jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiao Huang | Qianli Xing | Jinglong Ji | Bo Yang
Source: arXiv (Cornell University) (preprint, 2024-01-22)
Field: Materials Science — Materials Chemistry

ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction Property prediction is a fundamental task in crystal material research. To model atoms and structures, structures represented as graphs are widely used and graph learning-based methods have achieved significant progress. Bond angles and bond distances are two key structural information that greatly influence crystal properties. However, most of the existing works only consider bond distances and overlook bond angles. The main challenge lies in the time cost of handling bond angles, which leads to a significant increase in inference time. To solve this issue, we first propose a crystal structure modeling based on dual scale neighbor partitioning mechanism, which uses a larger scale cutoff for edge neighbors and a smaller scale cutoff for angle neighbors. Then, we propose a novel Atom-Distance-Angle Graph Neural Network (ADA-GNN) for property prediction tasks, which can process node information and structural information separately. The accuracy of predictions and inference time are improved with the dual scale modeling and the specially designed architecture of ADA-GNN. The experimental results validate that our approach achieves state-of-the-art results in two large-scale material benchmark datasets on property prediction tasks. Inference ; Computer science ; Benchmark (surveying) ; Artificial neural network ; Algorithm ; Property (philosophy) ; Graph ; Crystal (programming language) ; Scale (ratio) ; Artificial intelligence ; Node (physics) ; Theoretical computer science ; Topology (electrical circuits) ; Mathematics ; Combinatorics ; Engineering ; Physics ; Structural engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Marvin Poul | Liam Huber | Jörg Neugebauer
Source: Research Square (preprint, 2024-08-12)
Field: Materials Science — Materials Chemistry

Automated Generation of Structure Datasets for Machine Learning Potentials and Alloys Binary number ; Computer science ; Completeness (order theory) ; Ternary operation ; Coincidence ; Set (abstract data type) ; Training set ; Point (geometry) ; Space (punctuation) ; Range (aeronautics) ; Sampling (signal processing) ; Algorithm ; Chemical space ; Binary classification ; Machine learning ; Artificial intelligence ; Materials science ; Mathematics ; Detector ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Ion-surface interactions and analysis
Authors: Ibrahim Altarabsheh | Xiang Chen
Source: Journal of Engineering Mechanics (article, 2024-10-17)
Field: Materials Science — Materials Chemistry

Atomistic-Informed and Machine Learning–Assisted Crystal Plasticity Modeling for Material Interfaces This paper presents a novel methodology that addresses the limited predictive capability of the existing crystal plasticity (CP) method in interfaces modeling. Our approach incorporates interfacial parameters generated from molecular dynamic (MD) simulations into the continuum-level crystal plasticity finite element analysis (CPFEA) model. To address the inherent scale mismatch between atomistic and continuum models, we employ two essential techniques—the nucleation theory and machine learning (ML) method. The nucleation theory is utilized in conjunction with the nudged elastic band (NEB) method to extrapolate the low strain-rate yield stresses from the high strain-rate MD simulation results. To overcome the length scale limitation of MD, we use a method that was recently developed by the authors—a two-step approach that utilizes MD-calculated stress–strain data to train a probabilistic ML model for predicting stress–strain behaviors at larger scale. The resulting flow parameters and extrapolated yield stresses are then integrated into the atomistic-informed interface region of the CPFEA model. This multiscale computational method that combines MD, CPFEA, nucleation theory, NEB and ML enables a grain-level large time scale crystal plasticity modeling with atomic accuracy at the interfaces, as demonstrated by carefully validating it through a bicrystal Cu model with experimental results. This validation highlights the importance of accurately describing interfaces in the modeling of material mechanical behavior. Notably, our proposed methodology is not limited to interfaces but can be applied to other microstructures requiring atomic accuracy. The method opens up new possibilities for comprehensively understanding and designing materials with complex microstructure for various engineering applications. Crystal plasticity ; Materials science ; Plasticity ; Computer science ; Human–computer interaction ; Composite material Machine Learning in Materials Science ; Microstructure and mechanical properties ; Force Microscopy Techniques and Applications
Authors: Pedro S.F. Mendes | Florence H. Vermeire | Thibaut Van Haute | Joris Thybaut
Source: Research Square (preprint, 2024-01-24)
Field: Materials Science — Materials Chemistry

Machine learning for catalyst design: data matters Catalysis ; Computer science ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Source: Propellants Explosives Pyrotechnics (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

The Impact of Artificial Intelligence and Machine Learning on Energetic Materials Research and Development Artificial intelligence/machine learning (AI/ML) techniques are rapidly becoming a powerful tool in the arsenal of analysts, modelers, and designers of energetic materials.Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, discover linkages between design parameters and material properties, identify optimal material designs (micro-morphologies, combinations of materials in composites, etc.), and point to designs with superior/targeted property and performance metrics.For example, AI/ML approaches can already scan molecular structures and identify those with optimal physical and chemical properties, construct reaction kinetic schemes from atomistic simulation data, predict grain (meso ) scale mechanical and thermal response to a range of loading regimes, function as surrogate models to bridge scales in multiscale simulations, and learn and predict material properties and behavior at macro-scale. Research development ; Artificial intelligence ; Computer science ; Machine learning ; Geology ; Test (biology) Machine Learning in Materials Science
Authors: Jingwen Zhou | Yanan Fu | Ling Liu | Chungen Liu
Source: arXiv (Cornell University) (preprint, 2024-11-22)
Field: Materials Science — Materials Chemistry

Constant-Potential Machine Learning Molecular Dynamics Simulations Reveal Potential-Regulated Cu Cluster Formation on MoS$_{2}$ Electrochemical processes play a crucial role in energy storage and conversion systems, yet their computational modeling remains a significant challenge. Accurately incorporating the effects of electric potential has been a central focus in theoretical electrochemistry. Although constant-potential ab initio molecular dynamics (CP-AIMD) has provided valuable insights, it is limited by its substantial computational demands. Here, we introduce the Explicit Electric Potential Machine Learning Force Field (EEP-MLFF) model. Our model integrates the electric potential as an explicit input parameter along with the atom-centered descriptors in the atomic neural network. This approach enables the evaluation of nuclear forces under arbitrary electric potentials, thus facilitating molecular dynamics simulations at a specific potential. By applying the proposed machine learning method to the Cu/1T$^{\prime}$-MoS$_{2}$ system, molecular dynamics simulations reveal that the potential-modulated Cu atom migration and aggregation lead to the formation of small steric Cu clusters (Single Clusters, SCs) at potentials below -0.1 V. The morphological transformations of adsorbed Cu atoms are elucidated through electronic structure analyses, which demonstrates that both Cu-S and Cu-Cu bonding can be effectively tuned by the applied electric potential. Our findings present an opportunity for the convenient manufacture of single metal cluster catalysts through potential modulation. Moreover, this theoretical framework facilitates the exploration of potential-regulated processes and helps investigate the mechanisms of electrochemical reactions. Molecular dynamics ; Constant (computer programming) ; Cluster (spacecraft) ; Dynamics (music) ; Chemical physics ; Chemistry ; Physics ; Statistical physics ; Materials science ; Nanotechnology ; Computer science ; Biological system ; Computational chemistry ; Biology Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ion-surface interactions and analysis
Authors: Jiaxin Tian | Kiana A. Treaster | Liangtao Xiong | Zixiao Wang | Austin M. Evans | Haoyuan Li
Source: Angewandte Chemie (article, 2024-07-03)
Field: Materials Science — Materials Chemistry

Taming Two‐Dimensional Polymerization by a Machine‐Learning Discovered Crystallization Model Abstract Rapidly synthesizing high‐quality two‐dimensional covalent organic frameworks (2D COFs) is crucial for their practical applications. While strategies such as slow monomer addition have been developed based on an empirical understanding of their formation process, quantitative guidance remains absent, which prohibits precise optimizations of the experimental conditions. Here, we use a machine‐learning approach that overcomes the challenges associated with bottom‐up model derivation for the non‐classical 2D COF crystallization processes. The resulting model, referred to as NEgen1, establishes correlations among the induction time, nucleation rate, growth rate, bond‐forming rate constants, and common solution synthesis conditions for 2D COFs that grow by a nucleation‐elongation mechanism. The results elucidate the detailed competition between the nucleation and growth dynamics in solution, which has been inappropriately described previously by classical, empirical models with assumptions invalid for 2D COF polymerization. By understanding the dynamic processes at play, the NEgen1 model reveals a simple strategy of gradually increasing monomer addition speed for growing large 2D COF crystals. This insight enables us to rapidly synthesize large COF‐5 colloids, which could only be achieved previously by prolonged reaction times or by introducing chemical modulators. These results highlight the potential for systematically improving the crystal quality of 2D COFs, which has wide‐reaching relevance for many of the applications where 2D COFs are speculated to be valuable. Nucleation ; Crystallization ; Polymerization ; Monomer ; Crystal growth ; Crystal (programming language) ; Materials science ; Nanotechnology ; Competition (biology) ; Covalent bond ; Chemical physics ; Computer science ; Chemistry ; Polymer ; Crystallography ; Organic chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Sarath Menon | Yury Lysogorskiy | A. Knoll | Niklas Leimeroth | Marvin Poul | Minaam Qamar | Jan Janßen | Matous Mrovec | Jochen Rohrer | Karsten Albe | Jörg Behler | Ralf Drautz | Jörg Neugebauer
Source: arXiv (Cornell University) (preprint, 2024-03-08)
Field: Materials Science — Materials Chemistry

From electrons to phase diagrams with classical and machine learning potentials: automated workflows for materials science with pyiron We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating systematic DFT databases, (ii) fitting the Density Functional Theory (DFT) data to empirical potentials or MLPs, and (iii) validating the potentials in a largely automatic approach. The power and performance of this framework are demonstrated for three conceptually very different classes of interatomic potentials: an empirical potential (embedded atom method - EAM), neural networks (high-dimensional neural network potentials - HDNNP) and expansions in basis sets (atomic cluster expansion - ACE). As an advanced example for validation and application, we show the computation of a binary composition-temperature phase diagram for Al-Li, a technologically important lightweight alloy system with applications in the aerospace industry. Workflow ; Phase diagram ; Electron ; Computer science ; Phase (matter) ; Artificial intelligence ; Physics ; Nuclear physics ; Quantum mechanics ; Database Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Naveen Kumar Thawait | Umakant Shrivastava
Source: International Journal of Scientific Research in Computer Science Engineering and Information Technology (article, 2024-06-15)
Field: Materials Science — Materials Chemistry

Machine Learning Techniques for Predicting Conductive Properties of New Materials The study "Machine Learning Techniques for Predicting Conductive Properties of New Materials" explores the application of advanced machine learning (ML) algorithms to predict the conductive properties of novel materials, accelerating the discovery and development process in materials science. Traditional methods for assessing conductive properties are often time-consuming and expensive, necessitating a more efficient approach. This research leverages various ML techniques, including supervised learning algorithms such as support vector machines, decision trees, and neural networks, to analyze large datasets of material properties and predict conductivity with high accuracy. Feature selection and engineering processes are employed to identify the most significant attributes influencing conductivity. The study also compares the performance of different ML models, optimizing hyperparameters to enhance prediction reliability. Results demonstrate that ML models can significantly reduce the experimental burden, offering rapid and precise predictions that align closely with empirical data. The integration of ML in materials science presents a transformative approach, enabling faster identification of promising conductive materials, thereby fostering advancements in electronics, energy storage, and other technological domains. The study highlights the potential of ML to revolutionize material property prediction, paving the way for accelerated innovation and application in various industries. Computer science ; Machine learning ; Materials science ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Edward Deleu | Haoran Li | Joe Li | Wonju Lee | Thomas Guillod | Charles R. Sullivan | Shukai Wang | Minjie Chen
Source:  (article, 2024-02-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multi-Material Power Magnetics Modeling with a Modular and Scalable Machine Learning Framework This paper presents a modular and scalable machine learning framework for multi-material magnetic core loss modeling. The neural network framework is trained to predict core loss based on a flux density excitation waveform B(t) as well as additional scalar inputs including temperature, frequency, and dc-bias in order to handle a wide range of operating conditions. The framework is implemented such that a large portion of the model, the feature extractor, is shared for multiple materials, while specific materials require very few parameters in individual feature mapping networks. This allows the framework to 1) effectively model various materials with a scalable neural network structure and low parameter count; 2) accurately predict core losses across a wide operation range; and 3) adaptively support new materials with additional material-specific mapping networks trained with limited new data. Modular design ; Computer science ; Scalability ; Power (physics) ; Programming language ; Physics ; Operating system Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Magnetic Properties of Alloys
Authors: Zhiwei Zhang | Fumin Wang | Qi Shen | Xubin Zhang | Guobing Li | Bingxin Zhu | Jinhua Shi | Shuai Liu | Qingzhao Liu | Zheng Wang | Qin Yang | Qingshan Zhao
Source: AIChE Journal (article, 2024-12-28)
Field: Materials Science — Materials Chemistry

Integrating machine learning to uncover homogeneous catalytic mechanisms in <i>N</i>‐vinyl‐pyrrolidone synthesis Abstract Combining machine learning, density functional theory (DFT) calculation, and kinetic modeling offers significant advantages in studying reactions under complex conditions, enabling a detailed exploration of reaction mechanisms. Artificial neural network (ANN) models accurately predict and analyze the impact of reaction conditions, overcoming the limitations of traditional kinetic studies in complex systems. Specifically, the homogeneous catalytic reaction for synthesizing N ‐vinylpyrrolidone from pyrrolidone and acetylene, which has stringent requirements, was selected. The ANN model effectively captured nonlinear relationships between residence time, moisture content, reaction temperature, catalyst concentration, reaction pressure, and catalyst preparation temperature. DFT calculations revealed the reaction pathways, leading to the development of a high‐precision predictive model. This approach not only uncovers potential reaction pathways and intermediates in complex reaction systems but also provides a new avenue for optimizing reaction conditions, resulting in more efficient catalyst design and process development in intricate reaction environments. Homogeneous ; Catalysis ; Homogeneous catalysis ; 2-Pyrrolidone ; Chemistry ; Chemical engineering ; Computer science ; Engineering ; Organic chemistry ; Thermodynamics ; Physics Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalysis and Hydrodesulfurization Studies
Authors: Dimitrios-Paraskevas Gerakinis | Eleonora Ricci | George Giannakopoulos | Vangelis Karkaletsis | Doros N. Theodorou | Niki Vergadou
Source:  (article, 2024-09-11)
Field: Materials Science — Materials Chemistry

Molecular Simulation of Coarse-grained Systems using Machine Learning Hierarchical multiscale methods are essential for molecular simulations of complex chemical systems, such as organic fluids and soft matter systems in order to reach longer length and time scales. Coarse-Graining (CG) is often the basis of multiscale schemes. Machine Learning (ML) techniques have been recently explored for the development of atomistic force fields based on quantum mechanical calculations. However, integrating ML methods into CG force fields for bulk molecular systems is still rare. In this work, Graph Convolutional Neural Network (GCNN) architectures were adopted to develop CG Machine Learned potentials for bulk amorphous systems, implementing a strategy that includes a force-matching scheme using benzene liquid as a test system. The ML-based CG force fields developed were evaluated by conducting molecular dynamics simulations at the CG level, and the extracted properties were compared with the atomistic simulations to assess the effectiveness of the ML CG interaction potentials. The impact of hyperparameters, loss function construction, and GCNN architecture size have been examined, providing valuable insights for ML-based CG approaches in bulk soft matter systems. Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Computational Drug Discovery Methods
Authors: Neha Harde
Source: International Journal of Innovative Research in Information Security (article, 2024-05-08)
Field: Materials Science — Materials Chemistry

Material Property Prediction Using Machine Learning The ability to accurately predict the properties of materials is crucial for numerous applications across various industries, including materials science, engineering, and manufacturing. With the advent of machine learning (ML) techniques, researchers have gained powerful tools to model and predict material properties based on their composition, structure, and processing conditions. This review paper provides a comprehensive overview of material property prediction using machine learning. It covers the historical development, available ML models, recent trends, and prospects in this rapidly evolving domain. Property (philosophy) ; Computer science ; Machine learning ; Domain (mathematical analysis) ; Artificial intelligence ; Predictive modelling ; Mathematics Machine Learning in Materials Science ; Mineral Processing and Grinding
Authors: Silvan Käser | Debasish Koner | Markus Meuwly
Source: arXiv (Cornell University) (preprint, 2024-11-27)
Field: Materials Science — Materials Chemistry

The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks Atomistic simulations are a powerful tool for studying the dynamics of molecules, proteins, and materials on wide time and length scales. Their reliability and predictiveness, however, depend directly on the accuracy of the underlying potential energy surface (PES). Guided by the principle of parsimony this work introduces KerNN, a combined kernel/neural network-based approach to represent molecular PESs. Compared to state-of-the-art neural network PESs the number of learnable parameters of KerNN is significantly reduced. This speeds up training and evaluation times by several orders of magnitude while retaining high prediction accuracy. Importantly, using kernels as the features also improves the extrapolation capabilities of KerNN far beyond the coverage provided by the training data which solves a general problem of NN-based PESs. KerNN applied to spectroscopy and reaction dynamics shows excellent performance on test set statistics and observables including vibrational bands computed from classical and quantum simulations. Artificial neural network ; Energy (signal processing) ; Computer science ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods