jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: F. Vogt | A. Mehner | P. Figueira | Shanshan Yu | F. Kerber | Thomas Pfrommer | W. Hackenberg | Domenico Bonaccini Calia
Source: Physical Review Research (article, 2023-06-01)
Field: Chemistry — Spectroscopy

Pure-rotational and rotational-vibrational Raman spectrum of the atmosphere at an altitude of 23 km Ground-based optical astronomical observations supported by or in the vicinity of laser guide-star systems can be contaminated by Raman-scattered laser photons. Anticipating, alleviating, and correcting for the impact of this self-inflicted contamination requires a detailed knowledge of the pure-rotational and rotational-vibrational spectrum of the molecules in our atmosphere. We present a 15.3-hr-deep combined spectrum of the 4LGSF's 589 $\mathrm{nm}\ensuremath{\approx}509$ THz sodium laser beams of Paranal observatory, acquired with the ESPRESSO spectrograph at a resolution $\ensuremath{\lambda}/\mathrm{\ensuremath{\Delta}}\ensuremath{\lambda}\ensuremath{\cong}140000\ensuremath{\approx}0.12$ ${\mathrm{cm}}^{\ensuremath{-}1}$ and an altitude of 23 km above mean sea level. We identify 865 Raman lines over the spectral range of [3770; 7900] \AA{}$\ensuremath{\approx}$ [+9540; --4315] ${\mathrm{cm}}^{\ensuremath{-}1}$, with relative intensities spanning $\ensuremath{\sim}5$ orders of magnitudes. These lines are associated to the most abundant molecules of dry air, including their isotopes: $^{14}\mathrm{N}^{14}\mathrm{N}, ^{14}\mathrm{N}^{15}\mathrm{N}, ^{16}\mathrm{O}^{16}\mathrm{O}, ^{16}\mathrm{O}^{17}\mathrm{O}, ^{16}\mathrm{O}^{18}\mathrm{O}$, and $^{12}\mathrm{C}^{16}\mathrm{O}^{16}\mathrm{O}$. The signal-to-noise of these observations implies that professional observatories can treat the resulting catalog of Raman lines as exhaustive (for the detected molecules, over the observed Raman shift range) for the purpose of predicting/correcting/exploiting Raman lines in astronomical data. Our observations also reveal that the four laser units of the 4LGSF do not all lase at the same central wavelength. We measure a blueshift of $+43\ifmmode\pm\else\textpm\fi{}10$ MHz $\ensuremath{\cong}\ensuremath{-}50\ensuremath{\mp}10$ fm with respect to ${\ensuremath{\lambda}}_{*}=5891.59120$ \AA{} for LGSU1/2, and $+94\ifmmode\pm\else\textpm\fi{}10$ MHz $\ensuremath{\cong}\ensuremath{-}109\ensuremath{\mp}10$ fm for LGSU3/4. These offsets, including the difference of $\ensuremath{\sim}50$ MHz between LGSU1/2 and LGSU3/4, are larger than the observed 4LGSF spectral stability of $\ifmmode\pm\else\textpm\fi{}3$ MHz over hours. They remain well within the operational requirements for creating artificial laser guide-stars, but hinder the assessment of the radial velocity accuracy of ESPRESSO at the required level of 10 m ${\mathrm{s}}^{\ensuremath{-}1}$. Altogether, our observations demonstrate how Raman lines can be exploited by professional observatories as highly-accurate, on-sky wavelength references. Physics ; Raman spectroscopy ; Atomic physics ; Lambda ; Spectral line ; Atmosphere (unit) ; Optics ; Meteorology Spectroscopy and Laser Applications ; Stellar, planetary, and galactic studies ; Laser Design and Applications
Authors: Jiarui Zhang | Wencheng Fang | Ruobing Wang | Chengxing Li | Jia Zheng | Xixi Zou | Sannian Song | Zhitang Song | Xilin Zhou
Source: Nanomaterials (article, 2023-03-15)
Field: Materials Science — Materials Chemistry

Nanoscale Phase Change Material Array by Sub-Resolution Assist Feature for Storage Class Memory Application High density phase change memory array requires both minimized critical dimension (CD) and maximized process window for the phase change material layer. High in-wafer uniformity of the nanoscale patterning of chalcogenides material is challenging given the optical proximity effect (OPE) in the lithography process and the micro-loading effect in the etching process. In this study, we demonstrate an approach to fabricate high density phase change material arrays with half-pitch down to around 70 nm by the co-optimization of lithography and plasma etching process. The focused-energy matrix was performed to improve the pattern process window of phase change material on a 12-inch wafer. A variety of patternings from an isolated line to a dense pitch line were investigated using immersion lithography system. The collapse of the edge line is observed due to the OPE induced shrinkage in linewidth, which is deteriorative as the patterning density increases. The sub-resolution assist feature (SRAF) was placed to increase the width of the lines at both edges of each patterning by taking advantage of the optical interference between the main features and the assistant features. The survival of the line at the edges is confirmed with around a 70 nm half-pitch feature in various arrays. A uniform etching profile across the pitch line pattern of phase change material was demonstrated in which the micro-loading effect and the plasma etching damage were significantly suppressed by co-optimizing the etching parameters. The results pave the way to achieve high density device arrays with improved uniformity and reliability for mass storage applications. Materials science ; Wafer ; Process window ; Lithography ; Critical dimension ; Etching (microfabrication) ; Multiple patterning ; Photolithography ; Immersion lithography ; Resist ; Optoelectronics ; Plasma etching ; Nanoscopic scale ; Phase-change memory ; Nanotechnology ; Optics ; Layer (electronics) Phase-change materials and chalcogenides ; Liquid Crystal Research Advancements ; Thin-Film Transistor Technologies
Authors: Markus J. Buehler
Source: arXiv (Cornell University) (preprint, 2023-10-30)
Field: Materials Science — Materials Chemistry

Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design Transformer neural networks show promising capabilities, in particular for uses in materials analysis, design and manufacturing, including their capacity to work effectively with both human language, symbols, code, and numerical data. Here we explore the use of large language models (LLMs) as a tool that can support engineering analysis of materials, applied to retrieving key information about subject areas, developing research hypotheses, discovery of mechanistic relationships across disparate areas of knowledge, and writing and executing simulation codes for active knowledge generation based on physical ground truths. When used as sets of AI agents with specific features, capabilities, and instructions, LLMs can provide powerful problem solution strategies for applications in analysis and design problems. Our experiments focus on using a fine-tuned model, MechGPT, developed based on training data in the mechanics of materials domain. We first affirm how finetuning endows LLMs with reasonable understanding of domain knowledge. However, when queried outside the context of learned matter, LLMs can have difficulty to recall correct information. We show how this can be addressed using retrieval-augmented Ontological Knowledge Graph strategies that discern how the model understands what concepts are important and how they are related. Illustrated for a use case of relating distinct areas of knowledge - here, music and proteins - such strategies can also provide an interpretable graph structure with rich information at the node, edge and subgraph level. We discuss nonlinear sampling strategies and agent-based modeling applied to complex question answering, code generation and execution in the context of automated force field development from actively learned Density Functional Theory (DFT) modeling, and data analysis. Computer science ; Generative grammar ; Data science ; Language model ; Domain knowledge ; Artificial intelligence Machine Learning in Materials Science
Authors: Dingshuo Chen | Yanqiao Zhu | Jieyu Zhang | Yuanqi Du | Zhixun Li | Qiang Liu | Shu Wu | Liang Wang
Source: arXiv (Cornell University) (preprint, 2023-09-15)
Field: Materials Science — Materials Chemistry

Uncovering Neural Scaling Laws in Molecular Representation Learning Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design. While there has been a surge of interest in advancing model-centric techniques, the influence of both data quantity and quality on molecular representations is not yet clearly understood within this field. In this paper, we delve into the neural scaling behaviors of MRL from a data-centric viewpoint, examining four key dimensions: (1) data modalities, (2) dataset splitting, (3) the role of pre-training, and (4) model capacity. Our empirical studies confirm a consistent power-law relationship between data volume and MRL performance across these dimensions. Additionally, through detailed analysis, we identify potential avenues for improving learning efficiency. To challenge these scaling laws, we adapt seven popular data pruning strategies to molecular data and benchmark their performance. Our findings underline the importance of data-centric MRL and highlight possible directions for future research. Variety (cybernetics) ; Computer science ; Benchmark (surveying) ; Data science ; Representation (politics) ; Pruning ; Artificial intelligence ; Machine learning ; Field (mathematics) ; Modalities ; Scaling ; Law ; Mathematics ; Political science ; Sociology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Stefan Ecklebe | Nicole Gehring
Source: arXiv (Cornell University) (preprint, 2023-01-13)
Field: Materials Science — Materials Chemistry

Backstepping-based tracking control of the vertical gradient freeze crystal growth process The vertical gradient freeze crystal growth process is the main technique for the production of high quality compound semiconductors that are vital for today's electronic applications. A simplified model of this process consists of two 1D diffusion equations with free boundaries for the temperatures in crystal and melt. Both phases are coupled via an ordinary differential equation that describes the evolution of the moving solid/liquid interface. The control of the resulting two-phase Stefan problem is the focus of this contribution. A flatness-based feedforward design is combined with a multi-step backstepping approach to obtain a controller that tracks a reference trajectory for the position of the phase boundary. Specifically, based on some preliminary transformations to map the model into a time-variant PDE-ODE system, consecutive decoupling and backstepping transformations are shown to yield a stable closed loop. The tracking controller is validated in a simulation that considers the actual growth of a Gallium arsenide single crystal. Backstepping ; Flatness (cosmology) ; Control theory (sociology) ; Feed forward ; Decoupling (probability) ; Crystal growth ; Ode ; Materials science ; Computer science ; Mathematics ; Control engineering ; Physics ; Adaptive control ; Mathematical analysis ; Engineering ; Thermodynamics ; Control (management) ; Artificial intelligence Solidification and crystal growth phenomena
Authors: Dane Morgan | Ryan Jacobs
Source: Open Access Government (article, 2023-10-25)
Field: Materials Science — Materials Chemistry

Exploring the role of machine learning in materials science and engineering Exploring the role of machine learning in materials science and engineering In this paper, Professor Dane Morgan and Research Scientist Ryan Jacobs, from the University of Wisconsin, Madison, discuss their adventures in the field of machine learning in the areas of materials science and engineering. This paper gives a very brief and inevitably biased overview of machine learning (ML) in Materials Science and Engineering (MS&amp;E), with examples taken from our own work with collaborators. We hope it conveys our excitement about the extraordinary potential of this new area of research. MS&amp;E focuses on developing materials with desired properties. It has led to materials innovations that underlie much of modern society, from the transistors in computers to the batteries in cars and smartphones. In recent decades, major advances in algorithms, computing power, and data access have made ML tools extremely powerful. Science and engineering ; Adventure ; Field (mathematics) ; Artificial intelligence ; Computer science ; Engineering ethics ; Engineering ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Muktar Seid Hussen | Yordan Kyosev | Kathrin Pietsch | Jessica Boll | Abera Kechi Kabish
Source: Fibres and Textiles (article, 2023-05-01)
Field: Materials Science — Polymers and Plastics

MULTI-CRITERIA NUMERICAL OPTIMIZATION OF MECHANICAL PROPERTIES IN ULTRASONIC WELDING PROCESS PARAMETERS OF PVC-COATED HYBRID TEXTILES FOR WEATHER PROTECTION A series of research was carried out to determine the correlation between ultrasonic welding process parameters and weld seam mechanical properties. However, multi-objective numerical optimization of coated hybrid textiles for weather protection has not been addressed. To ensure a comprehensive evaluation of ultrasonic weld seams, the research investigates the optimal solution of the multi-objective function of ultrasonic welding process parameters and formulates a single criteria objective function. Lapped and superimposed types of seams were applied based on 33 factorial designs of experiments for 6 and 12 mm welding widths. Single-criteria objective functions instead of three independent problems were developed as a generalized utility function. A single-criteria optimization method was introduced through predetermined weight and normalization within the range of acceptable/unacceptable values. Numerical and graphical optimization methods were also applied to determine possible optimal solutions through generalized utility functions. The best optimal value of the generalized utility function (0.670425 and 0.944374) was attained at welding speed (2 and 2.01564 m/min), power (93.756 and 117.973 W), and pressure force (198.803 and 239.756 N) of 6 and 12 mm welding widths, respectively. The acceptable range of satisfactory values was determined for the roof and wall of awnings and camping tents through standard, in which seam performance level indicated. Nonlinear quadratic numerical models were formulated to estimate the generalized utility function, and their results were close to the regressed diagonal line against the actual points. The statistical analysis was shown a statistically significant effect of welding process parameters on the generalized utility function. Welding ; Ultrasonic sensor ; Diagonal ; Mathematics ; Structural engineering ; Computer science ; Mechanical engineering ; Engineering ; Acoustics ; Geometry Textile materials and evaluations ; Mechanical Behavior of Composites ; Mechanical and Thermal Properties Analysis
Authors: Xianglai Liao | Lili Gui | Shulei Bi | Ang Gao | Zhenming Yu | Kun Xu
Source: IEEE photonics journal (article, 2023-12-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multi-Objective Optimization of Chiral Metasurface for Sensing Based on a Distributed Algorithm We propose a distributed multi-objective optimization (DMO) method for designing chiral plasmonic metasurface that satisfies multiple objectives simultaneously. We aim to improve the refractive index sensitivity of the archetypical Born-Kuhn type chiral plasmonic metasurface while ensuring that circular dichroism (CD) is as pronounced as possible at a designated resonance wavelength. By leveraging distributed technology, the proposed method significantly improves the time efficiency of the optimization process. The simulation results demonstrate approximately 33% enhancement in sensitivity by DMO, as well as greater than 100% boost in time efficiency compared to stand-alone optimization approaches. These findings highlight the potential of the proposed method to guide the design of chiral plasmonic metasurface sensors, enabling the simultaneous optimization of multiple objectives and facilitating advancements in chiral optics and sensing applications. Plasmon ; Sensitivity (control systems) ; Computer science ; Refractive index ; Circular dichroism ; Optoelectronics ; Algorithm ; Optics ; Materials science ; Electronic engineering ; Physics ; Engineering ; Chemistry Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Orbital Angular Momentum in Optics
Authors: Hyuk Jun Yoo | Nayeon Kim | Hee‐Seung Lee | Daeho Kim | Leslie Ching Ow Tiong | Hyobin Nam | Chansoo Kim | Seung Yong Lee | Kwan‐Young Lee | Dong-Hun Kim | Sang Soo Han
Source: arXiv (Cornell University) (preprint, 2023-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Bespoke Nanoparticle Synthesis and Chemical Knowledge Discovery Via Autonomous Experimentations The optimization of nanomaterial synthesis using numerous synthetic variables is considered to be extremely laborious task because the conventional combinatorial explorations are prohibitively expensive. In this work, we report an autonomous experimentation platform developed for the bespoke design of nanoparticles (NPs) with targeted optical properties. This platform operates in a closed-loop manner between a batch synthesis module of NPs and a UV- Vis spectroscopy module, based on the feedback of the AI optimization modeling. With silver (Ag) NPs as a representative example, we demonstrate that the Bayesian optimizer implemented with the early stopping criterion can efficiently produce Ag NPs precisely possessing the desired absorption spectra within only 200 iterations (when optimizing among five synthetic reagents). In addition to the outstanding material developmental efficiency, the analysis of synthetic variables further reveals a novel chemistry involving the effects of citrate in Ag NP synthesis. The amount of citrate is a key to controlling the competitions between spherical and plate-shaped NPs and, as a result, affects the shapes of the absorption spectra as well. Our study highlights both capabilities of the platform to enhance search efficiencies and to provide a novel chemical knowledge by analyzing datasets accumulated from the autonomous experimentations. Bespoke ; Reagent ; Computer science ; Nanoparticle ; Nanomaterials ; Nanotechnology ; Absorption (acoustics) ; Biochemical engineering ; Biological system ; Combinatorial chemistry ; Chemistry ; Materials science ; Engineering ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties
Authors: F. Adams | Austin McDannald | Ichiro Takeuchi | A. Gilad Kusne
Source: arXiv (Cornell University) (preprint, 2023-06-17)
Field: Materials Science — Materials Chemistry

Human-In-the-Loop for Bayesian Autonomous Materials Phase Mapping Autonomous experimentation (AE) combines machine learning and research hardware automation in a closed loop, guiding subsequent experiments toward user goals. As applied to materials research, AE can accelerate materials exploration, reducing time and cost compared to traditional Edisonian studies. Additionally, integrating knowledge from diverse sources including theory, simulations, literature, and domain experts can boost AE performance. Domain experts may provide unique knowledge addressing tasks that are difficult to automate. Here, we present a set of methods for integrating human input into an autonomous materials exploration campaign for composition-structure phase mapping. The methods are demonstrated on x-ray diffraction data collected from a thin film ternary combinatorial library. At any point during the campaign, the user can choose to provide input by indicating regions-of-interest, likely phase regions, and likely phase boundaries based on their prior knowledge (e.g., knowledge of the phase map of a similar material system), along with quantifying their certainty. The human input is integrated by defining a set of probabilistic priors over the phase map. Algorithm output is a probabilistic distribution over potential phase maps, given the data, model, and human input. We demonstrate a significant improvement in phase mapping performance given appropriate human input. Computer science ; Prior probability ; Probabilistic logic ; Automation ; Set (abstract data type) ; Domain (mathematical analysis) ; Human-in-the-loop ; Bayesian probability ; Domain knowledge ; Phase (matter) ; Artificial intelligence ; Machine learning ; Bayesian inference ; Data mining ; Engineering ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Catalysis and Oxidation Reactions
Authors: Nickolas Gantzler | Aryan Deshwal | Janardhan Rao Doppa | Cory M. Simon
Source: ChemRxiv (preprint, 2023-06-26)
Field: Materials Science — Materials Chemistry

Multi-fidelity Bayesian Optimization of Covalent Organic Frameworks for Xenon/Krypton Separations Our objective is to search a large candidate set of covalent organic frameworks (COFs) for the one with the largest equilibrium adsorptive selectivity for xenon (Xe) over krypton (Kr) at room temperature. To predict the Xe/Kr selectivity of a COF structure, we have access to two molecular simulation techniques: (1) a higher-fidelity, binary grand canonical Monte Carlo simulation and (2) a lower-fidelity Henry coefficient calculation that (a) approximates the adsorbed phase as dilute and, consequently, (b) incurs a smaller computational runtime than the higher-fidelity simulation. To efficiently search for the COF with the largest high-fidelity Xe/Kr selectivity, we employ a multi-fidelity Bayesian optimization (MFBO) approach. MFBO constitutes a sequential, automated feedback loop of (1) conduct a low- or high-fidelity molecular simulation of Xe/Kr adsorption in a COF, (2) use the simulation data gathered thus far to train a surrogate model that cheaply predicts, with quantified uncertainty, the low- and high-fidelity simulated Xe/Kr selectivity of COFs from their structural/chemical features, and then (3) plan the next simulation (i.e., choose the next COF and fidelity) in consideration of balancing exploration, exploitation, and cost. We find that MFBO acquires the optimal COF among the candidate set of 609 structures using only 38 low-fidelity and nine high-fidelity simulations, incurring only 2.5%, 5% on average, and 18% on average of the computational runtime of an exhaustive, random, and single-fidelity BO search, respectively. Krypton ; Fidelity ; Bayesian optimization ; Xenon ; Computer science ; Bayesian probability ; Molecular dynamics ; Algorithm ; Set (abstract data type) ; Covalent organic framework ; Monte Carlo method ; Statistical physics ; Chemistry ; Physics ; Computational chemistry ; Covalent bond ; Mathematics ; Statistics ; Artificial intelligence Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science