jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Suman Itani | Yibo Zhang | Jiadong Zang
Source: arXiv (Cornell University) (preprint, 2024-09-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The Northeast Materials Database for Magnetic Materials The discovery of magnetic materials with high operating temperature ranges and optimized performance is essential for advanced applications. Current data-driven approaches are limited by the lack of accurate, comprehensive, and feature-rich databases. This study aims to address this challenge by using Large Language Models (LLMs) to create a comprehensive, experiment-based, magnetic materials database named the Northeast Materials Database (NEMAD), which consists of 67,573 magnetic materials entries(www.nemad.org). The database incorporates chemical composition, magnetic phase transition temperatures, structural details, and magnetic properties. Enabled by NEMAD, we trained machine learning models to classify materials and predict transition temperatures. Our classification model achieved an accuracy of 90% in categorizing materials as ferromagnetic (FM), antiferromagnetic (AFM), and non-magnetic (NM). The regression models predict Curie (Néel) temperature with a coefficient of determination (R2) of 0.87 (0.83) and a mean absolute error (MAE) of 56K (38K). These models identified 25 (13) FM (AFM) candidates with a predicted Curie (Néel) temperature above 500K (100K) from the Materials Project. This work shows the feasibility of combining LLMs for automated data extraction and machine learning models to accelerate the discovery of magnetic materials. Transition (genetics) ; Database ; Computer science ; Materials science ; Chemistry Magnetic and transport properties of perovskites and related materials ; Magnetic Properties of Alloys ; Rare-earth and actinide compounds
Authors: Cameron J. Bodenschatz | Wissam A. Saidi | Jamesa L. Stokes | Rebekah I. Webster | Gustavo Costa
Source: Materials (article, 2024-01-05)
Field: Materials Science — Materials Chemistry

Theoretical Prediction of Thermal Expansion Anisotropy for Y2Si2O7 Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment Environmental barrier coatings (EBCs) are an enabling technology for silicon carbide (SiC)-based ceramic matrix composites (CMCs) in extreme environments such as gas turbine engines. However, the development of new coating systems is hindered by the large design space and difficulty in predicting the properties for these materials. Density Functional Theory (DFT) has successfully been used to model and predict some thermodynamic and thermo-mechanical properties of high-temperature ceramics for EBCs, although these calculations are challenging due to their high computational costs. In this work, we use machine learning to train a deep neural network potential (DNP) for Y<sub>2</sub>Si<sub>2</sub>O<sub>7</sub>, which is then applied to calculate the thermodynamic and thermo-mechanical properties at near-DFT accuracy much faster and using less computational resources than DFT. We use this DNP to predict the phonon-based thermodynamic properties of Y<sub>2</sub>Si<sub>2</sub>O<sub>7</sub> with good agreement to DFT and experiments. We also utilize the DNP to calculate the anisotropic, lattice direction-dependent coefficients of thermal expansion (CTEs) for Y<sub>2</sub>Si<sub>2</sub>O<sub>7</sub>. Molecular dynamics trajectories using the DNP correctly demonstrate the accurate prediction of the anisotropy of the CTE in good agreement with the diffraction experiments. In the future, this DNP could be applied to accelerate additional property calculations for Y<sub>2</sub>Si<sub>2</sub>O<sub>7</sub> compared to DFT or experiments. Anisotropy ; Thermal barrier coating ; Artificial neural network ; Materials science ; Thermal expansion ; Thermal ; Composite material ; Thermodynamics ; Artificial intelligence ; Computer science ; Physics ; Optics ; Coating Machine Learning in Materials Science ; Thermal properties of materials ; Nuclear Materials and Properties
Authors: Yu Gu | Zexi Zhang | Tianyi Gao | Rafael Gómez‐Bombarelli | Mao Chen
Source: Angewandte Chemie International Edition (article, 2024-07-26)
Field: Chemistry — Organic Chemistry

Low‐Dispersity Polymers via Free Radical Alternating Copolymerization: Effects of Charge‐Transfer‐Complexes Alternating copolymers are crucial for diverse applications. While dispersity (Ð, also known as molecular weight distribution, MWD) influences the properties of polymers, achieving low dispersities in alternating copolymers poses a notable challenge via free radical polymerizations (FRPs). In this work, we demonstrated an unexpected discovery that dispersities are affected by the participation of charge transfer complexes (CTCs) formed between monomer pairs during free radical alternating copolymerization, which have inspired the successful synthesis of various alternating copolymers with low dispersities (>30 examples, Ð=1.13-1.39) under visible-light irradiation. The synthetic method is compatible with binary, ternary and quaternary alternating copolymerizations and is expandable for both fluorinated and non-fluorinated monomer pairs. DFT calculations combined with model experiments indicated that CTC-absent reaction exhibits higher propagation rates and affords fewer radical terminations, which could contribute to low dispersities. Based on the integration of Monte Carlo simulation and Bayesian optimization, we established the relationship map between FRP parameter space and dispersity, further suggested the correlation between low dispersities and higher propagation rates. Our research sheds light on dispersity control via FRPs and creates a novel platform to investigate polymer dispersity through machine learning. Dispersity ; Copolymer ; Monomer ; Polymer ; Materials science ; Ternary operation ; Polymer chemistry ; Chemical engineering ; Computer science ; Composite material Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Luminescence and Fluorescent Materials
Authors: Liyuan Dong | Xiaolong Xu | Wei Li
Source: Inorganic Chemistry Communications (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

Bandgap prediction of hybrid metal halide perovskites by transfer machine learning: From three to two dimensionality Halide ; Curse of dimensionality ; Materials science ; Band gap ; Metal ; Optoelectronics ; Artificial intelligence ; Computer science ; Chemistry ; Inorganic chemistry ; Metallurgy Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Ruslan Lavshchenko | Gennadiy Lvov
Source: Bulletin of National Technical University KhPI Series System Analysis Control and Information Technologies (article, 2024-12-24)
Field: Materials Science — General Materials Science

ANALYSIS OF THE APPLICATIONS OF THE DATA-DRIVEN APPROACH IN EVALUATING THE THERMAL-PHYSICAL PROPERTIES OF COMPOSITES This research analyzes the potential and prospects of a data-driven methodology for examining the thermo-physical properties of composite materials. The research is to provide an analysis of the potential and benefits of employing data-driven procedures, especially in contrast to conventional methods. The analysis examines fundamental principles and advanced machine learning approaches utilized in materials science, highlighting their ability to improve the knowledge, optimization, and overall quality of composite materials. This study thoroughly examines the application of neural networks in forecasting thermal characteristics, highlighting its predictive skills and potential to transform the analysis of thermal properties in composite materials. Additionally, the research underscores the growing reliance on big data analytics in addressing complex challenges in material behavior, particularly under variable environmental conditions. A comparison assessment is performed between the data-driven methodology and traditional analytical methodologies, emphasizing the distinct advantages and drawbacks of each. This comparison elucidates how data-driven methodologies can enhance and refine the precision of thermo-physical analysis. The convergence of machine learning and material science is shown to not only facilitate more accurate predictions but also reduce experimentation time and costs. The report also delineates contemporary techniques for measuring and forecasting the thermo-physical properties of composites, emphasizing the advancements in new technologies in recent years. The function of computational tools and computer technology is elaborated upon, especially with the modeling of thermo-physical properties and the simulation of production processes for composite materials. This paper highlights the growing significance of these technologies in enhancing both theoretical and practical dimensions of material science. The research provides novel insights into composite manufacture, thereby advancing the future of materials science and the practical applications of composite materials. The results have significant implications for enhancing production processes, fostering innovation, and progressing the research of composite materials across diverse industries. Composite material ; Materials science ; Thermal ; Physics ; Thermodynamics Material Properties and Applications ; Surface Treatment and Coatings ; Thermal and Kinetic Analysis
Authors: Pengjie Shi | Zhiping Xu
Source: Journal of Applied Physics (article, 2024-08-09)
Field: Materials Science — Ceramics and Composites

Strength of 2D glasses explored by machine-learning force fields The strengths of glasses are intricately linked to their atomic-level heterogeneity. Atomistic simulations are frequently used to investigate the statistical physics of this relationship, compensating for the limited spatiotemporal resolution in experimental studies. However, theoretical insights are limited by the complexity of glass structures and the accuracy of the interatomic potentials used in simulations. Here, we investigate the strengths and fracture mechanisms of 2D silica, with all structural units accessible to direct experimental observation. We develop a neural network force field for fracture based on the deep potential-smooth edition framework. Representative atomic structures across crystals, nanocrystalline, paracrystalline, and continuous random network glasses are studied. We find that the virials or bond lengths control the initialization of bond-breaking events, creating nanoscale voids in the vitreous network. However, the voids do not necessarily lead to crack propagation due to a disorder-trapping effect, which is stronger than the lattice-trapping effect in a crystalline lattice, and occurs over larger length and time scales. Fracture initiation proceeds with void growth and coalescence and advances through a bridging mechanism. The fracture patterns are shaped by subsequent trapping and cleavage steps, often guided by voids forming ahead of the crack tip. These heterogeneous processes result in atomically smooth facets in crystalline regions and rough, amorphous edges in the glassy phase. These insights into 2D crystals and glasses, both sharing SiO2 chemistry, highlight the pivotal role of atomic-level structures in determining fracture kinetics and crack path selection in materials. Materials science ; Chemical physics ; Amorphous solid ; Nanocrystalline material ; Coalescence (physics) ; Nanoscopic scale ; Anisotropy ; Fracture (geology) ; Nanotechnology ; Composite material ; Crystallography ; Chemistry ; Optics ; Physics Glass properties and applications ; Material Dynamics and Properties ; Theoretical and Computational Physics
Authors: Paul Cuillier | Matthew G. Tucker | Yuanpeng Zhang
Source: Journal of Applied Crystallography (article, 2024-10-29)
Field: Materials Science — Materials Chemistry

Integrating machine learning interatomic potentials with hybrid reverse Monte Carlo structure refinements in <i>RMCProfile</i> Structure refinement with reverse Monte Carlo (RMC) is a powerful tool for interpreting experimental diffraction data. To ensure that the under-constrained RMC algorithm yields reasonable results, the hybrid RMC approach applies interatomic potentials to obtain solutions that are both physically sensible and in agreement with experiment. To expand the range of materials that can be studied with hybrid RMC, we have implemented a new interatomic potential constraint in <i>RMCProfile</i> that grants flexibility to apply potentials supported by the <i>Large-scale Atomic/Molecular Massively Parallel Simulator</i> (<i>LAMMPS</i>) molecular dynamics code. This includes machine learning interatomic potentials, which provide a pathway to applying hybrid RMC to materials without currently available interatomic potentials. To this end, we present a methodology to use RMC to train machine learning interatomic potentials for hybrid RMC applications. Reverse Monte Carlo ; Interatomic potential ; Molecular dynamics ; Computer science ; Flexibility (engineering) ; Computational science ; Monte Carlo method ; Statistical physics ; Range (aeronautics) ; Diffraction ; Materials science ; Physics ; Chemistry ; Computational chemistry ; Neutron diffraction ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Lane E. Schultz | Yiqi Wang | Ryan Jacobs | Dane Morgan
Source: arXiv (Cornell University) (preprint, 2024-05-28)
Field: Materials Science — Materials Chemistry

A General Approach for Determining Applicability Domain of Machine Learning Models Knowledge of the domain of applicability of a machine learning model is essential to ensuring accurate and reliable model predictions. In this work, we develop a new and general approach of assessing model domain and demonstrate that our approach provides accurate and meaningful domain designation across multiple model types and material property data sets. Our approach assesses the distance between data in feature space using kernel density estimation, where this distance provides an effective tool for domain determination. We show that chemical groups considered unrelated based on chemical knowledge exhibit significant dissimilarities by our measure. We also show that high measures of dissimilarity are associated with poor model performance (i.e., high residual magnitudes) and poor estimates of model uncertainty (i.e., unreliable uncertainty estimation). Automated tools are provided to enable researchers to establish acceptable dissimilarity thresholds to identify whether new predictions of their own machine learning models are in-domain versus out-of-domain. Property (philosophy) ; Kernel (algebra) ; Domain (mathematical analysis) ; Computer science ; Artificial intelligence ; Machine learning ; Kernel method ; Mathematics ; Support vector machine ; Discrete mathematics ; Mathematical analysis Machine Learning in Materials Science
Authors: Haodong Chen | Wenjun Ye | Songxiao Hui | Yang Yu
Source: Materials (article, 2024-04-13)
Field: Materials Science — Materials Chemistry

Data-Driven Design of Nickel-Free Superelastic Titanium Alloys In this paper, a CatBoost model for predicting superelastic strains of alloys was established by utilizing features construction and selection as well as model filtering and evaluation based on 125 existing data points of superelastic titanium alloys. The alloy compositions of a TiNbMoZrSnTa system were optimized and three nickel-free titanium alloys with potentially excellent superelastic properties were designed using the Bayesian optimization algorithm using a superelastic strain as the optimization target. The experimental results indicated that only Ti-12Nb-18Zr-2Sn and Ti-12Nb-16Zr-3Sn exhibited clear superelasticity due to the absence of relevant information about the alloys' β stability in the machine learning model. Through experimental optimization of the heat treatment regimens, Ti-12Nb-18Zr-2Sn and Ti-12Nb-16Zr-3Sn ultimately achieved recovery strains of 4.65% after being heat treated at 853 K for 10 min and 3.01% after being heat treated at 1073 K for 30 min, respectively. The CatBoost model in this paper possessed a certain ability to design nickel-free superelastic titanium alloys but it was still necessary to combine it with existing knowledge of material theory for effective utilization. Nickel titanium ; Materials science ; Nickel ; Metallurgy ; Titanium ; Shape-memory alloy Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Advanced materials and composites
Authors: Estefania Ascencio Medina | Shan He | Amirreza Daghighi | Kweeni Iduoku | Gerardo M. Casañola‐Martín | Sonia Arrasate | Humerto Gonzalez-Diaz | Bakhtiyor Rasulev
Source: Preprints.org (preprint, 2024-08-13)
Field: Chemistry — Analytical Chemistry

Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR) The dielectric constant (ε) reflects the ability of a material to align and orient electrical dipoles within its structure in response to an externally applied electric field; the greater the polarizability of molecules, the greater the value of dielectric constant. In this study a data set of 86 polymers was investigated to develop a structure-property quantitative relations (QSPR) model to predict the dielectric constant in polymers. An initial set of 1273 descriptors was used to select a best set of descriptors and to construct two machine learning models with Gradient Boosting Regressor (GB_A and GB_B). The best performing model (GB_A) with 8 descriptors, exhibited a performance of (R2train) = 0.938 and (R2test) = 0.802. The models were internally validated by 5 folds cross-validation, demonstrating robustness. Additionally, using the Accumulative Local Effect (ALE) technique, we analyzed the relationship between the 8 descriptors involved and the impact of these descriptors to dielectric constant of polymers. Quantitative structure–activity relationship ; Series (stratigraphy) ; Dielectric ; Property (philosophy) ; Constant (computer programming) ; Polymer ; Materials science ; Thermodynamics ; Chemistry ; Computer science ; Physics ; Composite material ; Stereochemistry ; Geology ; Philosophy Spectroscopy and Chemometric Analyses
Authors: Nurul Akmal Che Lah
Source: Engineering Analysis with Boundary Elements (article, 2024-12-04)
Field: Materials Science — Materials Chemistry

Properties optimisation of nanostructures via machine learning: Progress and perspective Perspective (graphical) ; Computer science ; Artificial intelligence ; Machine learning ; Nanostructure ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Nanowire Synthesis and Applications ; Nanotechnology research and applications
Authors: Rikesh Patel | Wenqi Li | Richard J. Smith | Matt Clark
Source: Scripta Materialia (article, 2024-10-17)
Field: Materials Science — Materials Chemistry

Developing neural networks to rapidly map crystallographic orientation using laser ultrasound measurements Rapid measurement of crystal orientation is critical in the materials discovery process as it facilitates real-time decision-making and quality control. Acoustic inspection methods rapidly characterise microstructure without the need for extensive infrastructure or expense – the laser ultrasonic method known as Spatially Resolved Acoustic Spectroscopy (SRAS) has been developed with this intent and accurately characterises crystal orientation by leveraging a combination of forward modelling and an exhaustive brute force process to obtain the best-fit orientation. While effective, this method is computationally demanding and time-intensive. We introduce a novel approach that utilises neural networks to classify measured acoustic signals into orientation planes to significantly expedite the characterisation process and demonstrate classification on real-world Inconel 617 and CMX4 specimens. A reduction in the orientation determination time from around 10 hours (brute force search) down to 15 seconds (neural network) was achieved while exhibiting an average plane angle difference of between 5.3 ∘ and 13.8 ∘ . Materials science ; Orientation (vector space) ; Ultrasound ; Laser ; Crystallography ; Artificial neural network ; Artificial intelligence ; Optics ; Acoustics ; Geometry ; Computer science Crystallization and Solubility Studies ; Mineral Processing and Grinding ; X-ray Diffraction in Crystallography
Authors: Frédéric Célerse | Matthew D. Wodrich | Sergi Vela | Simone Gallarati | Raimón Fabregat | Veronika Jurásková | Clémence Corminbœuf
Source: Journal of Chemical Information and Modeling (article, 2024-02-06)
Field: Materials Science — Materials Chemistry

From Organic Fragments to Photoswitchable Catalysts: The OFF–ON Structural Repository for Transferable Kernel-Based Potentials Structurally and conformationally diverse databases are needed to train accurate neural networks or kernel-based potentials capable of exploring the complex free energy landscape of flexible functional organic molecules. Curating such databases for species beyond "simple" drug-like compounds or molecules composed of well-defined building blocks (e.g., peptides) is challenging as it requires thorough chemical space mapping and evaluation of both chemical and conformational diversities. Here, we introduce the OFF-ON (organic fragments from organocatalysts that are non-modular) database, a repository of 7869 equilibrium and 67,457 nonequilibrium geometries of organic compounds and dimers aimed at describing conformationally flexible functional organic molecules, with an emphasis on photoswitchable organocatalysts. The relevance of this database is then demonstrated by training a local kernel regression model on a low-cost semiempirical baseline and comparing it with a PBE0-D3 reference for several known catalysts, notably the free energy surfaces of exemplary photoswitchable organocatalysts. Our results demonstrate that the OFF-ON data set offers reliable predictions for simulating the conformational behavior of virtually any (photoswitchable) organocatalyst or organic compound composed of H, C, N, O, F, and S atoms, thereby opening a computationally feasible route to explore complex free energy surfaces in order to rationalize and predict catalytic behavior. Catalysis ; Chemistry ; Kernel (algebra) ; Combinatorial chemistry ; Computer science ; Organic chemistry ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions