jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kapil Dev Sharma | Hrushikesh M. Gade | Neetu Kumari
Source: ACS Applied Materials & Interfaces (article, 2024-04-27)
Field: Materials Science — Materials Chemistry

A Theoretical Study of Oxygen Anion Diffusion through the A-Site Deficient SrTiO<sub>3</sub> Lattice Structure: A Machine Learning Approach The performance of strontium titanate-based perovskite materials, widely employed as electrode materials for reversible solid oxide cells, is directly characterized by their efficiency and their ability to facilitate the diffusion of generated oxygen ions. A technique frequently employed for enhancing oxygen ion diffusivity involves artificially generating A-site vacancies in these structures. In this study, the molecular-level mechanism of oxygen ion diffusion for a range of A-site deficient structures is extensively investigated using combined molecular dynamics simulations and machine learning-based technique. The analysis of molecular simulation trajectories yields diffusion parameters for the bulk system. Additionally, clustering analysis of time-overlapped locations of oxygen ions provides a spatial distribution of oxygen ion dislocations. Concurrently, tracking the motion of individual oxygen ions elucidates the contribution of each ion to the overall ionic conductance. Overall, the systematic generation of A-site deficiency is found to significantly influence oxygen ion dislocations, thereby impacting the performance of these materials in terms of oxide ion conduction. Materials science ; Chemical physics ; Ion ; Oxygen ; Oxide ; Diffusion ; Molecular dynamics ; Ionic conductivity ; Ionic bonding ; Strontium titanate ; Fast ion conductor ; Nanotechnology ; Electrode ; Physical chemistry ; Computational chemistry ; Thermodynamics ; Thin film ; Electrolyte ; Chemistry Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Fuel Cells and Related Materials
Authors: Zhongling Liang | Yingchan Guo | Xizheng Diao | Boone M. Prentice
Source: Journal of the American Society for Mass Spectrometry (article, 2024-07-02)
Field: Chemistry — Spectroscopy

Enhancing Spatial Resolution in Tandem Mass Spectrometry Ion/Ion Reaction Imaging Experiments through Image Fusion We have recently developed a charge inversion ion/ion reaction to selectively derivatize phosphatidylserine lipids via gas-phase Schiff base formation. This tandem mass spectrometry (MS/MS) workflow enables the separation and detection of isobaric lipids in imaging mass spectrometry, but the images acquired using this workflow are limited to relatively poor spatial resolutions due to the current time and limit of detection requirements for these ion/ion reaction imaging mass spectrometry experiments. This trade-off between chemical specificity and spatial resolution can be overcome by using computational image fusion, which combines complementary information from multiple images. Herein, we demonstrate a proof-of-concept workflow that fuses a low spatial resolution (i.e., 125 μm) ion/ion reaction product ion image with higher spatial resolution (i.e., 25 μm) ion images from a full scan experiment performed using the same tissue section, which results in a predicted ion/ion reaction product ion image with a 5-fold improvement in spatial resolution. Linear regression, random forest regression, and two-dimensional convolutional neural network (2-D CNN) predictive models were tested for this workflow. Linear regression and 2D CNN models proved optimal for predicted ion/ion images of PS 40:6 and SHexCer d38:1, respectively. Chemistry ; Mass spectrometry ; Ion ; Image resolution ; Mass spectrometry imaging ; Analytical Chemistry (journal) ; Tandem mass spectrometry ; Resolution (logic) ; Artificial intelligence ; Chromatography ; Computer science Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Qingwen Tian | Yuxin Xu | Yixuan Yang | Zhen Wang | Ziqi Liu | Pengju Yan | Xiaolin Li
Source: arXiv (Cornell University) (preprint, 2024-12-15)
Field: Chemistry — Spectroscopy

EquiFlow: Equivariant Conditional Flow Matching with Optimal Transport for 3D Molecular Conformation Prediction Molecular 3D conformations play a key role in determining how molecules interact with other molecules or protein surfaces. Recent deep learning advancements have improved conformation prediction, but slow training speeds and difficulties in utilizing high-degree features limit performance. We propose EquiFlow, an equivariant conditional flow matching model with optimal transport. EquiFlow uniquely applies conditional flow matching in molecular 3D conformation prediction, leveraging simulation-free training to address slow training speeds. It uses a modified Equiformer model to encode Cartesian molecular conformations along with their atomic and bond properties into higher-degree embeddings. Additionally, EquiFlow employs an ODE solver, providing faster inference speeds compared to diffusion models with SDEs. Experiments on the QM9 dataset show that EquiFlow predicts small molecule conformations more accurately than current state-of-the-art models. Equivariant map ; Matching (statistics) ; Flow (mathematics) ; Computer science ; Mathematics ; Artificial intelligence ; Algorithm ; Pure mathematics ; Geometry ; Statistics Mass Spectrometry Techniques and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation ; Analytical Chemistry and Chromatography
Authors: Eunseo Yang | Miri Seo | Hanee Rhee | Yugyeong Je | Hyun‐Jeong Jeong | Sang Wook Lee
Source: Nanomaterials (article, 2024-01-12)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Identification of Single-Layer Graphene via Color Variation Analysis Techniques such as using an optical microscope and Raman spectroscopy are common methods for detecting single-layer graphene. Instead of relying on these laborious and expensive methods, we suggest a novel approach inspired by skilled human researchers who can detect single-layer graphene by simply observing color differences between graphene flakes and the background substrate in optical microscope images. This approach implemented the human cognitive process by emulating it through our data extraction process and machine learning algorithm. We obtained approximately 300,000 pixel-level color difference data from 140 graphene flakes from 45 optical microscope images. We utilized the average and standard deviation of the color difference data for each flake for machine learning. As a result, we achieved F1-Scores of over 0.90 and 0.92 in identifying 60 and 50 flakes from green and pink substrate images, respectively. Our machine learning-assisted computing system offers a cost-effective and universal solution for detecting the number of graphene layers in diverse experimental environments, saving both time and resources. We anticipate that this approach can be extended to classify the properties of other 2D materials. Graphene ; Artificial intelligence ; Raman spectroscopy ; Optical microscope ; Materials science ; Computer science ; Pixel ; Substrate (aquarium) ; Process (computing) ; Layer (electronics) ; Microscope ; Machine learning ; Microscopy ; Nanotechnology ; Computer vision ; Optics ; Scanning electron microscope ; Composite material ; Physics Graphene research and applications ; Advanced Neural Network Applications ; Advanced Memory and Neural Computing
Authors: Giovanni Parolin | Bidhan Chandra Garain | Saikat Mukherjee | Giovanni Granucci | Stefano Corni | Mario Barbatti
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Conformational dynamics of the pyrene excimer The conformational dynamics of the pyrene excimer play a critical role in its unique fluorescence properties. Yet, the influence of multiple local minima on its excited-state behavior remains underexplored. Using a combination of time-dependent density functional theory (TD-DFT) and unsupervised machine learning analysis, we have identified and characterized a diverse set of stable excimer geometries in the first excited state. Our analysis reveals that rapid structural reorganization towards the most stable stacked-twisted conformer dominates the excimer's photophysics, outcompeting radiative relaxation. This conformer, which is primarily responsible for the characteristic red-shifted, structureless fluorescence emission, reconciles experimental observations of long fluorescence lifetimes and emission profiles. These findings provide new insights into the excited-state dynamics of excimers. They may inform the design of excimer-based materials in fields ranging from organic electronics to molecular sensing. Excimer ; Pyrene ; Excited state ; Dynamics (music) ; Chemical physics ; Fluorescence ; Maxima and minima ; Molecular dynamics ; Chemistry ; Computational chemistry ; Photochemistry ; Physics ; Atomic physics ; Organic chemistry ; Optics Photochemistry and Electron Transfer Studies ; Porphyrin and Phthalocyanine Chemistry ; Analytical Chemistry and Chromatography
Authors: Antoine Loew | Dewen Sun | Hai‐Chen Wang | Silvana Botti | Miguel A. L. Marques
Source: arXiv (Cornell University) (preprint, 2024-12-21)
Field: Materials Science — Materials Chemistry

Universal Machine Learning Interatomic Potentials are Ready for Phonons There has been an ongoing race for the past several years to develop the best universal machinelearning interatomic potential. This progress has led to increasingly accurate models for predictingenergy, forces, and stresses, combining innovative architectures with big data. Here, we benchmarkthese models on their ability to predict harmonic phonon properties, which are critical for under-standing the vibrational and thermal behavior of materials. Using around 10 000 ab initio phononcalculations, we evaluate model performance across various phonon-related parameters to test theuniversal applicability of these models. The results reveal that some models achieve high accuracyin predicting harmonic phonon properties. However, others still exhibit substantial inaccuracies,even if they excel in the prediction of the energy and the forces for materials close to dynamicalequilibrium. These findings highlight the importance of considering phonon-related properties inthe development of universal machine learning interatomic potentials. Phonon ; Physics ; Computer science ; Statistical physics ; Condensed matter physics Machine Learning in Materials Science
Authors: Lara Koehler | Pierre Ronceray | Martin Lenz
Source: Physical Review X (article, 2024-12-09)
Field: Materials Science — Materials Chemistry

How Do Particles with Complex Interactions Self-Assemble? In living cells, proteins self-assemble into large functional structures based on specific interactions between molecularly complex patches. Because of this complexity, protein self-assembly results from a competition between a large number of distinct interaction energies, of the order of one per pair of patches. However, current self-assembly models typically ignore this aspect, and the principles by which it determines the large-scale structure of protein assemblies are largely unknown. Here, we use Monte Carlo simulations and machine learning to start to unravel these principles. We observe that despite widespread geometrical frustration, aggregates of particles with complex interactions fall within only a few categories that often display high degrees of spatial order, including crystals, fibers, and oligomers. We then successfully identify the most relevant aspect of the interaction complexity in predicting these outcomes, namely, the particles’ ability to form periodic structures. Our results provide a first extensive characterization of the rich design space associated with identical particles with complex interactions and could inspire engineered self-assembling nano-objects as well as help us to understand the emergence of robust functional protein structures. Published by the American Physical Society 2024 Self-assembly ; Computer science ; Monte Carlo method ; Frustration ; Nanotechnology ; Statistical physics ; Scale (ratio) ; Materials science ; Physics ; Mathematics Pickering emulsions and particle stabilization ; Supramolecular Self-Assembly in Materials ; Advanced Materials and Mechanics
Authors: Hongfei Li | Yazhou Chen | Linsen Zhou | Yan Wang | Wei Cao | Zhaoming Qu
Source: Polymer Composites (article, 2024-08-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning to optimize nonlinear conductive performance of composites for self‐adaptive electromagnetic shielding Abstract Polymer‐based composites that exhibit a unique nonlinear response to high‐power electric fields have the potential to serve as intelligent electromagnetic shielding materials. The optimization of switching fields ( E b ) and nonlinear coefficient ( α ) of polymer‐based composites is of great interests for nonlinear conductive performance. Based on literature data, the prediction models for E b and α are first successfully established using machine learning (ML) methods. A stacking ensemble learning (SEL) strategy was used to combine five base machine learning models, showing superior predictive performance. The research focuses on the effect of key process parameters on nonlinear conducting composites. The feature importance analysis shows that the nonlinear properties of the composites are considerably impacted by the mass fraction, filler size, and sample thickness. The parameter optimization method to improve the performance of the composites was explored by using partial dependence plots analysis. By measuring the nonlinear response of CNT/ZnO composites under high electric fields, the effectiveness of the optimization strategy is experimentally verified. This work establishes the intrinsic relationship between composition and performance, which is helpful in designing intelligent self‐adaptive electromagnetic shielding for switchable electronic devices. Highlights Prediction models for E b and α using machine learning. Stacking ensemble learning for superior predictive performance Focuses on the effect of key process parameters on performance. Optimization strategy validated through experimental testing. Materials science ; Electromagnetic shielding ; Composite material ; Electrical conductor ; Nonlinear system Electromagnetic wave absorption materials ; Dielectric materials and actuators ; Smart Materials for Construction
Authors: Sharon E. Ashbrook
Source: Faraday Discussions (article, 2024-10-01)
Field: Chemistry — Spectroscopy

Concluding remarks: <i>Faraday Discussion</i> on NMR crystallography This <i>Faraday Discussion</i> explored the field of NMR crystallography, and considered recent developments in experimental and theoretical approaches, new advances in machine learning and in the generation and handling of large amounts of data. Applications to a wide range of disordered, amorphous and dynamic systems demonstrated the range and quality of information available from this approach and the challenges that are faced in exploiting automation and developing best practice. In these closing remarks I will reflect on the discussions on the current state of the art, questions about what we want from these studies, how accurate we need results to be, how we best generate models for complex materials and what machine learning approaches can offer. These remarks close with thoughts about the future direction of the field, who will be carrying out this type of research, how they might be doing it and what their focus will be, along with likely possible challenges and opportunities. Faraday cage ; Physics ; Magnetic field Molecular spectroscopy and chirality ; NMR spectroscopy and applications ; Advanced NMR Techniques and Applications
Authors: Sulieman Ibraheem Shelash Al-Hawary | Arif Sarı | Shavan Askar | Harikumar Pallathadka | Renas Rajab Asaad | M. K. Sharma
Source: International Journal of Pressure Vessels and Piping (article, 2024-06-18)
Field: Materials Science — Metals and Alloys

Guided analysis of fracture toughness and hydrogen-induced embrittlement crack growth rate in quenched-and-tempered steels using machine learning Materials science ; Hydrogen embrittlement ; Fracture toughness ; Embrittlement ; Toughness ; Artificial neural network ; Hydrostatic pressure ; Mean squared error ; Metallurgy ; Machine learning ; Mechanics ; Computer science ; Statistics ; Mathematics ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Fatigue and fracture mechanics ; Material Properties and Failure Mechanisms
Authors: Hong Meng | Zhongyu Tang | Hao Bai | Yaming Fu | Hulei Yu | Yanhui Chu
Source: Journal of the American Ceramic Society (article, 2024-05-22)
Field: Materials Science — Materials Chemistry

Accelerating the discovery of high‐entropy hexaborides by data‐driven prediction: From equimolar to non‐equimolar Abstract Estimating the synthesizability is the prerequisite to discovering novel high‐entropy ceramics with exotic properties. Herein, combined with the high‐throughput experiments and machine learning (ML) methods, the synthesizability of high‐entropy hexaborides (HEB 6 ) is investigated. To construct the database, 100 equimolar quinary HEB 6 samples synthesized using a self‐developed high‐throughput solid‐state reaction technique and 20 potential synthesizability descriptors calculated from fundamental parameters of constituent precursors are simultaneously collected. By employing the ML and the genetic algorithms, an optimal model consisting of five synthesizability descriptors (, , , , and ) is determined for predicting the synthesizability of equimolar HEB 6 with a high validation accuracy (93.0%), and 7 005 new equimolar quinary HEB 6 are then proposed. Moreover, the applicability of our established model on non‐equimolar HEB 6 is explored by the prediction of 30 synthesizability diagrams of non‐equimolar quinary HEB 6 . A high accuracy of 90.9% is further validated by the synthesis experiments of 11 non‐equimolar HEB 6 candidates. Our work establishes an effective ML model for assessing the synthesizability of both equimolar and non‐equimolar HEB 6 and paves a promising way to accelerate the discovery of new synthetic accessible high‐entropy ceramics. High entropy alloys ; Computer science ; Chemistry ; Data mining ; Organic chemistry Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; High Entropy Alloys Studies
Authors: Jonathan T. Willman | Romain Perriot | Christopher Ticknor
Source: The Journal of Chemical Physics (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

Atomic cluster expansion potential for large scale simulations of hydrocarbons under shock compression We present an Atomic Cluster Expansion (ACE) machine learned potential developed for high-fidelity atomistic simulations of hydrocarbons, targeting pressures and temperatures near and above supercritical fluid regimes for molecular fluids. A diverse set of stoichiometries were covered in training, including 1:0 (pure carbon), 1:4 (methane), and 1:1 (benzene), and rich bonding environments sampled at supercritical temperatures, hydrogen rich, reactive mixtures where metastable stoichiometries arise, including 1:2 (ethylene) and 1:3 (ethane). A high-fidelity training database was constructed by performing large-scale quantum molecular dynamic simulations [density functional theory (DFT) MD] of diamond, graphite, methane, and benzene. A novel approach to selecting structures from DFT MD is also presented, which allows for the rapid selection of unique DFT MD frames from complex trajectories. Comparisons to DFT and experimental data demonstrate that the presented ACE potential accurately reproduces isotherms, carbon melting curves, radial distribution functions, and shock Hugoniots for carbon and hydrocarbon systems for pressures up to 100 GPa and temperatures up to 6000 K for hydrocarbon systems and up to 9000 K for pure carbon systems. This work delivers a potential that can be used for accurate, large-scale simulations of shocked hydrocarbons and demonstrates a methodology for fitting and validating machine learning interatomic potentials to complex molecular environments, which can be applied to energetic materials in future works. Molecular dynamics ; Supercritical fluid ; Methane ; Carbon fibers ; Density functional theory ; Chemical physics ; Multiscale modeling ; Cluster (spacecraft) ; Cluster expansion ; Metastability ; Materials science ; Chemistry ; Thermodynamics ; Computational chemistry ; Physics ; Organic chemistry ; Computer science Machine Learning in Materials Science ; High-pressure geophysics and materials ; Crystallography and molecular interactions
Authors: Xi 玺 Wang 王 | Meng 孟 Tang 唐 | Ming-Xuan 明璇 Jiang 蒋 | Yang-Chun 阳春 Chen 陈 | Zhi-Xiao 智骁 Liu 刘 | Hui-Qiu 辉球 Deng 邓
Source: Chinese Physics B (article, 2024-03-21)
Field: Materials Science — Materials Chemistry

Properties of radiation defects and threshold energy of displacement in zirconium hydride obtained by new deep-learning potential Abstract Zirconium hydride (ZrH 2 ) is an ideal neutron moderator material. However, radiation effect significantly changes its properties, which affect its behavior and the lifespan of the reactor. The threshold energy of displacement is an important quantity of the number of radiation defects produced, which helps us to predict the evolution of radiation defects in ZrH 2 . Molecular dynamics (MD) and ab initio molecular dynamics (AIMD) are two main methods of calculating the threshold energy of displacement. The MD simulations with empirical potentials often cannot accurately depict the transitional states that lattice atoms must surpass to reach an interstitial state. Additionally, the AIMD method is unable to perform large-scale calculation, which poses a computational challenge beyond the simulation range of density functional theory. Machine learning potentials are renowned for their high accuracy and efficiency, making them an increasingly preferred choice for molecular dynamics simulations. In this work, we develop an accurate potential energy model for the ZrH 2 system by using the deep-potential (DP) method. The DP model has a high degree of agreement with first-principles calculations for the typical defect energy and mechanical properties of the ZrH 2 system, including the basic bulk properties, formation energy of point defects, as well as diffusion behavior of hydrogen and zirconium. By integrating the DP model with Ziegler–Biersack–Littmark (ZBL) potential, we can predict the threshold energy of displacement of zirconium and hydrogen in ε -ZrH 2 . Molecular dynamics ; Zirconium hydride ; Materials science ; Zirconium ; Displacement (psychology) ; Hydrogen ; Density functional theory ; Work (physics) ; Hydride ; Chemical physics ; Statistical physics ; Computational chemistry ; Thermodynamics ; Chemistry ; Physics ; Quantum mechanics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear reactor physics and engineering
Authors: Sina Zare Pakzad | Mohammad Nasr Esfahani | D. Canadinç | B. Erdem Alaca
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Insights into the Elasticity of Bottom-Up Silicon Nanowires Silicon nanowires ; Nanowire ; Elasticity (physics) ; Materials science ; Nanotechnology ; Computer science ; Composite material Machine Learning in Materials Science ; Nanowire Synthesis and Applications ; Advancements in Semiconductor Devices and Circuit Design