scripta Materialia (article, 2023-12-21)
Field: Materials Science — Materials Chemistry

Generic dual-phase classification models through deep learning semantic segmentation method and image gray-level optimization Segmentation ; Artificial intelligence ; Isothermal process ; Microstructure ; Materials science ; Dual (grammatical number) ; Pattern recognition (psychology) ; Gray (unit) ; Image (mathematics) ; Computer science ; Phase (matter) ; Ground truth ; Deep learning ; Machine learning ; Composite material ; Thermodynamics ; Physics Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Physics and Applications
Authors: Xilan Feng | Xiangrui Gong | Dapeng Liu | Li Yang | Ying Jiang | Yu Zhang
Source: Angewandte Chemie (article, 2023-10-12)
Field: Materials Science — Materials Chemistry

Bayesian Optimization‐guided Discovery of High‐performance Methane Combustion Catalysts based on Multi‐component PtPd@CeZrOx Core–Shell Nanospheres Abstract Formula regulation of multi‐component catalysts by manual search is undoubtedly a time‐consuming task, which has severely impeded the development efficiency of high‐performance catalysts. In this work, PtPd@CeZrO x core–shell nanospheres, as a successful case study, is explicitly demonstrated how Bayesian optimization (BO) accelerates the discovery of methane combustion catalysts with the optimal formula ratio (the Pt/Pd mole ratio ranges from 1/2.33–1/9.09, and Ce/Zr from 1/0.22–1/0.35), which directly results in a lower conversion temperature (T 50 approaching to 330 °C) than ones reported hitherto. Consequently, the best sample obtained could be efficiently developed after two rounds of iterations, containing only 18 experiments in all that is far less than the common human workload via the traditional trial‐and‐error search for optimal compositions. Further, this BO‐based machine learning strategy can be straightforward extended to serve the autonomous discovery in multi‐component material systems, for other desired properties, showing promising opportunities to practical applications in future. Catalysis ; Component (thermodynamics) ; Workload ; Methane ; Combustion ; Computer science ; Bayesian optimization ; Bayesian probability ; Task (project management) ; Materials science ; Nanotechnology ; Chemistry ; Biological system ; Machine learning ; Physical chemistry ; Artificial intelligence ; Thermodynamics ; Organic chemistry ; Physics ; Engineering Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Dongping Chang | Pengcheng Xu | Minjie Li | Wencong Lu
Source: Chemometrics and Intelligent Laboratory Systems (article, 2023-10-29)
Field: Materials Science — Materials Chemistry

OCPMDM 2.0: An intelligent solution for materials data mining Computer science ; Process (computing) ; Property (philosophy) ; Computation ; Analytics ; Data mining ; Machine learning ; Data science Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; X-ray Diffraction in Crystallography
Authors: Szymon Buchaniec | Marek Gnatowski | Hiroshi Hasegawa | Grzegorz Brus
Source: Energies (article, 2023-07-27)
Field: Materials Science — Materials Chemistry

A Surrogate Model of the Butler-Volmer Equation for the Prediction of Thermodynamic Losses of Solid Oxide Fuel Cell Electrode Solid oxide fuel cells are becoming increasingly important in various applications, from households to large-scale power plants. However, these electrochemical energy conversion devices have complex behavior that is difficult to understand and optimize. A numerical simulation is a primary tool for analysis and optimization-design. One of the most significant challenges in this field is improving microscale transport phenomena and electrode reaction models. Two main categories of simulation are black-box and white-box models. The former requires large experimental datasets and lacks physical constraints, while the latter inherits the inaccuracy of typical electrochemical reaction models. Here we show a micro-scale artificial neural network-supported numerical simulation that allows for overcoming those issues. In our research, we substituted one equation in the system, an electrochemical model, with an artificial neural network prediction. The data-driven prediction is constrained and must satisfy all reminded balance equations in the system. The results show that the proposed model can simulate an anode-electrode’s thermodynamic losses with improved accuracy compared with the classical approach. The coefficient of determination R2 for the proposed model was equal to 0.8810 for 800 °C, 0.8720 for 900 °C, and 0.8436 for 1000 °C. The findings open a way for improving the accuracy and computational complexity of electrochemical models in solid oxide fuel cell simulations. Microscale chemistry ; Solid oxide fuel cell ; Artificial neural network ; Anode ; Computer science ; Scale (ratio) ; Oxide ; Black box ; Electrode ; Biological system ; Materials science ; Chemistry ; Artificial intelligence ; Mathematics ; Physics Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Jiajie Guo | Yali Zhang | Min Huang | Yishen Xu | Huiying Fan | Wei Liu | Yun Lai | Lei Gao | Jie Luo
Source: Optics & Laser Technology (article, 2023-08-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromagnetically large cylinders with duality symmetry by hybrid neural networks Artificial neural network ; Physics ; Duality (order theory) ; Dipole ; Symmetry (geometry) ; Polarization (electrochemistry) ; Magnetism ; Homogeneous space ; Quantum mechanics ; Computer science ; Mathematics ; Artificial intelligence ; Geometry Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Optical Polarization and Ellipsometry
Authors: Shizuka Suzuki | Takuro Dazai | Yukio Yamamoto | Hideomi Koinuma | Ryota Takahashi
Source: Japanese Journal of Applied Physics (article, 2023-10-10)
Field: Materials Science — Materials Chemistry

Accelerating the combinatorial optimization process for phosphor materials by Bayesian optimization Abstract We investigated the acceleration of the combinatorial optimization process for phosphor materials using a machine learning method based on Bayesian optimization. Combinatorial pulsed laser deposition can be used to create a library of single-crystalline films with varying chemical compositions. However, the systematic evaluation of the target functional properties requires a long measurement time, impairing rapid material screening. In this study, Bayesian optimization was applied to sequential measurements of the photoluminescence (PL) properties of Eu x Y 2−x O 3 films to accelerate the combinatorial high-throughput evaluation. Although a conventional combinatorial PL evaluation of a binary composition-gradient film is composed of a sequential measurement of 80 points, the autonomous PL mapping technique based on Bayesian optimization drastically reduced the measurement points to only six points, demonstrating that the optimum chemical composition can be identified in a shorter experimental time. Bayesian optimization ; Phosphor ; Process (computing) ; Bayesian probability ; Process optimization ; Computer science ; Materials science ; Mathematical optimization ; Process engineering ; Chemical engineering ; Mathematics ; Artificial intelligence ; Optoelectronics ; Engineering ; Programming language Machine Learning in Materials Science
Authors: Anjana S. Desai | Aparna Ashok | Brajesh Pandey | Neeru Bhagat
Source: Materials Today Proceedings (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Meta-analysis on plant-mediated synthesized gold and silver nanoparticles Surface plasmon resonance ; Nanoparticle ; Silver nanoparticle ; Colloidal gold ; Nanotechnology ; Artificial intelligence ; Computer science ; Cytotoxicity ; Machine learning ; Materials science ; Chemistry ; Mathematics ; In vitro Nanoparticles: synthesis and applications ; Laser-Ablation Synthesis of Nanoparticles
Authors: Joseph Konadu Boahen | Ahmed S.G. Khalil | Mohsen A. Hassan | Samir A. Elsagheer Mohamed
Source: Nuclear Science and Techniques (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

EJUSTCO: Monte Carlo radiation transport code hybrid with ANN model for gamma-ray shielding simulation Electromagnetic shielding ; Monte Carlo method ; Physics ; Radiation ; Variance reduction ; Gamma ray ; Photon ; Computational physics ; Computer science ; Simulation ; Nuclear engineering ; Optics ; Nuclear physics ; Engineering ; Mathematics ; Statistics Radiation Shielding Materials Analysis ; Graphite, nuclear technology, radiation studies ; Radioactivity and Radon Measurements
Authors: Kota Noda | Yasushi Shibuta
Source: Computational Materials Science (article, 2023-08-21)
Field: Materials Science — Materials Chemistry

Prediction of potential energy profiles of molecular dynamic simulation by graph convolutional networks A graph convolutional networks (GCN)-based machine learning (ML) model is constructed to predict physical properties of metallic materials from graph representation of atomic configuration of molecular dynamics (MD) simulation. The developed ML model is employed for the prediction of time variation of the potential energy of a solid–liquid biphasic system of nickel. The learned ML model gives a good prediction on the property of training data. Moreover, it is confirmed that the ML model has generalization performance sufficient to make adequate predictions on unknown graph structures despite the lack of information on interatomic distances in the graph representation. It is significant in this study to show that the graph representation can be a good notation for the prediction of various properties from MD simulations since there is no established notation for atomic configuration of MD simulation especially for large-scale system of metallic materials. Notation ; Graph ; Computer science ; Representation (politics) ; Molecular graph ; Molecular dynamics ; Interatomic potential ; Generalization ; External Data Representation ; Theoretical computer science ; Algorithm ; Chemistry ; Mathematics ; Computational chemistry ; Artificial intelligence Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: M. Shahnawaz Khan | Dong‐Sheng Li | Jianbei Qu | XuHui Xu | Hui Ying Yang
Source:  (review, 2023-02-14)
Field: Chemistry — Inorganic Chemistry

A Review of Metal-Organic Framework (MOF) Materials as an Effective Photocatalyst for Degradation of Organic Pollutants Water plays a vital role in all aspects of life. In recent years, water pollution has increased due to various organic and inorganic pollutants caused by human activities. Organic pollutants are hard to degrade in nature. Henceforth cost-effective and sustainable approaches are needed to degrade these organic pollutants to purify wastewater. Organic dye is a major source of organic pollutants from coloring industries, which must be removed during water treatment. The photoactive metal-organic frameworks (MOFs) offer an ultimate strategy to construct catalysts to degrade pollutants present in wastewater. Tuning the metal ions/cluster and organic ligands for better photocatalytic activity of MOFs is the need for an hour. This review comprehensively reported various MOFs, their composites, and POM-based MOF materials for the ultimate photo-degradation of various pollutants in the aqueous phase. Furthermore, special mention has been given to POM-based MOF material, which has an advantage over other materials due to their conducting behavior and high stability. A brief discussion on MOF-based photocatalysts is explored using various theoretical approaches such as density functional theory (DFT) and machine learning (ML). Although there are various article dealing with the basic concept of MOF-based photocatalyst but the area of combining design structure-activity with high-performance theoretical analysis have not been discussed in detail yet. Thus, this article eventually paved the way for applying different structural features to modulate hazardous materials’ photodegradation properties toward organic pollutants. Pollutant ; Photodegradation ; Photocatalysis ; Wastewater ; Degradation (telecommunications) ; Metal-organic framework ; Materials science ; Environmental science ; Waste management ; Biochemical engineering ; Catalysis ; Computer science ; Chemistry ; Environmental engineering ; Organic chemistry ; Engineering ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Marjolein de Jager | Frank Smallenburg | Laura Filion
Source: The Journal of Chemical Physics (article, 2023-10-03)
Field: Materials Science — Materials Chemistry

In search of a precursor for crystal nucleation of hard and charged colloids The interplay between crystal nucleation and the structure of the metastable fluid has been a topic of significant debate over recent years. In particular, it has been suggested that even in simple model systems such as hard or charged colloids, crystal nucleation might be foreshadowed by significant fluctuations in local structure around the location where the nucleus first arises. We investigate this using computer simulations of spontaneous nucleation events in both hard and charged colloidal systems. To detect local structural variations, we use both standard and unsupervised machine learning methods capable of finding hidden structures in the metastable fluid phase. We track numerous nucleation events for the face-centered cubic and body-centered cubic crystals on a local level and demonstrate that all signs of crystallinity emerge simultaneously from the very start of the nucleation process. We thus conclude that we observe no precursor for the crystal nucleation of hard and charged colloids. Nucleation ; Metastability ; Crystal (programming language) ; Chemical physics ; Crystallinity ; Colloidal crystal ; Materials science ; Colloidal particle ; Colloid ; Crystallography ; Physics ; Chemistry ; Thermodynamics ; Physical chemistry ; Computer science ; Quantum mechanics Material Dynamics and Properties ; Theoretical and Computational Physics ; Pickering emulsions and particle stabilization
Authors: Patrick Lauer | Lukas Arnold | Fabian Brännström
Source: Fire Safety Journal (article, 2023-01-19)
Field: Materials Science — Materials Chemistry

Inverse modelling of pyrolization kinetics with ensemble learning methods To simulate fire spread, especially the pyrolysis process – the thermal decomposition of a solid material – must be predicted. Yet, needed material dependent reaction kinetic parameters cannot be directly measured. Common methods infer them from small scale tests with inverse modelling approaches, which are computationally costly. Here, a novel machine learning based approach utilising extremely randomized trees (ERT) is presented and evaluated. It aims to derive these parameters almost instantly due to an inverse pre-trained surrogate model. The approach consists of an ERT classifier, a non-linear least squares optimiser and an ERT regressor. A thorough hyperparameter study was conducted. The model is evaluated with a synthetic thermogravimetric analysis (TGA) dataset. Calculated from an Arrhenius model, it contains data for more than 18⋅106 synthetic materials consisting of up to three components. The method is also applied on real experimental data, here polymethyl methacrylate (PMMA), gained from the Measurement and Computation of Fire Phenomena (MacFP) working group. Evaluation of the model demonstrated that it can instantly predict reaction kinetic parameters from TGA experiments for synthetic and real materials. Systematic analysis showed an overall R2 score of 0.77 for the complete model predictions. The code and datasets are published as open access. Inverse ; Hyperparameter ; Thermogravimetric analysis ; Computer science ; Computation ; Experimental data ; Algorithm ; Materials science ; Mathematics ; Statistics ; Engineering Thermal and Kinetic Analysis ; Fire dynamics and safety research ; Thermochemical Biomass Conversion Processes
Authors: Salman A. Khan | Stavros Caratzoulas | Dionisios G. Vlachos
Source: The Journal of Physical Chemistry C (article, 2023-11-07)
Field: Materials Science — Materials Chemistry

Catalyst Cluster-Induced Support Restructuring Supported subnanometer metal clusters are active and selective for many reactions. These catalysts present a distribution of active sites that cannot be precisely characterized using microscopy and spectroscopy and form an intractably large configurational space that is not readily sampled using ab initio computational methods. Consequently, the distribution of active sites is poorly understood. While it is generally accepted that the support strongly impacts the cluster properties, less is known about the effect of clusters on the support. Here, we develop actively trained machine learning potentials (MLPs) with basin-hopping to discover low-energy supported metal clusters. We apply the framework to γ-Al2O3(110)-supported Pt clusters. Unexpectedly, small Pt clusters cause significant multiple types of support restructuring. Small clusters partially embed into the support, reducing the fraction of surface-exposed atoms while metal atoms and larger clusters do not cause strong support restructuring. Unlike the belief that the metal dispersion decreases with an increase in size, we show that intermediate-sized clusters have the largest fraction of exposed surface atoms. Lastly, we show that the coordination number is a decent descriptor of cluster stability. Our framework can readily be applied to other supported cluster catalysts. Cluster (spacecraft) ; Restructuring ; Chemical physics ; Catalysis ; Curse of dimensionality ; Economies of agglomeration ; Metal ; Dispersion (optics) ; Materials science ; Chemistry ; Crystallography ; Nanotechnology ; Computer science ; Physics ; Machine learning ; Chemical engineering Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Pascal Pernot
Source: arXiv (Cornell University) (preprint, 2023-03-13)
Field: Materials Science — Materials Chemistry

Validation of uncertainty quantification metrics: a primer based on the consistency and adaptivity concepts The practice of uncertainty quantification (UQ) validation, notably in machine learning for the physico-chemical sciences, rests on several graphical methods (scattering plots, calibration curves, reliability diagrams and confidence curves) which explore complementary aspects of calibration, without covering all the desirable ones. For instance, none of these methods deals with the reliability of UQ metrics across the range of input features (adaptivity). Based on the complementary concepts of consistency and adaptivity, the toolbox of common validation methods for variance- and intervals- based UQ metrics is revisited with the aim to provide a better grasp on their capabilities. This study is conceived as an introduction to UQ validation, and all methods are derived from a few basic rules. The methods are illustrated and tested on synthetic datasets and representative examples extracted from the recent physico-chemical machine learning UQ literature. Toolbox ; Computer science ; Uncertainty quantification ; Calibration ; Reliability (semiconductor) ; Consistency (knowledge bases) ; Machine learning ; GRASP ; Range (aeronautics) ; Variance (accounting) ; Data mining ; Artificial intelligence ; Mathematics ; Statistics ; Software engineering ; Engineering Machine Learning in Materials Science ; Fault Detection and Control Systems ; Scientific Measurement and Uncertainty Evaluation