jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chenghan Sun | Siddharth Sonti | Zekun Chen | Davide Donadio | Surl-Hee Ahn | Ambarish Kulkarni
Source: ChemRxiv (preprint, 2023-11-14)
Field: Materials Science — Materials Chemistry

Elucidating the Fluxionality and Dynamics ofZeolite-Confined Au Nanoclusters UsingMachine Learning Potentials Sub-nanometric clusters (NCs) of transition metal (TM) atoms, typically consisting of fewer than 15 atoms, have exhibited remarkable catalytic activity in various industrial reactions. However, these NCs are thermodynamically unstable and susceptible to deactivation due to sintering effects. Previous experiments have proposed zeolites as effective structural supports to stabilize these NCs. Still, there has been limited exploration of the long-timescale dynamics, including fluxionality and diffusivity, of these zeolite-confined TM NCs (TM@zeolites). Traditionally, investigating dynamics on the timescale of a few nanoseconds (ns) has been challenging using conventional \emph{ab initio} molecular dynamics (AIMD) simulations. This paper uses a self-adaptive workflow that leverages two state-of-the-art machine learning potential (MLP) packages, SchNetPack and Neuroevolution potential (NEP). This workflow was developed via multiple iterations of training, utilizing both AIMD and classical molecular dynamics (MD) based metadynamics simulations under an adaptive sampling framework known as query-by-committee active learning. The result was a highly accurate and robust MLP capable describing the diffusion of Au nanoclusters in zeolites. The MLP was found to be transferable across different temperatures and scalable to different zeolite topologies. Nanoclusters ; Molecular dynamics ; Metadynamics ; Zeolite ; Chemical physics ; Materials science ; Workflow ; Computer science ; Computational chemistry ; Statistical physics ; Nanotechnology ; Chemistry ; Physics ; Catalysis Machine Learning in Materials Science ; Neural Networks and Applications ; Theoretical and Computational Physics
Authors: Yunxi Dong | Bowen Zheng | Hang Li | Hong Tang | Yi Huang | Sensong An | Hualiang Zhang
Source: arXiv (Cornell University) (preprint, 2023-08-01)
Field: Materials Science — Surfaces, Coatings and Films

High-fidelity achromatic metalens imaging via deep neural network Meta-optics are attracting intensive interest as alternatives to traditional optical systems comprising multiple lenses and diffractive elements. Among applications, single metalens imaging is highly attractive due to the potential for achieving significant size reduction and simplified design. However, single metalenses exhibit severe chromatic aberration arising from material dispersion and the nature of singlet optics, making them unsuitable for full-color imaging requiring achromatic performance. In this work, we propose and validate a deep learning-based single metalens imaging system to overcome chromatic aberration in varied scenarios. The developed deep learning networks computationally reconstruct raw imaging captures through reliably refocusing red, green and blue channels to eliminate chromatic aberration and enhance resolution without altering the metalens hardware. The networks demonstrate consistent enhancement across different aperture sizes and focusing distances. Images outside the training set and real-world photos were also successfully reconstructed. Our approach provides a new means to achieve achromatic metalenses without complex engineering, enabling practical and simplified implementation to overcome inherent limitations of meta-optics. Achromatic lens ; Chromatic aberration ; Optics ; Lens (geology) ; Computer science ; Chromatic scale ; Optical aberration ; Artificial intelligence ; Computer vision ; Physics ; Wavefront Optical Coatings and Gratings ; Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications
Authors: Pengjie Shi | Zhiping Xu
Source: arXiv (Cornell University) (preprint, 2023-10-30)
Field: Materials Science — Materials Chemistry

A Planning-and-Exploring Approach to Extreme-Mechanics Force Fields Extreme mechanical processes such as strong lattice distortion and bond breakage during fracture are ubiquitous in nature and engineering, which often lead to catastrophic failure of structures. However, understanding the nucleation and growth of cracks is challenged by their multiscale characteristics spanning from atomic-level structures at the crack tip to the structural features where the load is applied. Molecular simulations offer an important tool to resolve the progressive microstructural changes at crack fronts and are widely used to explore processes therein, such as mechanical energy dissipation, crack path selection, and dynamic instabilities (e.g., kinking, branching). Empirical force fields developed based on local descriptors based on atomic positions and the bond orders do not yield satisfying predictions of fracture, even for the nonlinear, anisotropic stress-strain relations and the energy densities of edges. High-fidelity force fields thus should include the tensorial nature of strain and the energetics of rare events during fracture, which, unfortunately, have not been taken into account in both the state-of-the-art empirical and machine-learning force fields. Based on data generated by first-principles calculations, we develop a neural network-based force field for fracture, NN-F$^3$, by combining pre-sampling of the space of strain states and active-learning techniques to explore the transition states at critical bonding distances. The capability of NN-F$^3$ is demonstrated by studying the rupture of h-BN and twisted bilayer graphene as model problems. The simulation results confirm recent experimental findings and highlight the necessity to include the knowledge of electronic structures from first-principles calculations in predicting extreme mechanical processes. Force field (fiction) ; Fracture mechanics ; Statistical physics ; Dissipation ; Molecular dynamics ; Elastic energy ; Materials science ; Fracture (geology) ; Breakage ; Anisotropy ; Mechanics ; Computer science ; Structural engineering ; Physics ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Advancements in Semiconductor Devices and Circuit Design
Authors: Geon-Soo Ha | Md Al Mamunur Rashid | Da Hae Oh | Jeong‐Myeong Ha | Chun‐Jae Yoo | Byong‐Hun Jeon | Bonwook Koo | Keunhong Jeong | Kwang Ho Kim
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Biomaterials

Integrating Experimental and Computational Approaches for Deep Eutectic Solvent-Catalyzed Glycolysis of Post-Consumer Polyethylene Terephthalate Polyethylene terephthalate ; Eutectic system ; Catalysis ; Polyethylene ; Deep eutectic solvent ; Solvent ; Materials science ; Glycolysis ; Chemical engineering ; Process engineering ; Polymer science ; Organic chemistry ; Chemistry ; Composite material ; Engineering ; Microstructure biodegradable polymer synthesis and properties ; Machine Learning in Materials Science ; Polymer crystallization and properties
Authors: PengJie Wang | Ya Li | YuHeng Liang | Xiaolin Zhu | TongFei Li | Wei Lv
Source:  (article, 2023-10-27)
Field: Materials Science — Polymers and Plastics

Optimization of Performance Models for Interlayer Meltblown Nonwovens with Random Forest In the context of rapid societal and technological advancements, there has been a pronounced elevation in public health awareness. Among various protective measures, meltblown nonwoven materials have emerged as an indispensable component in protective masks, offering a crucial respiratory barrier for users. This study endeavors to provide an in-depth examination of this pivotal material. By systematically analyzing extensive datasets of meltblown fabric manufacturing processes and taking into account key parameters such as receiving distance, air velocity, and material thickness, this paper, grounded on historical production parameters, harnesses advanced machine learning techniques to meticulously design and optimize the performance control model for interlayer melt-blown nonwoven materials. The central aim of this research is to further enhance the efficacy of this material in health protection applications. Context (archaeology) ; Materials science ; Component (thermodynamics) ; Computer science ; Random forest ; Composite material ; Mechanical engineering ; Process engineering ; Artificial intelligence ; Engineering ; Geology Textile materials and evaluations ; Thermoregulation and physiological responses ; Color perception and design
Authors: Diba Behnoudfar | Cory M. Simon | Joshua Schrier
Source: ChemRxiv (preprint, 2023-06-05)
Field: Materials Science — Materials Chemistry

Data-driven imputation of miscibility of aqueous solutions via graph-regularized logistic matrix factorization Aqueous, two-phase systems (ATPSs) may form upon mixing two solutions of independently water-soluble compounds. Many separation, purification, and extraction processes rely on ATPSs. Predicting the miscibility of solutions can accelerate and reduce the cost of the discovery of new ATPSs for these applications. Whereas previous machine learning approaches to ATPS prediction used physicochemical properties of each solute as a descriptor, in this work, we show how we can impute missing miscibility outcomes directly from an incomplete collection of pairwise miscibility experiments. We use graph-regularized logistic matrix factorization to learn a latent vector of each solution from (i) the observed entries in the pairwise miscibility matrix and (ii) a graph (nodes: solutes, edges: shared relationships) indicating the general category of the solute (i.e., polymer, surfactant, salt, protein). Using an experimental dataset of the pairwise miscibility of 68 solutions from Peacock et al. [ACS Appl. Mater. Interfaces 2021, 13, 9], we show that graph-regularized logistic matrix factorization more accurately predicts missing (im)miscibility outcomes of pairs of solutions than ordinary logistic matrix factorization and random forest classifiers using physicochemical features of the compounds. Miscibility ; Pairwise comparison ; Aqueous solution ; Factorization ; Graph ; Matrix (chemical analysis) ; Mathematics ; Chemistry ; Thermodynamics ; Combinatorics ; Polymer ; Statistics ; Chromatography ; Algorithm ; Organic chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Geon-Soo Ha | Md Al Mamunur Rashid | Da Hae Oh | Jeong‐Myeong Ha | Chun‐Jae Yoo | Byong‐Hun Jeon | Bonwook Koo | Keunhong Jeong | Kwang Ho Kim
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Integrating Experimental and Computational Approaches for Deep Eutectic Solvent-Catalyzed Glycolysis of Post-Consumer Polyethylene Terephthalate Polyethylene terephthalate ; Eutectic system ; Catalysis ; Deep eutectic solvent ; Polyethylene ; Glycolysis ; Solvent ; Materials science ; Chemical engineering ; Organic chemistry ; Business ; Process engineering ; Chemistry ; Composite material ; Engineering Machine Learning in Materials Science
Authors: Yogesh Khatri | Vaidehi Atpadkar | Aashi Agarwal | Arti Kashyap
Source:  (article, 2023-05-01)
Field: Materials Science — Materials Chemistry

Magnetic MXene: A Machine learning model with small data MXenes, comprising of atomically thin layers of transition metal carbides, nitrides, and carbonitrides, exhibit properties which are not found in their corresponding bulk materials. Interestingly, because of the presence of transition metal, MXenes may also provide the candidate materials for observing low-dimensional magnetism. This can be of interest to various applications such as spintronic devices, data storage, and electromagnetic interference shielding. Here, we focus on the magnetic MXenes which are only a few in number out of known MXenes. We propose a machine learning model for the prediction of magnetic MXenes using a carefully designed descriptor. MXenes ; Spintronics ; Materials science ; Magnetism ; Carbide ; Electromagnetic shielding ; Computer science ; Nanotechnology ; Condensed matter physics ; Ferromagnetism ; Physics ; Metallurgy ; Composite material MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Memory and Neural Computing
Authors: Xingyuan San | Junwei Hu | Mingyi Chen | Haiyang Niu | Paul J. M. Smeets | Jie Deng | Kunmo Koo | Roberto dos Reis | Vinayak P. Dravid | Xiaobing Hu
Source: arXiv (Cornell University) (preprint, 2023-06-14)
Field: Materials Science — Biomaterials

Unlocking the Structural Mystery of Vaterite CaCO3 Calcium carbonate (CaCO3), the most abundant biogenic mineral on earth, plays a crucial role in various fields. Of the four polymorphs, calcite, aragonite, vaterite, and amorphous CaCO3, vaterite is the most enigmatic one due to an ongoing debate regarding its structure that has persisted for nearly a century. In this work, based on systematic transmission electron microscopy characterizations, elaborate crystallographic analysis and machine learning aided molecular dynamics simulations with ab initio accuracy, we reveal that vaterite can be regarded as a polytypic structure. The basic phase is a monoclinic lattice possessing pseudohexagonal symmetry. Direct imaging and atomic-scale simulations provide evidence that a single grain of vaterite can have three orientation variants. Additionally, we find that vaterite undergoes a second-order phase transition. These atomic scale insights provide a comprehensive understanding of the structure of vaterite and offer new perspectives on the biomineralization process of calcium carbonate. Vaterite ; Calcite ; Aragonite ; Calcium carbonate ; Amorphous calcium carbonate ; Biomineralization ; Crystallography ; Chemical physics ; Atomic units ; Chemistry ; Materials science ; Mineralogy ; Chemical engineering ; Physics ; Composite material Calcium Carbonate Crystallization and Inhibition ; Minerals Flotation and Separation Techniques ; Paleontology and Stratigraphy of Fossils
Authors: Eman Mustafa | Abdel Salam Malek | Sherwet El Gholmy | Adel El Geiheini | Sherien El kateb
Source: Industrial Technology Journal (article, 2023-10-08)
Field: Materials Science — Polymers and Plastics

Prediction of Fabric Stiffness Based On Fabric Construction This research presents a statistical tool to predict fabric stiffness based on fabric construction to avoid material waste, effort and time consuming in laboratories testing. Three different blended material of fabric were used that made from wool and polyester with different percentages of material yarns, counts and type (single/ply) of weft yarn, and fabric weave pattern (plain, twill and satin). Quantitative and qualitative variables were used in prediction by applying regression analysis and Artificial Neural Network (ANN) with a suitable coding system for different variables. Three independent models were derived to predict fabric stiffness by using regression analysis and compared to ANN model. Model (3) is the best prediction tool depending on regression analysis to predict fabric stiffness due to higher interpretation of the relation between variables used in prediction of fabric stiffness. Whereas, ANN achieved higher performance of validation (MSE = 7.41E-05) and total value of (R2= 0.967) when comparing to regression analysis model. Stiffness ; Structural engineering ; Materials science ; Composite material ; Engineering Textile materials and evaluations ; Industrial Vision Systems and Defect Detection
Authors: G. Narasimha | Saban M. Hus | Arpan Biswas | Rama K. Vasudevan | Maxim Ziatdinov
Source: arXiv (Cornell University) (preprint, 2023-10-26)
Field: Materials Science — Materials Chemistry

Autonomous convergence of STM control parameters using Bayesian Optimization Scanning Tunneling microscopy (STM) is a widely used tool for atomic imaging of novel materials and its surface energetics. However, the optimization of the imaging conditions is a tedious process due to the extremely sensitive tip-surface interaction, and thus limits the throughput efficiency. Here we deploy a machine learning (ML) based framework to achieve optimal-atomically resolved imaging conditions in real time. The experimental workflow leverages Bayesian optimization (BO) method to rapidly improve the image quality, defined by the peak intensity in the Fourier space. The outcome of the BO prediction is incorporated into the microscope controls, i.e., the current setpoint and the tip bias, to dynamically improve the STM scan conditions. We present strategies to either selectively explore or exploit across the parameter space. As a result, suitable policies are developed for autonomous convergence of the control-parameters. The ML-based framework serves as a general workflow methodology across a wide range of materials. Bayesian optimization ; Setpoint ; Workflow ; Scanning tunneling microscope ; Computer science ; Throughput ; Convergence (economics) ; Parameter space ; Exploit ; Process (computing) ; Microscope ; Range (aeronautics) ; Artificial intelligence ; Machine learning ; Nanotechnology ; Materials science ; Optics ; Physics ; Mathematics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Xu Wang | Yunbo Hao | Kai Zhao | Chunsheng Sha | Weiqi Li | Jiali Gao
Source:  (article, 2023-06-28)
Field: Materials Science — Materials Chemistry

Prediction of single-track deposition quality in additive manufacturing of NiTi shape memory alloy Nickel-titanium (NiTi) shape memory alloy (SMA) has excellent application potential in aerospace because of its shape memory effect and super elasticity, but is still limited by poor machinability and weldability in traditional processing techniques. The coaxial powder feeding laser deposition technology opens a new window in the processing of NiTi SMA components. In this paper, prediction models between the process parameters (laser power, scanning speed and powder feeding rate) and process state parameter (melted pool temperature), deposition quality (track width, track height, microhardness) in NiTi alloy laser metal deposition based on the Back Propagation Neural Networks (BPNN) and Random Forest (RF) algorithms were estab-lished. Thirty single tracks were deposited and measured as training groups. The results show that the average prediction error based on the BPNN model for microhardness, track width, track height and melted pool temperature are 0.37%, 1.88%, 4.45% and 0.91%, respectively, which are better than the RF model. Then, BPNN model was further used to predict deposied quality under the combination of five new process parameters groups. Objective of this study was to provide a guidance for the subsequent optimization of process parameters for the improvement of the deposition accuracy of NiTi alloy parts. Nickel titanium ; Materials science ; Shape-memory alloy ; Indentation hardness ; Deposition (geology) ; Machinability ; Titanium alloy ; Metallurgy ; Composite material ; Alloy ; Machining Shape Memory Alloy Transformations ; Additive Manufacturing Materials and Processes ; High Entropy Alloys Studies
Authors: Enrico Brugnolotto | Preslav Aleksandrov | Marilyne Sousa | Vihar Georgiev
Source:  (preprint, 2023-09-11)
Field: Materials Science — Materials Chemistry

Machine Learning based Nanowire Classification method based on Nanowire Array Scanning Electron Microscope Images &lt;p&gt;This article introduces an innovative classification methodology for identifying nanowires within scanning electron microscope images. Our approach employs advanced image manipulation techniques in conjunction with machine learning-based recognition algorithms. The effectiveness of our proposed method is demonstrated through its application to the categorization of scanning electron microscopy images depicting nanowires arrays. The method’s capability to isolate and distinguish individual nanowires within an array is the primary factor in the observed accuracy. The foundational data set for model training comprises scanning electron microscopy images featuring 240 III-V nanowire arrays grown with metal organic chemical vapor deposition on silicon substrates. Each of these arrays consists of 66 nanowires. The results underscore the model’s proficiency in discerning distinct wire configurations and detecting parasitic crystals. Our approach yields an average F1 score of 0.91, indicating high precision and recall. Such a high level of performance and accuracy of the ML methods demonstrate the viability of our technique for not only academic but also practical commercial implementation and usage.&lt;/p&gt; Nanowire ; Scanning electron microscope ; Categorization ; Materials science ; Chemical vapor deposition ; Artificial intelligence ; Nanotechnology ; Pattern recognition (psychology) ; Computer science Machine Learning in Materials Science ; Image Processing Techniques and Applications ; Cell Image Analysis Techniques
Authors: Davide Avagliano | Marta Skreta | Sebastian Arellano-Rubach | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2023-09-14)
Field: Materials Science — Materials Chemistry

DELFI: A computer oracle for recommending the best density functional for excited states calculations Density functional theory is known as the workhorse of computational quantum chemistry. One of its main limitations, if not the main one, is that choosing the right functional to employ is a non-trivial task left for human experts. The choice is particularly hard for excited states calculations, when using the time-dependent formulation of DFT (TD-DFT). This is not only due to the approximations and limitations of the method, but also because the photophysical properties of a molecule are defined by a manifold of states that all need to be properly described in a balanced manner to obtain an accurate photochemical picture. This includes not only the relative energy of the states, but also capturing the correct character, order and intensity of the transitions. In this work, we developed a scoring system to quantitatively define the accuracy of an excited states calculation by simultaneously considering at the same time all these properties of a manifold of states. The scoring system is generalizable to any level of theory, we here applied it to a large dataset of organic molecules, calculating 38 scores for as many common functionals of different type and rung, against a higher accuracy method. We used these scores to train a graph attention neural network that is used to predict the 38 scores for molecules represented as 2D graphs. We call this oracle DELFI (Data-driven EvaLuation of Functionals by Inference), which can be used to predict the ranking of functionals to calculate optical properties of organic molecules. A corresponding web application allows to easily run DELFI and analyze the results, alleviating the hurdle of choosing the right functional for TD-DFT calculations. Oracle ; Density functional theory ; Excited state ; Computer science ; Inference ; Estimator ; Manifold (fluid mechanics) ; Ranking (information retrieval) ; Theoretical computer science ; Algorithm ; Statistical physics ; Artificial intelligence ; Computational chemistry ; Mathematics ; Chemistry ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Various Chemistry Research Topics