jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guowei Mu | Quanmin Dai | Shuying Chai | Peng Yang
Source: AIP Advances (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Ultrasonic propagation characteristics of partial discharge in oil-impregnated paper traction transformer Partial Discharges (PDs) are a significant factor in reducing the insulation life of traction transformers. In recent years, the Acoustic Emission (AE) method has become the most advanced method for detecting PD signals in transformers. The AE method utilizes AE sensors placed on the transformer tank to detect ultrasonic signals emitted by PD and determine the Time Of Arrival (TOA) of the head wave. The windings and cores of a traction transformer consist mainly of metal, which greatly affects the propagation of PD ultrasonic waves. This paper establishes a 110 kV “pressure acoustic, transient” physical field model of the traction transformer with dimensions of 4.63 × 1.48 × 2.84 m3. The model is used to carry out the PD pressure acoustic physical field simulation study of the traction transformer, to clarify the physical characteristics of the ultrasound of the PD defects, and to establish observation points on the transformer tanks to receive ultrasonic time-domain waveforms for PD detection. The simulation results indicate that PD ultrasonic waves exhibit complex propagation characteristics, including reflection, refraction, and reverberation, as they pass through the windings and cores to the observation points. The TOA of the head wave in the ultrasound time-domain waveform is indicated by the first maximum value of the wave crest line. Finally, this paper proposes a multi-level localization method based on the AE method to determine which winding generates the PD in the large-scale traction transformer using only four dynamically moving observation points. Acoustics ; Ultrasonic sensor ; Partial discharge ; Transformer ; Electromagnetic coil ; Sound pressure ; Waveform ; Materials science ; Acoustic emission ; Time domain ; Traction (geology) ; Voltage ; Electrical engineering ; Physics ; Engineering ; Computer science ; Mechanical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Aerosol Filtration and Electrostatic Precipitation
Authors: Ce Liu | Suryansh Kumar | Shuhang Gu | Radu Timofte | Yao Yao | Luc Van Gool
Source: arXiv (Cornell University) (preprint, 2024-07-03)
Field: Chemistry — Analytical Chemistry

Stereo Risk: A Continuous Modeling Approach to Stereo Matching We introduce Stereo Risk, a new deep-learning approach to solve the classical stereo-matching problem in computer vision. As it is well-known that stereo matching boils down to a per-pixel disparity estimation problem, the popular state-of-the-art stereo-matching approaches widely rely on regressing the scene disparity values, yet via discretization of scene disparity values. Such discretization often fails to capture the nuanced, continuous nature of scene depth. Stereo Risk departs from the conventional discretization approach by formulating the scene disparity as an optimal solution to a continuous risk minimization problem, hence the name "stereo risk". We demonstrate that $L^1$ minimization of the proposed continuous risk function enhances stereo-matching performance for deep networks, particularly for disparities with multi-modal probability distributions. Furthermore, to enable the end-to-end network training of the non-differentiable $L^1$ risk optimization, we exploited the implicit function theorem, ensuring a fully differentiable network. A comprehensive analysis demonstrates our method's theoretical soundness and superior performance over the state-of-the-art methods across various benchmark datasets, including KITTI 2012, KITTI 2015, ETH3D, SceneFlow, and Middlebury 2014. Matching (statistics) ; Computer science ; Artificial intelligence ; Computer vision ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses
Authors: Leo Shan Wenzhang Zhou Grace Zhao
Source: arXiv (Cornell University) (preprint, 2024-05-28)
Field: Materials Science — Polymers and Plastics

Boosting General Trimap-free Matting in the Real-World Image Image matting aims to obtain an alpha matte that separates foreground objects from the background accurately. Recently, trimap-free matting has been well studied because it requires only the original image without any extra input. Such methods usually extract a rough foreground by itself to take place trimap as further guidance. However, the definition of 'foreground' lacks a unified standard and thus ambiguities arise. Besides, the extracted foreground is sometimes incomplete due to inadequate network design. Most importantly, there is not a large-scale real-world matting dataset, and current trimap-free methods trained with synthetic images suffer from large domain shift problems in practice. In this paper, we define the salient object as foreground, which is consistent with human cognition and annotations of the current matting dataset. Meanwhile, data and technologies in salient object detection can be transferred to matting in a breeze. To obtain a more accurate and complete alpha matte, we propose a network called \textbf{M}ulti-\textbf{F}eature fusion-based \textbf{C}oarse-to-fine Network \textbf{(MFC-Net)}, which fully integrates multiple features for an accurate and complete alpha matte. Furthermore, we introduce image harmony in data composition to bridge the gap between synthetic and real images. More importantly, we establish the largest general matting dataset \textbf{(Real-19k)} in the real world to date. Experiments show that our method is significantly effective on both synthetic and real-world images, and the performance in the real-world dataset is far better than existing matting-free methods. Our code and data will be released soon. Boosting (machine learning) ; Artificial intelligence ; Computer science ; Image (mathematics) ; Computer vision Textile materials and evaluations
Authors: Murat Cihan Sorkun | Elham Nour Ghassemi | Cihan Yatbaz | J. M. V. A. Koelman | Süleyman Er
Source: Artificial Intelligence Chemistry (article, 2024-04-04)
Field: Materials Science — Materials Chemistry

RedPred, a machine learning model for the prediction of redox reaction energies of the aqueous organic electrolytes Aqueous Organic Redox Flow Batteries (AORFBs) are considered as one of the most appealing technologies for large-scale energy storage due to their electroactive organic materials, which are abundant, easy to produce, and recyclable. A prevailing challenge for the redox chemistries applied in AORFBs is to achieve high power and energy density. The chemical design and molecular engineering of the electroactive compounds is an effective approach for the optimization of their physicochemical properties. Among them, the reaction energy of redox couples is often used as a proxy for the measured potentials. In this study, we present RedPred, a machine learning (ML) model that predicts the one-step two-electron two-proton redox reaction energy of redox-active molecule pairs. RedPred comprises an ensemble of Artificial Neural Networks, Random Forests, and Graph Convolutional Networks, trained using the RedDB database, which contains over 15,000 reactant-product pairs for AORFBs. We evaluated RedPred’s performance using six different molecular encoders and five prominent ML algorithms applied in chemical science. The predictive capability of RedPred was tested on both its training chemical space and the chemical space outside its training domain using two separate test datasets. We released a user-friendly web tool with open-source code to promote software sustainability and broad use. Redox ; Aqueous solution ; Electrolyte ; Chemistry ; Chemical engineering ; Computer science ; Inorganic chemistry ; Organic chemistry ; Physical chemistry ; Engineering ; Electrode Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Dario Massa | Daniel Cieśliński | Amirhossein D. Naghdi | Stefanos Papanikolaou
Source: AIP Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Substitutional alloying using crystal graph neural networks Materials discovery, especially for applications that require extreme operating conditions, requires extensive testing that naturally limits the ability to inquire the wealth of possible compositions. Machine Learning (ML) has nowadays a well-established role in facilitating this effort in systematic ways. The increasing amount of available accurate Density Functional Theory (DFT) data represents a solid basis upon which new ML models can be trained and tested. While conventional models rely on static descriptors, generally suitable for a limited class of systems, the flexibility of Graph Neural Networks (GNNs) allows for direct learning representations on graphs, such as the ones formed by crystals. We utilize crystal graph neural networks (CGNNs) known to predict crystal properties with DFT level accuracy through graphs by encoding the atomic (node/vertex), bond (edge), and global state attributes. In this work, we aim at testing the ability of the CGNN MegNet framework in predicting a number of properties of systems previously unseen in the model, which are obtained by adding a substitutional defect to bulk crystals that are included in the training set. We perform DFT validation to assess the accuracy in the prediction of formation energies and structural features (such as elastic moduli). Using CGNNs, one may identify promising paths in alloy discovery. Computer science ; Artificial neural network ; Parameterized complexity ; Crystal (programming language) ; Artificial intelligence ; Flexibility (engineering) ; Density functional theory ; Graph ; Machine learning ; Moduli ; Algorithm ; Graph theory ; Theoretical computer science ; Mathematics ; Computational chemistry ; Chemistry ; Physics ; Combinatorics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Amir Omranpour | Jan Elsner | K. Nikolas Lausch | Jörg Behler
Source: arXiv (Cornell University) (preprint, 2024-11-01)
Field: Materials Science — Materials Chemistry

Machine Learning Potentials for Heterogeneous Catalysis The sustainable production of many bulk chemicals relies on heterogeneous catalysis. The rational design or improvement of the required catalysts critically depends on insights into the underlying mechanisms at the atomic scale. In recent years, substantial progress has been made in applying advanced experimental techniques to complex catalytic reactions in operando, but in order to achieve a comprehensive understanding, additional information from computer simulations is indispensable in many cases. In particular, ab initio molecular dynamics (AIMD) has become an important tool to explicitly address the atomistic level structure, dynamics, and reactivity of interfacial systems, but the high computational costs limit applications to systems consisting of at most a few hundred atoms for simulation times of up to tens of picoseconds. Rapid advances in the development of modern machine learning potentials (MLP) now offer a new approach to bridge this gap, enabling simulations of complex catalytic reactions with ab initio accuracy at a small fraction of the computational costs. In this perspective, we provide an overview of the current state of the art of applying MLPs to systems relevant for heterogeneous catalysis along with a discussion of the prospects for the use of MLPs in catalysis science in the years to come. Catalysis ; Computer science ; Chemistry ; Organic chemistry Machine Learning in Materials Science
Authors: Likun Wang | Na Li | Mengyao Cao | Yun Zhu | Xiewei Xiong | Li Li | Tong Zhu | Hao Pei
Source: Advanced Science (article, 2024-09-19)
Field: Materials Science — Materials Chemistry

Predicting DNA Reactions with a Quantum Chemistry‐Based Deep Learning Model In this study, a deep learning model based on quantum chemistry is introduced to enhance the accuracy and efficiency of predicting DNA reaction parameters. By integrating quantum chemical calculations with self-designed descriptor matrices, the model offers a comprehensive description of energy variations and considers a broad range of relevant factors. To overcome the challenge of limited labeled data, an active learning method is employed. The results demonstrate that this model outperforms existing methods in predicting DNA hybridization free energies and strand displacement rate constants, thus advancing the understanding of DNA molecular interactions, and aiding in the precise design and optimization of DNA-based systems. DNA ; Quantum chemistry ; Chemistry ; Nanotechnology ; Computational biology ; Biophysics ; Materials science ; Biology ; Biochemistry ; Reaction mechanism Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Natalia Kireeva | А. Yu. Tsivadze
Source: Solid State Ionics (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

Using machine learning towards enhancement of electrochemical activity in OER/ORR half-reactions of MXene cathode materials for Li-air batteries Electrochemistry ; Cathode ; Materials science ; Chemical engineering ; Nanotechnology ; Chemistry ; Electrode ; Physical chemistry ; Engineering MXene and MAX Phase Materials ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Abdulrahman Aldossary | Jorge A. Campos-Gonzalez-Angulo | Sergio Pablo‐García | Shi Xuan Leong | Ella Miray Rajaonson | Luca Thiede | Gary Tom | Andrew Z. Wang | Davide Avagliano | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2024-02-16)
Field: Materials Science — Materials Chemistry

In silico chemical experiments in the Age of AI: From quantum chemistry to machine learning and back Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry offers solutions to scalability and accelerating the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. Through a review of existing computational methods, ML models, and their intertwining, this paper also outlines a roadmap for future research, identifying areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science. Computer science ; Chemical space ; In silico ; Scalability ; Artificial intelligence ; Transferability ; Computational model ; Machine learning ; Set (abstract data type) ; Biochemical engineering ; Chemistry ; Drug discovery ; Engineering ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Jieyu Liu | Junze Zhang | Haigang Deng | Shuoao Wang | Xingxing Jiang | Li Wang | Changhong Wang
Source: Chinese Chemical Letters (article, 2024-11-20)
Field: Materials Science — Materials Chemistry

Understanding the activity origin of Pd-anchored single-atom alloy catalysts for NO-to-NH3 conversion by DFT studies and machine learning Catalysis ; Atom (system on chip) ; Alloy ; Density functional theory ; Chemistry ; Crystallography ; Materials science ; Computational chemistry ; Computer science ; Metallurgy ; Organic chemistry ; Embedded system Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Hydrogen Storage and Materials
Authors: Nithin Lalith | Aayush R. Singh | Joseph A. Gauthier
Source: ChemPhysChem (article, 2024-03-22)
Field: Materials Science — Materials Chemistry

The Importance of Reaction Energy in Predicting Chemical Reaction Barriers with Machine Learning Models Improving our fundamental understanding of complex heterocatalytic processes increasingly relies on electronic structure simulations and microkinetic models based on calculated energy differences. In particular, calculation of activation barriers, usually achieved through compute-intensive saddle point search routines, remains a serious bottleneck in understanding trends in catalytic activity for highly branched reaction networks. Although the well-known Brønsted-Evans-Polyani (BEP) scaling - a one-feature linear regression model - has been widely applied in such microkinetic models, they still rely on calculated reaction energies and may not generalize beyond a single facet on a single class of materials, e. g., a terrace sites on transition metals. For highly branched and energetically shallow reaction networks, such as electrochemical CO<sub>2</sub> reduction or wastewater remediation, calculating even reaction energies on many surfaces can become computationally intractable due to the combinatorial explosion of states that must be considered. Here, we investigate the feasibility of activation barrier prediction without knowledge of the reaction energy using linear and nonlinear machine learning (ML) models trained on a new database of over 500 dehydrogenation activation barriers. We also find that inclusion of the reaction energy significantly improves both classes of ML models, but complex nonlinear models can achieve performance similar to the simplest BEP scaling when predicting activation barriers on new systems. Additionally, inclusion of the reaction energy significantly improves generalizability to new systems beyond the training set. Our results suggest that the reaction energy is a critical feature to consider when building models to predict activation barriers, indicating that efforts to reliably predict reaction energies through, e. g., the Open Catalyst Project and others, will be an important route to effective model development for more complex systems. Chemical reaction ; Chemistry ; Computer science ; Computational chemistry ; Nanotechnology ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yufan Chen | Ching Ting Leung | Yong Huang | Jianwei SUN | Hao Chen | Hanyu Gao
Source: Research Square (preprint, 2024-02-09)
Field: Materials Science — Materials Chemistry

MolNexTR: A Generalized Deep Learning Modelfor Molecular Image Recognition Computer science ; Deep learning ; Image (mathematics) ; Artificial intelligence ; Psychology Machine Learning in Materials Science
Authors: Nadeem Muhammad | Mudassir Hussain Tahir | Mohamed Mohamed Soliman | Sumaira Naeem | Anthony M.S. Pembere | Mohamed H. Helal
Source: Materials Today Communications (article, 2024-11-19)
Field: Materials Science — Materials Chemistry

Design of polymers with higher atomization energy with help of machine learning and chemical space generation Materials science ; Polymer ; Space (punctuation) ; Energy (signal processing) ; Chemical space ; Engineering physics ; Process engineering ; Chemical engineering ; Nanotechnology ; Composite material ; Computer science Machine Learning in Materials Science ; Polymer crystallization and properties ; Advanced Polymer Synthesis and Characterization
Authors: Yuheng Wang | Guang Lin | Shengfeng Yang
Source: APL Machine Learning (article, 2024-02-22)
Field: Materials Science — Materials Chemistry

Integrating uncertainty into deep learning models for enhanced prediction of nanocomposite materials’ mechanical properties In this paper, we present a novel deep-learning framework that incorporates quantified uncertainty for predicting the mechanical properties of nanocomposite materials, specifically taking into account their morphology and composition. Due to the intricate microstructures of nanocomposites and their dynamic changes under diverse conditions, traditional methods, such as molecular dynamics simulations, often impose significant computational burdens. Our machine learning models, trained on comprehensive material datasets, provide a lower computational cost alternative, facilitating rapid exploration of design spaces and more reliable predictions. We employ both convolutional neural networks and feedforward neural networks for our predictions, training separate models for yield strength and ultimate tensile strength. Furthermore, we integrate uncertainty quantification into our models, thereby providing confidence intervals for our predictions and making them more reliable. This study paves the way for advancements in predicting the properties of nanocomposite materials and could potentially be expanded to cover a broad spectrum of materials in the future. Nanocomposite ; Computer science ; Feed forward ; Convolutional neural network ; Artificial intelligence ; Ultimate tensile strength ; Deep learning ; Machine learning ; Uncertainty quantification ; Materials science ; Biochemical engineering ; Nanotechnology ; Engineering ; Composite material ; Control engineering Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Probabilistic and Robust Engineering Design
Authors: H. Maeda | Stephen Wu | Rika Marui | Erina Yoshida | Kan Hatakeyama‐Sato | Yuta Nabae | Shiori Nakagawa | Meguya Ryu | Ryohei Ishige | Yoh Noguchi | Yasuhiko Hayashi | Masashi Ishii | Isao Kuwajima | Felix Jiang | Xuan Thang Vu | Sven Ingebrandt | Masatoshi Tokita | Junko Morikawa | Ryo Yoshida | Teruaki Hayakawa
Source: ChemRxiv (preprint, 2024-08-12)
Field: Materials Science — Materials Chemistry

Discovery of liquid crystalline polymers with high thermal conductivity using machine learning It is highly desirable to enhance the heat conduction of polymeric heat-dissipating materials for applications in power electronics, automotive components, light-emitting diodes, and telecommunication equipment. However, the thermal conductivity of polymeric materials is one to three orders of magnitude lower than that of metals and ceramics due to phonon scattering in amorphous regions. Several attempts have been made to overcome this limitation by increasing heat flow along the orientation axis of the self-assembly of liquid crystalline polymers, but the design of liquid crystalline polymers remains largely empirical. In this study, we developed a machine learning model that can predict with over 96% accuracy whether a polymer will form a liquid crystalline state based on its compositional or structural characteristics. By exploring the inverse mapping of this model, we identified a comprehensive set of chemical structures for liquid crystalline polyimides. De novo monomer synthesis and polymerization reactions of six selected polymers were performed, and all synthesized polymers were experimentally confirmed to form liquid crystalline structures with thermal conductivities ranging from 0.722 to 1.26 W m−1 K−1. These are the first liquid crystalline polymers predicted and discovered via machine learning. Materials science ; Polymer ; Thermal conductivity ; Amorphous solid ; Ceramic ; Monomer ; Thermal conduction ; Chemical engineering ; Composite material ; Organic chemistry ; Chemistry Machine Learning in Materials Science ; Thermal properties of materials ; Injection Molding Process and Properties