jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hyundo Park | Jiwon Roh | Hyungtae Cho | Insoo Ro | Junghwan Kim
Source: Journal of Materials Chemistry A (article, 2023-12-14)
Field: Materials Science — Materials Chemistry

Accelerating active catalyst discovery: a probabilistic prediction-based screening methodology with applications in dry reforming of methane Dry reforming of methane (DRM) is a promising technology for syngas production from CH 4 and CO 2 . However, discovering feasible and efficient catalysts remains challenging despite recent advancements in machine learning. Herein, we present a novel probabilistic prediction-based, high-throughput screening methodology that demonstrates outstanding performance, with a coefficient of determination ( R 2 ) of 0.936 and root-mean-square error (RMSE) of 6.66. Additionally, experimental validation was performed using 20 distinct catalysts to ensure the accurate verification of the model, 17 of which were previously unreported combinations. Our model accurately predicts CH 4 conversion rates and probability values by considering catalyst design, pretreatment, and operating variables, providing reliable insights into catalyst performance. The proposed probabilistic prediction-based screening methodology, which we introduce for the first time in the field of catalysis, holds significant potential for accelerating the discovery of catalysts for DRM reactions and expanding their application scope in other crucial industrial processes. Thus, the methodology effectively addresses a key challenge in the development of active catalysts for energy and environmental research. Probabilistic logic ; Syngas ; Methane ; Catalysis ; Scope (computer science) ; Carbon dioxide reforming ; Computer science ; Process engineering ; Statistical model ; Biochemical engineering ; Environmental science ; Machine learning ; Chemistry ; Artificial intelligence ; Engineering Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Zening Yang | Weiwei Sun | Zhengyu Sun | Mutian Zhang | Jin Yu | Yubin Wen
Source: Scripta Materialia (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

Explainable predictions of multi-component oxides enabled by attention-based neural networks Interpretability ; Adaptability ; Thermal expansion ; Artificial neural network ; Component (thermodynamics) ; Materials science ; Range (aeronautics) ; Computer science ; Machine learning ; Artificial intelligence ; Reduction (mathematics) ; Task (project management) ; Mean squared error ; Systems engineering ; Mathematics ; Engineering ; Statistics ; Thermodynamics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Thermal Expansion and Ionic Conductivity
Authors: Ruoqi Dang | Wenshan Yu
Source: Materials (article, 2023-01-31)
Field: Materials Science — Materials Chemistry

Standard Deviation Effect of Average Structure Descriptor on Grain Boundary Energy Prediction The structural complexities of grain boundaries (GBs) result in their complicated property contributions to polycrystalline metals and alloys. In this study, we propose a GB structure descriptor by linearly combining the average two-point correlation function (PCF) and standard deviation of PCF via a weight parameter, to reveal the standard deviation effect of PCF on energy predictions of Cu, Al and Ni asymmetric tilt GBs (i.e., Σ3, Σ5, Σ9, Σ11, Σ13 and Σ17), using two machine learning (ML) methods; i.e., principal component analysis (PCA)-based linear regression and recurrent neural networks (RNN). It is found that the proposed structure descriptor is capable of improving GB energy prediction for both ML methods. This suggests the discriminatory power of average PCF for different GBs is lifted since the proposed descriptor contains the data dispersion information. Meanwhile, we also show that GB atom selection methods by which PCF is evaluated also affect predictions. Standard deviation ; Principal component analysis ; Energy (signal processing) ; Mathematics ; Linear regression ; Tilt (camera) ; Grain boundary ; Dispersion (optics) ; Statistics ; Algorithm ; Biological system ; Materials science ; Physics ; Optics ; Geometry ; Microstructure Microstructure and mechanical properties ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Colton C. Seegmiller | Sterling G. Baird | Hasan M. Sayeed | Taylor D. Sparks
Source: ChemRxiv (preprint, 2023-04-04)
Field: Materials Science — Materials Chemistry

Discovering Chemically Novel, High-TemperatureSuperconductors One of the biggest unsolved problems in condensed matter physics is what mechanism causes high-temperature superconductivity and if there is a material that can exhibit superconductivity at both room temperature and atmospheric pressure. Among the many important properties of a superconductor, the critical temperature (Tc) or transition temperature is the point at which a material transitions into a superconductive state. In this implementation, machine learning is used to predict the critical temperatures of chemically unique compounds in an attempt to identify new chemically novel, high-temperature superconductors. The training data set (SuperCon) consists of known superconductors and their critical temperatures, and the testing data set (NOMAD) consists of around 700,000 novel chemical formulae. The chemical formulae in these data sets are first passed through a collection of rapid screening tools, SMACT, to check for chemical validity. Next, the DiSCoVeR algorithm is used to train on the SuperCon data to form a model, and then screens through batches of the formulae in the NOMAD data set. Having a combination of a chemical distance metric, density-aware dimensionality reduction, clustering, and a regression model, the DiSCoVeR algorithm serves as a tool to identify and assess these superconducting compositions [1]. This research and implementation resulted in the screening of chemically novel compositions exhibiting critical temperatures upwards of 150 K, which correlates to superconductors in the cuprate class. This implementation demonstrates a process of performing machine learning-assisted superconductor screening (while exploring chemically distinct spaces) which can be utilized in the materials discovery process. Superconductivity ; Cuprate ; Curse of dimensionality ; High-temperature superconductivity ; Cluster analysis ; Set (abstract data type) ; Chemical space ; Computer science ; Materials science ; Physics ; Machine learning ; Condensed matter physics ; Chemistry ; Drug discovery Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yasemin Bayğu | İnci Çapan | Rıfat Çapan | Matem Erdoğan | Yaser Açıkbaş | Nilgün Kabay | Yaşar Gök | Kemal Büyükkabasakal
Source: Inorganic Chemistry Communications (article, 2023-02-20)
Field: Materials Science — Materials Chemistry

Sensor application and mathematical modeling of new Zn(II) phthalocyanine containing 26-membered tetraoxadithia macrocycle moieties Phthalonitrile ; Phthalocyanine ; Quartz crystal microbalance ; Materials science ; Substrate (aquarium) ; Moiety ; Thin film ; Benzene ; Analytical Chemistry (journal) ; Chemical engineering ; Chemistry ; Physical chemistry ; Organic chemistry ; Nanotechnology ; Adsorption Porphyrin and Phthalocyanine Chemistry ; Conducting polymers and applications ; Organic Electronics and Photovoltaics
Authors: Ericsson Tetteh Chenebuah | Michel Nganbe | Alain Tchagang
Source:  (article, 2023-06-05)
Field: Materials Science — Materials Chemistry

Target-learning the Latent Space of a Variational Autoencoder model for the Inverse Design of Stable Perovskites At the forefront of discoverable materials are perovskites that stand out as some of the most chemically diverse and multifunctional energy materials. Theoretically, the estimated number of ternary perovskites exceeds a hundred thousand distinct compounds, notwithstanding that only a small fraction of this estimate has been reported in existing crystal databases. Therefore, the study takes advantage of the reliable, inexpensive and rapid opportunity offered by deep generative modeling for accelerating the search for unknown perovskites. In the process of making such findings, an inverse design-modeling scheme is resolved, which aims at assimilating deterministic target properties with their corresponding perovskite structure. The inverse design pipeline is architectured by combining a generative Variational AutoEncoder (VAE) model with Target-Learning (TL) feed-forward neural networks to form the TL-VAE perovskite generator, thereby making the complete modeling process semi-supervisory. The TL feed-forward neural network model serves the purpose of organizing the non-linear latent space of the VAE model and further assists in isolating deterministic target properties that are of interest to the core objective of the study. The property to be target-learned in the latent space is the formation energy, which is a crucial indicator for calibrating perovskite stability. The results report the discovery of promising new perovskite candidates, which are unique and polymorphic material variants. Upon conducting Density Functional Theory (DFT) validation on the newly identified perovskites, candidates that undergo full geometrical relaxation are recommended for further investigation and/or synthesization. In conclusion, the study demonstrates the efficacy of the inverse design TL-VAE model for the generation of stable ternary perovskites. Autoencoder ; Generative model ; Inverse ; Space (punctuation) ; Perovskite (structure) ; Computer science ; Ternary operation ; Artificial intelligence ; Artificial neural network ; Stability (learning theory) ; Generative grammar ; Materials science ; Machine learning ; Mathematics ; Chemistry ; Crystallography ; Geometry Machine Learning in Materials Science ; Perovskite Materials and Applications ; Solid-state spectroscopy and crystallography
Authors: Cihat Aydin
Source: Traitement du signal (article, 2023-12-30)
Field: Materials Science — Surfaces, Coatings and Films

Enhanced Material Classification via MobileSEMNet: Leveraging MobileNetV2 for SEM Image Analysis Background: Scanning electron microscopy (SEM) has been instrumental in elucidating material details, enabling the use of SEM images for machine learning applications.This study introduces a novel automated classification model employing deep learning techniques to classify materials using SEM imagery with high accuracy.Materials and Methods: A publicly accessible dataset comprising over 20,000 SEM images across 10 classes was utilized.The dataset was bifurcated into training and testing subsets for model development.The advanced MobileSEMNet model was trained using a pre-trained MobileNetV2 architecture, wherein deep features were extracted via the global average pooling layer.Feature extraction proceeded with fixed-size patch division using the MobileNetV2 network.During feature selection, the neighborhood component analysis (NCA) was employed to distill the most informative 512 features from the feature vector.Subsequently, a classification toolkit determined the Support Vector Machine (SVM) as the optimal shallow classifier.The model was refined using 10-fold cross-validation.Results: The primary aim to surpass the classification accuracy of the standalone MobileNetV2 was achieved; the MobileNetV2 secured a 94.85% accuracy rate, whereas the proposed MobileSEMNet model attained a 96.87% accuracy rate on the test data.Conclusions: MobileSEMNet has proven to be highly effective in the classification of SEM images within a significant dataset, signaling its potential utility in material science.The results unequivocally underscore the model's capacity for automated material type detection, thereby enhancing analytical precision and automation in the field.This work underscores the advantages of deep learning methodologies in advancing material classification in various scientific domains. Image (mathematics) ; Artificial intelligence ; Computer science ; Computer vision ; Pattern recognition (psychology) ; Environmental science Electron and X-Ray Spectroscopy Techniques ; Non-Destructive Testing Techniques ; Machine Learning in Materials Science
Authors: Florbela Pereira
Source: ChemistrySelect (article, 2023-04-24)
Field: Materials Science — Materials Chemistry

Machine Learning for the Prediction of Ionization Potential and Electron Affinity Energies Obtained by Density Functional Theory Abstract Quantum chemical (QC) calculations based on density functional theory (DFT) provide increasingly accurate estimates of various properties, but with a relatively high computational cost. Machine learning (ML) techniques can be envisaged to extract new knowledge from these large volumes of data, creating empirical models to fast predict QC calculations in new situations. Here, ML algorithms were explored for the fast estimation of ionization potential (IP) and electron affinity (EA) energies calculated by DFT using the B3LYP and PBE0 with 6–31G** basic set on molecular descriptors generated from DFT‐optimized geometries. A database of 9,410 and 9,627 small organic structures for IP and EA energies modelling were used, respectively. Several ML algorithms such as random forest, support vector machines, deep learning multilayer perceptron networks, and light gradient‐boosting machine were screened. The best performance was achieved with a consensus regression model predicted an external test set of 972 and 963 small organic molecules achieving a mean absolute error up to 0.23 eV and 0.32 eV for modelling IP and EA energies, respectively. Density functional theory ; Electron affinity (data page) ; Boosting (machine learning) ; Gradient boosting ; Test set ; Ionization energy ; Computer science ; Support vector machine ; Machine learning ; Multilayer perceptron ; Artificial intelligence ; Ionization ; Perceptron ; Artificial neural network ; Electron ; Random forest ; Algorithm ; Molecule ; Chemistry ; Computational chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Shigeyuki Matsumoto | Shoichi Ishida | Kei Terayama | Yasushi Okuno
Source: Biophysics and Physicobiology (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Quantitative analysis of protein dynamics using a deep learning technique combined with experimental cryo-EM density data and MD simulations Protein functions associated with biological activity are precisely regulated by both tertiary structure and dynamic behavior. Thus, elucidating the high-resolution structures and quantitative information on in-solution dynamics is essential for understanding the molecular mechanisms. The main experimental approaches for determining tertiary structures include nuclear magnetic resonance (NMR), X-ray crystallography, and cryogenic electron microscopy (cryo-EM). Among these procedures, recent remarkable advances in the hardware and analytical techniques of cryo-EM have increasingly determined novel atomic structures of macromolecules, especially those with large molecular weights and complex assemblies. In addition to these experimental approaches, deep learning techniques, such as AlphaFold 2, accurately predict structures from amino acid sequences, accelerating structural biology research. Meanwhile, the quantitative analyses of the protein dynamics are conducted using experimental approaches, such as NMR and hydrogen-deuterium mass spectrometry, and computational approaches, such as molecular dynamics (MD) simulations. Although these procedures can quantitatively explore dynamic behavior at high resolution, the fundamental difficulties, such as signal crowding and high computational cost, greatly hinder their application to large and complex biological macromolecules. In recent years, machine learning techniques, especially deep learning techniques, have been actively applied to structural data to identify features that are difficult for humans to recognize from big data. Here, we review our approach to accurately estimate dynamic properties associated with local fluctuations from three-dimensional cryo-EM density data using a deep learning technique combined with MD simulations. Structural biology ; Molecular dynamics ; Deep learning ; Cryo-electron microscopy ; Computer science ; Protein dynamics ; Biological system ; Experimental data ; Resolution (logic) ; Artificial intelligence ; Chemistry ; Physics ; Nuclear magnetic resonance ; Computational chemistry ; Biology ; Mathematics Enzyme Structure and Function ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Ji Qi | Tsz Wai Ko | Brandon C. Wood | Tuan Anh Pham | Shyue Ping Ong
Source: arXiv (Cornell University) (preprint, 2023-07-24)
Field: Materials Science — Materials Chemistry

Robust Training of Machine Learning Interatomic Potentials with Dimensionality Reduction and Stratified Sampling Machine learning interatomic potentials (MLIPs) enable the accurate simulation of materials at larger sizes and time scales, and play increasingly important roles in the computational understanding and design of materials. However, MLIPs are only as accurate and robust as the data they are trained on. In this work, we present DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling as an approach to select a robust training set of structures from a large and complex configuration space. By applying DIRECT sampling on the Materials Project relaxation trajectories dataset with over one million structures and 89 elements, we develop an improved materials 3-body graph network (M3GNet) universal potential that extrapolate more reliably to unseen structures. We further show that molecular dynamics (MD) simulations with universal potentials such as M3GNet can be used in place of expensive \textit{ab initio} MD to rapidly create a large configuration space for target materials systems. Combined with DIRECT sampling, we develop a highly reliable moment tensor potential for Ti-H system without the need for iterative optimization. This work paves the way towards robust high throughput development of MLIPs across any compositional complexity. Computer science ; Dimensionality reduction ; Curse of dimensionality ; Sampling (signal processing) ; Interatomic potential ; Machine learning ; Artificial intelligence ; Computational science ; Molecular dynamics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Maciej J. Karcz | Luca Messina | Eiji Kawasaki | Serenah Rajaonson | Didier Bathellier | Maylise Nastar | Thomas Schüler | Émeric Bourasseau
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Semi-supervised generative approach to chemical disorder: application to point-defect formation in uranium–plutonium mixed oxides Chemical disorder has a major impact on the characterization of the atomic-scale properties of highly complex chemical compounds, such as the properties of point defects. Due to the vast amount of possible atomic configurations, the study of such properties becomes intractable if treated with direct sampling. In this work, we propose an alternative approach, in which samples are selected based on the local atomic composition around the defect, and the defect formation energy is obtained as a function of this local composition with a reduced computational cost. We apply this approach to (U, Pu)O<sub>2</sub> nuclear fuels. The formation-energy distribution is computed using machine-learning generative methods, and used to investigate the impact of chemical disorder and the range of influence of local composition on the defect properties. The predicted distributions are then used to calculate the concentration of thermal defects. This approach allows for the first time for the computation of the latter property with a physically meaningful exploration of the configuration space, and opens the way to a more efficient determination of physico-chemical properties in other chemically-disordered compounds such as high-entropy alloys. Plutonium ; Uranium ; Generative grammar ; Point (geometry) ; Materials science ; Chemistry ; Computer science ; Radiochemistry ; Metallurgy ; Artificial intelligence ; Mathematics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Radioactive element chemistry and processing
Authors: Hyeonsu Kim | Jeheon Woo | Seonghwan Kim | Seokhyun Moon | Jun Hyeong Kim | Woo Youn Kim
Source: arXiv (Cornell University) (preprint, 2023-03-28)
Field: Materials Science — Materials Chemistry

GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoising As quantum chemical properties have a dependence on their geometries, graph neural networks (GNNs) using 3D geometric information have achieved high prediction accuracy in many tasks. However, they often require 3D geometries obtained from high-level quantum mechanical calculations, which are practically infeasible, limiting their applicability to real-world problems. To tackle this, we propose a new training framework, GeoTMI, that employs denoising process to predict properties accurately using easy-to-obtain geometries (corrupted versions of correct geometries, such as those obtained from low-level calculations). Our starting point was the idea that the correct geometry is the best description of the target property. Hence, to incorporate information of the correct, GeoTMI aims to maximize mutual information between three variables: the correct and the corrupted geometries and the property. GeoTMI also explicitly updates the corrupted input to approach the correct geometry as it passes through the GNN layers, contributing to more effective denoising. We investigated the performance of the proposed method using 3D GNNs for three prediction tasks: molecular properties, a chemical reaction property, and relaxed energy in a heterogeneous catalytic system. Our results showed consistent improvements in accuracy across various tasks, demonstrating the effectiveness and robustness of GeoTMI. Computer science ; Robustness (evolution) ; Property (philosophy) ; Limiting ; Information geometry ; Algorithm ; Quantum chemical ; Graph ; Quantum ; Theoretical computer science ; Artificial intelligence ; Mathematics ; Geometry Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Surface Chemistry and Catalysis