jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maryam Astero | Juho Rousu
Source: Research Square (preprint, 2024-01-18)
Field: Materials Science — Materials Chemistry

Learning symmetry-aware atom mapping in chemical reactions through deep graph matching <title>Abstract</title> Accurate atom mapping, which establishes correspondences between atoms in reactants and products, is a crucial step in analyzing chemical reactions. In this paper, we present a novel end-to-end approach that formulates the atom mapping problem as a deep graph matching task. Our proposed model, AMNet (Atom Matching Network), utilizes molecular graph representations and employs various atom and bond features using graph neural networks to capture the intricate structural characteristics of molecules, ensuring precise atom correspondence predictions. Notably, AMNet incorporates the consideration of molecule symmetry, enhancing accuracy while simultaneously reducing computational complexity. The integration of the Weisfeiler-Lehman isomorphism test for symmetry identification refines the model's predictions. Furthermore, our model maps the entire atom in a chemical reaction, offering a comprehensive approach beyond focusing solely on the main molecules in reactions. We evaluated the performance of the model on a subset of USPTO reaction datasets, and the result reveals an average accuracy of 97.3% on mapped atoms and a top@10 accuracy of 99.7%, indicating 99.7% of reactions are correctly mapped when the correct mapped atom is in the first top 10 predicted atom. Atom (system on chip) ; Graph isomorphism ; Matching (statistics) ; Computer science ; Graph ; Molecule ; Isomorphism (crystallography) ; Symmetry (geometry) ; Chemistry ; Algorithm ; Biological system ; Theoretical computer science ; Mathematics ; Crystallography ; Line graph ; Geometry ; Crystal structure Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Meysam Bagheri Tagani
Source: Research Square (preprint, 2024-09-24)
Field: Materials Science — Materials Chemistry

Exploring the Thermal and Mechanical Properties of Semi Metallic XCr₂S₄ (X: Ag, Au) Monolayers Monolayer ; Materials science ; Metal ; Thermal ; Metallurgy ; Composite material ; Nanotechnology ; Thermodynamics ; Physics 2D Materials and Applications ; MXene and MAX Phase Materials ; Chalcogenide Semiconductor Thin Films
Authors: Jeff Adie | Cheng Siong Chin | Jichun Li | Simon See
Source:  (article, 2024-05-15)
Field: Materials Science — Materials Chemistry

GAIA-Chem: A Framework for Global AI-Accelerated Atmospheric Chemistry Modelling The inclusion of atmospheric chemistry in global climate projections is currently limited by the high computational expense of modelling the many reactions of chemical species. Recent rapid advancements in artificial intelligence (AI) provide us with new tools for reducing the cost of numerical simulations. The application of these tools to atmospheric chemistry is still somewhat nascent and multiple challenges remain due to the reaction complexities and the high number of chemical species. In this work, we present GAIA-Chem, a global AI-accelerated atmospheric chemistry framework for large-scale, multi-fidelity, data-driven chemical simulations. GAIA-Chem provides an environment for testing different approaches to data-driven species simulation. GAIA-Chem includes curated training and validation datasets, support for offline and online training schemes, and comprehensive metrics for model intercomparison. We use GAIA-Chem to evaluate two Deep Neural Network (DNN) models: a standard autoencoder scheme based on convolutional LSTM nodes, and a transformer-based model. We show computational speedups of 1,280x over numerical methods for the chemical solver and a 2.8x reduction in RMSE when compared to previous works. Computer science ; Autoencoder ; Atmospheric chemistry ; Artificial neural network ; Solver ; Convolutional neural network ; Deep learning ; Artificial intelligence ; Machine learning ; Chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Air Quality Monitoring and Forecasting
Authors: Heather J. Kulik | Ilia Kevlishvili | Jafer Vakil | David W. Kastner | Xiao Huang | Stephen L. Craig
Source: Research Square (preprint, 2024-09-13)
Field: Materials Science — Surfaces, Coatings and Films

High-Throughput Discovery of Ferrocene Mechanophores with Enhanced Reactivity and Network Toughening Toughening ; Throughput ; Reactivity (psychology) ; Ferrocene ; Nanotechnology ; Computer science ; Combinatorial chemistry ; Business ; Chemistry ; Materials science ; Medicine ; Electrochemistry ; Telecommunications Polymer Surface Interaction Studies ; Luminescence and Fluorescent Materials ; Force Microscopy Techniques and Applications
Authors: Gyoung S. Na | Ye Cheol Rho
Source:  (article, 2024-03-16)
Field: Chemistry — Analytical Chemistry

Learning $M$-Order Spectrum Graphs to Identify Unknown Chemical Compounds from Infrared Spectroscopy Data Infrared (IR) spectrum analysis is an efficient and generally applicable method for identifying unknown chemical compounds. However, the analysis quality of the IR spectrum essentially depends on a labor-intensive manual search of human experts. We propose a graph-based machine learning approach for automatically identifying unknown chemical compounds from the input IR spectra without the manual analysis of human experts. To this end, we define a graph representation of unstructured spectrum data and devise a sequence model based on graph neural networks. We evaluated the proposed method in three common tasks of the IR spectrum analysis: material class classification, functional group detection, and compound identification. In these experiments, the proposed method achieved state-of-the-art prediction accuracy. In particular, the proposed method showed a prediction accuracy of 41.82%-85.10% in identifying unknown chemical compounds from their IR spectra, which is one of the most challen]ging tasks in chemical analysis. Spectroscopy ; Infrared spectroscopy ; Infrared ; Order (exchange) ; Spectrum (functional analysis) ; Computer science ; Materials science ; Physics ; Optics ; Astronomy ; Business ; Quantum mechanics Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Nahla Khraief | M. Hidayath Ansari | Salah Eldeen F. Hegazi | Anjali Appukuttan | Bincy Vincent | Huda Fatima
Source: International Journal of Electrical and Electronics Engineering (article, 2024-04-30)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Structure Prediction for QM9 Quantum Datasets The inner arrangement of the quantum mechanics dataset QM9 is investigated in this study. The dataset contains 1000 organic molecules as well as being defined in terms of electronic properties. To estimate the atomic composition using inverse molecular design attributes, one must understand the structure and properties of such data. The study used methods for detecting outliers, clustering, and intrinsic dimension analysis. The dataset was found to have descriptive dimensions far higher than their intrinsic dimensionality. Inliner items make up the majority of the inner core area of the QM9 data, whereas outliers dominate the outside region. The atom count in a molecule is strongly related to its outlier or inner character. Despite structural differences, important variables for inverse molecular design are very predictable. The molecular representation was estimated using Graph Neural Network (GNN), a modern Machine Learning (ML) algorithm. This study also did feature extraction and preprocessing before this algorithm. This proposed technique works for the outcomes. Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science
Authors: Nasrollah Majidian | M.Sc. Karrar. Mahdi. Saleh | Mohammad Samipourgiri
Source: Iranian Polymer Journal (article, 2024-07-22)
Field: Materials Science — Polymers and Plastics

Kinetics study of catalytic pyrolysis of polystyrene polymer using response surface method Materials science ; Polystyrene ; Pyrolysis ; Kinetics ; Polymer ; Catalysis ; Chemical engineering ; Response surface methodology ; Composite material ; Organic chemistry ; Chromatography Polymer crystallization and properties ; Fiber-reinforced polymer composites ; Microplastics and Plastic Pollution
Authors: Laura Budewig | Sang-Kil Son | Zoltán Jurek | Malik Muhammad Abdullah | Marina Tropmann-Frick | Robin Santra
Source: Physical Review Research (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

X-ray-induced atomic transitions via machine learning: A computational investigation Intense x-ray free-electron laser pulses can induce multiple sequences of one-photon ionization and accompanying decay processes in atoms, producing highly charged atomic ions. Considering individual quantum states during these processes provides more precise information about the x-ray multiphoton ionization dynamics than the common configuration-based approach. However, in such a state-resolved approach, extremely huge-sized rate-equation calculations are inevitable. Here we present a strategy that embeds machine-learning models into a framework for atomic state-resolved ionization dynamics calculations. Machine learning is employed for the required atomic transition parameters, whose calculations possess the computationally most expensive steps. We find for argon that both feedforward neural networks and random forest regressors can predict these parameters with acceptable, but limited accuracy. State-resolved ionization dynamics of argon, in terms of charge-state distributions and electron and photon spectra, are also presented. Comparing fully calculated and machine-learning-based results, we demonstrate that the proposed machine-learning strategy works in principle and that the performance, in terms of charge-state distributions and electron and photon spectra, is good. Our work establishes a first step toward accelerating the calculation of atomic state-resolved ionization dynamics induced by high-intensity x rays. Published by the American Physical Society 2024 Computer science ; Physics ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Chaoyi Huang | Chunming Yang | Yingjie He
Source:  (article, 2024-05-24)
Field: Materials Science — Materials Chemistry

Using interpretable machine learning techniques to analyze the thermal decomposition behavior of high-energy compounds for dataset partitioning In recent years, machine learning has shown great potential in the field of molecular research. However, for energy-containing molecules with small datasets, the difficulty of model prediction is high, and improving model prediction performance is a challenge. This paper aims to analyze the thermal decomposition behavior of energetic compounds using interpretable machine learning techniques, build a dataset containing nearly 900 energetic compound data, and employ interpretable machine learning methods to construct a set of molecular descriptors. After reasonable partitioning of the dataset, directed message passing neural network (D-MPNN) is used for training and performance evaluation. The method in this paper can improve model training effectiveness, with a decrease of approximately 15% in MAE and RMSE, and an increase of about 8% in <Formula format="inline"><TexMath><?TeX ${R}^2$ ?></TexMath><File name="a00--inline1" type="gif"/></Formula>. This has important value for model training on small-scale datasets. Decomposition ; Computer science ; Artificial intelligence ; Energy (signal processing) ; Machine learning ; Thermal decomposition ; Statistics ; Chemistry ; Mathematics Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Semiconductor materials and devices
Authors: Jack Yang | Ziqi Yin | Sean Li
Source: ChemRxiv (preprint, 2024-12-23)
Field: Materials Science — Materials Chemistry

Accounting for the Vibrational Contribution to the Configurational Entropy in Disordered Solids with Machine Learned Forcefields: A Case Study of Garnet Electrolyte Li7La3Zr2O12 Accounting for lattice vibrations to accurately determine the phase stabilities of site-disordered solids is a long-standing challenge in computational material designs, due to the high computational cost associated with sampling the vast configurational space to obtain the converged thermodynamic quantities. One example is the garnet electrolyte Li7La3Zr2O12, the high-temperature and high-ion-mobility cubic phase of which is disordered in its Li+ site occupations, such that both the vibrational and configurational entropic contributions to its phase stability cannot be ignored. Understanding the subtle interplay between vibrational and configurational entropies in this material will therefore play a critical role in the rational manipulation of dopants and defects to stabilise cubic Li7La3Zr2O12 at room temperature for practical applications. Here, by developing machine learned forcefields based on an equivariant message-passing neural network SO3krates, we follow a strict statistical thermodynamic protocol to quantify the phase stability of cubic Li7La3Zr2O12 through structural optimisations, as well as molecular dynamic simulations at 300 and 1500 K, for a total of 70,120 configurations of cubic Li7La3Zr2O12. Although this only covers a tiny fraction of the configurational space (7x10^34 configurations in total), we are able to deterministically show the vibrational contributions to the total configurational free energy at 1500 K are significant (on the order of 1 eV/atom) in correctly ordering the stability of the cubic Li7La3Zr2O12 over its tetragonal counterpart, thanks to the high data efficiency, accuracy, stability and good transferability of the transformer-based equivariant network architecture behind SO3krates. Therefore, our work opens up new avenue to accelerate the accurate computational designs of disordered solids, such as solid electrolytes, for technologically important applications. Electrolyte ; Entropy (arrow of time) ; Configuration entropy ; Materials science ; Statistical physics ; Thermodynamics ; Accounting ; Chemistry ; Physical chemistry ; Physics ; Business Solid-state spectroscopy and crystallography
Authors: Emily Shinkle
Source: arXiv (Cornell University) (preprint, 2024-06-17)
Field: Materials Science — Materials Chemistry

Coarse-grained methanol trajectory Coarse-graining is a molecular modeling technique in which an atomistic system is represented in a simplified fashion that retains the most significant system features that contribute to a target output, while removing the degrees of freedom that are less relevant. This reduction in model complexity allows coarse-grained molecular simulations to reach increased spatial and temporal scales compared to corresponding all-atom models. A core challenge in coarse-graining is to construct a force field that represents the interactions in the new representation in a way that preserves the atomistic-level properties. Many approaches to building coarse-grained force fields have limited transferability between different thermodynamic conditions as a result of averaging over internal fluctuations at a specific thermodynamic state point. Here, we use a graph-convolutional neural network architecture, the Hierarchically Interacting Particle Neural Network with Tensor Sensitivity (HIP-NN-TS), to develop a highly automated training pipeline for coarse grained force fields which allows for studying the transferability of coarse-grained models based on the force-matching approach. We show that this approach not only yields highly accurate force fields, but also that these force fields are more transferable through a variety of thermodynamic conditions. These results illustrate the potential of machine learning techniques such as graph neural networks to improve the construction of transferable coarse-grained force fields. Transferability ; Graph ; Computer science ; Artificial neural network ; Statistical physics ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Physics Machine Learning in Materials Science
Authors: Maksym Szemer | Szymon Buchaniec | Tomasz Prokop | Grzegorz Brus
Source: arXiv (Cornell University) (preprint, 2024-10-04)
Field: Materials Science — Materials Chemistry

Topology-Informed Machine Learning for Efficient Prediction of Solid Oxide Fuel Cell Electrode Polarization Machine learning has emerged as a potent computational tool for expediting research and development in solid oxide fuel cell electrodes. The effective application of machine learning for performance prediction requires transforming electrode microstructure into a format compatible with artificial neural networks. Input data may range from a comprehensive digital material representation of the electrode to a selected set of microstructural parameters. The chosen representation significantly influences the performance and results of the network. Here, we show a novel approach utilizing persistence representation derived from computational topology. Using 500 microstructures and current-voltage characteristics obtained with 3D first-principles simulations, we have prepared an artificial neural network model that can replicate current-voltage characteristics of unseen microstructures based on their persistent image representation. The artificial neural network can accurately predict the polarization curve of solid oxide fuel cell electrodes. The presented method incorporates complex microstructural information from the digital material representation while requiring substantially less computational resources (preprocessing and prediction time approximately 1 min) compared to our high-fidelity simulations (simulation time approximately 1 hour) to obtain a single current-potential characteristic for one microstructure. Polarization (electrochemistry) ; Electrode ; Solid oxide fuel cell ; Fuel cells ; Topology (electrical circuits) ; Oxide ; Computer science ; Materials science ; Artificial intelligence ; Machine learning ; Chemical engineering ; Electrical engineering ; Chemistry ; Engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Machine Learning and ELM
Authors: Desmarita Leni | Ade Usra Berli | Dytchia Septi Kesuma | Haris Haris | R. Sumiati
Source: Jurnal Engine Energi Manufaktur dan Material (article, 2024-04-18)
Field: Materials Science — Materials Chemistry

Evaluasi Pemodelan Augmentasi Data Sifat Mekanik Aluminium Menggunakan Generative Adversarial Networks Materials informatics is a new approach in material science that integrates information technology and material science to optimize the discovery of new materials more efficiently and innovatively. In materials informatics, experimental and simulation data are combined with data-driven methods such as big data, data augmentation, and machine learning to gain a deeper understanding of material properties. However, limitations in the availability of samples with desired characteristics and the lack of accurate experimental data pose challenges in materials informatics. In this study, we attempt to address these challenges by modeling the augmentation of mechanical properties of aluminum using Generative Adversarial Networks (GAN). GAN is used to generate synthetic data of aluminum's mechanical properties that closely resemble experimental data. This modeling is trained using experimental testing data consisting of aluminum's mechanical properties and chemical elements in the alloy, obtained from the material database. The dataset comprises 9 chemical element variables in the aluminum alloy and 2 mechanical property variables. The synthetic data generated from the modeling is evaluated using descriptive statistics, Pearson correlation, and Kolmogorov-Smirnov (KS) test to assess the extent to which the synthetic data resembles the original data. The evaluation results indicate that the distribution of synthetic data is similar to the original data. The Pearson correlation results show that most variables of chemical elements and mechanical properties of aluminum in the synthetic data have a correlation that is quite similar to the original data. The KS test results also indicate that the distribution of synthetic data does not significantly differ from the distribution of the original data. This indicates that the synthetic data generated has a high resemblance to the experimental data, enabling its use in materials informatics research. Thus, modeling the augmentation of aluminum's mechanical property data using GAN provides a significant contribution to expanding data availability in material science. Experimental data ; Test data ; Property (philosophy) ; Synthetic data ; Materials informatics ; Data modeling ; Pearson product-moment correlation coefficient ; Artificial neural network Machine Learning in Materials Science ; Data Mining and Machine Learning Applications ; Material Selection and Properties
Authors: Kentaro Fuku | Takefumi Yoshida
Source: ChemRxiv (preprint, 2024-09-08)
Field: Chemistry — Analytical Chemistry

Unsupervised machine learning-based image recognition of raw IR spectra: Toward chemist-like chemical structural classification Artificial intelligence ; Chemist ; Pattern recognition (psychology) ; Computer science ; Raw data ; Image (mathematics) ; Machine learning ; Unsupervised learning ; Chemistry ; Organic chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Z. M. Qiu | Luozhijie Jin | Zijian Du | Hongyu Chen | Yan Cen | Siqi Sun | Yongfeng Mei | Hao Zhang
Source: arXiv (Cornell University) (preprint, 2024-09-10)
Field: Materials Science — Materials Chemistry

VQCrystal: Leveraging Vector Quantization for Discovery of Stable Crystal Structures Discovering functional crystalline materials through computational methods remains a formidable challenge in materials science. Here, we introduce VQCrystal, an innovative deep learning framework that leverages discrete latent representations to overcome key limitations in current approaches to crystal generation and inverse design. VQCrystal employs a hierarchical VQ-VAE architecture to encode global and atom-level crystal features, coupled with a machine learning-based inter-atomic potential(IAP) model and a genetic algorithm to realize property-targeted inverse design. Benchmark evaluations on diverse datasets demonstrate VQCrystal's advanced capabilities in representation learning and novel crystal discovery. Notably, VQCrystal achieves state-of-the-art performance with 91.93\% force validity and a Fréchet Distance of 0.152 on MP-20, indicating both strong validity and high diversity in the sampling process. To demonstrate real-world applicability, we apply VQCrystal for both 3D and 2D material design. For 3D materials, the density-functional theory validation confirmed that 63.04\% of bandgaps and 99\% of formation energies of the 56 filtered materials matched the target range. Moreover, 437 generated materials were validated as existing entries in the full database outside the training set. For the discovery of 2D materials, 73.91\% of 23 filtered structures exhibited high stability with formation energies below -1 eV/atom. Our results highlight VQCrystal's potential to accelerate the discovery of novel materials with tailored properties. Vector quantization ; Quantization (signal processing) ; Computer science ; Theoretical computer science ; Algorithm X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies ; Crystallography and molecular interactions