jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohamad Moosavi
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

(Invited) bridging MOFs to Applications Via Machine Learning MOFs (metal-organic frameworks) are materials with the potential to significantly improve several energy-related applications by allowing customization of their properties. However, traditional scientific methods make the process of discovery of scalable materials that perform well in industrial processes slow and complicated. This is due to the need for a holistic approach that considers the entire design process, including exploring vast materials spaces, linking them to their properties, synthesis, and process design and engineering. To tackle this challenge, we are developing tools using machine learning and artificial intelligence to help navigate the vast design space, uncover hidden relationships between material properties and performance, and accelerate MOF design. In this discussion, I'll elaborate on how these novel tools are being used to quantify the structural diversity of large MOF material databases, develop physics-constrained machine learning methods for low-data design tasks, learn from failures to design better experiments and perform the inverse design of MOFs. Bridging (networking) ; Computer science ; Computer security Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Jiayin Li | Jing Xu | Evgeny V. Rebrov | Annemie Bogaerts
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Prediction and Optimization of Plasma-Catalytic Dry Reforming of Methane in a Dbd Reactor Carbon dioxide reforming ; Methane ; Catalysis ; Methane reformer ; Plasma ; Chemistry ; Environmental science ; Chemical engineering ; Engineering ; Syngas ; Physics ; Organic chemistry ; Steam reforming ; Nuclear physics Catalytic Processes in Materials Science
Authors: Sida Sun | Shigeo Maruyama | Yan Li
Source: arXiv (Cornell University) (preprint, 2024-11-29)
Field: Materials Science — Materials Chemistry

Chirality-Dependent Kinetics of Single-Walled Carbon Nanotubes from Machine-Learning Force Fields The origin of the chirality of single-walled carbon nanotubes (SWCNTs) has been a long-standing dispute. Molecular dynamics (MD) simulations driven by machine-learning force fields (MLFF), which can study the interface dynamics under near ab-initio accuracy, provides a powerful technique to reveal the formation mechanism of SWCNTs. Here, we develop a cobalt-carbon MLFF and perform growth simulations on a cobalt catalyst to investigate the chirality preference of the growth of SWCNTs under the vapor-liquid-solid (VLS) regime. Through microkinetic modeling, we reproduce the observed growth and defect kinetics, demonstrating their dependence on the chirality. It is observed that while the initial chirality assignment is likely related to the configurational degeneracy of the nanotube caps, pentagon defects immediately form and resolve after nucleation. Such processes, which we name as diameter control mechanisms, not only control the diameter toward an optimum but also shift the chirality distribution drastically. Our work therefore offers a microkinetic modeling workflow for the chirality-dependent kinetics of the SWCNTs, highlighting the important contribution of the defect kinetics to the chirality origination. Chirality (physics) ; Carbon nanotube ; Kinetics ; Materials science ; Nanotechnology ; Chemical physics ; Physics ; Classical mechanics ; Chiral symmetry ; Quantum mechanics Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Advanced Physical and Chemical Molecular Interactions
Authors: Isaiah A. Moses | Wesley F. Reinhart
Source: arXiv (Cornell University) (preprint, 2024-07-30)
Field: Materials Science — Materials Chemistry

Transfer Learning for Multi-material Classification of Transition Metal Dichalcogenides with Atomic Force Microscopy Deep learning models are widely used for the data-driven design of materials based on atomic force microscopy (AFM) and other scanning probe microscopy. These tools enhance efficiency in inverse design and characterization of materials. However, limited and imbalanced experimental materials data typically available is a major challenge. Also important is the need to interpret trained models, which have typically been complex enough to be uninterpretable by humans. Here, we present a systemic evaluation of transfer learning strategies to accommodate low-data scenarios in materials synthesis and a model latent feature analysis to draw connections to the human-interpretable characteristics of the samples. Our models show accurate predictions in five classes of transition metal dichalcogenides (TMDs) (MoS$_2$, WS$_2$, WSe$_2$, MoSe$_2$, and Mo-WSe$_2$) with up to 89$\%$ accuracy on held-out test samples. Analysis of the latent features reveals a correlation with physical characteristics such as grain density, DoG blob, and local variation. The transfer learning optimization modality and the exploration of the correlation between the latent and physical features provide important frameworks that can be applied to other classes of materials beyond TMDs to enhance the models' performance and explainability which can accelerate the inverse design of materials for technological applications. Transition metal ; Atomic force microscopy ; Nanotechnology ; Materials science ; Chemical physics ; Engineering physics ; Condensed matter physics ; Chemistry ; Physics ; Catalysis Machine Learning in Materials Science ; 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Dario Massa | Kaszuba, Grzegorz | Stefanos Papanikolaou | Piotr Sankowski
Source: arXiv (Cornell University) (preprint, 2024-01-17)
Field: Materials Science — Materials Chemistry

Transfer Learning in Materials Informatics: structure-property relationships through minimal but highly informative multimodal input In this work we propose simple, effective and computationally efficient transfer learning approaches for structure-property relation predictions in the context of materials, with highly informative input from different modalities. As materials properties stand from their electronic structure, representations are extracted directly from datasets of electronic charge density profile images using Neural Networks. We demonstrate transferability of the existing pre-trained Convolutional Neural Networks and Large Language Models knowledge to physics domain data, exploring a wide set of compositions for the regression of energetics- or structure- related properties, and the role of semantic crystallographic information in the context of multimodal approaches. We test the applicability of the CLIP multimodal model, and employ as well a training protocol for building a more interpretable and versatile stacked custom solution from different pre-trained modalities. The study offers a promising avenue for enhancing the effectiveness of descriptor identification in physical systems, shedding light on the power of multimodal transfer learning for materials property prediction. Instead of using the well-established GNN-based approaches, we explore the transfer learning of image- and text-based architectures, which can impact decision making for new low-cost AI methods in the field of Materials and Chemoinformatics. Computer science ; Transfer of learning ; Artificial intelligence ; Machine learning ; Artificial neural network ; Modalities ; Convolutional neural network ; Deep learning ; Transferability ; Ab initio ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Nuclear Physics and Applications
Authors: Indrajeet Mandal | Sajid Mannan | Yuanqing Lu | Nitya Nand Gosvami | Lothar Wondraczek | N. M. Anoop Krishnan
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Physics-Informed Machine Learning for Na-Ion Conductivity and Activation Energy Conductivity ; Ion ; Energy (signal processing) ; Physics ; Engineering physics ; Computer science ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Dian Rong | Haiyan Wang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Few-Shot Deep Learning Method for Spectra-Based Data Discriminatory Analysis Shot (pellet) ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Deep learning ; Chemistry Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Kohei Tada | Hiroyuki Ozaki | Tetsu Kiyobayashi
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Stabilities of Lithium and Sodium Titanates Predicted by Neural Network Potentials: A Comparison with Density Functional Theory Calculation Lithium (medication) ; Density functional theory ; Sodium ; Artificial neural network ; Chemistry ; Inorganic chemistry ; Computational chemistry ; Computer science ; Organic chemistry ; Machine learning ; Medicine ; Internal medicine Thermal and Kinetic Analysis ; Advancements in Battery Materials ; Machine Learning in Materials Science
Authors: Srihari M. Kastuar | Christopher Rzepa | Srinivas Rangarajan | Chinedu E. Ekuma
Source: arXiv (Cornell University) (preprint, 2024-06-21)
Field: Materials Science — Materials Chemistry

A High-Throughput and Data-Driven Computational Framework for Novel Quantum Materials Two-dimensional layered materials, such as transition metal dichalcogenides (TMDs), possess intrinsic van der Waals gap at the layer interface allowing for remarkable tunability of the optoelectronic features via external intercalation of foreign guests such as atoms, ions, or molecules. Herein, we introduce a high-throughput, data-driven computational framework for the design of novel quantum materials derived from intercalating planar conjugated organic molecules into bilayer transition metal dichalcogenides and dioxides. By combining first-principles methods, material informatics, and machine learning, we characterize the energetic and mechanical stability of this new class of materials and identify the fifty (50) most stable hybrid materials from a vast configurational space comprising $\sim 10^5$ materials, employing intercalation energy as the screening criterion. Throughput ; Computer science ; Quantum ; Computational science ; Physics ; Quantum mechanics ; Telecommunications Machine Learning in Materials Science
Authors: Guangyu Hu | Marat I. Latypov
Source: arXiv (Cornell University) (preprint, 2024-01-29)
Field: Materials Science — Materials Chemistry

AnisoGNN: graph neural networks generalizing to anisotropic properties of polycrystals We present AnisoGNNs -- graph neural networks (GNNs) that generalize predictions of anisotropic properties of polycrystals in arbitrary testing directions without the need in excessive training data. To this end, we develop GNNs with a physics-inspired combination of node attributes and aggregation function. We demonstrate the excellent generalization capabilities of AnisoGNNs in predicting anisotropic elastic and inelastic properties of two alloys. Anisotropy ; Artificial neural network ; Graph ; Mathematics ; Materials science ; Computer science ; Combinatorics ; Artificial intelligence ; Physics ; Optics Machine Learning in Materials Science
Authors: Haipeng Ren | Jiale Liu | Liu Yang | Xungai Wang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Machine Learning Based on Finite Element Method to Predict Engineering Constants of Weft Plain Knitted Composites Finite element method ; Composite material ; Constant (computer programming) ; Materials science ; Plain weave ; Element (criminal law) ; Structural engineering ; Engineering ; Computer science ; Yarn Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Mechanical Behavior of Composites
Authors: Yuki Inada | M. Fujioka | Haruhiko Morito | Tohru Sugahara | Hisanori Yamane | Yukari Katsura
Source: Open MIND (preprint, 2024-07-30)
Field: Materials Science — Materials Chemistry

Elemental Reactivity Maps for Materials Discovery When searching for novel inorganic materials, limiting the combination of constituent elements can greatly improve the search efficiency. In this study, we used machine learning to predict elemental combinations with high reactivity for materials discovery. The essential issue for such prediction is the uncertainty of whether the unreported combinations are non-reactive or not just investigated, though the reactive combinations can be easily collected as positive datasets from the materials databases. To construct the negative datasets, we developed a process to select reliable non-reactive combinations by evaluating the similarity between unreported and reactive combinations. The machine learning models were trained by both datasets and the prediction results were visualized by two-dimensional heatmaps: elemental reactivity maps to identify elemental combinations with high reactivity but no reported stable compounds. The maps predicted high reactivity (i.e., synthesizability) for the Co_Al_Ge ternary system, and two novel ternary compounds were synthesized: Co4Ge3.19Al0.81 and Co2Al1.24Ge1.76. Reactivity (psychology) ; Computer science ; Medicine Machine Learning in Materials Science ; History and advancements in chemistry ; Advanced Materials Characterization Techniques
Authors: Bing Han | Yuhai Zhao
Source:  (article, 2024-04-04)
Field: Materials Science — Materials Chemistry

Electrolyzer performance prediction method based on knowledge graph Accurately predicting the performance outcomes of electrolytic cells holds paramount significance for subsequent production and decision-making within the aluminum electrolysis industry. Presently, the level of automation in electrolytic cell performance prediction methods is limited, heavily relying on manual assessments, which fail to accurately assess the stability of cell conditions and other related issues. This paper leverages knowledge graphs and graph convolutional neural networks for predicting electrolytic cell performance. Firstly, an electrolytic cell parameter knowledge graph is constructed to facilitate the structured processing of production data. Secondly, the graph convolutional neural network model is improved to enhance the performance of electrolyzer performance prediction by aggregating neighbourhood information through four layers of attention. Finally, validation is conducted through smelting experiments. Experimental results demonstrate that the proposed prediction method exhibits a high degree of accuracy, offering valuable insights for practical applications. Computer science ; Automation ; Convolutional neural network ; Graph ; Machine learning ; Artificial intelligence ; Data mining ; Artificial neural network ; Theoretical computer science ; Engineering Machine Learning in Materials Science ; Machine Learning and ELM ; Industrial Technology and Control Systems
Authors: Dibyendu Maity | Suman Chakrabarty
Source: arXiv (Cornell University) (preprint, 2024-09-27)
Field: Chemistry — Spectroscopy

IceCoder: Identification of Ice phases in molecular simulation using variational autoencoder The identification and classification of different phases of ice within molecular simulations is a challenging task due to the complex and varied phase space of ice, which includes numerous crystalline and amorphous forms. Traditional order parameters often struggle to differentiate between these phases, especially under conditions of thermal fluctuations. In this work, we present a novel machine learning-based framework, \textit{IceCoder}, which combines a variational autoencoder (VAE) with the Smooth Overlap of Atomic Positions (SOAP) descriptor to classify a large number of ice phases effectively. Our approach compresses high-dimensional SOAP vectors into a two-dimensional latent space using VAE, facilitating the visualization and distinction of various ice phases. We trained the model on a comprehensive dataset generated through molecular dynamics (MD) simulations and demonstrated its ability to accurately detect various phases of crystalline ice as well as liquid water at the molecular level. IceCoder provides a robust and generalizable tool for tracking ice phase transitions in simulations, overcoming limitations of traditional methods. This approach may be generalized to detect polymorphs in other molecular crystals as well, leading to new insights into the microscopic mechanisms underlying nucleation, growth, and phase transitions, while maintaining computational efficiency. Autoencoder ; Identification (biology) ; Statistical physics ; Computer science ; Geology ; Physics ; Artificial intelligence ; Artificial neural network ; Biology Mass Spectrometry Techniques and Applications ; nanoparticles nucleation surface interactions ; Phase Equilibria and Thermodynamics
Authors: Pedram Mirchi | Christophe Adessi | Samy Mérabia | Ali Rajabpour
Source: arXiv (Cornell University) (preprint, 2024-03-06)
Field: Materials Science — Materials Chemistry

Lattice thermal conductivity and mechanical properties of the single-layer penta-NiN2 explored by a deep-learning interatomic potential Penta-NiN2, a novel pentagonal 2D sheet with potential nanoelectronic applications, is investigated in terms of its lattice thermal conductivity, stability, and mechanical behavior. A deep learning interatomic potential (DLP) is firstly generated from ab-initio molecular dynamics (AIMD) data and then utilized for classical molecular dynamics simulations. The DLP's accuracy is verified, showing strong agreement with AIMD results. The dependence of thermal conductivity on size, temperature, and tensile strain, reveals important insights into the material's thermal properties. Additionally, the mechanical response of penta-NiN2 under uniaxial loading is examined, yielding a Young's modulus of approximately 368 GPa. The influence of vacancy defects on mechanical properties is analyzed, demonstrating significant reduction in modulus, fracture stress, and ultimate strength. This study also investigates the influence of strain on phonon dispersion relations and phonon group velocity in penta-NiN2, shedding light on how alterations in the atomic lattice affect the phonon dynamics and, consequently, impact the thermal conductivity. This investigation showcases the ability of deep learning based interatomic potentials in studying the properties of 2D Penta-NiN2. Interatomic potential ; Thermal conductivity ; Lattice (music) ; Materials science ; Condensed matter physics ; Thermal ; Statistical physics ; Composite material ; Chemistry ; Thermodynamics ; Physics ; Computational chemistry ; Molecular dynamics Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Memory and Neural Computing
Authors: Steven W. Hall | Porhouy Minh | Sapna Sarupria
Source: arXiv (Cornell University) (preprint, 2024-05-15)
Field: Chemistry — Physical and Theoretical Chemistry

LeaPP: Learning Pathways to Polymorphs through machine learning analysis of atomic trajectories Understanding the mechanisms underlying crystal formation is crucial. For most systems, crystallization typically goes through a nucleation process that involves dynamics that happen at short time and length scales. Due to this, molecular dynamics serves as a powerful tool to study this phenomenon. Existing approaches to study the mechanism often focus analysis on static snapshots of the global configuration, potentially overlooking subtle local fluctuations and history of the atoms involved in the formation of solid nuclei. To address this limitation, we propose a methodology that categorizes nucleation pathways into reactive pathways based on the time evolution of constituent atoms. Our approach effectively captures the diverse structural pathways explored by crystallizing Lennard-Jones-like particles and solidifying Ni$_3$Al, providing a more nuanced understanding of nucleating pathways. Moreover, our methodology enables the prediction of the resulting polymorph from each reactive trajectory. This deep learning-assisted comprehensive analysis offers an alternative view of crystal nucleation mechanisms and pathways. Artificial intelligence ; Computer science ; Cognitive science ; Machine learning ; Psychology History and advancements in chemistry ; Various Chemistry Research Topics ; Machine Learning in Materials Science
Authors: Kevin Ortner
Source: World Journal of Materials Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

APPLICATION PROGRESS OF MATERIALS GENOME TECHNOLOGY IN THE FIELD OF NEW ENERGY MATERIALS Materials genome integrates high-throughput computing, high-throughput preparation, high-throughput detection and database systems of materials. It is a "paradigm revolution" in materials research and development. With its profound scientific connotation and significant application potential, it will accelerate New materials discovery and applications. This article focuses on the use of materials genome in the research and development of new energy materials to shorten the "discovery-development-production-application" cycle of new energy materials. It introduces the internationally representative Materials Project and OQMD two material genome platforms, as well as some important the application of materials genome computing technologies, such as material conformation characterization, high-throughput computing and screening, machine learning, neural network technology, optimization algorithms and new high-throughput preparation and characterization technologies, in the research and development of new energy materials, and the next step the development of materials genome puts forward prospects, such as developing high-precision high-throughput computing, using artificial intelligence to develop high-throughput experimental systems and platforms, generating material big data, and making full use of material big data through intelligent computing to create computing and experiments. The integrated materials genome big data artificial intelligence system accelerates the discovery and application of new energy materials. Throughput ; Big data ; Computer science ; Field (mathematics) ; Characterization (materials science) ; Data science ; Nanotechnology ; Systems engineering ; Engineering ; Materials science ; Data mining Machine Learning in Materials Science