jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shivani Bhawsar | Mengqi Fang | Abdus Salam Sarkar | Siwei Chen | Eui‐Hyeok Yang
Source: MRS Bulletin (article, 2024-06-25)
Field: Materials Science — Materials Chemistry

Deep learning-based multimodal analysis for transition-metal dichalcogenides Abstract In this study, we present a novel approach to enable high-throughput characterization of transition-metal dichalcogenides (TMDs) across various layers, including mono-, bi-, tri-, four, and multilayers, utilizing a generative deep learning-based image-to-image translation method. Graphical features, including contrast, color, shapes, flake sizes, and their distributions, were extracted using color-based segmentation of optical images, and Raman and photoluminescence spectra of chemical vapor deposition-grown and mechanically exfoliated TMDs. The labeled images to identify and characterize TMDs were generated using the pix2pix conditional generative adversarial network (cGAN), trained only on a limited data set. Furthermore, our model demonstrated versatility by successfully characterizing TMD heterostructures, showing adaptability across diverse material compositions. Graphical abstract Impact Statement Deep learning has been used to identify and characterize transition-metal dichalcogenides (TMDs). Although studies leveraging convolutional neural networks have shown promise in analyzing the optical, physical, and electronic properties of TMDs, they need extensive data sets and show limited generalization capabilities with smaller data sets. This work introduces a transformative approach—a generative deep learning (DL)-based image-to-image translation method—for high-throughput TMD characterization. Our method, employing a DL-based pix2pix cGAN network, transcends traditional limitations by offering insights into the graphical features, layer numbers, and distributions of TMDs, even with limited data sets. Notably, we demonstrate the scalability of our model through successful characterization of different heterostructures, showcasing its adaptability across diverse material compositions. Transition metal ; Materials science ; Nanotechnology ; Engineering physics ; Engineering ; Chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; 2D Materials and Applications
Authors: Filippo Bigi | Marcel Langer | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2024-12-16)
Field: Materials Science — Materials Chemistry

The dark side of the forces: assessing non-conservative force models for atomistic machine learning The use of machine learning to estimate the energy of a group of atoms, and the forces that drive them to more stable configurations, has revolutionized the fields of computational chemistry and materials discovery. In this domain, rigorous enforcement of symmetry and conservation laws has traditionally been considered essential. For this reason, interatomic forces are usually computed as the derivatives of the potential energy, ensuring energy conservation. Several recent works have questioned this physically constrained approach, suggesting that directly predicting the forces yields a better trade-off between accuracy and computational efficiency, and that energy conservation can be learned during training. This work investigates the applicability of such non-conservative models in microscopic simulations. We identify and demonstrate several fundamental issues, from ill-defined convergence of geometry optimization to instability in various types of molecular dynamics. Given the difficulty in monitoring and correcting the lack of energy conservation, direct forces should be used with great care. We show that the best approach to exploit the acceleration they afford is to use them in conjunction with conservative forces. A model can be pre-trained efficiently on direct forces, then fine-tuned using backpropagation. At evaluation time, both force types can be used together to avoid unphysical effects while still benefitting almost entirely from the computational efficiency of direct forces. Great Rift ; Computer science ; Physics ; Statistical physics ; Astronomy Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Ion-surface interactions and analysis
Authors: Su Kalayar Swe | Heeso Noh
Source: Photonics (article, 2024-10-14)
Field: Materials Science — Surfaces, Coatings and Films

Inverse Design of Reflectionless Thin-Film Multilayers with Optical Absorption Utilizing Tandem Neural Network The traditional approach to optical design faces limitations as photonic devices grow increasingly complex, requiring advanced functionalities. Recently, machine learning algorithms have gained significant interest for extracting structural designs from customized wavelength spectra, surpassing traditional simulation methods known for their time-consuming nature and resource-demanding computational requirements. This study focuses on the inverse design of a reflectionless multilayer thin-film structure across a specific wavelength region, utilizing a tandem neural network (TNN) approach. The method effectively addresses the non-uniqueness problem in training inverse neural networks. Data generation via the transfer matrix method (TMM) involves simulating the optical behavior of a multilayer structure comprising alternating thin films of silicon dioxide (SiO2) and silicon (Si). This innovative design considers both reflection and absorption properties to achieve near-zero reflection. We aimed to manipulate the structure’s reflectivity by implementing low-index and high-index layers along with Si absorption layers to attain specific optical properties. Our TNN demonstrated an MSE accuracy of less than 0.0005 and a maximum loss of 0.00781 for predicting the desired spectrum range, offering advanced capabilities for forecasting arbitrary spectra. This approach provides insights into designing multilayer thin-film structures with near-zero reflection and highlights the potential for controlling absorption materials to enhance optical performance. Tandem ; Inverse ; Thin film ; Absorption (acoustics) ; Materials science ; Artificial neural network ; Optoelectronics ; Optics ; Computer science ; Physics ; Nanotechnology ; Composite material ; Artificial intelligence ; Mathematics Optical Coatings and Gratings ; Photonic Crystals and Applications ; Semiconductor Lasers and Optical Devices
Authors: Siyu Hu | Tong Zhao | Qiuchen Sha | Enji Li | Xiangyu Meng | Liping Liu | Lin‐Wang Wang | Guangming Tan | Weile Jia
Source:  (article, 2024-02-20)
Field: Materials Science — Materials Chemistry

Training one DeePMD Model in Minutes: a Step towards Online Learning Neural Network Molecular Dynamics (NNMD) has become a major approach in material simulations, which can speedup the molecular dynamics (MD) simulation for thousands of times, while maintaining ab initio accuracy, thus has a potential to fundamentally change the paradigm of material simulations. However, there are two time-consuming bottlenecks of the NNMD developments. One is the data access of ab initio calculation results. The other, which is the focus of the current work, is reducing the training time of NNMD model. The training of NNMD model is different from most other neural network training because the atomic force (which is related to the gradient of the network) is an important physical property to be fit. Tests show the traditional stochastic gradient methods, like the Adam algorithms, cannot efficiently deploy the multisample minibatch algorithm. As a result, a typical training (taking the Deep Potential Molecular Dynamics (DeePMD) as an example) can take many hours. In this work, we designed a heuristic minibatch quasi-Newtonian optimizer based on Extended Kalman Filter method. An early reduction of gradient and error is adopted to reduce memory footprint and communication. The memory footprint, communication and settings of hyper-parameters of this new method are analyzed in detail. Computational innovations such as customized kernels of the symmetry-preserving descriptor are applied to exploit the computing power of the heterogeneous architecture. Experiments are performed on 8 different datasets representing different real case situations, and numerical results show that our new method has an average speedup of 32.2 compared to the Reorganized Layer-wised Extended Kalman Filter with 1 GPU, reducing the absolute training time of one DeePMD model from hours to several minutes, making it one step toward online training. Computer science ; Speedup ; Memory footprint ; Heuristic ; Artificial neural network ; Artificial intelligence ; Reduction (mathematics) ; Algorithm ; Parallel computing Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Machine Learning and ELM
Authors: Yoel Zimmermann | Joonas Keski-Rahkonen | Anton M. Graf | Eric J. Heller
Source: Entropy (preprint, 2024-06-28)
Field: Materials Science — Materials Chemistry

Rise and Fall of Anderson Localization by Lattice Vibrations: A Time-Dependent Machine Learning Approach The intricate relationship between electrons and the crystal lattice is a linchpin in condensed matter, traditionally described by the Fröhlich model encompassing the lowest-order lattice-electron coupling. Recently developed quantum acoustics, emphasizing the wave nature of lattice vibrations, has enabled the exploration of previously uncharted territories of electron-lattice interaction not accessible with conventional tools such as perturbation theory. In this context, our agenda here is two-fold. First, we showcase the application of machine learning methods to categorize various interaction regimes within the subtle interplay of electrons and the dynamical lattice landscape. Second, we shed light on a nebulous region of electron dynamics identified by the machine learning approach and then attribute it to transient localization, where strong lattice vibrations result in a momentary Anderson prison for electronic wavepackets, which are later released by the evolution of the lattice. Overall, our research illuminates the spectrum of dynamics within the Fröhlich model, such as transient localization, which has been suggested as a pivotal factor contributing to the mysteries surrounding strange metals. Furthermore, this paves the way for utilizing time-dependent perspectives in machine learning techniques for designing materials with tailored electron-lattice properties. Lattice (music) ; Electron ; Physics ; Particle in a one-dimensional lattice ; Theoretical physics ; Lattice vibration ; Quantum ; Artificial intelligence ; Computer science ; Statistical physics ; Quantum mechanics ; Lattice field theory ; Phonon ; Acoustics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Advanced Chemical Physics Studies
Authors: Maria Jose Campbell
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-06-28)
Field: Materials Science — Materials Chemistry

VIPER: A General Model for Prediction of Enzyme Substrates Abstract Enzymes, nature’s catalysts, possess remarkable properties such as high stereo-, regio-, and chemo-specificity. These properties allow enzymes to greatly simplify complex synthetic processes, resulting in improved yields and reduced manufacturing costs compared to traditional chemical methods. However, the lack of experimental characterization of enzyme substrates, with only a few thousand out of tens of millions of known enzymes in Uniprot having annotated substrates, severely limits the ability of chemists to repurpose enzymes for industrial applications. Previous machine learning models aimed at predicting enzyme substrates have been hampered by poor generalization to new substrates. Here, we introduce VIPER (Virtual Interaction Predictor for Enzyme Reactivity), a model that achieves an average 30% improvement over the previous state-of-the-art model (ProSmith) in reaction prediction for unseen substrates. Furthermore, we reveal flaws in previous enzyme-substrate reaction datasets, and introduce a novel high-quality enzyme-substrate reaction dataset to alleviate these issues. VIPeR ; Biochemical engineering ; Computer science ; Enzyme ; Artificial intelligence ; Generalization ; Machine learning ; Benchmarking ; Viper Venoms ; Substrate specificity ; Chemistry ; Biochemistry ; Engineering ; Mathematics ; Snake venom Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Enzyme Catalysis and Immobilization
Authors: Jiawei Wu | Yulan Han | Menglei Jia | Jiayan Xu | P. Hu
Source: ChemRxiv (preprint, 2024-04-05)
Field: Materials Science — Materials Chemistry

Identifying the Active Site on ZnxCryOz for HC-O Bond Cleavage in Syn-gas Conversion The excellent performance of ZnxCryOz catalysts, used in the process of converting CO/H2 to methanol and light olefins, is highly attractive, but the microstructure of ZnxCryOz structure under the syngas conversion conditions remains elusive experimentally and theoretically because of the limitation of the detecting facilities/methods. By using the genetic-algorithm-based global structural search accelerated by machine learning in combination with a local cluster sampling strategy in the active learning scheme, we reveal the structure/composition evolution of ZnxCryOz structures and uncover that the catalytic activities of these catalysts strongly depend on the Zn/Cr ratios under the syngas conversion conditions. The possible active phase at the thermodynamically stable condition is identified and the critical active site influencing the catalytic property is unraveled. We show that the catalyst Zn2Cr2O5, which consists of a thin ZnO layer and the ZnCr2O4 structures, achieves a high catalytic activity for syngas conversion and its X-ray diffraction patterns are in agreement with the experimental result. Importantly, the presence of a hexahedral configuration ([ZnCrO2]hex) is found to affect the catalyst activity significantly, and this result is further supported by the analysis based on the structure-activity relationship. Cleavage (geology) ; Active site ; Chemistry ; Bond ; Bond cleavage ; Stereochemistry ; Business ; Materials science ; Catalysis ; Organic chemistry ; Finance Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Kohei Shimamura | Shinnosuke Hattori | Ken-ichi Nomura | Akihide Koura | Fuyuki Shimojo
Source: International Journal of Heat and Mass Transfer (article, 2024-08-27)
Field: Materials Science — Materials Chemistry

Thermal conductivity calculation using homogeneous non-equilibrium molecular dynamics simulation with Allegro Homogeneous ; Thermal conductivity ; Molecular dynamics ; Dynamics (music) ; Thermodynamics ; Statistical physics ; Materials science ; Conductivity ; Mechanics ; Physics Machine Learning in Materials Science ; Thermal properties of materials ; Fuel Cells and Related Materials
Authors: Zeyu Zhang | Yuliang Shi | Farnaz A. Shakib
Source: ChemRxiv (preprint, 2024-04-02)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Thermodynamic Stability and Electronic Properties of 2D Layered Conductive Metal-Organic Frameworks 2D layered metal-organic frameworks (MOFs) are a new class of multifunctional materials that can provide electrical conductivity on top of the conventional structural characteristics of MOFs, offering potential applications in electronics and optics. Here, for the first time, we employ Machine Learning (ML) techniques to predict the thermodynamic stability and electronic properties of layered electrically conductive (EC) MOFs, bypassing expensive ab initio calculations for the design and discovery of new materials. Proper feature engineering is a very important factor in utilizing ML models for such purposes. Here, we show that a combination of elemental features, using generic statistical reduction methods and crystal structure information curated from the recently introduced EC-MOF database, leads to a reasonable prediction of the thermodynamic and electronic properties of EC MOFs. We utilize these features in training a diverse range of ML classifiers and regressors. Evaluating the performance of these different models, we show that an ensemble learning approach in the form of stacking ML models can lead to higher accuracy and more reliability on the predictive power of ML to be employed in future MOF research. Metal-organic framework ; Electrical conductor ; Chemical stability ; Stability (learning theory) ; Materials science ; Key (lock) ; Metal ; Thermodynamics ; Nanotechnology ; Computer science ; Chemistry ; Machine learning ; Physical chemistry ; Physics ; Metallurgy ; Composite material ; Adsorption Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Conducting polymers and applications
Authors: Subin Kim | Nojoon Myoung | Seung‐Hyun Jun | Ara Go
Source: Current Applied Physics (article, 2024-08-30)
Field: Materials Science — Materials Chemistry

Neural network-based recognition of multiple nanobubbles in graphene Graphene ; Artificial neural network ; Computer science ; Nanotechnology ; Artificial intelligence ; Materials science Graphene research and applications ; Gas Sensing Nanomaterials and Sensors ; Minerals Flotation and Separation Techniques
Authors: D.V. Shein | Dmitry Zav’yalov
Source: The European Physical Journal Plus (article, 2024-09-30)
Field: Materials Science — Materials Chemistry

Calculating thermal properties of phosphorene using a deep learning force field Phosphorene ; Field (mathematics) ; Thermal ; Photothermal therapy ; Materials science ; Engineering physics ; Computer science ; Nanotechnology ; Physics ; Mathematics ; Thermodynamics Machine Learning in Materials Science ; Thermal properties of materials ; 2D Materials and Applications
Authors: Karthik Balasubramanian | Sukriti Manna | Suvo Banik | Srilok Srinivasan | Rohit Batra | Subramanian Sankaranarayanan
Source: Computational Materials Science (article, 2024-11-06)
Field: Materials Science — Materials Chemistry

Machine learning enabled discovery of superhard and ultrahard carbon polymorphs Carbon fibers ; Materials science ; Nanotechnology ; Composite material ; Composite number Boron and Carbon Nanomaterials Research ; Diamond and Carbon-based Materials Research ; Boron Compounds in Chemistry
Authors: Nicholas Humphrey | Selin Bac | Shaama Mallikarjun Sharada
Source: The Journal of Chemical Physics (article, 2024-09-20)
Field: Materials Science — Materials Chemistry

A configuration sampling study of reaction intermediates constituting catalytic cycles for CO oxidation with Pt1/TiO2 We combine ab initio molecular dynamics (AIMD) simulations with an unsupervised machine learning approach to automate the search for possible configurations of CO oxidation reaction intermediates catalyzed by the atomically dispersed Pt1/TiO2 catalyst. Following the example of Roncoroni and co-workers [Phys. Chem. Chem. Phys. 25, 13741 (2023)], we employ t-distributed stochastic neighbor embedding and hierarchical density-based spatial clustering of applications with noise to reduce the dimensionality and cluster AIMD snapshots based on the local coordination environment of Pt. We identify new local minima, particularly in cases where CO2 is bound to the active site, because it can coordinate in various ways with both the metal and support. The new minima constitute additional elementary steps in some proposed pathways for CO oxidation, resulting in turnover frequencies that differ from prior estimates by several orders of magnitude. This work, therefore, demonstrates that configuration sampling is a necessary component of computational studies of catalytic cycles for atomically dispersed catalysts. Maxima and minima ; Catalysis ; Curse of dimensionality ; Cluster (spacecraft) ; Embedding ; Cluster analysis ; Sampling (signal processing) ; Molecular dynamics ; Component (thermodynamics) ; Work (physics) ; Reaction coordinate ; Ab initio ; Chemistry ; Computer science ; Hierarchical clustering ; Computational chemistry ; Chemical physics ; Materials science ; Physics ; Mathematics ; Quantum mechanics ; Organic chemistry ; Artificial intelligence Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Mutao Xu | Yuhang Deng | Xingxiu Gao | Qijie Jin | Wei Yan | Liguo Chen | Jian Yang | Jing Song | Changcheng Zhou | Haitao Xu
Source: Molecules (article, 2024-11-25)
Field: Materials Science — Materials Chemistry

Impact of Alkali Metals on CeO2-WO3/TiO2 Catalysts for NH3-Selective Catalytic Reduction and Lifetime Prediction of Catalysts Ce-based catalysts have been widely used in the removal of nitrogen oxides from industrial flue gas because of their good catalytic performance and environmental friendliness. However, the mechanism of alkali metal poisoning in Ce-based catalysts remains to be further studied. This work involves the preparation of the K/Na-poisoned CeWTi catalyst via the impregnation method for assessing its performance in NO removal. Experiments show that both K and Na exhibit detrimental effects on the CeWTi catalyst, and the loading of alkali metal reduces the specific surface area and pore volume of the catalyst. Furthermore, the presence of alkaline metals results in a notable decline in the CeWTi acid concentration, particularly in Lewis acid sites. Concurrently, the levels of Ce<sup>3+</sup>, oxygen vacancies, and reducing agents on the catalyst surface decrease, leading to diminished reduction capability and eventual catalyst deactivation. The application of a BP neural network for catalyst activity prediction yielded an average relative error of approximately 0.73%, indicating enhanced accuracy in prediction outcomes. This work explored the cause of alkali metal poisoning of the CeWTi catalyst and provided an effective prediction method for the lifetime of CeWTi catalyst, which provided theoretical guidance for the engineering application of Ce-based catalysts. Catalysis ; Alkali metal ; Selective catalytic reduction ; Inorganic chemistry ; Flue gas ; Chemistry ; Metal ; Catalyst poisoning ; Materials science ; Chemical engineering ; Catalyst support ; Organic chemistry Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Ammonia Synthesis and Nitrogen Reduction
Authors: Alessia Serena Perna
Source: Materials research proceedings (article, 2024-04-19)
Field: Materials Science — Materials Chemistry

Artificial intelligence approaches for enhanced coating performance Abstract. Cold spray (CS) is an innovative manufacturing technology designed to produce metallic layers on diverse materials. This process involves propelling metallic particles at supersonic speeds using pressurized gas, causing them to impact the target surface and achieve adhesion through mechanical interlocking between the powders and the substrate. Integrating Artificial Intelligence (AI) techniques can enhance the understanding and quality of this additive manufacturing process. This work focuses on predicting the characteristics of particle deformation upon collision by exploring multiple Machine Learning (ML) and Deep Learning (DL) techniques with the aim of identifying the most suitable approach. The used dataset is mixed data, composed of experimental data and FEM data, generated by Finite Element models (FEM). The input parameters for the model are categorized into three macro-categories: process, powder, and substrate. The research aims to forecast particle behavior through this multidimensional approach and contribute valuable insights for optimizing the cold spray manufacturing process by applying DL methodologies. Computer science ; Coating ; Artificial intelligence ; Materials science ; Nanotechnology High voltage insulation and dielectric phenomena ; Surface Roughness and Optical Measurements ; Material Properties and Processing