jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: F. Wei
Source:  (article, 2024-12-29)
Field: Chemistry — Inorganic Chemistry

Synthetic prediction of MOF based on RF and K-nearest neighbor In this paper, we propose a MOF synthetic prediction model that integrates the Random Forest (RF) and K-nearest neighbor (KNN) algorithms. By applying advanced machine learning techniques to analyze MOF synthesis data, the model learns from historical synthesis records to automatically predict the synthesis conditions and properties of new MOF materials. The RF algorithm employs an ensemble approach, utilizing multiple decision trees to effectively capture complex nonlinear relationships and reduce overfitting, enhancing model robustness. Meanwhile, KNN offers a non-parametric method that excels in local approximation, ensuring precise predictions based on the nearest historical data points. By combining these algorithms, the model leverages RF's capability to handle high-dimensional data and KNN's sensitivity to local variations, resulting in superior prediction accuracy and stability. Moreover, feature importance analysis from the RF component provides insights into key factors influencing MOF properties, facilitating informed decision-making in the synthesis process. Experimental findings confirm that the model not only achieves high accuracy but also demonstrates strong generalization in predicting MOF synthesis. k-nearest neighbors algorithm ; Computer science ; Materials science ; Pattern recognition (psychology) ; Artificial intelligence Metal-Organic Frameworks: Synthesis and Applications
Authors: Austin Zadoks | Antimo Marrazzo | Nicola Marzari
Source: npj Computational Materials (article, 2024-12-02)
Field: Materials Science — Materials Chemistry

Spectral operator representations Machine learning in atomistic materials science has grown to become a powerful tool, with most approaches focusing on atomic geometry, typically decomposed into local atomic environments. This approach, while well-suited for machine-learned interatomic potentials, is conceptually at odds with learning complex intrinsic properties of materials, often driven by spectral properties commonly represented in reciprocal space (e.g., band gaps or mobilities) which cannot be readily partitioned in real space. For such applications, methods that represent the electronic rather than the atomic structure could be more promising. In this work, we present a general framework focused on electronic-structure descriptors that take advantage of the natural symmetries and inherent interpretability of physical models. We apply this framework first to material similarity and then to accelerated screening, where a model trained on 217 materials correctly labels 75% of entries in the Materials Cloud 3D database, which meet common screening criteria for promising transparent-conducting materials. Interpretability ; Computer science ; Reciprocal ; Operator (biology) ; Space (punctuation) ; Similarity (geometry) ; Homogeneous space ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Mathematics ; Chemistry ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: S. Santosh | V. Sampath | R. R. Mouliswar
Source: Journal of Materials Engineering and Performance (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Elevated Temperature Deformation Behavior of Novel NiTiAg Shape Memory Alloys: A Comparison Between Various Constitutive Models and Experimental Flow Curves Materials science ; Constitutive equation ; Shape-memory alloy ; Deformation (meteorology) ; Composite material ; Flow (mathematics) ; Flow stress ; Metallurgy ; Mechanics ; Thermodynamics ; Alloy ; Finite element method Shape Memory Alloy Transformations
Authors: Kun Luo | Xiao Han | Jonathan Cappola | Dian Li | Yufeng Zheng | Lin Li | Feng Yan | Qi An
Source: Advanced Engineering Materials (article, 2024-04-02)
Field: Materials Science — Materials Chemistry

Hyper‐Elastic Deformation via Martensitic Phase Transformation in Cadmium Telluride Cadmium telluride (CdTe) is a highly promising material for photovoltaics (PV) and photodetectors due to its light‐absorbing properties. However, efficient design and use of flexible devices require a deep understanding of its atomic‐level deformation mechanism. Herein, uniaxial compression deformation of CdTe monocrystalline with varying crystal orientations is investigated using molecular dynamics (MD) with a newly developed machine‐learning force field (ML‐FF), alongside in‐situ micropillar compression experiments. The findings reveal that CdTe bulk deformation is dominated by reversible martensitic phase transformation, whereas CdTe pillar deformation is primarily driven by dislocation nucleation and movement. CdTe monocrystals possess exceptional super‐recoverable deformation along the &lt;100&gt; orientation due to hyper‐elastic processes induced by martensitic transformation. This discovery not only sheds light on the peculiarities observed in micropillar experimental measurements, but also provides pivotal insights into the fundamental deformation behaviors of CdTe and similar II–VI compounds under various stress conditions. These insights are crucial for the innovative design and enhanced functionality of future flexible electronic devices. Materials science ; Cadmium telluride photovoltaics ; Deformation (meteorology) ; Deformation mechanism ; Dislocation ; Nucleation ; Diffusionless transformation ; Phase (matter) ; Martensite ; Optoelectronics ; Composite material ; Microstructure ; Thermodynamics Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials ; Boron and Carbon Nanomaterials Research
Authors: Robert Tempke | Terence Musho
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Autonomous generation of single photon emitting materials The utilization of machine learning in Materials Science underscores the critical importance of the quality and quantity of data in training models effectively. Unlike fields such as image processing and natural language processing, there is limited availability of atomistic datasets, leading to biases in training data. Particularly in the domain of materials discovery, there exists an issue of continuity in atomistic datasets. Experimental data sourced from literature and patents is usually only available for favorable data, resulting in bias in the training dataset. This study focuses on developing a SMILES-based model for generating synthetic datasets of quantum materials using a variational autoencoder. This study centers on the generation of a synthetic dataset of quantum materials specifically for quantum sensing applications, with a focus on two-level quantum molecules that exhibit a dipole blockade. The proposed technique offers an improved sampling algorithm by incorporating newly generated data into the sampling algorithm to create a more normally distributed dataset. Through this technique, the study was able to generate over 1 000 000 candidate quantum materials from a small dataset of only 8000 materials. The generated dataset identified several iodine-containing molecules as promising single photon emitting materials for potential quantum sensing applications. Photon ; Materials science ; Nanotechnology ; Optoelectronics ; Optics ; Physics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Electronic and Structural Properties of Oxides
Authors: Xinjian Ouyang | Yanxing Zhang | Zhilong Wang | Feng Zhang | Weijia Chen | Yuan Zhuang | Jie Xiao | Laijun Liu | Dawei Wang
Source: Acta Physica Sinica (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Modeling ferroelectric phase transitions with graph convolutional neural networks Ferroelectric materials are widely used in functional devices, however, it has been a long-standing issue to achieve convenient and accurate theoretical modeling of them. Herein, a noval approach to modeling ferroelectric materials is proposed by using graph convolutional neural networks (GCNs). In this approach, the potential energy surface of ferroelectric materials is described by GCNs, which then serves as a calculator to conduct large-scale molecular dynamics simulations. Given atomic positions, the well-trained GCN model can provide accurate predictions of the potential energy and atomic forces, with an accuracy reaching up to 1 meV per atom. The accuracy of GCNs is comparable to that of &lt;i&gt;ab inito&lt;/i&gt; calculations, while the computing speed is faster than that of &lt;i&gt;ab inito&lt;/i&gt; calculations by a few orders. Benefiting from the high accuracy and fast prediction of the GCN model, we further combine it with molecular dynamics simulations to investigate two representative ferroelectric materials—bulk GeTe and CsSnI&lt;sub&gt;3&lt;/sub&gt;, and successfully produce their temperature-dependent structural phase transitions, which are in good agreement with the experimental observations. For GeTe, we observe an unusual negative thermal expansion around the region of its ferroelectric phase transition, which has been reported in previous experiments. For CsSnI&lt;sub&gt;3&lt;/sub&gt;, we correctly obtain the octahedron tilting patterns associated with its phase transition sequence. These results demonstrate the accuracy and reliability of GCNs in the modeling of potential energy surfaces for ferroelectric materials, thus providing a universal approach for investigating them theoretically. Ferroelectricity ; Computer science ; Convolutional neural network ; Phase transition ; Graph ; Phase (matter) ; Materials science ; Theoretical computer science ; Artificial intelligence ; Condensed matter physics ; Physics ; Optoelectronics ; Quantum mechanics ; Dielectric Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Hendra Hendra | Henryk A. Witek
Source: Nanomaterials (article, 2024-01-12)
Field: Materials Science — Materials Chemistry

Energy Decomposition Scheme for Rectangular Graphene Flakes We show-to our own surprise-that total electronic energies for a family of <i>m</i> × <i>n</i> rectangular graphene flakes can be very accurately represented by a simple function of the structural parameters <i>m</i> and <i>n</i> with errors not exceeding 1 kcal/mol. The energies of these flakes, usually referred to as multiple zigzag chains <i>Z</i>(<i>m</i>,<i>n</i>), are computed for <i>m</i>, <i>n</i> < 21 at their optimized geometries using the DFTB3 methodology. We have discovered that the structural parameters <i>m</i> and <i>n</i> (and their simple algebraic functions) provide a much better basis for the energy decomposition scheme than the various topological invariants usually used in this context. Most terms appearing in our energy decomposition scheme seem to have simple chemical interpretations. Our observation goes against the well-established knowledge stating that many-body energies are complicated functions of molecular parameters. Our observations might have far-reaching consequences for building accurate machine learning models. Simple (philosophy) ; Zigzag ; Decomposition ; Context (archaeology) ; Algebraic number ; Surprise ; Graphene ; Energy (signal processing) ; Scheme (mathematics) ; Function (biology) ; Basis (linear algebra) ; Physics ; Statistical physics ; Computer science ; Computational chemistry ; Materials science ; Mathematics ; Quantum mechanics ; Geometry ; Mathematical analysis ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Onurcan Kaya | Hyeongjoon Kim | Byeongkyu Kim | Galvani, Thomas | Luigi Colombo | Lanza, Mario | Hyeon‐Jin Shin | Ivan Cole | Hyeon Suk Shin | Stephan Roche
Source: arXiv (Cornell University) (preprint, 2024-02-02)
Field: Materials Science — Materials Chemistry

Amorphous Boron Nitride as an Ultrathin Copper Diffusion Barrier for Advanced Interconnects This study focuses on amorphous boron nitride ($\rm α$-BN) as a novel diffusion barrier for advanced semiconductor technology, particularly addressing the critical challenge of copper diffusion in back-end-of-line (BEOL) interconnects. Owing to its ultralow dielectric constant and robust barrier properties, $\rm α$-BN is examined as an alternative to conventional low-k dielectrics. The investigation primarily employs theoretical modelling, using a Gaussian Approximation Potential, to simulate and understand the atomic-level interactions. This machine learning-based approach allows the performance of realistic simulations of amorphous structure of $\rm α$-BN, enabling the exploration of the impact of different film morphologies on barrier efficacy. Furthermore, we studied the electronic and optical properties of the films using a simple Tight-Binding model. In addition to the theoretical studies, we performed diffusion studies of copper through PECVD $\rm α$-BN on Si. The results from both the theoretical and experimental investigations highlight the potential of $\rm α$-BN as a highly effective diffusion barrier, suitable for integration in nanoelectronics. This research shows that $\rm α$-BN is a promising candidate for BEOL interconnects but also demonstrates the synergy of advanced computational models and experimental methods in material innovation for semiconductor applications. Boron nitride ; Diffusion barrier ; Diffusion ; Amorphous solid ; Materials science ; Nitride ; Boron ; Chemical physics ; Chemical engineering ; Engineering physics ; Crystallography ; Nanotechnology ; Chemistry ; Physics ; Thermodynamics ; Engineering ; Organic chemistry Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Metal and Thin Film Mechanics
Authors: Achiya Khanam | Gerardo M. Casañola‐Martín | Amirreza Daghighi | Dean C. Webster | Bakhtiyor Rasulev
Source: ChemRxiv (preprint, 2024-05-13)
Field: Chemistry — Analytical Chemistry

Development of QSAR Models on the Fouling-Release Performance of Silicone Oil-modified Siloxane Polyurethane Coatings Polymer coatings with a fouling release activity is an effective method to prevent marine biological fouling caused by large number of marine organisms (over 4000) and ensure stability and safety of marine facilities. In this study, a novel cheminformatics-based approach was employed to investigate the fouling-release performance of siloxane-PU coating system. The structure-fouling release activity relationship was modeled for a set of 18 siloxane PU coatings by incorporation phenylmethyl silicones oils (PMM-1025, PMM-1043, PMM-5021, PMM-6025, PMM-0021, PMM-0025 of 1, 2 and 5 wt%). A specific structural descriptors encoding approach was applied for these systems, based on a mixture-based encoding, to feed the complex polymeric systems in machine learning algorithms. Several predictive quantitative structure-fouling release relationships models were developed using machine learning techniques, from multiple linear regression to nonlinear random forest methods, followed by scoring them based on high performance accuracy and validation with rigorous internal fit R2 train values between 0.73 to 0.95 and external predictivity R2ext between 0.68 to 0.88. Random Forest method was the best nonlinear one for predicting diatom removal activity. This work indicates that both linear and nonlinear machine learning-based modeling can be beneficial to predict fouling release properties of polymer coatings with an effective fouling release performance. Siloxane ; Fouling ; Silicone oil ; Silicone ; Polyurethane ; Quantitative structure–activity relationship ; Materials science ; Chemical engineering ; Polymer science ; Composite material ; Chemistry ; Engineering ; Stereochemistry ; Polymer Petroleum Processing and Analysis ; Engineering Diagnostics and Reliability ; Risk and Safety Analysis
Authors: Li Shao | Chuanguo Zhang | Xianlong Wang | Zhi Zeng
Source: International Journal of Modern Physics C (article, 2024-04-19)
Field: Chemistry — Spectroscopy

A machine learning methodology for investigating the liquid–liquid transition of hydrogen under high-pressure Due to its unique properties such as superconductivity and superfluidity, high-pressure properties of hydrogen attract a lot of attention. However, the Liquid–Liquid Transition (LLT) of hydrogen under high-pressure and high-temperature is of particular significance for understanding its metallization. We propose a data-driven machine learning approach based on the density functional theory data to fit the potential energy surface into a deep neural network form. This method overcomes the simulation scale limitations of first-principles approaches to investigate the dissociation behavior of hydrogen molecules under high pressure. Our findings reveal an LLT curve exhibiting a first-order continuous change along with transition zone corresponding to hydrogen molecule dissociation. This study offers valuable insights into the LLT phenomenon and the metallization of hydrogen under high pressure. Liquid liquid ; Liquid hydrogen ; Transition (genetics) ; Computer science ; Materials science ; Thermodynamics ; Statistical physics ; Hydrogen ; Chemistry ; Physics ; Chromatography ; Organic chemistry Spectroscopy and Laser Applications ; Phase Equilibria and Thermodynamics ; Quantum, superfluid, helium dynamics
Authors: Satadeep Bhattacharjee | Pritam Das | Swetarekha Ram | Seung‐Cheol Lee
Source: arXiv (Cornell University) (preprint, 2024-01-31)
Field: Materials Science — Materials Chemistry

A Hybrid Machine Learning Framework for Predicting Hydrogen Storage Capacities in Metal Hydrides: Unsupervised Feature Learning with Deep Neural Networks In this study, we present a sophisticated hybrid machine-learning framework that significantly improves the accuracy of predicting hydrogen storage capacities in metal hydrides. This is a critical challenge due to the scarcity of experimental data and the complexity of high-dimensional feature spaces. Our approach employs the power of unsupervised learning through the use of a state-of-the-art autoencoder. This autoencoder is trained on elemental descriptors obtained from Mendeleev software, enabling the extraction of a meaningful and lower dimensional latent space from the input data. This latent representation serves as the basis for our deep multi-layer perceptron (MLP) model, which consists of five layers and shows good precision in predicting hydrogen storage capacities. Furthermore, our results show very good agreement with the results of density functional theory (DFT). In addition to addressing the limitations caused by limited and unevenly distributed data in the field of hydrogen storage materials, we also focus on discovering new materials that show promising opportunities for hydrogen storage. These materials were identified using both feature-based approaches and predictions generated by a large language model. Finally, our investigation into the effectiveness of transferring weights from the autoencoder to the MLP, in addition to the latent features, suggests that while this strategy slightly improves model performance indicated by a slightly higher R$^2$ value and lower RMSE, it emphasizes the intricate challenge of adapting pre-trained weights for specific supervised tasks. Hydrogen storage ; Machine learning ; Computer science ; Supervised learning ; Metal ; Artificial intelligence ; Hydrogen ; Materials science ; Chemistry ; Metallurgy ; Artificial neural network ; Organic chemistry Hydrogen Storage and Materials ; Nuclear Materials and Properties
Authors: Shichao Zhu | Mengmeng Wang | Maoyuan Li | Mingjie Zhang | Gang Jin
Source: Polymer (article, 2024-11-08)
Field: Chemistry — Analytical Chemistry

Non-destructive measurement of the dispersion of high-density polyethylene/Polystyrene blends using near-infrared diffuse reflectance spectroscopy based on deep learning Polystyrene ; Diffuse reflectance infrared fourier transform ; Materials science ; Dispersion (optics) ; Diffuse reflection ; Polyethylene ; Infrared spectroscopy ; Infrared ; Reflectivity ; Spectroscopy ; Composite material ; Optics ; Polymer ; Chemistry ; Organic chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Tomoko Imasaka | Katsunori Yoshinaga | Totaro Imasaka
Source: Analytical Chemistry (article, 2024-06-05)
Field: Chemistry — Spectroscopy

Machine Learning for Characterizing Biofuels Based on Femtosecond Laser Ionization Mass Spectrometry A sample mixture of fatty acid methyl esters (FAMEs) was measured by femtosecond laser ionization mass spectrometry (fsLIMS) using the fifth (206 nm) and fourth (257 nm) harmonic emissions of an ytterbium (Yb) laser (1030 nm). Molecular ions were observed as the major signals in this technique, providing valuable information concerning the molecular weight and the number of double bonds in the molecule. The mass spectral data were then used as explanatory variables in machine learning based on artificial intelligence (AI) to correlate with objective variables such as the cetane number, kinematic viscosity, specific gravity, a higher heating value, an iodine value, flash point, oxidative stability index, and a cloud point measured for reference biofuel samples containing various FAMEs. The properties of biofuels, i.e., the objective variables, were evaluated from the mass spectral data obtained for unknown samples. The errors in the evaluation were a few percent when the distribution of the FAMEs in the unknown biofuel sample was similar to those of the biofuels used for machine learning. As demonstrated herein, the present approach, involving a combination of fsLIMS and AI, has the potential for use in evaluating the properties of a biofuel and then in solving of environmental issues associated with global warming. Chemistry ; Biofuel ; Mass spectrometry ; Flash point ; Analytical Chemistry (journal) ; Ionization ; Femtosecond ; Laser ; Chromatography ; Ion ; Organic chemistry ; Optics ; Physics Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Biodiesel Production and Applications