jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aleksander E. P. Durumeric | Yaoyi Chen | Frank Noé | Cecilia Clementi
Source: arXiv (Cornell University) (preprint, 2024-07-01)
Field: Materials Science — Materials Chemistry

Learning data efficient coarse-grained molecular dynamics from forces and noise Machine-learned coarse-grained (MLCG) molecular dynamics is a promising option for modeling biomolecules. However, MLCG models currently require large amounts of data from reference atomistic molecular dynamics or substantial computation for training. Denoising score matching -- the technology behind the widely popular diffusion models -- has simultaneously emerged as a machine-learning framework for creating samples from noise. Models in the first category are often trained using atomistic forces, while those in the second category extract the data distribution by reverting noise-based corruption. We unify these approaches to improve the training of MLCG force-fields, reducing data requirements by a factor of 100 while maintaining advantages typical to force-based parameterization. The methods are demonstrated on proteins Trp-Cage and NTL9 and published as open-source code. Dynamics (music) ; Noise (video) ; Computer science ; Molecular dynamics ; Data science ; Statistical physics ; Artificial intelligence ; Physics ; Chemistry ; Computational chemistry ; Acoustics Machine Learning in Materials Science
Authors: Yajie Cao | Baojun Wang | Maohong Fan | Lixia Ling | Riguang Zhang
Source: Chemical Engineering Science (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

DFT calculations and machine learning for the study of ethane dehydrogenation on the heteroatom-doped graphene supported Pt SACs Dehydrogenation ; Heteroatom ; Graphene ; Doping ; Chemistry ; Nanotechnology ; Materials science ; Computational chemistry ; Organic chemistry ; Optoelectronics ; Catalysis Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Saketh Sridhara | Krishnan Suresh
Source: Journal of Mechanical Design (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

A Visual Representation of Engineering Catalogs Using Variational Autoencoders Abstract Catalogs have been used for over a century for designing engineering systems. While catalogs are excellent repositories of engineering information, they are difficult to navigate and visualize, specifically to spot clusters, gaps, substitutes, and outliers. Inspired by Ashby charts for material selection, we propose here a visual representation of engineering catalogs using neural networks. In particular, we employ variational autoencoders (VAEs) to project catalog data onto a lower-dimensional latent space. The latent space can then be visualized to explore the underlying structure of the catalog. Specifically, catalog creators can identify gaps and outliers in their data, while end-users can compare catalogs from competitors and easily find substitutes. Contours can be superimposed on the latent space to enable selection based on user-defined attributes; these contours are generalizations of design indices associated with Ashby charts. Various examples of catalogs ranging from materials and bearings, to motors and batteries are illustrated using the proposed method. By using these examples, we (1) study the impact of the latent space dimension on the representational error, (2) illustrate how designers can easily choose alternate configurations based on their design requirements, and (3) identify gaps in catalog offerings, providing a stimulus for new product development. Computer science ; Representation (politics) ; Dimension (graph theory) ; Outlier ; Space (punctuation) ; Artificial intelligence ; Data mining ; Selection (genetic algorithm) ; Machine learning ; Information retrieval Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Design Education and Practice
Authors: Pedro Augusto Ramos | Ireneia Melo | Victor Henrique de Mattos Medeiros | Pedro Brito
Source: Journal of Alloys and Metallurgical Systems (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

Influence of heat treatment time on microstructure evolution of austempered nodular cast iron evaluated by image segmentation This study aimed to investigate the microstructural characteristics of a pearlitic austempered nodular cast iron (ADI) prepared with different austempering times. In austempering of ductile irons, the material is austenitiezed and rapidly cooled to temperatures typically between 420 and 290°C in a salt bath to allow the formation of a microstructure composed of graphite nodules dispersed in a residual austenitic matrix, stabilized with a high carbon content, and acicular ferrite, known as “ausferrite”. Presently, microstructure characterization by optical and scanning electron microscopy were carried out after different austempering times. Neural network image segmentation using ImageJ® software was employed to perform quantitative phase analysis, and the results were compared with volume fractions obtained by XRD measurements, one of the traditional methods for determining the residual austenite content in ferrous alloys. It was found that both carbon content in austenite and austenite fraction increase with austempering time and that graphite nodule geometry becomes gradually more irregular. The proposed methodology for quantitative analysis allowed classification of microstructure constituents with high accuracy in comparison to the XRD results. Cast iron ; Microstructure ; Austempering ; Materials science ; Metallurgy ; Segmentation ; Artificial intelligence ; Bainite ; Computer science Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Pablo García‐Risueño | Eva Armengol | Àngel García-Cerdaña | J. M. García‐Lastra | David Carrasco‐Busturia
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Electron–vibrational renormalization in fullerenes through <i>ab initio</i> and machine learning methods The effect of nuclear vibrations on the electronic eigenvalues and the HOMO-LUMO gap is known for several kinds of carbon-based materials, like diamond, diamondoids, carbon nanoclusters, carbon nanotubes and others, like hydrogen-terminated oligoynes and polyyne. However, it has not been widely analysed in another remarkable kind which presents both theoretical and technological interest: fullerenes. In this article we present the study of the HOMO, LUMO and gap renormalizations due to zero-point motion of a relatively large number (163) of fullerenes and fullerene derivatives. We have calculated this renormalization using density-functional theory with the frozen-phonon method, finding that it is non-negligible (above 0.1 eV) for systems with relevant technological applications in photovoltaics and that the strength of the renormalization increases with the size of the gap. In addition, we have applied machine learning methods for classification and regression of the renormalizations, finding that they can be approximately predicted using the output of a computationally cheap ground state calculation. Our conclusions are supported by recent research in other systems. Fullerene ; Renormalization ; Diamondoid ; HOMO/LUMO ; Ab initio ; Density functional theory ; Carbon nanotube ; Band gap ; Nanoclusters ; Materials science ; Chemical physics ; Computational chemistry ; Physics ; Chemistry ; Nanotechnology ; Condensed matter physics ; Quantum mechanics ; Molecule Fullerene Chemistry and Applications ; Advanced Chemical Physics Studies ; Machine Learning in Materials Science
Authors: Tong Xie | Yuwei Wan | Yixuan Liu | Yuchen Zeng | Wang, Shaozhou | Wenjie Zhang | Grazian, Clara | Chunyu Kit | Ouyang, Wanli | Dongzhan Zhou | Bram Hoex
Source: arXiv (Cornell University) (preprint, 2024-12-16)
Field: Materials Science — Materials Chemistry

DARWIN 1.5: Large Language Models as Materials Science Adapted Learners Materials discovery and design aim to find compositions and structures with desirable properties over highly complex and diverse physical spaces. Traditional solutions, such as high-throughput simulations or machine learning, often rely on complex descriptors, which hinder generalizability and transferability across different material systems. Moreover, These descriptors may inadequately represent macro-scale material properties, which are influenced by structural imperfections and compositional variations in real-world samples, thus limiting their practical applicability. To address these challenges, we propose DARWIN 1.5, the largest open-source large language model tailored for materials science. By leveraging natural language as input, DARWIN eliminates the need for task-specific descriptors and enables a flexible, unified approach to material property prediction and discovery. Our approach integrates 6M material domain papers and 21 experimental datasets from 49,256 materials across modalities while enabling cross-task knowledge transfer. The enhanced model achieves up to 59.1% improvement in prediction accuracy over the base LLaMA-7B architecture and outperforms SOTA machine learning approaches across 8 materials design tasks. These results establish LLMs as a promising foundation for developing versatile and scalable models in materials science. Darwin (ADL) ; Computer science ; Mathematics education ; Linguistics ; Psychology ; Philosophy ; Software engineering Machine Learning in Materials Science
Authors: Arman Ter-Petrosyan | Michael Holden | Jenna A. Bilbrey | Sarah Akers | Christina Doty | Kayla Yano | Le Wang | Rajendra Paudel | Eric J. Lang | Khalid Hattar | R. Comès | Yingge Du | Bethany E. Matthews | Steven R. Spurgeon
Source: arXiv (Cornell University) (preprint, 2024-11-15)
Field: Materials Science — Materials Chemistry

Revealing the Evolution of Order in Materials Microstructures Using Multi-Modal Computer Vision The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microstructural order. Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data, which is difficult to scale, challenging to reproduce, and lacks the ability to reveal latent associations needed for mechanistic models. Here, we demonstrate a multi-modal machine learning (ML) approach to describe order from electron microscopy analysis of the complex oxide La$_{1-x}$Sr$_x$FeO$_3$. We construct a hybrid pipeline based on fully and semi-supervised classification, allowing us to evaluate both the characteristics of each data modality and the value each modality adds to the ensemble. We observe distinct differences in the performance of uni- and multi-modal models, from which we draw general lessons in describing crystal order using computer vision. Modal ; Order (exchange) ; Microstructure ; Computer science ; Computer vision ; Artificial intelligence ; Computer graphics (images) ; Materials science ; Business ; Composite material X-ray Diffraction in Crystallography ; Industrial Vision Systems and Defect Detection ; Metallurgy and Material Forming
Authors: Dihua Ouyang | Tianyu Cui | Qiantao Zhang | Haoxiang Dai | Xiaowen Qin | Youqiu Hu
Source: Journal of Chemometrics (article, 2024-11-17)
Field: Chemistry — Analytical Chemistry

Rapid Detection of Stabilizer Content in Double‐Base Propellant Based on Artificial Neural Network Combined With Near‐Infrared Spectroscopy ABSTRACT During long‐term storage, double‐base propellants are prone to chemical decomposition of internal nitrate esters, leading to decreased burn rate, reduced strength, and degraded ballistic performance. Adding an appropriate amount of Centralite‐II is crucial for ensuring storage safety. This study proposes a novel method combining near‐infrared spectroscopy (NIRS) with artificial intelligence to rapidly and non‐destructively detect the content of Centralite‐II in double‐base propellants. The optimal modeling wavelength ranges of 4000–4600 cm −1 and 5700–6100 cm −1 were identified, and the raw spectral data were preprocessed using standard normal variate (SNV) transformation to improve the signal‐to‐noise ratio. Principal component analysis (PCA) was then applied to reduce data dimensionality, and the first three principal components were used as inputs for a backpropagation (BP‐ANN) neural network. The resulting PCA‐BP‐ANN model showed excellent performance on the training set, with an of 0.9830 and an of 0.0376%. During independent validation, the model demonstrated strong generalization ability, achieving an of 0.9824 and an of 0.3179%, comparative analysis with other models, including BP, PLS, ELM, SVR, and LSTM, indicated that the PCA‐BP‐ANN model exhibited superior prediction accuracy and generalization capability. This method provides a rapid and non‐destructive approach for assessing the stabilizer content in double‐base propellants and expands the application of NIRS and AI techniques in the field of energetic materials. Stabilizer (aeronautics) ; Propellant ; Base (topology) ; Artificial neural network ; Infrared spectroscopy ; Infrared ; Near-infrared spectroscopy ; Chemometrics ; Spectroscopy ; Analytical Chemistry (journal) ; Materials science ; Biological system ; Chemistry ; Computer science ; Chromatography ; Artificial intelligence ; Engineering ; Organic chemistry ; Mathematics ; Optics ; Aerospace engineering ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fault Detection and Control Systems
Authors: Laura Marsollek | Julius Lamprecht | Norbert Kockmann
Source: Crystals (article, 2024-11-30)
Field: Materials Science — Materials Chemistry

Investigation of AI Algorithms for Photometric Online Analysis in a Draft Tube Baffle Crystallizer The rapid advancement of AI algorithms presents new opportunities for sensing technologies based on image recognition, such as real-time crystallization monitoring. This work investigates the use of computer vision to detect and size crystals in a lab scale draft tube baffle crystallizer (DTBC). A specially developed analytical bypass was implemented on the DTBC to enable a low-influence analysis without invasive intrusion into the process. By utilizing AI models such as YouOnlyLookOnce version 8 (YOLOv8), YOLOv8 Segmentation (YOLO8seg), and the convolutional network for biomedical image segmentation U-Net, this study assesses their effectiveness in determining crystal size distributions from photometric images. While U-Net was deemed unsuitable due to computational demands and accuracy issues, YOLOv8 and YOLO8seg performed better in terms of efficiency and precision. YOLO8seg, specifically, achieved the highest accuracy, with a mean average precision (mAP) of 82.3%, and excelling in detecting larger crystals, but underperforming with crystals smaller than 90 µm. Despite this limitation, YOLO8seg was able to compete with the manual methods and was superior to the state-of-the-art algorithm mask region convolutional neural network (Mask R-CNN) in terms of accuracy. The study suggests that specific training and adaptation of the imaging conditions could further improve the crystal detection performance. Baffle ; Draft tube ; Hull ; Computer science ; Algorithm ; Tube (container) ; Computer graphics (images) ; Mechanical engineering ; Engineering ; Marine engineering Crystallization and Solubility Studies
Authors: Ting Cao
Source: Nature Computational Science (article, 2024-08-16)
Field: Materials Science — Materials Chemistry

Accelerating predictions of electronic transport and superconductivity Superconductivity ; Coupling (piping) ; Range (aeronautics) ; Condensed matter physics ; Relaxation (psychology) ; Electron transport chain ; Phonon ; Statistical physics ; Physics ; Materials science ; Chemistry ; Psychology ; Neuroscience Machine Learning in Materials Science ; High-pressure geophysics and materials ; Electronic and Structural Properties of Oxides
Authors: James H. Merrill | Yixuan Han | Connie Roth
Source: Macromolecules (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

A Bayesian Inference Approach to Accurately Fitting the Glass Transition Temperature in Thin Polymer Films We present a Bayesian inference-based nonlinear least-squares fitting approach developed to reliably fit challenging, noisy data in an automated and robust manner. The advantages of using Bayesian inference for nonlinear fitting are demonstrated by applying this approach to a set of temperature-dependent film thickness <i>h</i>(<i>T</i>) data collected by ellipsometry for thin films of polystyrene (PS) and poly(2-vinylpyridine) (P2VP). The glass transition experimentally presents as a continuous transition in thickness characterized by a change in slope that in thin films with broadened transitions can become particularly subtle and challenging to fit. This Bayesian fitting approach is implemented using existing open-source Python libraries that make these powerful methods accessible with desktop computers. We show how this Bayesian approach is more versatile and robust than existing methods by comparing it to common fitting methods currently used in the polymer science literature for identifying <i>T</i> <sub>g</sub>. As Bayesian inference allows for fitting to more complex models than existing methods in the literature do, our discussion includes an in-depth evaluation of the best functional form for capturing the behavior of <i>h</i>(<i>T</i>) data with temperature-dependent changes in thermal expansivity. This Bayesian fitting approach is easily automated, capable of reliably fitting noisy and challenging data in an unsupervised manner, and ideal for machine learning approaches to materials development. Glass transition ; Polymer ; Inference ; Bayesian inference ; Materials science ; Thermodynamics ; Bayesian probability ; Statistical physics ; Thin film ; Computer science ; Physics ; Composite material ; Nanotechnology ; Artificial intelligence Material Dynamics and Properties ; Phase Equilibria and Thermodynamics ; Composite Material Mechanics
Authors: Tongan Yan | Zhengqing Zhang | Chongli Zhong
Source: Journal of Chemical & Engineering Data (article, 2024-06-27)
Field: Chemistry — Inorganic Chemistry

Machine Learning Assisted Discovery of Efficient MOFs for One-Step C<sub>2</sub>H<sub>4</sub> Purification from Ternary C<sub>2</sub>H<sub>2</sub>/C<sub>2</sub>H<sub>4</sub>/C<sub>2</sub>H<sub>6</sub> Mixtures Purifying C2H4 from a mixture of C2H2/C2H4/C2H6 using a single adsorbent is crucial industrially. Yet, the challenge lies in their similar physicochemical properties, leading to low separation efficiency. Additionally, the lack of understanding regarding the structure–performance relationships hinders the development of high-performance metal–organic frameworks (MOFs). In this study, machine learning assisted high-throughput molecular simulation methods are employed to discover efficient MOFs for one-step C2H4 purification. The general material design strategies were proposed based on the analysis of 14,142 CoRE MOF simulation data. These include locking open metal sites, ensuring relative mass proportion of H atoms in the range of 2–4%, optimizing the largest cavity diameter to span 5–7 Å (ultramicropore), and fine-tuning φ within 0.5–0.6. Further using the computational insights obtained, 10 materials were identified with both C2H2/C2H4 and C2H6/C2H4 selectivities exceeding 3 from 137,953 hypothetical MOFs and 303,991 generated MOFs through additional molecular simulations. Our study not only provides screened and designed potential candidates for efficient one-step C2H4 purification from ternary C2H2/C2H4/C2H6 mixtures but also provides useful information for further performance improvement. Ternary operation ; Materials science ; Crystallography ; Computer science ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Inorganic Fluorides and Related Compounds
Authors: Chenxing Luo | Yang Sun | Renata M. Wentzcovitch
Source: Physical Review Materials (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Elasticity and acoustic velocities of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>δ</mml:mi><mml:mtext>−</mml:mtext><mml:mi>AlOOH</mml:mi></mml:mrow></mml:math> at extreme conditions: A methodology assessment Hydrous phases play a fundamental role in the deep-water cycle on Earth. Understanding their stability and thermoelastic properties is essential for constraining their abundance using seismic tomography. However, determining their elastic properties at extreme conditions is notoriously challenging. The challenges stem from the complex behavior of hydrogen bonds under high pressures and temperatures ($P,T\mathrm{s}$). In this study, we evaluate how advanced molecular dynamics simulation techniques can address these challenges by investigating the adiabatic elasticity and acoustic velocities of $\ensuremath{\delta}\text{\ensuremath{-}}\mathrm{AlOOH}$, a critical and prototypical high-pressure hydrous phase. We compared the performances of three methods to assess their viability and accuracy. The thermoelastic tensor was computed up to 140 GPa and temperatures up to 2700 K using molecular dynamics with a DeePMD machine-learning interatomic potential based on the SCAN meta-GGA functional. The excellent agreement with ambient condition single-crystal ultrasound measurements and the correct description of velocity changes induced by H-bond disorder-symmetrization transition observed at 10 GPa in Brillouin scattering measurements underscores the accuracy and efficacy of our approach. Materials science ; Elasticity (physics) ; Composite material Solid-state spectroscopy and crystallography ; Perovskite Materials and Applications ; Gas Sensing Nanomaterials and Sensors