jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Atalar, Kemal
Source: Spiral (Imperial College London) (article, 2024-08-16)
Field: Materials Science — Materials Chemistry

Ab-initio tight-binding models for twisted bilayer transition metal dichalcogenides Atomically thin semiconductors, such as transition metal dichalcogenides (TMDs), exhibit unique electronic and optical properties, and introducing a twist angle between two TMD layers can lead to exotic quantum phases. To navigate the complex landscape of materials and twist angles and understand the nature of these emergent states, this thesis presents the development and application of \emph{ab initio} tight-binding Hamiltonians for the study of twisted homo- and heterobilayers of MoS$_2$, MoSe$_2$, WS$_2$ and WSe$_2$. A novel \emph{ab initio} tight-binding model for twisted bilayer TMDs is introduced, incorporating a position-dependent interlayer interaction that considers metal-chalcogen hopping and explicitly depend on the distance between layers. This model enhances the qualitative and quantitative description of moir{\'e} valence bands and extends to ten different homo- and heterobilayers. Additionally, advancements are made to include long-range intralayer hopping terms to provide a very accurate representation of the conduction band manifold. The application of this tight-binding model reveals various moiré flat bands and investigates the effects of atomic reconstructions on flat-band properties in twisted bilayer TMDs. Insights are gained regarding the material-specific band flattening and band gap evolution as a function of twist angle, alongside an analysis of the ordering variations of moiré valence states originating from the monolayer $\Gamma$ and $K$ valleys. Further developments extend the model to include position-dependent intralayer interactions, employing separate Wannier Hamiltonian and band structure fitting approaches. In addition to the Slater-Koster description of the hopping integrals, a machine learning model is developed to map atomic environments to Wannier on-site energies. The accuracy and transferability of both Wannier and band-fitted tight-binding models are assessed for the cases of biaxial strain, random displacements, and temperature dependent band structures. Wannier function ; Bilayer ; Twist ; Hamiltonian (control theory) ; Monolayer ; Transition metal ; Electronic structure ; Band gap ; Bilayer graphene 2D Materials and Applications ; Advanced Physical and Chemical Molecular Interactions ; Machine Learning in Materials Science
Authors: John Findley | Samir Budhathoki | Janice A. Steckel
Source:  (article, 2024-06-19)
Field: Chemistry — Inorganic Chemistry

Machine-Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture To cope with legacy greenhouse gas emissions and to achieve net-zero emissions by 2050, the U.S. Department of Energy (DOE) is funding efforts to develop direct air capture (DAC), a method for removing CO2 directly from air. Metal organic frameworks (MOFs) have been well studied as DAC sorbent materials due to their tunable structural and compositional properties. Thermodynamic simulations using force fields are often used to provide predictions of a material’s performance in many separations. However, these force fields often make assumptions about bonds and the physics of the adsorption process. A new class of force fields called machine-learned force fields (MLFFs) use machine learning to form quantitative relationships between a material’s chemical structure and the forces and energies predicted by more accurate quantum mechanical calculations, such as dispersion-corrected density functional theory (DFT). In this work, models were developed to achieve DFT-level accuracy for the forces and energies associated with MOF flexibility and CO2 adsorption using MLFFs. These methods were parametrized based on thousands of DFT calculations of CO2 in flexible MOFs and used to predict MOF structural properties as well as CO2 adsorption in several MOFs. Field (mathematics) ; Computer science ; Metal-organic framework ; Force field (fiction) ; Environmental science ; Aerospace engineering ; Artificial intelligence ; Engineering ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Membrane Separation and Gas Transport
Authors: Bin Han | Kuang Yu
Source: arXiv (Cornell University) (preprint, 2024-06-26)
Field: Materials Science — Materials Chemistry

Refining Potential Energy Surface through Dynamical Properties via Differentiable Molecular Simulation Recently, machine learning potentials (MLP) largely enhances the reliability of molecular dynamics, but its accuracy is limited by the underlying $\textit{ab initio}$ methods. A viable approach to overcome this limitation is to refine the potential by learning from experimental data, which now can be done efficiently using modern automatic differentiation technique. However, potential refinement is mostly performed using thermodynamic properties, leaving the most accessible and informative dynamical data (like spectroscopy) unexploited. In this work, through a comprehensive application of adjoint and gradient truncation methods, we show that both memory and gradient explosion issues can be circumvented in many situations, so the dynamical property differentiation is well-behaved. Consequently, both transport coefficients and spectroscopic data can be used to improve the density functional theory based MLP towards higher accuracy. Essentially, this work contributes to the solution of the inverse problem of spectroscopy by extracting microscopic interactions from vibrational spectroscopic data. Refining (metallurgy) ; Differentiable function ; Surface (topology) ; Potential energy surface ; Energy (signal processing) ; Biological system ; Molecular dynamics ; Computer science ; Statistical physics ; Mathematics ; Physics ; Chemistry ; Computational chemistry ; Physical chemistry ; Molecule ; Mathematical analysis ; Biology ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Chemical Physics Studies
Authors: Promise Longe | Shadfar Davoodi | Mohammad Mehrad
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Prediction of Carbon Dioxide Adsorption on Metal-Organic Frameworks: A Combined Machine Learning and Optimization Approach Adsorption ; Carbon dioxide ; Metal-organic framework ; Metal ; Computer science ; Materials science ; Artificial intelligence ; Environmental science ; Chemistry ; Metallurgy ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Phase Equilibria and Thermodynamics
Authors: Seongsoo Jeong | Yong Kyoung Kim | Suel Hye Hur | Hyojoo Bang | Ho Jin Kim | Hoeil Chung
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Explainable Extreme Gradient Boosting as a Machine Learning Tool for Discrimination of the Geographical Origin of Chili Peppers Using Laser Ablation-Inductively Coupled Plasma Mass Spectrometry, X-Ray Fluorescence, and Near-Infrared Spectroscopy Boosting (machine learning) ; Mass spectrometry ; Ablation ; Gradient boosting ; Analytical Chemistry (journal) ; Fluorescence ; Inductively coupled plasma ; Laser ablation ; Inductively coupled plasma mass spectrometry ; Plasma ; Chemistry ; Laser ; Physics ; Optics ; Computer science ; Artificial intelligence ; Environmental chemistry ; Chromatography ; Random forest ; Nuclear physics ; Aerospace engineering ; Engineering Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Mohd Sharil Salleh | Khalid Isa Isa | Fakroul Ridzuan Hashim
Source: Journal of Advanced Research in Applied Mechanics (article, 2024-08-30)
Field: Materials Science — Materials Chemistry

Prediction Model using a Multi-Layer Perceptron Neural Network for Military Plastic Explosive PE4 Blast Performance in a Significant Effect of Tropicalisation Over the years, plastic explosive PE4 has been imported from the United Kingdom and is fully utilised in military activities and drills. This study is to discover a ratio of the plastic explosive PE4 explosion performance in tropical conditions and develop a multilayer perceptron neural network model for the prediction of plastic explosive in Malaysia. Several environmental tests have been performed in Kem Kongkoi, Jelebu, Negeri Sembilan. Six parameters were considered, including environmental temperature, distance measurement, explosive material, variety of shapes, weight and ignition point. In this paper, the Bayesian Regularization, Levenberg-Marquardt algorithm and Scaled Conjugate Gradient model were developed. Every model was tested with 24 datasets to discover the root mean square error and regression performance. The Bayesian Regularization model provides the best prediction model as it has a mean square error of 0.0005 and a regression performance value that is close to 1 at 0.9992. Explosive material ; Artificial neural network ; Perceptron ; Multilayer perceptron ; Layer (electronics) ; Computer science ; Artificial intelligence ; Materials science ; Composite material ; Chemistry High-Velocity Impact and Material Behavior
Authors: Darja Cvetkovic | Marija Mitrović Dankulov | Aleksandar Bogojević | Saša Lazović | Darija Obradović
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Molecular and Graph-Based Theory: A Reliable Solution for Estimating Three-Dimensional Hansen Solubility Solubility ; Graph theory ; Graph ; Mathematics ; Computer science ; Combinatorics ; Chemistry ; Organic chemistry Crystallization and Solubility Studies ; Thermal and Kinetic Analysis ; Chemistry and Chemical Engineering
Authors: Rajan Prasad | Antony Satya Vivek Vardhan Akisetty | Punit Goel | Gokul Subramanian | Abdul Rafi Shaik | Krishan Kumar
Source:  (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Framework GAN Optimization and Strategy on Novel Molecules Span the Space The pharmaceutical industry places a high value on research into drug design. However, several obstacles could affect this process, such as ineffectiveness, off-target delivery, time consumption, and excessive cost. One interesting new approach to de novo drug creation is the use of deep learning algorithms to create individualised drug-like compounds. The produced compounds, however, did not take stereochemistry into account, as is always the case with molecules with a specific target. Using a feedback loop and deep models for encoder-decoder, generative adversarial networks (GANs), and predictors, this study presents a framework for optimisation. The Encoder-Decoder generates a novel molecular representation by translating molecule string notations into latent space vectors. Concurrently, GAN may learn to produce novel compounds by duplicating the distribution of training data. During each training epoch, the produced distribution is incrementally moved closer to the space of the intended characteristics by incorporating and evaluating the produced molecules based on the multiobjective desired property. The results show that the suggested model can produce chemically plausible, new compounds. Reconstructing datasets with 99%, including stereochemical data, is accomplished using the suggested Encoder-Decoder approach. Optimising the impartial GAN to produce compounds with a strong empathy for binding to the Adenosine A<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2a</inf> and Kappa Opioid receptors demonstrated the model's capacity to discover hitherto unexplored areas of the chemical universe. In addition to being unique, the produced compounds have internal diversity levels of 0.89 and exterior diversity levels of 0.95. Span (engineering) ; Computer science ; Space (punctuation) ; Materials science ; Engineering ; Structural engineering Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Synthesis of Tetrazole Derivatives
Authors: Franz Dammaß | Karl A. Kalina | Markus Kaestner
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Neural Networks Meet Phase-Field: A Hybrid Fracture Model Field (mathematics) ; Phase (matter) ; Fracture (geology) ; Artificial neural network ; Computer science ; Geology ; Artificial intelligence ; Physics ; Geotechnical engineering ; Mathematics Material Properties and Failure Mechanisms ; Neural Networks and Applications ; Non-Destructive Testing Techniques
Authors: Alexandru Haidarlî | Bogdan Alexandrescu | Rodica Constantinescu
Source:  (article, 2024-10-16)
Field: Materials Science — Materials Chemistry

Prediction of the Magnetic Interaction between Atoms using a Neural Network The main goal of the analysis carried out in this paper was to establish the best algorithm among four neural network algorithms chosen to approach and solve the problem of predicting scalar coupling constants between two atoms within the same molecule. It will be shown that the most important aspect of the analyzed deep neural network is related to the input features on which the training is done. Artificial neural network ; Computer science ; Artificial intelligence Machine Learning in Materials Science
Authors: Xiaoxia Yang | Zhishuai Zheng | Hairong Zheng | Xiaoping Liu
Source: Дефектоскопия (article, 2024-10-19)
Field: Chemistry — Organic Chemistry

Deep Learning Method of Precious Wood Image Classification Based on Microscopic Computed Tomography Correctly identifying precious wood species is crucial for import and export trade and furniture material identification. This study utilizes nondestructive testing (Microscopic Computed Tomography, Micro-CT) to capture microscopic images of the transverse, radial, and tangential sections of 24 precious wood species, creating a comprehensive dataset. The SLConNet deep learning model is developed, enhancing recognition accuracy through multi-scale convolution and an improved residual block structure. The experiment results show that the classification accuracy of the transverse, radial and tangential sections is 98.72, 96.75 and 95.36 % respectively when the gain value is 0.8. The model outperforms traditional models like Alexnet, ResNet50, Inception-V3, and Xception. This research highlights the efficiency of nondestructive testing in obtaining a large number of microscopic wood images, compared to traditional anatomical methods. The SLConNet model showcases high accuracy in precision, recall, and specificity, suggesting its potential for widespread applications in wood classification. Computed tomography ; Artificial intelligence ; Image (mathematics) ; Computer vision ; Deep learning ; Computer science ; Radiology ; Medicine Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Image Processing and 3D Reconstruction
Authors: Zhang, Yibo
Source: University of New Hampshire Scholars Repository (University of New Hampshire at Manchester) (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

ARTIFICIAL INTELLIGENCE DRIVEN MAGNETIC MATERIALS DISCOVERY: FROM DATA EXTRACTION TO PROPERTY PREDICTION This dissertation presents an integrated artificial intelligence framework aimed at accelerating magnetic materials discovery by addressing key challenges in materials science: improving experimental characterization techniques, efficiently extracting structured data from scientific literature, developing comprehensive materials databases, and creating reliable predictive models. We first develop a UNet-enhanced vector field electron tomography (VFET) approach for three-dimensional magnetic structure reconstruction. This method addresses the missing wedge problem in experimental data collection while maintaining computational efficiency, providing reliable experimental data crucial for materials research. We introduce GPTArticleExtractor, a novel workflow leveraging large language models to automatically extract key information from scientific literature. Applied to 22,120 articles from the Journal of Magnetism and Magnetic Materials, this system demonstrated the effectiveness of AI-driven data extraction by generating a database of 2,035 magnetic materials with detailed structural and magnetic properties. Expanding upon GPTArticleExtractor's methodology, we develop the Northeast Materials Database (NEMAD), a comprehensive experiment-based magnetic materials database containing 26,706 entries. By enhancing and scaling up our extraction approach, NEMAD consolidates chemical compositions, magnetic phase transition temperatures, structural details, and magnetic properties from multiple data sources, including our experimental data and extracted literature data. Utilizing NEMAD's extensive dataset, we develop machine learning models for magnetic property prediction. Our classification model achieves 90\% accuracy in categorizing materials as ferromagnetic, antiferromagnetic, or non-magnetic. The regression models exhibit high accuracy in predicting Curie and Néel temperatures, with R² values of 0.86 and 0.85, respectively. These models successfully identify 62 promising ferromagnetic candidates with predicted Curie temperatures above 500 K and 19 antiferromagnetic compounds with predicted Néel temperatures exceeding 100 K. This integrated framework demonstrates the potential of AI-driven approaches in accelerating materials discovery by combining enhanced experimental techniques, automated knowledge extraction, comprehensive database development, and accurate property prediction. The methodologies and tools developed in this work lay a robust foundation for future advancements in materials science research. Magnetism ; Workflow ; Curie temperature ; Property (philosophy) ; Data extraction ; Experimental data ; Ferromagnetism ; Characterization (materials science) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Victor H. R. Nogueira | Rishabh Sharma | Rafael V. C. Guido | Michael J. Keiser
Source: ChemRxiv (preprint, 2024-07-11)
Field: Materials Science — Materials Chemistry

Fuzz testing molecular representation using deep variational anomaly generation As efforts to improve the robustness of molecular representations advance, so does the need for methods to test and validate them. We use a Variational Auto-Encoder (VAE), an unsupervised deep learning model, to generate anomalous samples of a well-known molecular string format called SELF-referencIng Embedded Strings (SELFIES). This exercise questions a fundamental SELFIES assumption -- that they are always valid when converted to another string representation, SMILES. Interestingly, we discover that specific regions in the VAE's latent space are particularly effective at generating SELFIES that defy this assumption. This organization of validity in the latent space, which proceeds continuously and radially, helps us better understand the factors affecting the molecular representation's reliability. We propose that the VAE and associated anomaly generation approach offer an effective tool for assessing the robustness of molecular representations. We also explore why some SELFIES strings (version 2.1.1) might be invalid and suggest changes to improve them, aiming to spark further discussion on molecular string representations. Anomaly (physics) ; Representation (politics) ; Anomaly detection ; Artificial intelligence ; Computer science ; Physics ; Quantum mechanics ; Political science Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Yifan Wang | Linlin Zhong
Source: arXiv (Cornell University) (preprint, 2024-07-13)
Field: Materials Science — Surfaces, Coatings and Films

DeepCSNet: a deep learning method for predicting electron-impact doubly differential ionization cross sections Electron-impact ionization cross sections of atoms and molecules are essential for plasma modelling. However, experimentally determining the absolute cross sections is not easy, and ab initio calculations become computationally prohibitive as molecular complexity increases. Existing AI-based prediction methods suffer from limited data availability and poor generalization. To address these issues, we propose DeepCSNet, a deep learning approach designed to predict electron-impact ionization cross sections using limited training data. We present two configurations of DeepCSNet: one tailored for specific molecules and another for various molecules. Both configurations can typically achieve a relative L2 error less than 5%. The present numerical results, focusing on electron-impact doubly differential ionization cross sections, demonstrate DeepCSNet's generalization ability, predicting cross sections across a wide range of energies and incident angles. Additionally, DeepCSNet shows promising results in predicting cross sections for molecules not included in the training set, even large molecules with more than 10 constituent atoms, highlighting its potential for practical applications. Electron ionization ; Differential (mechanical device) ; Ionization ; Cross-validation ; Artificial intelligence ; Computer science ; Atomic physics ; Physics ; Ion Electron and X-Ray Spectroscopy Techniques ; Ion-surface interactions and analysis ; Radiation Effects in Electronics
Authors: Xiaokun Yang | Xuejuan Hu | A.J. Li | Tan Zhang | Shiqian Liu | Qingyang Wu | Minfei Li | Yifei Liang | Yadan Tan | Hengliang Wang | Jianze Ye
Source: Journal of Electronic Materials (article, 2024-12-29)
Field: Chemistry — Organic Chemistry

Lifetime Assessment of Polymethyl Methacrylate (PMMA) Plastic Lens, Used in LED-Based Products Materials science ; Light-emitting diode ; Polymethyl methacrylate ; Lens (geology) ; Photothermal therapy ; LED lamp ; Optics ; Color temperature ; Optoelectronics ; Composite material ; Transmittance ; Accelerated aging ; Wavelength ; Polymer ; Nanotechnology Photopolymerization techniques and applications ; Industrial Vision Systems and Defect Detection ; Color Science and Applications