jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yunhao Fan | Sheng Zhang | Gia-Wei Chern
Source: arXiv (Cornell University) (preprint, 2024-03-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Coarsening of chiral domains in itinerant electron magnets: A machine learning force field approach Frustrated itinerant magnets often exhibit complex noncollinear or noncoplanar magnetic orders which support topological electronic structures. A canonical example is the anomalous quantum Hall state with a chiral spin order stabilized by electron-spin interactions on a triangular lattice. While a long-range magnetic order cannot survive thermal fluctuations in two dimensions, the chiral order which results from the breaking of a discrete Ising symmetry persists even at finite temperatures. We present a scalable machine learning (ML) framework to model the complex electron-mediated spin-spin interactions that stabilize the chiral magnetic domains in a triangular lattice. Large-scale dynamical simulations, enabled by the ML force-field models, are performed to investigate the coarsening of chiral domains after a thermal quench. While the chiral phase is described by a broken $Z_2$ Ising-type symmetry, we find that the characteristic size of chiral domains increases linearly with time, in stark contrast to the expected Allen-Cahn domain growth law for a non-conserved Ising order parameter field. The linear growth of the chiral domains is attributed to the orientational anisotropy of domain boundaries. Our work also demonstrates the promising potential of ML models for large-scale spin dynamics of itinerant magnets. Magnet ; Electron ; Condensed matter physics ; Field (mathematics) ; Physics ; Materials science ; Engineering physics ; Quantum electrodynamics ; Mechanical engineering ; Theoretical physics ; Engineering ; Quantum mechanics ; Mathematics ; Pure mathematics Magnetic Properties and Applications ; Computational Physics and Python Applications ; Magnetic properties of thin films
Authors: Yu. K. Undalov
Source:  (article, 2024-12-11)
Field: Chemistry — Spectroscopy

CALCULATION OF FORMULAS OF HOMOLOGOUS SERIES OF CHEMICAL COMPOUNDS(IN GENERAL): FOUR-COMPONENT SYSTEMS (Aa+ – Bb+ – Dd+ – Cc-) AND (La3+ – Ni2+ – Ni3+ – O2-) Homologous series ; Component (thermodynamics) ; Series (stratigraphy) ; Homologous chromosome ; Chemistry ; Computer science ; Thermodynamics ; Physics ; Organic chemistry ; Biology ; Biochemistry Adsorption, diffusion, and thermodynamic properties of materials ; Chemical Thermodynamics and Molecular Structure
Authors: Honghui Shang | Xiongzhi Zeng | Ming Gong | Yangju Wu | Shaojun Guo | Haoran Qian | Chen Zha | Zhijie Fan | Kai Yan | Xiaobo Zhu | Zhenyu Li | Yi Luo | Jian-Wei Pan | Jinlong Yang
Source: arXiv (Cornell University) (preprint, 2024-05-15)
Field: Materials Science — Materials Chemistry

Rapidly Achieving Chemical Accuracy with Quantum Computing Enforced Language Model Finding accurate ground state energy of a many-body system has been a major challenge in quantum chemistry. The integration of classic and quantum computers has shed new light on resolving this outstanding problem. Here we propose QiankunNet-VQE, a transformer based language models enforced with quantum computing to learn and generate quantum states. It has been implemented using up to 12 qubits and attaining an accuracy level competitive with state-of-the-art classical methods. By leveraging both quantum and classical resources, this scheme overcomes the limitations of variational quantum eigensolver(VQE) without the need for cumbersome error mitigation. Moreover, QiankunNet-VQE provides a different route to achieve a practical quantum advantage for solving many-electron Schrödinger equation without requiring extremely precise preparation and measurement of the ground-state wavefunction on quantum computer. Computer science ; Quantum chemical ; Theoretical computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Text Analysis Techniques
Authors: Zihan Zhang | Tian Cui | Yansun Yao | Tianchen Ma | Qiwen Jiang | Haojun Jia | Bartomeu Monserrat | Chris J. Pickard | Defang Duan
Source: arXiv (Cornell University) (preprint, 2024-08-22)
Field: Materials Science — Materials Chemistry

The Critical Metallization of Hydrogen in Pressurized LaBeH8 Hydride Behaviours of hydrogen, such as fluidity and metallicity, are crucial for our understanding of planetary interiors and the emerging field of high-temperature superconducting hydrides. These behaviours were discovered in complex phase diagrams of hydrogen and hydrides, however, the transition mechanism of behaviours driven by temperature, pressure and chemical compression remain unclear, particularly in the processes of metallization. Until now, a comprehensive theoretical framework to quantify atomization and metallization of hydrogen in phase diagram of hydrides has been lacking. In this study, we address this gap by combining molecular dynamics and electronic structure analysis to propose a theoretical framework, which clarify the content and properties of atomic hydrogen under various temperature and pressure conditions and chemical compression exerted by non-hydrogen elements in hydrides. Applying this framework to the superconducting hydride LaBeH8, we identify three general hydrogen orderings within its phase diagram: molecular, sublattice and warm hydrogens. During the phase transition from molecule to sublattice, hydrogen exhibits different properties from three general hydrogen orderings, such as fast superionicity, metallicity and unusual atomic content response to temperature. These abnormal behaviours were defined as the critical metallization of hydrogen, which not only suggests a potential synthesis route for the metastable phase but also provides valuable insights into the complex synthetic products of superconducting hydrides. Hydride ; Hydrogen ; Materials science ; Chemistry ; Organic chemistry Hydrogen Storage and Materials ; Rare-earth and actinide compounds ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Lukáš Frk | Pavel Baláž | Elguja Archemashvili | Martin Žonda
Source: arXiv (Cornell University) (preprint, 2024-11-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unsupervised Machine Learning Phase Classification for Falicov-Kimball Model We apply various unsupervised machine learning methods for phase classification to investigate the finite-temperature phase diagram of the spinless Falicov-Kimball model in two dimensions. Using only particle occupation snapshots from Monte Carlo simulations as input, each technique, including a straightforward classification based on principal component analysis (PCA), successfully identifies the phase boundary between ordered and disordered phases, independent of the type of phase transition. Remarkably, these techniques also distinguish between the weakly localized and Anderson-localized regimes within the disordered phase, accurately identifying their crossover, which is a challenging task for standard methods. Among the machine learning approaches used, PCA based analysis outperforms more complex methods, such as neural network predictors and autoencoders. These results underscore the effectiveness of simple unsupervised techniques in examining phase transitions and electron localization in complex correlated systems. Phase (matter) ; Artificial intelligence ; Computer science ; Machine learning ; Physics ; Quantum mechanics Magnetic Properties and Applications
Authors: Dominik Gehringer | Lorenz Romaner | David Holec
Source: arXiv (Cornell University) (preprint, 2024-07-16)
Field: Materials Science — Materials Chemistry

Segregation to grain boundaries in disordered systems: an application to a Ni-based multi-component alloy Segregation to defects, in particular to grain boundaries (GBs), is an unavoidable phenomenon leading to changed material behavior over time. With the increase of available computational power, unbiased quantum-mechanical predictions of segregation energies, which feed classical thermodynamics models of segregation (e.g., McLean isotherm), become available. In recent years, huge progress towards predictions closely resembling experimental observations was made by considering the statistical nature of the segregation process due to competing segregation sites at a single GB and/or many different types of co-existing GBs. In the present work, we further expand this field by explicitly showing how compositional disorder, present in real engineering alloys (e.g. steels or Ni-based superalloys), gives rise to a spectrum of segregation energies. With the example of a $Σ5$ GB in a Ni-based model alloy (Ni-Co-Cr-Ti-Al), we show that the segregation energies of Fe, Mn, W, Nb, and Zr are significantly different from those predicted for pure elemental Ni. We further use the predicted segregation energy spectra in a statistical evaluation of GB enrichment, which allows for extracting segregation enthalpy and segregation entropy terms related to the chemical complexity in multi-component alloys. Superalloy ; Grain boundary ; Materials science ; Metallurgy ; Alloy ; Microstructure Solidification and crystal growth phenomena ; Metallurgy and Material Forming ; Microstructure and mechanical properties
Authors: Desmarita Leni | Femi Earnestly | Nike Angelia | Elsa Nofriyanti | Adriansyah Adriansyah
Source: Jurnal Mesin Nusantara (article, 2024-06-28)
Field: Materials Science — Materials Chemistry

Mengoptimalkan Analisis Sifat Mekanik Material Berbasis Data Dengan Pandas Profiling The analysis of mechanical properties based on data is a method used to analyze the mechanical properties of a material using data, typically obtained from a material database. This process encounters several challenges, such as large volume of data, complexity in data processing, as well as difficulties in data visualization and interpretation. In this study, Pandas Profiling, a Python library designed specifically for automated dataset analysis, was employed. The dataset used consisted of tensile test results for various austenitic stainless steel types such as SUS 304, SUS 316, SUS 321, SUS 347, and NCF 800H. This dataset comprised 1916 samples with attributes related to mechanical properties and factors influencing them. The analysis results using Pandas Profiling indicated a strong negative correlation between heat treatment temperature and Yield Strength (YS) and Ultimate Tensile Strength (UTS). Additionally, a positive correlation was found between chemical elements such as Copper (Cu) and Nickel (Ni) with Elongation (EL). Furthermore, the analysis results revealed that stainless steel treated with water cooling exhibited a higher average UTS value, measuring at 493 MPa, compared to air cooling, which only reached 403 MPa. Pandas Profiling offers an effective solution to overcome common challenges in data-based mechanical property analysis, including dealing with large data volumes, complex data processing, as well as challenges in data visualization and interpretation. By utilizing Pandas Profiling, researchers can easily comprehend the dataset comprehensively, identify patterns, trends, and relationships among variables, and optimize the analysis process of data-based material mechanical properties. Profiling (computer programming) ; Computer science ; Operating system Material Selection and Properties ; Management and Optimization Techniques
Authors: Matthew Matzelle | Wei-Chi Chiu | Caiyun Hong | B. Ghosh | Pengxu Ran | R.S. Markiewicz | B. Barbiellini | Changxi Zheng | Sheng Li | Ruihua He | Arun Bansil
Source: arXiv (Cornell University) (preprint, 2024-05-09)
Field: Materials Science — Surfaces, Coatings and Films

Recycling failed photoelectrons via tertiary photoemission A key insight of Einstein's theory of the photoelectric effect is that a minimum energy is required for photoexcited electrons to escape from a material. For the past century it has been assumed that photoexcited electrons of lower energies make no contribution to the photoemission spectrum. Here we demonstrate the conceptual possibility that the energy of these 'failed' photoelectrons-primary or secondary-can be partially recycled to generate new 'tertiary' electrons of energy sufficient to escape. Such a 'recycling' step goes beyond the traditional three steps of the photoemission process (excitation, transport, and escape), and, as we illustrate, it can be realized through a novel Auger mechanism that involves three distinct minority electronic states in the material. We develop a phenomenological three-band model to treat this mechanism within a revised four-step framework for photoemission, which contains robust features of linewidth narrowing and population inversion under strong excitation, reminiscent of the lasing phenomena. We show that the conditions for this recycling mechanism are likely satisfied in many quantum materials with multiple flat bands properly located away from the Fermi level, and elaborate on the representative case of SrTiO3 among other promising candidates. We further discuss how this mechanism can explain the recent observation of anomalous intense coherent photoemission from a SrTiO3 surface, and predict its manifestations in related experiments, including the 'forbidden' case of photoemission with photon energies lower than the work function. Our study calls for paradigm shifts across a range of fundamental and applied research fields, especially in the areas of photoemission, photocathodes, and flat-band materials. Photoelectric effect ; Inverse photoemission spectroscopy ; Angle-resolved photoemission spectroscopy ; Materials science ; Optoelectronics ; Physics ; Condensed matter physics ; Electronic structure Electron and X-Ray Spectroscopy Techniques ; Photocathodes and Microchannel Plates ; Nuclear Physics and Applications
Authors: Akitoshi Shiotari | Shuyi Liu | George Trenins | Toshiki Sugimoto | Martin Wolf | Mariana Rossi | Takashi Kumagai
Source: arXiv (Cornell University) (preprint, 2024-11-17)
Field: Chemistry — Spectroscopy

Picocavity-Enhanced Raman Spectroscopy of Physisorbed H$_2$ and D$_2$ Molecules We report on tip-enhanced Raman spectroscopy of H2 and D2 molecules physisorbed within a plasmonic picocavity at 10 K. The intense Raman peaks resulting from the rotational and vibrational transitions are observed at subnanometer gap distances of the junction formed by an Ag tip and an Ag(111) surface, where a picocavity-enhanced field plays a crucial role. A significant redshift of the H-H stretch frequency is observed as the gap distance decreases, while the D-D stretch frequency is unaffected. Density functional theory, path-integral molecular dynamics, and quantum anharmonic vibrational energy calculations suggest that this unexpected isotope effect is explained by a different molecular density between H2 and D2 on the surface. Raman spectroscopy ; Molecule ; Spectroscopy ; Chemistry ; Materials science ; Photochemistry ; Nanotechnology ; Physics ; Optics ; Organic chemistry ; Quantum mechanics Spectroscopy and Laser Applications ; Spectroscopy and Quantum Chemical Studies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Hui Li | Zijian Sun | Lei Yang | Weihua Zhu
Source: Molecules (article, 2024-12-05)
Field: Materials Science — Materials Chemistry

Impacts of Vinyl Group on the Diffusion and Thermal Properties of Branched Silicon-Containing Arylacetylene Resins by Molecular Dynamics Simulations Branched-structure polyarylacetylene resins with low curing temperatures and excellent thermal stability are promising candidates for high-performance resin-matrix composites. In this work, the effects of different vinyl/acetylene group ratios on the thermal properties of star-shaped silicon-containing arylacetylene (SSA) resins and branched silicon-containing arylacetylene (BSA) resins were investigated by molecular dynamics simulations. The calculated interaction energies, diffusion behaviors, and glass transition temperatures (<i>T</i><sub>g</sub>) indicated that an increase in the vinyl/acetylene group ratio positively affected the rheological and thermal properties of the branched resins. After the introduction of the vinyl groups, the <i>T</i><sub>g</sub> value of SSA2 was greater than that of SSA0. An energy decomposition approach (EDA) analysis further confirmed that the involvement of the vinyl groups in the BSA and SSA resins provided larger interaction energies dominated by dispersion effects, leading to better thermal performance. This work may provide us with a deep understanding of the incorporation of vinyl groups in silicon-containing arylacetylene resins. Molecular dynamics ; Silicon ; Diffusion ; Group (periodic table) ; Materials science ; Thermal ; Chemical physics ; Chemical engineering ; Polymer chemistry ; Chemistry ; Organic chemistry ; Computational chemistry ; Thermodynamics ; Physics ; Optoelectronics ; Engineering Carbon Nanotubes in Composites ; Organic Electronics and Photovoltaics ; Thermal properties of materials
Authors: Waqwoya Abebe | J. Strube | Luanzheng Guo | Nathan R. Tallent | Oceane Bel | Steven R. Spurgeon | Christina Doty | Ali Jannesari
Source: arXiv (Cornell University) (preprint, 2024-04-09)
Field: Materials Science — Surfaces, Coatings and Films

SAM-I-Am: Semantic Boosting for Zero-shot Atomic-Scale Electron Micrograph Segmentation Image segmentation is a critical enabler for tasks ranging from medical diagnostics to autonomous driving. However, the correct segmentation semantics - where are boundaries located? what segments are logically similar? - change depending on the domain, such that state-of-the-art foundation models can generate meaningless and incorrect results. Moreover, in certain domains, fine-tuning and retraining techniques are infeasible: obtaining labels is costly and time-consuming; domain images (micrographs) can be exponentially diverse; and data sharing (for third-party retraining) is restricted. To enable rapid adaptation of the best segmentation technology, we propose the concept of semantic boosting: given a zero-shot foundation model, guide its segmentation and adjust results to match domain expectations. We apply semantic boosting to the Segment Anything Model (SAM) to obtain microstructure segmentation for transmission electron microscopy. Our booster, SAM-I-Am, extracts geometric and textural features of various intermediate masks to perform mask removal and mask merging operations. We demonstrate a zero-shot performance increase of (absolute) +21.35%, +12.6%, +5.27% in mean IoU, and a -9.91%, -18.42%, -4.06% drop in mean false positive masks across images of three difficulty classes over vanilla SAM (ViT-L). Segmentation ; Computer science ; Artificial intelligence ; Boosting (machine learning) ; Pattern recognition (psychology) ; Computer vision Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advancements in Photolithography Techniques
Authors: Michiko Yoshitake | Yūta Suzuki | Ryo Igarashi | Yoshitaka Ushiku | Keisuke Nagato
Source: arXiv (Cornell University) (preprint, 2024-09-05)
Field: Materials Science — Materials Chemistry

MaterialBENCH: Evaluating College-Level Materials Science Problem-Solving Abilities of Large Language Models A college-level benchmark dataset for large language models (LLMs) in the materials science field, MaterialBENCH, is constructed. This dataset consists of problem-answer pairs, based on university textbooks. There are two types of problems: one is the free-response answer type, and the other is the multiple-choice type. Multiple-choice problems are constructed by adding three incorrect answers as choices to a correct answer, so that LLMs can choose one of the four as a response. Most of the problems for free-response answer and multiple-choice types overlap except for the format of the answers. We also conduct experiments using the MaterialBENCH on LLMs, including ChatGPT-3.5, ChatGPT-4, Bard (at the time of the experiments), and GPT-3.5 and GPT-4 with the OpenAI API. The differences and similarities in the performance of LLMs measured by the MaterialBENCH are analyzed and discussed. Performance differences between the free-response type and multiple-choice type in the same models and the influence of using system massages on multiple-choice problems are also studied. We anticipate that MaterialBENCH will encourage further developments of LLMs in reasoning abilities to solve more complicated problems and eventually contribute to materials research and discovery. Mathematics education ; Computer science ; Management science ; Psychology ; Engineering Machine Learning in Materials Science
Authors: Alok Gupta | Anju Singh
Source: International Journal of Science and Research Archive (article, 2024-01-30)
Field: Chemistry — Organic Chemistry

Refractive index, conductometric, and fluorescence probe study on interaction of valine with aqueous solution of anionic surfactant, sodium dodecyl surfactant (SDS), at 298.15, 303.15, 308.15 and 313.15 K Interactions between amino acid (valine) and surfactant (sodium dodecyl sulphate) was investigated using refractive index and conductance data. These data were used to calculate molar refraction (RD), and limiting molar conductance (∧m0) and these parameter further utilized to find out solute-solvent interactions between amino acid and surfactant at 298.15, 303.15, 308.15 and 313.15 K temperatures. Also fluorescence measurements using pyrene as a photophysical probe have also been carried out for the present amino acid–surfactant systems. Pulmonary surfactant ; Chemistry ; Aqueous solution ; Fluorescence ; Sodium ; Sodium dodecyl sulfate ; Valine ; Chromatography ; Organic chemistry ; Amino acid ; Biochemistry ; Optics Surfactants and Colloidal Systems ; Thermodynamic properties of mixtures ; Analytical Chemistry and Sensors
Authors: Lai WEI | Xue Ling | JunHao WU | Wei Zheng | Liguo Zhang | Zhang Wei-hua | YiNan CUI
Source: Zhongguo kexue. Wulixue Lixue Tianwenxue (article, 2024-01-01)
Field: Materials Science — Ceramics and Composites

Macroscopic and microscopic radiation effects on glass materials 玻璃材料在核工业及航天航空、深空探测等领域发挥着重要的作用。然而，强辐照效应常常会导致玻璃材料变色、脆化、光学性能退化等，极大制约了其服役的可靠性和耐久性。设计抗辐照玻璃的核心便是深入理解其辐照损伤的宏微观物理过程，然而鲜有论文对其展开深入系统的总结和论述。本文从辐照效应的位移损伤和离子损伤原子级物理图像出发，阐述了玻璃中色心缺陷的形成机制，综述了损伤演化带来的光学及力热特性的变化，继而总结了不同损伤特征的实验监测方法，及宏微观损伤的多尺度计算方法和模型，旨在为抗辐照及耐辐照玻璃设计提供指导和依据。 Radiation ; Materials science ; Optics ; Physics Glass properties and applications ; Luminescence Properties of Advanced Materials ; Radiation Shielding Materials Analysis
Authors: James Paolo Rili
Source: arXiv (Cornell University) (preprint, 2024-04-29)
Field: Materials Science — Materials Chemistry

Machine Learning Interatomic Potentials with Keras API A neural network is used to train, predict, and evaluate a model to calculate the energies of 3-dimensional systems composed of Ti and O atoms. Python classes are implemented to quantify atomic interactions through symmetry functions and to specify prediction algorithms. The hyperparameters of the model are optimised by minimising validation RMSE, which then produced a model that is accurate to within 100 eV. The model could be improved by proper testing of symmetry function calculations and addressing properties of features and targets. Physics ; Computer science ; Statistical physics Machine Learning in Materials Science