jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexa Tyberg | Yunhao Fan | Gia-Wei Chern
Source: arXiv (Cornell University) (preprint, 2024-11-29)
Field: Materials Science — Materials Chemistry

Machine learning force-field model for kinetic Monte Carlo simulations of itinerant Ising magnets We present a scalable machine learning (ML) framework for large-scale kinetic Monte Carlo (kMC) simulations of itinerant electron Ising systems. As the effective interactions between Ising spins in such itinerant magnets are mediated by conducting electrons, the calculation of energy change due to a local spin update requires solving an electronic structure problem. Such repeated electronic structure calculations could be overwhelmingly prohibitive for large systems. Assuming the locality principle, a convolutional neural network (CNN) model is developed to directly predict the effective local field and the corresponding energy change associated with a given spin update based on Ising configuration in a finite neighborhood. As the kernel size of the CNN is fixed at a constant, the model can be directly scalable to kMC simulations of large lattices. Our approach is reminiscent of the ML force-field models widely used in first-principles molecular dynamics simulations. Applying our ML framework to a square-lattice double-exchange Ising model, we uncover unusual coarsening of ferromagnetic domains at low temperatures. Our work highlights the potential of ML methods for large-scale modeling of similar itinerant systems with discrete dynamical variables. Monte Carlo method ; Kinetic Monte Carlo ; Magnet ; Ising model ; Statistical physics ; Field (mathematics) ; Physics ; Force field (fiction) ; Kinetic energy ; Magnetic field ; Condensed matter physics ; Classical mechanics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science
Authors: Qi Ren | Yingzhuo Lun | Bonan Zhu | Gang Tang | Jiawang Hong
Source: arXiv (Cornell University) (preprint, 2024-10-08)
Field: Materials Science — Materials Chemistry

Identifying Highly Deformable van der Waals Layered Chalcogenides with Superior Thermoelectric Performance Using Deformability Factors and Interpretable Machine Learning Van der Waals layered chalcogenide-based flexible thermoelectric devices show great potential for applications in wearable electronics. However, materials that are both highly deformable and exhibit superior thermoelectric performance are extremely limited. There is an urgent need for methods that can efficiently predict both deformability and thermoelectric performance to enable high-throughput screening of these materials. In this study, over 1000 van der Waals layered chalcogenides were high-throughput screened from material databases, the deformability of which were predicted with our previously developed deformability factor. An accurate and efficient model based on machine learning methods were developed to predict the thermoelectric properties. Several candidate materials with both deformability and thermoelectric potential were successfully discovered. Among them, NbSe2Br2 was verified by first principles calculations, achieving ZTmax value of 1.35 at 1000K, which is currently the highest value among flexible inorganic thermoelectric materials. And the power factor value of 8.1 μWcm-1K-2 at 300K also surpassed most organic and inorganic flexible thermoelectric materials. Its high deformability mainly attributed to the small slipping energy that allows interlayer slip and the small in-plane modulus that allows deformation before failure. The high ZTmax is mainly contributed by the extremely low thermal conductivity and the high Seebeck coefficient along the out-of-plane direction at high temperature. The high power factor at room temperature is mainly comes from the high conductivity in the in-plane direction. This study is expected to accelerate the development and application of flexible thermoelectric devices based on inorganic semiconductor materials. Thermoelectric effect ; van der Waals force ; Materials science ; Computer science ; Artificial intelligence ; Thermodynamics ; Physics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Dimitri Boivin | Pascal Brault | Andrea Jagodar | Thomas Vaubois | Edern Menou | Barthélemy Aspe | Amaël Caillard | Pascal Andreazza | Marjorie Cavarroc | Anne‐Lise Thomann
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2024-12-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Numerical vs Experimental Sputtering Deposition Phase formation prediction in magnetron sputtered Cu(Ti)Zn thin films : Numerical vs Experimental approaches <div>Numerical vs Experimental Sputtering Deposition In this work we evaluated the ability of three numerical methods to predict the phase formation in Cu-Zn binary and Cu-Ti-Zn ternary alloy thin films deposited by DC-magnetron sputter deposition. Molecular dynamics simulations were carried out to simulate the growth of the alloy film and study the organization at the atomic level. A Machine Learning (ML) approach trained with a recently published bulk HEA (high entropy alloy) database was used to determine the presence of an amorphous phase, solid solutions or/and intermetallics. Finally, CALPHAD (CALculation of PHAse Diagrams) thermodynamic modeling based on Gibbs energy calculations allows to simulate the phase diagrams. This was done taking the experimental energy distribution functions of film forming species as inputs for the calculations. In this paper we discuss both agreements and limits of each method depending on their starting hypotheses and expected domains of validity. Crystalline phases formed in experimental films were investigated by grazing incidence X-ray diffraction (GIXRD). Comparison with CALPHAD results highlight that for pure Ti or binary Cu-Zn films, the thermodynamically stable phases are formed in the films. Less agreement was found at low or high percentage of Ti introduced in the Cu-Zn system, and drastic differences were observed for compositions close to equimolarity. In those cases, the out of equilibrium nature of the magnetron sputtering deposition technique is evidenced. The very limited agreement between GIXRD and ML approach is explained by the available database which is exclusively based on bulk alloys. Chemical composition of the alloy does not itself determine the stabilized phases: elaboration techniques are to be taken into account too. Molecular Dynamics (MD) simulations bring information on a possible segregation of the Zn element to the surface and grain boundaries. An interesting result is the very good agreement evidenced between the diffraction patterns calculated on the simulated films and that experimentally measured GIXRD </div> Deposition (geology) ; Materials science ; Thin film ; Sputter deposition ; Phase (matter) ; Sputtering ; Metallurgy ; Cavity magnetron ; Analytical Chemistry (journal) ; Chemistry ; Nanotechnology ; Geology Copper Interconnects and Reliability ; Metal and Thin Film Mechanics ; Semiconductor materials and devices
Authors: Escobar, Roberto Magdiel
Source: ScholarWorks @ UTRGV (The University of Texas Rio Grande Valley) (article, 2024-12-01)
Field: Chemistry — Organic Chemistry

Density Functional Theory and Machine Learning Analysis of C—C Reductive Coupling From Terminal Cyanido and Cyaphido Complexes Carbon–carbon (C–C) bond activation has gained increased attention as a direct method for the synthesis of pharmaceuticals and industrial chemistry. Due to the thermodynamic stability and kinetic inaccessibility of the C–C bonds, however, activation of C–C bonds by homogeneous transition-metal catalysts under mild homogeneous conditions is still a challenge. Most of the systems in which the activation occurs either have aromatization or relief of ring strain as the primary driving force. The activation of unstrained C–C bonds of phosphaalkynes does not have this advantage. This study employs Density Functional Theory (DFT) calculations using the Gaussian16 package to elucidate and compare Pt(0) and Ni(0) mediated C–CP reductive coupling in phosphaalkynes and C—CN in benzonitriles. Additionally, machine learning techniques, including linear regression and linear ridge algorithms, provide a stronger foundation for understanding how substituents in the ortho, meta, and para positions influence Gibbs free energy changes. These models help chemists identify correlations that may not seem readily apparent or linear, revealing that while meta and para positions exhibit strong correlations, the ortho position presents distinct electronic and steric effects. Density functional theory ; Homogeneous ; Steric effects ; Gibbs free energy ; Position (finance) ; Terminal (telecommunication) ; Coupling (piping) ; Catalysis ; Kinetic energy Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Hayato Wakai | Atsuto Seko | Hirosato Izuta | Takayuki Nishiyama | Isao Tanaka
Source: arXiv (Cornell University) (preprint, 2024-01-26)
Field: Materials Science — Materials Chemistry

Predictive power of polynomial machine learning potentials for liquid states in 22 elemental systems The polynomial machine learning potentials (MLPs) described by polynomial rotational invariants have been systematically developed for various systems and used in diverse applications in crystalline states. In this study, we systematically investigate the predictive power of the polynomial MLPs for liquid structural properties in 22 elemental systems with diverse chemical bonding properties, including those showing anomalous melting behavior, such as Si, Ge, and Bi. We compare liquid structural properties obtained from molecular dynamics simulations using the density functional theory (DFT) calculation, the polynomial MLPs, and other interatomic potentials in the literature. The current results demonstrate that the polynomial MLPs consistently exhibit high predictive power for liquid structural properties with the same accuracy as that of typical DFT calculations. Polynomial ; Power (physics) ; Mathematics ; Computer science ; Control theory (sociology) ; Applied mathematics ; Artificial intelligence ; Physics ; Thermodynamics ; Mathematical analysis Machine Learning in Materials Science ; Theoretical and Computational Physics ; Protein Structure and Dynamics
Authors: Sakhinana Sagar Srinivas | Chidaksh Ravuru | Geethan Sannidhi | Venkataramana Runkana
Source: arXiv (Cornell University) (preprint, 2024-08-23)
Field: Materials Science — Surfaces, Coatings and Films

Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption Semiconductor imaging and analysis are critical yet understudied in deep learning, limiting our ability for precise control and optimization in semiconductor manufacturing. We introduce a small-scale multimodal framework for analyzing semiconductor electron microscopy images (MAEMI) through vision-language instruction tuning. We generate a customized instruction-following dataset using large multimodal models on microscopic image analysis. We perform knowledge transfer from larger to smaller models through knowledge distillation, resulting in improved accuracy of smaller models on visual question answering (VQA) tasks. This approach eliminates the need for expensive, human expert-annotated datasets for microscopic image analysis tasks. Enterprises can further finetune MAEMI on their intellectual data, enhancing privacy and performance on low-cost consumer hardware. Our experiments show that MAEMI outperforms traditional methods, adapts to data distribution shifts, and supports high-throughput screening. Scale (ratio) ; Electron micrographs ; Computer science ; Micrograph ; Artificial intelligence ; Natural language processing ; Computer graphics (images) ; Computer vision ; Optics ; Cartography ; Physics ; Electron microscope ; Geography ; Scanning electron microscope Electron and X-Ray Spectroscopy Techniques
Authors: Juan Antonio Garcı́a | Anass Bouchnita
Source: Biosystems (article, 2024-11-07)
Field: Materials Science — Materials Chemistry

Exploring the spatial effects influencing the EGFR/ERK pathway dynamics with machine learning surrogate models Dynamics (music) ; Computer science ; MAPK/ERK pathway ; Artificial intelligence ; Biology ; Psychology ; Cell biology ; Signal transduction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Radiomics and Machine Learning in Medical Imaging
Authors: Bhagyashri Bachhav | Zhao Wu | Bernd Markert | Franz Bamer
Source: PAMM (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence‐Based Detection of Local Spots in Network Materials Prone to Mechanical Failure ABSTRACT Predicting the failure of a noncrystalline solid such as silica glass is challenging due to the complex fracture phenomenon. Failure originates from particular local zones where the atomic structure is highly susceptible to local rearrangement. The local yield stress (LYS) method predicts such locations of plastic events during external deformation. Despite the precise prediction accuracy of the location of the failure initiation, the LYS method requires substantial computational resources as it evaluates each local zone separately for rearrangement and is time‐consuming. Owing to new age development in artificial neural networks, a relation can be established between the plastic events and the atomic structure. In this study, a neural network is trained to identify this relation and, hence, predict the locations of atomic rearrangements. This approach results in a heatmap indicating soft spots that highly correlate with elementary events of fracture and allows for reliable incorporation of structural identification to coarser mechanical frameworks that need local information for a physically meaningful description. The advantage of this method is that it can be trained on a specific material and can predict the local soft spots for unseen structural information without the need for multiple time‐consuming molecular simulations. Artificial neural network ; Fracture (geology) ; Relation (database) ; Biological system ; Computer science ; Spots ; Deformation (meteorology) ; Artificial intelligence ; Pattern recognition (psychology) ; Materials science ; Data mining ; Chemistry ; Composite material Machine Learning in Materials Science ; Building materials and conservation ; Cultural Heritage Materials Analysis
Authors: Vahid Attari | Raymundo Arróyave
Source: arXiv (Cornell University) (preprint, 2024-11-27)
Field: Materials Science — Materials Chemistry

Decoding Non-Linearity and Complexity: Deep Tabular Learning Approaches for Materials Science Materials data, especially those related to high-temperature properties, pose significant challenges for machine learning models due to extreme skewness, wide feature ranges, modality, and complex relationships. While traditional models like tree-based ensembles (e.g., XGBoost, LightGBM) are commonly used for tabular data, they often struggle to fully capture the subtle interactions inherent in materials science data. In this study, we leverage deep learning techniques based on encoder-decoder architectures and attention-based models to handle these complexities. Our results demonstrate that XGBoost achieves the best loss value and the fastest trial duration, but deep encoder-decoder learning like Disjunctive Normal Form architecture (DNF-nets) offer competitive performance in capturing non-linear relationships, especially for highly skewed data distributions. However, convergence rates and trial durations for deep model such as CNN is slower, indicating areas for further optimization. The models introduced in this study offer robust and hybrid solutions for enhancing predictive accuracy in complex materials datasets. Decoding methods ; Linearity ; Computer science ; Deep learning ; Artificial intelligence ; Algorithm ; Electronic engineering ; Engineering Machine Learning in Materials Science ; Mineral Processing and Grinding ; Advanced Materials Characterization Techniques
Authors: Thomas Bocklitz
Source:  (article, 2024-06-18)
Field: Chemistry — Analytical Chemistry

Data science for inverse modelling of photonic measurement processes and FAIR data management of photonic data Photonic techniques are optimal tools to characterise samples in various research disciplines such as remote sensing, materials characterisation, life sciences and medicine. To exploit the full potential of these techniques, the entire data life cycle of photonic data needs to be investigated and optimised. The photonic data lifecycle starts with data generation and planning of the corresponding study/experiment, followed by data modelling using artificial intelligence (AI) techniques such as chemometrics, machine learning (ML) and deep learning (DL), and it ends by data storage and archiving. <br/><br/> In this contribution, we will present our studies aimed at the generation of correction procedures and inverse modelling tools for photonic data and heir measurement processes using data science methods. We will also present our research activities towards a repository for sharing vibrational spectroscopic data (VibSpecDB), which is embedded in the National Research Data Infrastructure Initiative in Germany (NFDI) and its chemistry consortium (NFDI4Chem). <br/><br/> <strong>Acknowledgements</strong> <br/><br/> This work is supported by the BMBF, funding program Photonics Research Germany (13N15466 (LPI-BT1-FSU), 13N15710 (LPI-BT3-FSU), 13N15708 (LPI-BT3-IPHT)) and is integrated into the Leibniz Center for Photonics in Infection Research (LPI). The LPI initiated by Leibniz-IPHT, Leibniz-HKI, Friedrich Schiller University Jena and Jena University Hospital is part of the BMBF national roadmap for research infrastructures. Parts are funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 441958208 (NFDI4Chem). Photonics ; Data management ; Computer science ; Inverse ; Exploit ; Data modeling ; Artificial intelligence ; Data science ; Data mining ; Physics ; Database ; Mathematics ; Optoelectronics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Jason M. Porter | Schuyler Dick
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Chemistry — Electrochemistry

Exploring Ion Transport Dynamics in Liquid Electrolytes: Insights from in-Situ FTIR Imaging of Lithium Electrodes While solid-state batteries have demonstrated potential in enhancing metal electrode stability, achieving the same stability in liquid electrolytes remains elusive. The instability of the SEI plays a pivotal role in the stability of metal electrodes in liquid electrolytes. Despite several promising strategies to stabilize metal electrodes in liquid electrolytes, the intricate relationship between the composition of the liquid electrolyte and the formation and stability of the SEI is still not fully understood. This study delves into the constraints of ion transport in liquid electrolytes, specifically focusing on the process of lithium plating and stripping on metal electrodes. A customized short pathlength transmission flow cell, equipped with symmetric lithium electrodes, is employed to simultaneously image lithium deposition, capture electrochemical measurements, and perform in-situ Fourier Transform Infrared (FTIR) imaging. This methodology illuminates the complex relationship between electrolyte transport, local composition, and the morphology of lithium plating. The research utilizes quantitative analysis of electrolyte components, Li + , PF6 - , EC, and EMC, through spectral analysis with chemometric methods and machine learning. The composition of the local electrolyte, which undergoes significant changes under rapid charge conditions, impacts SEI composition, and by extension lithium plating morphology. In situ FTIR imaging, with a resolution of five microns, is used to evaluate the role of preferential diffusion and/or migration of solvent and ionic species. Synchronized optical imaging, with a superior resolution of one micron, providing visual images of lithium structures that are correlated with solvent and ion concentration distributions measured in the infrared. The findings establish a link between ion transport dynamics and the efficiency of lithium plating and stripping, which has far-reaching implications for the performance and safety of electrochemical systems. Electrolyte ; Lithium (medication) ; Electrode ; Fourier transform infrared spectroscopy ; In situ ; Ion ; Materials science ; Inorganic chemistry ; Analytical Chemistry (journal) ; Chemistry ; Chemical engineering ; Physical chemistry ; Chromatography ; Organic chemistry ; Psychology ; Engineering Electrochemical Analysis and Applications ; Spectroscopy and Quantum Chemical Studies ; Chemical and Physical Properties in Aqueous Solutions
Authors: Kinjal Mondal | Samantha Felton | Bryan W. Berger | Jeffery B. Klauda
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-09-26)
Field: Materials Science — Materials Chemistry

Effect of Mutations on Smlt1473 Binding to Various Substrates Using Molecular Dynamics Simulations Abstract Smlt1473 is a polysaccharide lyase from Stenotrophomonas maltophilia whose crystal structure was solved recently using X-ray crystallography. There was an effort to study the effect of mutations on the activity of Smlt1473 binding to various substrates like hyaluronic acid(HA), mannuronic acid(ManA), and alginate. In this study, we use molecular docking and molecular dynamics simulations to investigate the effect of binding of various substrates (HA and ManA) to Smlt1473 and two of its mutants H221F and R312L. We further studied the stability in the binding of Smlt1473 to its various substrates as well as the role of fluctuations. Machine-Learning based clustering algorithms were used to group the entire simulation trajectory into various stable states. The molecular interactions Smlt1473 to the substrates were calculated and the importance of specific residues were tested with observed activity assays due to residue mutations. Overall, we find that the R218 plays an important role in substrate binding and thus impacting the activity due to the H221F mutant and R/L312 itself plays an important role in the R312 mutation. In addition, we have also found three more residues K56, R107, and R164 important for substrate binding which we further proceed to confirm using wet lab mutagenesis studies. Molecular dynamics ; Dynamics (music) ; Computational biology ; Chemistry ; Statistical physics ; Biology ; Physics ; Computational chemistry Anodic Oxide Films and Nanostructures ; Electron and X-Ray Spectroscopy Techniques ; RNA Interference and Gene Delivery