jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A. A. Vakhrushev | А. В. Матвеев | Anna V. Nartova
Source: Kinetics and Catalysis (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Application of a Convolutional Neural Network for Automated Analysis of X-ray Photoelectron Spectra of Heterogeneous Catalysts A convolutional neural network was used to solve the problem of segmentation of XPS spectra. The developed combination of recognition using a machine learning model and a post-processing algorithm provided fast automatic analysis of XPS data. The results of determining the positions and areas of peaks were in good agreement with both the results of manual analysis and handbook values. The proposed approach was applied to analyze the XPS spectra of heterogeneous catalysts (Pd/Al2O3 and Sr2TiO4) and chemical compounds used in the preparation of catalysts (AgCl and TiO2). Catalysis ; Chemistry ; X-ray photoelectron spectroscopy ; Spectral line ; Convolutional neural network ; X-ray ; Heterogeneous catalysis ; Chemical engineering ; Artificial intelligence ; Organic chemistry ; Computer science ; Optics ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Catalytic Processes in Materials Science
Authors: Mohammed Al‐Fahdi | Riccardo Rurali | Jianjun Hu | Christopher Wolverton | Ming Hu
Source: arXiv (Cornell University) (preprint, 2024-10-21)
Field: Materials Science — Materials Chemistry

Accelerating Discovery of Extreme Lattice Thermal Conductivity by Crystal Attention Graph Neural Network (CATGNN) Using Chemical Bonding Intuitive Descriptors Designing materials with targeted lattice thermal conductivity (LTC) demands electronic-level insight into chemical bonding. We introduce two bonding descriptors, namely normalized negative integrated crystal orbital Hamilton populations (-ICOHP) and normalized integrated crystal orbital bond index (ICOBI), that strongly correlate with LTC and rattling (mean-squared displacement), surpassing empirical rules and the unnormalized -ICOHP across &gt;4,500 inorganic crystals by first-principles. We train a Crystal Attention Graph Neural Network (CATGNN) to predict these descriptors and screen ~200,000 database structures for extreme LTCs. From 367 (533) candidates with low (high) normalized -ICOHP and normalized ICOBI, first-principles validation identifies 106 dynamically stable compounds with LTC &lt;5 W/mK (68% &lt;2 W/mK) and 13 stable compounds with LTC &gt;100 W/mK. The descriptors' low cost and clear physical meaning provide a rapid, reliable route to high-throughput discovery and inverse design of crystalline materials with ultralow or ultrahigh LTC for applications in thermal insulation, thermoelectrics, and electronics cooling. Thermal conductivity ; Graph ; Artificial neural network ; Lattice (music) ; Computer science ; Materials science ; Crystal (programming language) ; Artificial intelligence ; Statistical physics ; Theoretical computer science ; Physics ; Composite material Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Sejun Kim | Won June Kim | Changho Kim | Eok Kyun Lee | Hyungjun Kim
Source: Journal of the American Chemical Society (article, 2024-09-12)
Field: Materials Science — Materials Chemistry

PROFiT-Net: Property-Networking Deep Learning Model for Materials There is a growing need to develop artificial intelligence technologies capable of accurately predicting the properties of materials. This necessitates the expansion of material databases beyond the scope of density functional theory, and also the development of deep learning (DL) models that can be effectively trained with a limited amount of high-fidelity data. We developed a DL model utilizing a crystal structure representation based on the orbital field matrix (OFM), which was modified to incorporate information on elemental properties and valence electron configurations. This model, effectively capturing the interrelation between the elemental properties in the crystal, was coined the PRoperty-networking Orbital Field maTrix-convolutional neural Network (PROFiT-Net). Remarkably, PROFiT-Net demonstrated high accuracy in predicting the dielectric constant, experimental band gaps, and formation enthalpies compared with other leading DL models. Moreover, our model accurately identifies physical patterns, such as avoiding the prediction of unphysical negative band gaps and exhibiting a Penn-model-like trend while maintaining the scalability. We envision that PROFiT-Net will accelerate the development of functional materials. Chemistry ; Net (polyhedron) ; Net profit ; Profit (economics) ; Property (philosophy) ; Microeconomics ; Economics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: D. R. Nizin | Т.А. Низина | V. P. Selyaev | I. P. Spirin
Source: Stroitel nye Materialy (article, 2024-12-15)
Field: Materials Science — General Materials Science

Accounting for the moisture state of polymeric materials when developing machine learning models The paper provides the results of studying the dependence of elastic-strength properties of unfilled epoxy polymers on moisture content by example of 18 different compounds. It includes the analysis of possible effects associated with changes in free moisture content in the polymer matrix structure, including a change in nature of behavior under load from brittle to viscous with a multiple increase in relative deformations at rupture, as well as quasi-embrittlement, manifested in the elimination or reduction of forced highly elastic deformations on the deformation curve. In addition to the shape corresponding to the one close to linear dependence of the change in tensile strength and modulus of elasticity on moisture content with a maximum around W~0%, the study revealed other forms of interrelation of parameters under consideration: with a local maximum of values in the “optimal moisture content” area differing from W~0%; with “plateau” sections around both extreme humidity conditions. The similarity of the effects occurring around moisture content of W~0% for epoxy polymer samples both in the control state and after prolonged climatic aging is shown. A hypothesis was formulated regarding the existence of a pattern common to epoxy polymers of change in the nature of the dependence of mechanical strength on moisture content during field climatic aging. Based on a joint analysis of the dependence curves of elastic-strength parameters on moisture content, the most representative epoxy polymer compounds for field studies were selected to compile training datasets for a machine learning model predicting changes in the elastic-strength properties of polymer materials exposed to environmental factors. Moisture ; State (computer science) ; Process engineering ; Accounting ; Environmental science ; Materials science ; Computer science ; Composite material ; Business ; Engineering Material Properties and Applications ; Mechanical and Thermal Properties Analysis
Authors: Alex K. Chew | Matthew Sender | Zachary Kaplan | Anand Chandrasekaran | Jackson Chief Elk | Andrea Browning | H. Shaun Kwak | Mathew D. Halls | Mohammad Atif Faiz Afzal
Source: ChemRxiv (preprint, 2024-01-17)
Field: Materials Science — Materials Chemistry

Advancing Material Property Prediction: Using Physics-Informed Machine Learning Models for Viscosity In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially in the material science domain where data is limited. To address this, we integrate physics-informed descriptors from molecular dynamics (MD) simulations to enhance the accuracy and interpretability of ML models. Our current study focuses on accurately predicting viscosity in liquid systems using MD descriptors. In this work, we curated a comprehensive dataset of over 4,000 small organic molecules’ viscosities from scientific literature, publications, and online databases. This dataset enabled us to develop quantitative structure–property relationships (QSPR) consisting of descriptor-based and graph neural network models to predict temperature-dependent viscosities for a wide range of viscosities with considerable accuracy. The QSPR models reveal that including MD descriptors improves prediction accuracies of experimental viscosities, particularly at the small data set scale of fewer than a thousand data points. Furthermore, feature importance tools reveal that intermolecular interactions captured by MD descriptors are most important for accurate viscosity predictions. Finally, the QSPR models can accurately capture the inverse relationship between viscosity and temperature for six battery-relevant solvents, some of which were not included in the original data set. Our research highlights the effectiveness of incorporating MD descriptors into QSPR models, which leads to improved accuracy for properties that are difficult to predict when using physics-based models alone or when limited data is available. Interpretability ; Quantitative structure–activity relationship ; Applicability domain ; Molecular descriptor ; Machine learning ; Artificial intelligence ; Viscosity ; Experimental data ; Computer science ; Predictive modelling ; Cheminformatics ; Set (abstract data type) ; Artificial neural network ; Data mining ; Mathematics ; Thermodynamics ; Chemistry ; Computational chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Dat Nguyen | Phuc T. Le
Source: Technical Education Science/Giáo dục Kỹ thuật (article, 2024-06-28)
Field: Materials Science — Materials Chemistry

Leveraging Graph Neural Networks for Enhanced Prediction of Molecular Solubility via Transfer Learning In this study, we explore the potential of graph neural networks (GNNs), in combination with transfer learning, for the prediction of molecular solubility, a crucial property in drug discovery and materials science. Our approach begins with the development of a GNN-based model to predict the dipole moment of molecules. The extracted dipole moment, alongside a selected set of molecular descriptors, feeds into a subsequent predictive model for water solubility. This two-step process leverages the inherent correlations between molecular structure and its physical properties, thus enhancing the accuracy and generalizability. Our data showed that GNN models with attention mechanism and those utilize bond properties outperformed other models. Especially, 3D GNN models such as ViSNet exhibited outstanding performance, with an R2 value of 0.9980. For the prediction of water solubility, the inclusion of dipole moments greatly enhanced the predictive power of various machine learning models. Our methodology demonstrates the effectiveness of GNNs in capturing complex molecular features and the power of transfer learning in bridging related predictive tasks, offering a novel approach for computational predictions in chemistry. Transfer of learning ; Computer science ; Solubility ; Artificial intelligence ; Artificial neural network ; Graph ; Machine learning ; Chemistry ; Theoretical computer science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yannick Couzinié | Yuya Seki | Yusuke Nishiya | Hirofumi Nishi | Taichi Kosugi | Shu Tanaka | Yu‐ichiro Matsushita
Source: arXiv (Cornell University) (preprint, 2024-08-07)
Field: Materials Science — Materials Chemistry

Machine learning supported annealing for prediction of grand canonical crystal structures This study investigates the application of Factorization Machines with Quantum Annealing (FMQA) to address the crystal structure problem (CSP) in materials science. FMQA is a black-box optimization algorithm that combines machine learning with annealing machines to find samples to a black-box function that minimize a given loss. The CSP involves determining the optimal arrangement of atoms in a material based on its chemical composition, a critical challenge in materials science. We explore FMQA's ability to efficiently sample optimal crystal configurations by setting the loss function to the energy of the crystal configuration as given by a predefined interatomic potential. Further we investigate how well the energies of the various metastable configurations, or local minima of the potential, are learned by the algorithm. Our investigation reveals FMQA's potential in quick ground state sampling and in recovering relational order between local minima. Annealing (glass) ; Artificial intelligence ; Grand Challenges ; Crystal structure prediction ; Computer science ; Machine learning ; Crystal structure ; Physics ; Chemistry ; Crystallography ; Thermodynamics ; Operating system Machine Learning in Materials Science
Authors: Yicheng Chen | Wenjie Yan | Zhanfeng Wang | Jianming Wu | Xinyue Xu
Source: arXiv (Cornell University) (preprint, 2024-08-12)
Field: Materials Science — Materials Chemistry

Constructing accurate and efficient general-purpose atomistic machine learning model with transferable accuracy for quantum chemistry Density Functional Theory (DFT) has been a cornerstone in computational science, providing powerful insights into structure-property relationships for molecules and materials through first-principles quantum-mechanical (QM) calculations. However, the advent of atomistic machine learning (ML) is reshaping the landscape by enabling large-scale dynamics simulations and high-throughput screening at DFT-equivalent accuracy with drastically reduced computational cost. Yet, the development of general-purpose atomistic ML models as surrogates for QM calculations faces several challenges, particularly in terms of model capacity, data efficiency, and transferability across chemically diverse systems. This work introduces a novel extension of the polarizable atom interaction neural network (namely, XPaiNN) to address these challenges. Two distinct training strategies have been employed, one direct-learning and the other $Δ$-ML on top of a semi-empirical QM method. These methodologies have been implemented within the same framework, allowing for a detailed comparison of their results. The XPaiNN models, in particular the one using $Δ$-ML, not only demonstrate competitive performance on standard benchmarks, but also demonstrate the effectiveness against other ML models and QM methods on comprehensive downstream tasks, including non-covalent interactions, reaction energetics, barrier heights, geometry optimization and reaction thermodynamics, etc. This work represents a significant step forward in the pursuit of accurate and efficient atomistic ML models of general-purpose, capable of handling complex chemical systems with transferable accuracy. Quantum chemistry ; Computer science ; Quantum ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Chemistry ; Algorithm ; Statistical physics ; Physics ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science
Authors: Arturo Lopez-Solorzano | Eréndira Rendón | Sonia Martínez‐Gallegos | R. Alejo
Source: MRS Advances (article, 2024-02-27)
Field: Chemistry — Analytical Chemistry

Classification of crystallographic materials through machine learning Materials science ; k-nearest neighbors algorithm ; Plasmon ; Similarity (geometry) ; Wavelength ; Euclidean distance ; Artificial intelligence ; Computer science ; Optoelectronics ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Geochemistry and Geologic Mapping ; Water Quality Monitoring and Analysis
Authors: Shuhao Zhang | Michael Chigaev | Olexandr Isayev | Richard A. Messerly | Nicholas Lubbers
Source: ChemRxiv (preprint, 2024-12-09)
Field: Materials Science — Materials Chemistry

Including physics-informed atomization constraints in neural networks for reactive chemistry Machine learning interatomic potentials (MLIPs) have emerged as powerful tools for investigating atomistic systems with high accuracy and relatively low computational cost. However, a common challenge with current MLIPs is their limited ability to accurately predict the relative energies of systems containing isolated or nearly isolated atoms, which often appear in various chemical processes. To address this limitation, we present a technique for modifying existing neural network architectures in a minimal way which accounts for the energies of isolated atoms and the atomization energy (AE) of a system. Using this technique, we build a model architecture we call HIP-NN-AE, an enhanced version of the Hierarchically Interacting Particle Neural Network (HIP-NN). Our results demonstrate that the HIP-NN-AE model significantly outperforms the previous HIP-NN model in multiple scenarios involving isolated atoms, such as complete atomization and bond dissociation processes. We also show that this innovation does not compromise the model performance on other tasks, including barrier height and conformational energy variation. The HIP-NN-AE model thus offers a robust solution to the challenges posed by isolated atoms in energy prediction tasks. Artificial neural network ; Dissociation (chemistry) ; Computer science ; Deep neural networks ; Energy (signal processing) ; Particle (ecology) ; Artificial intelligence ; Biological system ; Statistical physics ; Machine learning ; Atomic physics ; Simulation ; Physics ; Chemistry ; Physical chemistry ; Quantum mechanics ; Biology Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Machine Learning and ELM
Authors: Simone Perego | Luigi Bonati
Source: ChemRxiv (preprint, 2024-10-09)
Field: Materials Science — Materials Chemistry

Data-efficient modeling of catalytic reactions via enhanced sampling and on-the-fly learning of machine learning potentials Simulating catalytic reactivity under operative conditions poses a significant challenge due to the dynamic nature of the catalysts and the high computational cost of electronic structure calculations. Machine learning potentials offer a promising avenue to simulate dynamics at a fraction of the cost, but they require datasets containing all relevant configurations, particularly reactive ones. Here we present a scheme to construct reactive potentials in a data-efficient manner. This is achieved by combining enhanced sampling methods first with Gaussian processes to discover transition paths and then with graph neural networks to obtain a uniformly accurate description. The necessary configurations are extracted via a Data-Efficient Active Learning (DEAL) procedure based on local environment uncertainty. We validated our approach by studying several reactions related to the decomposition of ammonia on iron-cobalt alloy catalysts. Our scheme proved efficient, requiring only ~1,000 DFT calculations per reaction, and robust, sampling reactive configurations from the different accessible pathways. Using this potential, we calculated free energy profiles and characterized reaction mechanisms, showing the ability to provide microscopic insights into complex processes under dynamic conditions. Computer science ; Gaussian process ; Gaussian ; Artificial neural network ; Catalysis ; Kriging ; Artificial intelligence ; Biological system ; Machine learning ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Akash Pallath | Nicholas B. Rego | Andrew L. Ferguson | Amish J. Patel
Source: Biophysical Journal (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Unraveling the effect of chemical patterning on hydrophobicity using machine learning Interpretability ; Biological system ; Polar ; Nanotechnology ; Computer science ; Chemistry ; Artificial intelligence ; Materials science ; Physics ; Biology Machine Learning in Materials Science ; Software Engineering Research ; Mass Spectrometry Techniques and Applications
Authors: Alex K. Chew | Mohammad Atif Faiz Afzal | Zach Kaplan | Eric M. Collins | Suraj Gattani | Mayank Misra | Anand Chandrasekaran | Karl Leswing | Mathew D. Halls
Source: ChemRxiv (preprint, 2024-06-18)
Field: Materials Science — Materials Chemistry

Leveraging High-throughput Molecular Simulations and Machine Learning for Formulation Design Formulations, or mixtures of chemical ingredients, are ubiquitously found across material science applications, such as themoplastics, consumer packaged goods, and energy storage devices. However, finding formulations with optimal properties is difficult because of the non-obvious connection between the individual ingredient structures and compositions to downstream mixture properties. Computational approaches that could traverse the expansive design space offer a promising solution to finding formulations with improved properties while minimizing the number of experiments. In this work, we generated a large formulation dataset using high-throughput classical molecular dynamics simulations that resulted in more than 30,000 solvent mixtures ranging between pure component to five component systems. We developed three formulation-property relationship approaches to create machine learning models which use the ingredient structure and composition as input to predict a formulation property: formulation descriptor aggregation (FDA), formulation descriptor Set2Set (FDS2S), and formulation graph (FG). We found that FDS2S, a new approach that uses a Set2Set layer to aggregate molecular descriptors of individual ingredients, outperforms all other approaches in accurately predicting density, heat of vaporization, and enthalpy of mixing that were computed from molecular simulations. Feature importance analysis of FDA models reveal that specific substructures are important to predicting these formulation properties, which is useful in the design of formulations to achieve target properties. When leveraging an active learning framework to iteratively suggest the next ingredient and composition to experiment on, we found that formulation-property relationships can identify formulations with the highest property values at least two to three times faster than randomly guessing. The results demonstrate that formulation-property relationships provide valuable insight to suggest the next experiment even when starting from a limited dataset of ~100 examples. Our research demonstrates the utility of high-throughput simulations and machine learning algorithms applied to designing formulations with promising properties, which could broadly accelerate the design of new materials for a wide range of applications, such as improving the performance of liquid electrolytes for batteries, fuel mixtures for oil and gas, solvent additives for perfumes or paints, and more. Throughput ; Computer science ; Computer architecture ; Artificial intelligence ; Machine learning ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods