jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vijay Choyal | Mahesh Pralhad Patil | Nitin Luhadiya | S. I. Kundalwal
Source: Journal of Physics Materials (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Machine-learned interatomic potentials for accurate analysis of the mechanical properties of boron nitride sheets Abstract We introduced a novel machine-learned interatomic potential (MLIP) by thoroughly discussing the step–by–step MLIP creation process using precise but limited data. This study explored the mechanical properties of hexagonal boron nitride (hBN) nanosheets and addressed the challenges of accurately predicting their structural properties. We explored the use of ab initio molecular dynamics and classical molecular dynamics (CMD) simulation techniques, emphasizing the necessity for a more effective and efficient solution. We also discussed the machine learning procedure to construct an effective interatomic potential. Furthermore, we address techniques for evaluating the performance and robustness of MLIPs on unseen datasets. Using the newly formed MLIP in a CMD simulation, we investigated the mechanical attributes of hBN nanosheets, exploring the fluctuations in sheet strength across a range of dimensions, temperatures, and varying numbers of layers. We obtained an average Young’s modulus in the range of 980–1000 GPa at 1 K, whereas the average failure stress and strain were approximately 106 GPa and 0.16, respectively. Our results demonstrate significant improvements in the accuracy of hBN nanosheets compared to prior studies, highlighting the effectiveness of MLIP in achieving higher precision with minimal computational cost. This study offers comprehensive analysis and theoretical exploration, delivering valuable insights into MLIP and the mechanical properties of hBN nanosheets, and paves the way for future applications in materials science and engineering. Boron nitride ; Materials science ; Boron ; Nitride ; Interatomic potential ; Nanotechnology ; Chemical physics ; Computational chemistry ; Chemistry ; Molecular dynamics ; Organic chemistry Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Thermal properties of materials
Authors: Jun Liu | Yangfan Li | Dingjie Lu | Shuai Cao | N. Sridhar
Source:  (article, 2024-12-03)
Field: Materials Science — Materials Chemistry

Real-Time Evaluation of Effective Thermal Conductivity Profile for the Redistribution Layer (RDL) This paper presents a novel approach for rapid evaluation of effective thermal property distribution in Redistribution Layers (RDLs) used in advanced packaging. The methodology combines GDSii file processing, finite element analysis (FEM), and machine learning to overcome the limitations of existing methods. The process involves dividing the RDL into small patches, assembling them into a structured mesh, conducting FEM analysis using an optimized in-house code for data generation, and training a U-Net-based machine learning model to predict effective thermal property distributions. This approach significantly reduces computational time compared to traditional methods, enabling simultaneous evaluation of the entire RDL material property distribution. The method achieves a balance between accuracy and efficiency, reducing evaluation time from days to seconds while maintaining reasonable prediction accuracy. This innovation facilitates thermal characterization and design optimization in chiplet-based packaging, potentially accelerating the development of next-generation electronic devices through improved co-design practices. Redistribution (election) ; Thermal conductivity ; Materials science ; Layer (electronics) ; Conductivity ; Composite material ; Physics Advancements in Solid Oxide Fuel Cells
Authors: Wenhao Jing | Zihao Jiao | Mengmeng Song | Ya Liu | Liejin Guo
Source: Green Energy & Environment (article, 2024-06-28)
Field: Materials Science — Materials Chemistry

An active learning workflow for predicting hydrogen atom adsorption energies on binary oxides based on local electronic transfer features Machine learning combined with density functional theory (DFT) enables rapid exploration of catalyst descriptors space such as adsorption energy, facilitating rapid and effective catalyst screening. However, there is still a lack of models for predicting adsorption energies on oxides, due to the complexity of elemental species and the ambiguous coordination environment. This work proposes an active learning workflow (LeNN) founded on local electronic transfer features (e) and the principle of coordinate rotation invariance. By accurately characterizing the electron transfer to adsorption site atoms and their surrounding geometric structures, LeNN mitigates abrupt feature changes due to different element types and clarifies coordination environments. As a result, it enables the prediction of ∗H adsorption energy on binary oxide surfaces with a mean absolute error (MAE) below 0.18 eV. Moreover, we incorporate local coverage (θl) and leverage neutral network ensemble to establish an active learning workflow, attaining a prediction MAE below 0.2 eV for 5419 multi-∗H adsorption structures. These findings validate the universality and capability of the proposed features in predicting ∗H adsorption energy on binary oxide surfaces. Adsorption ; Workflow ; Density functional theory ; Leverage (statistics) ; Binary number ; Electron transfer ; Materials science ; Transfer of learning ; Computer science ; Chemistry ; Computational chemistry ; Artificial intelligence ; Physical chemistry ; Mathematics ; Database Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; CO2 Reduction Techniques and Catalysts
Authors: J. Charlie Maier | Chun-I Wang | Nicholas E. Jackson
Source: The Journal of Chemical Physics (article, 2024-01-09)
Field: Materials Science — Materials Chemistry

Distilling coarse-grained representations of molecular electronic structure with continuously gated message passing Bottom-up methods for coarse-grained (CG) molecular modeling are critically needed to establish rigorous links between atomistic reference data and reduced molecular representations. For a target molecule, the ideal reduced CG representation is a function of both the conformational ensemble of the system and the target physical observable(s) to be reproduced at the CG resolution. However, there is an absence of algorithms for selecting CG representations of molecules from which complex properties, including molecular electronic structure, can be accurately modeled. We introduce continuously gated message passing (CGMP), a graph neural network (GNN) method for atomically decomposing molecular electronic structure sampled over conformational ensembles. CGMP integrates 3D-invariant GNNs and a novel gated message passing system to continuously reduce the atomic degrees of freedom accessible for electronic predictions, resulting in a one-shot importance ranking of atoms contributing to a target molecular property. Moreover, CGMP provides the first approach by which to quantify the degeneracy of "good" CG representations conditioned on specific prediction targets, facilitating the development of more transferable CG representations. We further show how CGMP can be used to highlight multiatom correlations, illuminating a path to developing CG electronic Hamiltonians in terms of interpretable collective variables for arbitrarily complex molecules. Computer science ; Message passing ; Parallel computing Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Molecular Junctions and Nanostructures
Authors: Seongsoo Jeong | Yong-Kyoung Kim | Suel Hye Hur | Hyojoo Bang | Hojin Kim | Hoeil Chung
Source: Journal of Agriculture and Food Research (article, 2024-09-30)
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 The spectroscopic discrimination of chili pepper samples according to geographical origin was executed using analytical techniques coupled with machine learning. First, laser ablation-inductively coupled plasma mass spectrometry (LA-ICP-MS), X-ray fluorescence (XRF), and near-infrared (NIR) spectroscopy were chosen for simple and rapid sample measurements. Second, to secure discrimination accuracy, eXtreme Gradient Boosting (XGBoost), a tree-based ensemble technique, was adopted as a potential classifier. Also, for explainable machine learning modeling, SHaply Additive exPlanation (SHAP) values of employed variables were calculated to assess how they contribute to the discrimination. The use of XGBoost improved discrimination accuracies in all three measurements compared to k-nearest neighbor (k-NN), support vector machine (SVM), and partial least squares-discriminant analysis (PLS-DA). The accuracy was 96.2 % using the LA-ICP-MS data. When the XRF and NIR data were combined, the accuracy improved to 97.5 %. The accuracy improvement was attributed to the combination of complementary atomic and molecular spectroscopic signatures of the samples. Mass spectrometry ; Boosting (machine learning) ; Laser ablation ; Ablation ; Analytical Chemistry (journal) ; Inductively coupled plasma mass spectrometry ; Plasma ; Fluorescence ; Inductively coupled plasma ; Laser ; Chemistry ; Materials science ; Optics ; Physics ; Chromatography ; Artificial intelligence ; Computer science ; Aerospace engineering ; Engineering Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Pesticide Residue Analysis and Safety
Authors: Yanze Wang | Tinghao Zhang | Wenjing Zhao | Weidong Xu | Zhongbin Wu | Yung Doug Suh | Yuezhou Zhang | Xiaowang Liu | Wei Huang
Source: Angewandte Chemie (article, 2024-10-29)
Field: Materials Science — Materials Chemistry

Machine Learning‐Guided Discovery of Copper(I)‐Iodide Cluster Scintillators for Efficient X‐ray Luminescence Imaging Abstract Developing efficient scintillators with environmentally friendly compositions, adaptable band gaps, and robust chemical stability is crucial for modern X‐ray radiography. While copper(I)‐iodide cluster crystals show promise, the vast design space of inorganic cores and organic ligands poses challenges for conventional approaches. In this study, we present machine learning‐guided discovery of copper(I)‐iodide cluster scintillators for efficient X‐ray luminescence imaging. Our findings reveal that combining base learning models with fused features enhances model generalization, achieving an impressive determination coefficient of 0.88. By leveraging this approach, we obtain a high‐performance Cu(I)‐I cluster scintillator, named copper iodide‐(1‐Butyl‐1,4‐diazabicyclo[2.2.2]octan‐1‐ium) 2 , which exhibit radioluminescence 56 times stronger than that of PbWO 4 , and enables a detection limit for X‐rays of 19.6 nGy air s −1 . Furthermore, we demonstrate the versatility of these scintillators by incorporating them as microfillers in the fabrication of flexible composite scintillators for X‐ray imaging, achieving a static resolution of 20 lp mm −1 and demonstrating promising performance for dynamic X‐ray imaging. Scintillator ; Luminescence ; Iodide ; Copper ; Materials science ; Radioluminescence ; Cluster (spacecraft) ; Optoelectronics ; Chemistry ; Optics ; Detector ; Computer science ; Physics ; Inorganic chemistry Machine Learning in Materials Science ; Radiation Detection and Scintillator Technologies ; Lanthanide and Transition Metal Complexes
Authors: Amol B. Pandhare | Swapnajit V. Mulik | Dhanaji B. Malavekar | Jin Hyeok Kim | Vishwajeet M. Khot | Pawan Kumar | Santosh S. Sutar | Tukaram D. Dongale | Rajendra P. Patil | Sagar D. Delekar
Source: Langmuir (article, 2024-11-22)
Field: Materials Science — Biomaterials

Chitosan-Functionalized Lithium Iron Oxide Nanoparticles for Magnetic Hyperthermia Applications In this study, various compositions of α-Fe<sub>2</sub>O<sub>3</sub>, Li<sub>3<i>x</i></sub>Fe<sub>2-x</sub>O<sub>3</sub>, where <i>x</i> = 0.1, 0.3, and 0.5, along with chitosan (CTS)-coated Li<sub>1.5</sub>Fe<sub>1.5</sub>O<sub>3</sub> nanomaterials (NMs), were synthesized using a sol-gel method. Rietveld refinement analysis indicated a predominance of the rhombohedral phase for lower Li-doped content (<i>x</i> = 0.1) and a transition to cubic crystal structures at higher Li-doped content (<i>x</i> = 0.3 and 0.5) within the host lattice. Field emission scanning electron microscopy (FE-SEM) images revealed irregular spherical morphologies, while transmission electron microscopy (TEM) images showed average particle sizes ranging from 19 to 40 nm across the various NMs. Superconducting quantum interference device (SQUID) analysis demonstrated a ferromagnetic nature with the highest saturation magnetization measured at 49.84 emu/g for Li<sub>1.5</sub>Fe<sub>1.5</sub>O<sub>3</sub> NMs. X-ray photoelectron spectra (XPS) exhibited Fe 2p<sub>3/2</sub> and Fe 2p<sub>1/2</sub> peaks at 712.60 and 726.13 eV, respectively, Li 1s at 57.58 eV, and O 1s at 533.44 eV for the representative samples; these characteristic XPS peaks shifted to a lower binding energy for CTS-coated Li<sub>1.5</sub>Fe<sub>1.5</sub>O<sub>3</sub> NMs. Hyperthermia studies demonstrated that the Li-doped samples reached a temperature range between 42 and 44 °C under an alternating current (AC) magnetic field applied at 167.6 to 335.2 Oe, with a constant frequency of 278 kHz. The specific absorption rate (SAR) was recorded as 265.11 W/g for Li<sub>1.5</sub>Fe<sub>1.5</sub>O<sub>3</sub> and 153.48 W/g for CTS-coated Li<sub>1.5</sub>Fe<sub>1.5</sub>O<sub>3</sub> NMs, both surpassing the SAR values of the other samples. Furthermore, various machine learning techniques were utilized to analyze how different synthesis conditions and material properties affected the heating efficiency and SAR values of the synthesized materials. The study also suggests an optimized set of guidelines and heuristics to enhance the heating performance and SAR values of these materials. Finally, magnetic CTS-coated Li<sub>1.5</sub>Fe<sub>1.5</sub>O<sub>3</sub> NMs exhibited a higher cell viability, as confirmed by MTT assays conducted on the NRK 52 E normal cell line. Chitosan ; Nanoparticle ; Iron oxide nanoparticles ; Lithium (medication) ; Iron oxide ; Magnetic nanoparticles ; Nanotechnology ; Chemical engineering ; Oxide ; Materials science ; Magnetic hyperthermia ; Hyperthermia ; Chemistry ; Metallurgy ; Medicine Nanoparticle-Based Drug Delivery ; Iron oxide chemistry and applications ; Characterization and Applications of Magnetic Nanoparticles
Authors: Tassiani Sarretto | Wil Gardner | Daniel Brungs | Sarbar Napaki | Paul J. Pigram | Shane R. Ellis
Source: Journal of the American Society for Mass Spectrometry (article, 2024-02-26)
Field: Chemistry — Spectroscopy

A Machine Learning-Driven Comparison of Ion Images Obtained by MALDI and MALDI-2 Mass Spectrometry Imaging Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) enables label-free imaging of biomolecules in biological tissues. However, many species remain undetected due to their poor ionization efficiencies. MALDI-2 (laser-induced post-ionization) is the most widely used post-ionization method for improving analyte ionization efficiencies. Mass spectra acquired using MALDI-2 constitute a combination of ions generated by both MALDI and MALDI-2 processes. Until now, no studies have focused on a detailed comparison between the ion images (as opposed to the generated <i>m/z</i> values) produced by MALDI and MALDI-2 for mass spectrometry imaging (MSI) experiments. Herein, we investigated the ion images produced by both MALDI and MALDI-2 on the same tissue section using correlation analysis (to explore similarities in ion images for ions common to both MALDI and MALDI-2) and a deep learning approach. For the latter, we used an analytical workflow based on the Xception convolutional neural network, which was originally trained for human-like natural image classification but which we adapted to elucidate similarities and differences in ion images obtained using the two MSI techniques. Correlation analysis demonstrated that common ions yielded similar spatial distributions with low-correlation species explained by either poor signal intensity in MALDI or the generation of additional unresolved signals using MALDI-2. Using the Xception-based method, we identified many regions in the t-SNE space of spatially similar ion images containing MALDI and MALDI-2-related signals. More notably, the method revealed distinct regions containing only MALDI-2 ion images with unique spatial distributions that were not observed using MALDI. These data explicitly demonstrate the ability of MALDI-2 to reveal molecular features and patterns as well as histological regions of interest that are not visible when using conventional MALDI. MALDI imaging ; Chemistry ; Mass spectrometry imaging ; Mass spectrometry ; Ionization ; Matrix-assisted laser desorption/ionization ; Ion ; Analytical Chemistry (journal) ; Mass spectrum ; Analyte ; Desorption ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Lei Zhang | Markus Stricker
Source: SoftwareX (article, 2024-02-15)
Field: Materials Science — Materials Chemistry

MatNexus: A comprehensive text mining and analysis suite for materials discovery In the evolving landscape of materials science, the exponential growth in scientific publications presents both an opportunity and a challenge. Efficiently extracting valuable insights from this vast volume of literature requires specialized tools that go beyond traditional methods. Here, we introduce MatNexus , a software package designed for the automated collection, processing, and analysis of text from scientific articles in the realm of materials science. MatNexus stands out with its integrated suite of modules, which facilitates the retrieval of scientific articles, processes textual data to uncover latent knowledge, generation of vector representations suitable for machine learning applications, and offers advanced visualization capabilities for these word embeddings. Our tool addresses the critical need for effective and reproducable text mining in materials science, an area marked by increasing complexity and data intensity. By making the exploration of materials more efficient and insightful, as exemplified in our case study on electrocatalysts, MatNexus represents a significant advancement in the field. It offers an end-to-end solution for harnessing the wealth of information available in scientific literature, thus aiding the discovery and innovation process in materials science. • MatNexus for automated article handling in material science. • Features article analysis and ML-ready data vectors. • Boosts material exploration and visualization. Suite ; Computer science ; Data science ; Visualization ; Scientific literature ; Software ; Information retrieval ; Scientific discovery ; Biomedical text mining ; World Wide Web ; Data mining ; Text mining ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Text and Document Classification Technologies
Authors: Hosein Fooladi | Steffen Hirte | Johannes Kirchmair
Source: Journal of Chemical Information and Modeling (article, 2024-05-13)
Field: Materials Science — Materials Chemistry

Quantifying the Hardness of Bioactivity Prediction Tasks for Transfer Learning Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, validation, and application. Several modern strategies aim to mitigate the challenges associated with data scarcity by learning from data on related tasks. These knowledge-sharing approaches encompass transfer learning, multitask learning, and meta-learning. A key question remaining to be answered for these approaches is about the extent to which their performance can benefit from the relatedness of available source (training) tasks; in other words, how difficult ("hard") a test task is to a model, given the available source tasks. This study introduces a new method for quantifying and predicting the hardness of a bioactivity prediction task based on its relation to the available training tasks. The approach involves the generation of protein and chemical representations and the calculation of distances between the bioactivity prediction task and the available training tasks. In the example of meta-learning on the FS-Mol data set, we demonstrate that the proposed task hardness metric is inversely correlated with performance (Pearson's correlation coefficient <i>r</i> = -0.72). The metric will be useful in estimating the task-specific gain in performance that can be achieved through meta-learning. Transfer of learning ; Computer science ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; RNA and protein synthesis mechanisms
Authors: Xu Guang | Shengfeng Gan | Bo Guo | Yang Li
Source: Rapid Communications in Mass Spectrometry (article, 2024-02-23)
Field: Chemistry — Spectroscopy

Application of clustering strategy for automatic segmentation of tissue regions in mass spectrometry imaging We used a clustering algorithm to construct tissue automatic segmentation in MSI datasets. The performance was evaluated by comparing it with the stained image and calculating clustering validation indexes. The results indicated that SAI is important for automatic tissue segmentation in MSI, different from traditional clustering validation measures. Compared to the reports that used internal clustering validation measures such as DBI, our method offers more effective evaluation of clustering results for MSI segmentation. We envision that the proposed automatic image segmentation strategy can facilitate deep learning in molecular feature extraction and biomarker discovery for the biomedical applications of MSI. Mass spectrometry imaging ; Cluster analysis ; Artificial intelligence ; Segmentation ; Computer science ; Mass spectrometry ; Pattern recognition (psychology) ; Computer vision ; Chemistry ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Mingshan Yang | Xiangyu Li | Weiqiu Chen
Source: Applied Energy (article, 2024-06-21)
Field: Materials Science — Materials Chemistry

A robust lattice Boltzmann scheme for high-throughput predicting effective thermal conductivity of reinforced composites Lattice Boltzmann methods ; Thermal conductivity ; Composite material ; Materials science ; Throughput ; Conductivity ; Thermal ; Lattice (music) ; Scheme (mathematics) ; Computer science ; Mechanics ; Physics ; Mathematics ; Thermodynamics ; Mathematical analysis ; Acoustics Thermal properties of materials ; Phase Change Materials Research ; Composite Material Mechanics