jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiaoyue Yuan | Runqing Yang | Lokanath Patra | Bolin Liao
Source: AIP Advances (article, 2024-08-01)
Field: Materials Science — Materials Chemistry

Enhancing magnetocaloric material discovery: A machine learning approach using an autogenerated database by large language models Magnetic cooling based on the magnetocaloric effect is a promising solid-state refrigeration technology for a wide range of applications in different temperature ranges. Previous studies have mostly focused on near room temperature (300 K) and cryogenic temperature (&amp;lt;10 K) ranges, while important applications such as hydrogen liquefaction call for efficient magnetic refrigerants for the intermediate temperature range of 10–100 K. For efficient use in this range, new magnetocaloric materials with matching Curie temperatures need to be discovered, while conventional experimental approaches are typically time-consuming and expensive. Here, we report a computational material discovery pipeline based on a materials database containing more than 6000 entries auto-generated by extracting reported material properties from the literature using a large language model. We then use this database to train a machine learning model that can efficiently predict the magnetocaloric properties of materials based on their chemical composition. We further verify the magnetocaloric properties of the predicted compounds using ab initio atomistic spin dynamics simulations to complete the computational material discovery. Using this approach, we identify 11 new promising magnetocaloric materials for the target temperature range. Our work demonstrates the potential of combining large language models, machine learning, and ab initio simulations to efficiently discover new functional materials. Magnetic refrigeration ; Refrigerant ; Curie temperature ; Ab initio ; Materials science ; Atmospheric temperature range ; Computer science ; Machine learning ; Artificial intelligence ; Thermodynamics ; Magnetization ; Ferromagnetism ; Chemistry ; Condensed matter physics ; Physics ; Magnetic field Machine Learning in Materials Science ; Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics
Authors: Haodong Rao | Dong Liu | Jungang Nan | J.Y. Wang
Source: Materials Letters (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Machine learning assisted microtextured regions segmentation in a near-α titanium alloy Electron backscatter diffraction ; Materials science ; Segmentation ; Titanium alloy ; Titanium ; Alloy ; Enhanced Data Rates for GSM Evolution ; Microstructure ; Metallurgy ; Artificial intelligence ; Computer science Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses
Authors: D. Yu. Alekseev | Mikhail Logunov | Mikhail Lazarev | Sergey S. Zhukov | Nikita Orekhov
Source: Computational Materials Science (article, 2024-12-20)
Field: Materials Science — Materials Chemistry

Thermal stability of monolayer fullerene networks: A molecular dynamics study with machine-learning potential Fullerene ; Molecular dynamics ; Monolayer ; Thermal stability ; Chemical physics ; Materials science ; Nanotechnology ; Thermal ; Stability (learning theory) ; Dynamics (music) ; Chemistry ; Computational chemistry ; Physics ; Computer science ; Thermodynamics ; Machine learning ; Organic chemistry Machine Learning in Materials Science ; Graphene research and applications ; Fullerene Chemistry and Applications
Authors: Kun Luo | Zhaorui Liu | Rui Yu | Tengfei Xu | Dominik Legut | Xing Yin | Ruifeng Zhang
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Electrochemical stability of biodegradable Zn–Cu alloys through machine-learning accelerated high-throughput discovery Zn-Cu alloys have attracted great attention as biodegradable alloys owing to their excellent mechanical properties and biocompatibility, with corrosion characteristics being crucial for their suitability for biomedical applications. However, the unresolved identification of intermetallic compounds in Zn-Cu alloys affecting corrosion and the complexity of the application environment hamper the understanding of their electrochemical behavior. Utilizing high-throughput first-principles calculations and machine-learning accelerated evolutionary algorithms for screening the most stable compounds in Zn-Cu systems, a dataset encompassing the formation energy of 2033 compounds is generated. It reveals that most of the experimentally reported Zn-Cu compounds can be replicated, especially the structure of <i>R</i>32 CuZn<sub>5</sub> is first discovered which possesses the lowest formation energy of -0.050 eV per atom. Furthermore, the simulated X-ray diffraction pattern matches perfectly with the experimental ones. By formulating 342 potential electrochemical reactions based on the binary compounds, the Pourbaix diagrams for Zn-Cu alloys are constructed to clarify the fundamental competition between different phases and ions. The calculated equilibrium potential of CuZn<sub>5</sub> is higher than that of Zn through the forward reaction Zn + CuZn<sub>5</sub> ⇌ CuZn<sub>5</sub> + Zn<sup>2+</sup> + 2e<sup>-</sup>, resulting in microcell formation owing to the stronger charge density localization in Zn compared to CuZn<sub>5</sub>. The presence of chlorine accelerates the corrosion of Zn through the reaction Zn + CuZn<sub>5</sub> + 6Cl<sup>-</sup> + 6H<sub>2</sub>O ⇌ Cu + 6ZnOHCl + 6H<sup>+</sup> + 12e<sup>-</sup>, where the formation of ZnOHCl disrupts the ZnO passive film and expands the corrosion pH range from 9.2 to 8.8. Our findings reveal an accurate quantitative corrosion mechanism for Zn-Cu alloys, providing an effective pathway to investigate the corrosion resistance of biodegradable alloys. Throughput ; Electrochemistry ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Computer science ; Electrode ; Engineering ; Physical chemistry ; Telecommunications Corrosion Behavior and Inhibition ; Metal Extraction and Bioleaching ; Machine Learning in Materials Science
Authors: Upendra Kumar | Hyeon Woo Kim | Sobhit Singh | Sung Beom Cho | Hyunseok Ko
Source: Journal of Materials Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Designing Pr-based advanced photoluminescent materials using machine learning and density functional theory Density functional theory ; Photoluminescence ; Materials science ; Band gap ; Debye model ; Perovskite (structure) ; Stability (learning theory) ; Debye ; Machine learning ; Condensed matter physics ; Optoelectronics ; Computational chemistry ; Computer science ; Crystallography ; Physics ; Chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications
Authors: Pranay Sinha | Ablokit Joshi | Rik Dey | Shikhar Misra
Source: Journal of Chemical Information and Modeling (article, 2024-11-03)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Materials Discovery from Electronic Band Structure Traditional methods of materials discovery, often relying on intuition and trial-and-error experimentation, are time-consuming and limited in their ability to explore the vast design space effectively. The emergence of machine learning (ML) as a powerful tool for pattern recognition has opened exciting opportunities to revolutionize materials discovery. This work explores the application of ML techniques to assist in the discovery of materials using band structure data. The electronic band structure, which describes the energy levels of electrons in a material, holds vital information regarding its electronic and optical properties. The band structure data of 63,588 materials, including metals and insulators, have been retrieved from the Materials Project database. The data were grouped into 85 batches based on the band path in the first Brillouin zone. Three ML clustering algorithms were trained on the band structure data after performing feature selection and engineering, followed by noise reduction. The models were validated by comparing the materials' properties in a cluster. Drug discovery ; Computer science ; Data science ; Bioinformatics ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Kaoru Hisama | Gerardo Valadez Huerta | Michihisa Koyama
Source: Journal of Computational Chemistry (article, 2024-08-23)
Field: Materials Science — Materials Chemistry

Molecular dynamics of liquid–electrode interface by integrating <scp>Coulomb</scp> interaction into universal neural network potential Computational understanding of the liquid-electrode interface faces challenges in efficiently incorporating reactive force fields and electrostatic potentials within reasonable computational costs. Although universal neural network potentials (UNNPs), representing pretrained machine learning interatomic potentials, are emerging, current UNNP models lack explicit treatment of Coulomb potentials, and methods for integrating additional charges on the electrode remain to be established. We propose a method to analyze liquid-electrode interfaces by integrating a UNNP, known as the preferred potential, with Coulomb potentials using the ONIOM method. This approach extends the applicability of UNNPs to electrode-liquid interface systems. Through molecular dynamics simulations of graphene-water and graphene oxide (GO)-water interfaces, we demonstrate the effectiveness of our method. Our findings emphasize the necessity of incorporating long-range Coulomb potentials into the water potential to accurately describe water polarization at the interface. Furthermore, we observe that functional groups on the GO electrode influence both polarization and capacitance. Coulomb ; Electrode ; Molecular dynamics ; Polarization (electrochemistry) ; Graphene ; Electric potential ; Interface (matter) ; Capacitance ; Chemical physics ; Oxide ; Chemistry ; Computer science ; Materials science ; Nanotechnology ; Voltage ; Physics ; Computational chemistry ; Molecule ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Spectroscopy and Quantum Chemical Studies
Authors: Dongzhi Li | Xue‐Qing Gong
Source: The Journal of Physical Chemistry A (article, 2024-11-20)
Field: Materials Science — Materials Chemistry

Challenges with Literature-Derived Data in Machine Learning for Yield Prediction: A Case Study on Pd-Catalyzed Carbonylation Reactions The application of machine learning (ML) to predict reaction yields has shown remarkable accuracy when based on high-throughput computational and experimental data. However, the accuracy significantly diminishes when leveraging literature-derived data, highlighting a gap in the predictive capability of the current ML models. This study, focusing on Pd-catalyzed carbonylation reactions, reveals that even with a data set of 2512 reactions, the best-performing model reaches only an <i>R</i><sup>2</sup> of 0.51. Further investigations show that the models' effectiveness is predominantly confined to predictions within narrow subsets of data, closely related and from the same literature sources, rather than across the broader, heterogeneous data sets available in the literature. The reliance on data similarity, coupled with small sample sizes from the same sources, makes the model highly sensitive to inherent fluctuations typical of small data sets, adversely impacting stability, accuracy, and generalizability. The findings underscore the inherent limitations of current ML techniques in leveraging literature-derived data for predicting chemical reaction yields, highlighting the need for more sophisticated approaches to handle the complexity and diversity of chemical data. Generalizability theory ; Computer science ; Carbonylation ; Data set ; Machine learning ; Experimental data ; Set (abstract data type) ; Similarity (geometry) ; Training set ; Stability (learning theory) ; Artificial intelligence ; Sample (material) ; Data mining ; Chemistry ; Catalysis ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Bin Xi | Man Kit Chan | Kejie Bao | Wenjing Zhao | Ho Ming Chan | Hang Chen | Junyi Zhu
Source: The Journal of Physical Chemistry Letters (article, 2024-02-02)
Field: Materials Science — Materials Chemistry

Parameter-Free and Electron Counting Satisfied Material Representation for Machine Learning Potential Energy and Force Fields We proposed a parameter-free volume element representation that satisfies the electron counting model and obtains accurate machine learning potential energy and direct force fitting of randomly perturbed hexagonal BN. Our method preserves permutational, translational, and rotational invariance and can be extended to three-dimensional systems, verified by a system of bulk Si. As a result, we obtained 0.57 meV/atom potential energy root mean squared error (RMSE) and 59 meV/Å force RMSE for perturbed bulk BN systems and 0.43 meV/atom potential energy RMSE and 36 meV/Å force RMSE for perturbed Si systems. In addition, an unbiased perturbation-based data set construction scheme is introduced and a continuous population distribution is obtained with a training data set of 4500, which is about 1 order of magnitude smaller than standard methods based on first-principles molecular dynamics simulations and saves a large amount of computing resources. General validity of our model is verified by structure optimization, molecular dynamics simulations, and extrapolations. Mean squared error ; Molecular dynamics ; Representation (politics) ; Atom (system on chip) ; Statistical physics ; Electron ; Perturbation (astronomy) ; Algorithm ; Mathematics ; Computer science ; Physics ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Christian Rodríguez-Martínez | Tony Schwedek | Evelyn Salazar | Xim Bokhimi
Source: The Journal of Physical Chemistry C (article, 2024-01-18)
Field: Materials Science — Materials Chemistry

Utilizing Wyckoff Sites to Construct Machine-Learning-Driven Interatomic Potentials for Crystalline Materials: A Case Study on α-Alumina We present a methodology leveraging machine learning models to generate interatomic potentials for crystalline materials. This approach is rooted in the material’s crystallography in question. Specifically, we tap into the occupied Wyckoff sites, extracting the defining features that encapsulate the atomic local order in the material. Our choice for the target variable is the formation energy per atom, derived from the total energy of the structure’s representative cell. Our machine learning model’s architecture depends on the occupied Wyckoff sites. The diversity of these occupied sites conditions the layering scheme within the model. Atoms occupying a particular Wyckoff site were modeled with the architecture and learning parameters linked to the respective layer. To illustrate our method, we developed an interatomic potential for atomic interactions in α-alumina. For training purposes, we generated the samples through quantum mechanical computations. The evaluation of the learned interatomic potential involved conducting molecular dynamics calculations on a 2 × 2 × 2 supercell, yielding formation energies per atom deviating by less than 1.0 meV from the quantum mechanics results. The methodology described here paves the way for further innovations, potentially ushering in the creation of interatomic potentials that can be utilized for more than one material. Moreover, this approach provides valuable insights into the feasibility of substituting atoms within a compound by focusing exclusively on the specific Wyckoff sites that they occupy. Interatomic potential ; Atom (system on chip) ; Supercell ; Chemical physics ; Molecular dynamics ; Chemistry ; Crystallography ; Nanotechnology ; Materials science ; Physics ; Computational chemistry ; Computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Donggeon Lee | Sooran Kim | Ji‐Sang Park
Source: Current Applied Physics (article, 2024-01-02)
Field: Materials Science — Materials Chemistry

Machine learning of kesterite materials using cost-effective hybrid density functional theory Kesterite materials have received much attention for earth-abundant photovoltaic applications, however, their record cell efficiency is still lower than other thin film solar cells. We performed high-throughput calculations using hybrid density functional theory to find an alternative absorber material. The atomic structures were cost-effectively optimized by employing a downsampled k-point grid for the Fock exchange potential, and therefore the electronic band gap () was obtained in good agreement with the conventional hybrid calculation. Based on hybrid DFT calculations, we develop machine learning (ML) models to predict the Eg and suggest the empirical equation for Eg with the accuracy of the root-mean-square-error of 0.137 eV and R2 of 0.921. We found that the distance between group-IV and group-VI elements is important to estimate Eg. Using the developed ML models, we searched materials with predicted Eg within the optimum range of 1.0−1.5 eV and also compared their Eg with hybrid functional calculations. Based on our ML and DFT calculations, we suggest nine materials whose ML predicted and DFT calculated Eg in the optimum range for photovoltaic applications. Hybrid functional ; Density functional theory ; Kesterite ; Band gap ; Materials science ; Photovoltaic system ; Range (aeronautics) ; Computer science ; Algorithm ; Computational chemistry ; Chemistry ; Optoelectronics ; CZTS Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Quantum Dots Synthesis And Properties
Authors: Zhihong Wu | Lei Zhou | Peng‐Fei Hou | Yuyan Liu | Taoli Guo | Jincheng Liu
Source: ChemRxiv (preprint, 2024-10-16)
Field: Materials Science — Materials Chemistry

Catalytic Large Atomic Model (CLAM): A Machine-Learning-Based Interatomic Potential Universal Model Catalysis involves complex reactions with dynamic changes in catalyst morphology, challenging the capabilities of traditional Density Functional Theory (DFT) methods. To address this, we present the Catalytic Large Atomic Model (CLAM), a machine-learning-based interatomic potential designed for heterogeneous catalysis. Trained on a comprehensive dataset that includes metals, alloys, oxides, clusters, zeolites, 2D materials, and small molecules, CLAM ensures high accuracy across diverse catalytic systems. We also introduce a "local fine-tuning" algorithm that enhances the model’s applicability by accelerating structural optimizations and transition state searches while maintaining precision. Additionally, CLAM facilitates rapid reaction network construction and efficient kinetic analysis. This work advances computational catalysis by providing a universal and robust tool for catalyst design and mechanism exploration. Interatomic potential ; Catalysis ; Computer science ; Chemistry ; Molecular dynamics ; Computational chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Wojciech G. Stark | van der Oord | Ilyes Batatia | Yaolong Zhang | Bin Jiang | Gábor Csányi | Reinhard J. Maurer
Source: arXiv (Cornell University) (preprint, 2024-03-22)
Field: Materials Science — Materials Chemistry

Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces Simulations of chemical reaction probabilities in gas surface dynamics require the calculation of ensemble averages over many tens of thousands of reaction events to predict dynamical observables that can be compared to experiments. At the same time, the energy landscapes need to be accurately mapped, as small errors in barriers can lead to large deviations in reaction probabilities. This brings a particularly interesting challenge for machine learning interatomic potentials, which are becoming well-established tools to accelerate molecular dynamics simulations. We compare state-of-the-art machine learning interatomic potentials with a particular focus on their inference performance on CPUs and suitability for high throughput simulation of reactive chemistry at surfaces. The considered models include polarizable atom interaction neural networks (PaiNN), recursively embedded atom neural networks (REANN), the MACE equivariant graph neural network, and atomic cluster expansion potentials (ACE). The models are applied to a dataset on reactive molecular hydrogen scattering on low-index surface facets of copper. All models are assessed for their accuracy, time-to-solution, and ability to simulate reactive sticking probabilities as a function of the rovibrational initial state and kinetic incidence energy of the molecule. REANN and MACE models provide the best balance between accuracy and time-to-solution and can be considered the current state-of-the-art in gas-surface dynamics. PaiNN models require many features for the best accuracy, which causes significant losses in computational efficiency. ACE models provide the fastest time-to-solution, however, models trained on the existing dataset were not able to achieve sufficiently accurate predictions in all cases. Benchmarking ; Dynamics (music) ; Metal ; Molecular dynamics ; Computer science ; Artificial intelligence ; Materials science ; Chemistry ; Physics ; Metallurgy ; Computational chemistry ; Economics Machine Learning in Materials Science
Authors: Gaoqiang Ma | Junfeng Shi | Hao Xiong | Chunming Xiong | Ruidong Zhao | Xishun Zhang
Source: Geoenergy Science and Engineering (review, 2024-03-15)
Field: Chemistry — Analytical Chemistry

Machine learning assisted molecular modeling from biochemistry to petroleum engineering: A review Computer science ; Domain (mathematical analysis) ; Biochemical engineering ; Molecular dynamics ; Nanotechnology ; Data science ; Chemistry ; Engineering ; Mathematics ; Materials science Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques