jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaogen Yuan | Zhongchao Wei | Qiongxiong Ma | Wen Feng Ding | Jianping Guo
Source: ACS Applied Materials & Interfaces (article, 2024-05-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multitask Learning Deep Neural Networks Enable Embedded Design of Active Metamaterials In this study, we propose and implement a deep neural network framework based on multitask learning aimed at simplifying the forward modeling and inverse design process of photonic devices integrating active metasurfaces. We demonstrate and validate our approach by constructing a continuously tunable bandpass filter that is effective in the midwave infrared region. The key to this filter is the combination of a metasurface and Fabry-Perot (F-P) cavity structure of the tunable phase-change material Ge2Sb2Se4Te (GSST) and the precise control of the crystallinity of the GSST by a silicon-based heater. With the help of a deep learning framework, we are able to independently model the crystallinity and geometric parameters of the filter to maximize the use of GSST tuning for bandpass filtering. Our model discusses the self-attention mechanism and the effect of noise and compares several existing popular algorithms, and the results show that a multitask deep learning strategy can better assist the on-demand reverse design of photonic structures with phase change materials. This opens up new possibilities for personalization and functional extension of optical devices. Materials science ; Metamaterial ; Artificial neural network ; Deep learning ; Nanotechnology ; Artificial intelligence ; Systems engineering ; Computer science ; Optoelectronics ; Engineering Metamaterials and Metasurfaces Applications ; Advanced Memory and Neural Computing ; Polydiacetylene-based materials and applications
Authors: Wei Sun | Pengfei Wen | Sijie Zhu | Pengcheng Zhai
Source: Energies (article, 2024-04-27)
Field: Materials Science — Materials Chemistry

Geometrical Optimization of Segmented Thermoelectric Generators (TEGs) Based on Neural Network and Multi-Objective Genetic Algorithm In this study, a neural network and a multi-objective genetic algorithm were used to optimize the geometric parameters of segmented thermoelectric generators (TEGs) with trapezoidal legs, including the cold end width of thermoelectric (TE) legs (Wc), the ratios of cold-segmented length to the total lengths of the n- and p-legs (Sn,c and Sp,c), and the width ratios of the TE legs between the hot end and the cold end of the n- and p-legs (Kn and Kp). First, a neural network with high prediction accuracy was trained based on 5000 sets of parameters and the corresponding output power values of the TEGs obtained from finite element simulations. Then, based on the trained neural network, the multi-objective genetic algorithm was applied to optimize the geometric parameters of the segmented TEGs with the objectives of maximizing the output power (P) and minimizing the semiconductor volume (V). The optimal geometric parameters for different semiconductor volumes were obtained, and their variations were analyzed. The results indicated that the optimal Sn,c, Sp,c, Kn, and Kp remained almost unchanged when V increased from 52.8 to 216.2 mm3 for different semiconductor volumes. This work provides practical guidance for the design of segmented TEGs with trapezoidal legs. Thermoelectric generator ; Artificial neural network ; Genetic algorithm ; Thermoelectric effect ; Materials science ; Power (physics) ; Semiconductor ; Algorithm ; Maximum power principle ; Optimal design ; Computer science ; Topology (electrical circuits) ; Mathematics ; Physics ; Optoelectronics ; Artificial intelligence ; Machine learning ; Thermodynamics ; Combinatorics Advanced Thermoelectric Materials and Devices ; Environmental Education and Sustainability ; Thermal Radiation and Cooling Technologies
Authors: M. Lal | Akashdeep Singh | Ryan Mzik | Amirmasoud Lanjan | Seshasai Srinivasan
Source: Batteries (article, 2024-01-29)
Field: Materials Science — Materials Chemistry

AI-Based Nano-Scale Material Property Prediction for Li-Ion Batteries In this work, we propose a machine learning (ML)-based technique that can learn interatomic potential parameters for various particle–particle interactions employing quantum mechanics (QM) calculations. This ML model can be used as an alternative for QM calculations for predicting non-bonded interactions in a computationally efficient manner. Using these parameters as input to molecular dynamics simulations, we can predict a diverse range of properties, enabling researchers to design new and novel materials suitable for various applications in the absence of experimental data. We employ our ML-based technique to learn the Buckingham potential, a non-bonded interatomic potential. Subsequently, we utilize these predicted values to compute the densities of four distinct molecules, achieving an accuracy exceeding 93%. This serves as a strong demonstration of the efficacy of our proposed approach. Computer science ; Molecular dynamics ; Range (aeronautics) ; Property (philosophy) ; Particle (ecology) ; Scale (ratio) ; Work (physics) ; Interatomic potential ; Ion ; Statistical physics ; Materials science ; Computational science ; Computational chemistry ; Physics ; Chemistry ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Advancements in Battery Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Yusuf Burak Ozdemir | Oguzhan Orkut Okudur | Mario González | Clément Merckling
Source:  (article, 2024-04-07)
Field: Materials Science — Materials Chemistry

Predictive Modeling of Thin Film Yield Stress Using Machine Learning: A Simulation-Based Approach In this study, an experimental and simulation-based approach is used to evaluate the yield stress of integrated circuit (IC) industry-relevant thin film materials. An artificial neural network (ANN) is trained to predict the yield stress of thin films based on the load-displacement curves obtained from the finite element simulations of thin film nanoindentation (NI). The NI models include a wide range of film thickness values (100-500 nm) as well as elastic and plastic properties for the thin film material. The validity of the yield stress model is assessed through a series of thin film NI experiments and test simulations. The results show that the machine learning approach enables a rapid assessment of the yield stress for thin film materials, and the ANN model demonstrates accurate yield stress predictions with an error rate of 3.59%. Computer science ; Stress (linguistics) ; Machine learning ; Artificial intelligence Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design ; Electron and X-Ray Spectroscopy Techniques
Authors: Van-Quyen Nguyen | Phuoc‐Anh Le | Phi Long Nguyen | Tien-Lam Pham | Thi Viet Bac Phung | Kostya S. Novoselov | Laurent El Ghaoui
Source: Cell Reports Physical Science (article, 2024-07-09)
Field: Materials Science — Materials Chemistry

Learnable features for predicting properties of metal-organic frameworks with deep neural networks Materials science is being rapidly transformed by machine learning tools. This paper introduces a machine learning approach for predicting energy and other derived properties in metal-organic frameworks (MOFs). Using neural networks, our approach generates embedding characteristics for both local atomic structures and the overall MOF system by extracting hidden representations of pairwise interactions among atoms inside MOFs. These networks are trained using total energies derived from density functional theory computations, and they are shared for all paired terms. The model performs better than others in terms of total energy prediction, with a mean absolute error of about 0.09 eV/atom. Furthermore, we demonstrate the transferability of the learned features to accurately predict band gaps. t-Distributed stochastic neighbor embedding is utilized to gain insights into the meaningful patterns within the MOF space, while a K-means clustering model is carried out to detect distinct groups of MOFs. Embedding ; Artificial neural network ; Pairwise comparison ; Computer science ; Artificial intelligence ; Cluster analysis ; Atom (system on chip) ; Computation ; Transferability ; Machine learning ; Pattern recognition (psychology) ; Algorithm Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Jinyoung Jeong | Taehyun Park | Junho Song | Seungpyo Kang | Joonghee Won | Jungim Han | Kyoungmin Min
Source: Journal of Chemical Information and Modeling (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Integrating Data Mining and Natural Language Processing to Construct a Melting Point Database for Organometallic Compounds As semiconductor devices are miniaturized, the importance of atomic layer deposition (ALD) technology is growing. When designing ALD precursors, it is important to consider the melting point, because the precursors should have melting points lower than the process temperature. However, obtaining melting point data is challenging due to experimental sensitivity and high computational costs. As a result, a comprehensive and well-organized database for the melting point of the OMCs has not been fully reported yet. Therefore, in this study, we constructed a database of melting points for 1,845 OMCs, including 58 metal and 6 metalloid elements. The database contains CAS numbers, molecular formulas, and structural information and was constructed through automatic extraction and systematic curation. The melting point information was extracted using two methods: 1) 1,434 materials from 11 chemical vendor databases and 2) 411 materials identified through natural language processing (NLP) techniques with an accuracy of 86.3%, based on 2,096 scientific papers published over the past 29 years. In our database, the OMCs contain up to around 250 atoms and have melting points that range from -170 to 1610 °C. The main source is the Chemsrc database, accounting for 607 materials (32.9%), and Fe is the most common central metal or metalloid element (15.0%), followed by Si (11.6%) and B (6.7%). To validate the utilization of the constructed database, a multimodal neural network model was developed integrating graph-based and feature-based information as descriptors to predict the melting points of the OMCs but moderate performance. We believe the current approach reduces the time and cost associated with hand-operated data collection and processing, contributing to effective screening of potentially promising ALD precursors and providing crucial information for the advancement of the semiconductor industry. Construct (python library) ; Computer science ; Point (geometry) ; Information retrieval ; Natural (archaeology) ; Melting point ; Database ; Chemistry ; Programming language ; Mathematics ; Geography ; Organic chemistry ; Archaeology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: Pan-dong Lin | Jun-feng Nie | Shu-gang Cui | Wen-dong Cui | Lei He | Gui‐yong Xiao | Yu‐peng Lu
Source: Journal of Materials Research and Technology (article, 2024-06-21)
Field: Materials Science — Materials Chemistry

Atomic-scale three-dimensional irradiation-induced defect kinetics models for bcc Fe-based alloys The interaction between irradiation defects and dislocations could lead to macroscopic hardening and embrittlement. In this study, molecular dynamics (MD) and machine learning were conducted to study three-dimensional void- and Cu-cluster-induced kinetics models under interaction with dislocations in body-centered cubic Fe-based alloys. MD Results show that dislocation climb and shear are two types of interaction mechanism based on the defect size. Combined MD results and machine learning, it was found that the dislocation length increased linearly with an increase in the size of the defects after the interaction. In addition, the number of atoms in the Cu-rich cluster and the reduced number of vacancies in the voids had a square relationship with the defect size where 0.85D2 is for Cu-rich cluster and 0.50D2 is for void. Furthermore, atomic-scale three-dimensional irradiation-induced defect kinetics models were developed and incorporated into the crystal plasticity finite element model (CPFEM). We also analyzed the contributions of various defects to the increase in the yield stress during CPFEM simulation. Compared with DBH model, the prediction of MD data-driven CPFEM matches better with the experimental data. Cluster contributed the most to hardening with low obstacle strength due to the higher number density compared with dislocation loops and voids. The loops also contributed to hardening; however, their contribution was smaller than that of the clusters. Although the number density and size of voids were minimized, they may have contributed because of the high obstacle strengths. This work can indeed deepen the understanding of irradiation effect in Fe-based alloys. Materials science ; Void (composites) ; Dislocation ; Hardening (computing) ; Kinetics ; Molecular dynamics ; Cluster (spacecraft) ; Number density ; Crystallography ; Embrittlement ; Metallurgy ; Composite material ; Thermodynamics ; Computational chemistry Fusion materials and technologies ; Nuclear Materials and Properties ; High Temperature Alloys and Creep
Authors: George Mosi | Bernard W. Ikua | Samuel Karanja Kabini | James Wamai Mwangi
Source: Advances in Materials Physics and Chemistry (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Characterization and Modeling of Mechanical Properties of Additively Manufactured Coconut Fiber-Reinforced Polypropylene Composites In the face of the increased global campaign to minimize the emission of greenhouse gases and the need for sustainability in manufacturing, there is a great deal of research focusing on environmentally benign and renewable materials as a substitute for synthetic and petroleum-based products. Natural fiber-reinforced polymeric composites have recently been proposed as a viable alternative to synthetic materials. The current work investigates the suitability of coconut fiber-reinforced polypropylene as a structural material. The coconut fiber-reinforced polypropylene composites were developed. Samples of coconut fiber/polypropylene (PP) composites were prepared using Fused Filament Fabrication (FFF). Tests were then conducted on the mechanical properties of the composites for different proportions of coconut fibers. The results obtained indicate that the composites loaded with 2 wt% exhibited the highest tensile and flexural strength, while the ones loaded with 3 wt% had the highest compression strength. The ultimate tensile and flexural strength at 2 wt% were determined to be 34.13 MPa and 70.47 MPa respectively. The compression strength at 3 wt% was found to be 37.88 MPa. Compared to pure polypropylene, the addition of coconut fibers increased the tensile, flexural, and compression strength of the composite. In the study, an artificial neural network model was proposed to predict the mechanical properties of polymeric composites based on the proportion of fibers. The model was found to predict data with high accuracy. Materials science ; Composite material ; Polypropylene ; Fiber ; Characterization (materials science) Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Mechanical Engineering and Vibrations Research
Authors: Shingo Urata | Noriyoshi Kayaba
Source: The Journal of Chemical Physics (article, 2024-10-01)
Field: Materials Science — Ceramics and Composites

Li diffusion in oxygen–chlorine mixed anion borosilicate glasses using a machine-learning simulation Lithium-ion conducting borate glasses are suitable for solid-state batteries as an interfacial material between a crystalline electrolyte and an electrode, thanks to their superior formability. Chlorine has been known to improve the electron conductivity of borate glasses as a secondary anion. To examine the impact of chlorine on lithium dynamics, molecular dynamics (MD) simulations were performed with a machine-learning interatomic potential (MLIP). The accuracy of the MLIP in modeling chlorine-doped lithium borate (LBCl) and borosilicate (LBSCl) glasses was verified by comparing with available experimental data on density, neutron diffraction S(q), and glass transition temperatures (Tg). While the MLIP-MD simulations underestimated the density when an isobaric-isothermal (NPT) ensemble was used, the glass models relaxed using the NPT ensemble after a melt-quench simulation employing a canonical (NVT) ensemble possessed reasonable density. The LBCl and LBSCl glass models exhibited increased lithium ion diffusion, and the ions were found to travel longer distances with an increase in the chlorine content. According to the structural analyses, it was observed that chlorine ions primarily interacted with lithium ions rather than the network formers. Consequently, lithium ions that interacted with a higher amount of chlorine showed a moderate increase in mobility. In summary, the MLIP demonstrated reasonable accuracy in modeling chlorine-containing borate glasses and enabled the investigation of the effect of chlorine on electron conductivity. In contrast, the first sharp diffraction peaks in S(q) deviated from the experimental diffractions, suggesting that additional efforts are required to accurately model the middle-range structure of the glasses. Chlorine ; Borosilicate glass ; Ion ; Lithium (medication) ; Lithium borate ; Boron ; Materials science ; Conductivity ; Neutron diffraction ; Chemistry ; Analytical Chemistry (journal) ; Borate glass ; Physical chemistry ; Composite material ; Crystal structure ; Crystallography ; Metallurgy Glass properties and applications ; Advanced Battery Materials and Technologies ; Luminescence Properties of Advanced Materials
Authors: Linfeng Yu | Kexin Dong | Qi Yang | Yi Zhang | Xiong Zheng | Huimin Wang | Zhenzhen Qin | Guangzhao Qin
Source: Applied Physics Letters (article, 2024-11-18)
Field: Materials Science — Materials Chemistry

Revealing the correlation between asymmetric structure and low thermal conductivity in Janus-graphene via machine learning force constant potential Understanding the fundamental link between structure and functionalization is crucial for designing and optimizing functional materials, since different structural configurations could trigger materials to demonstrate diverse physical and chemical properties. However, the correlation between crystal structure and thermal conductivity (κ) remains unclear. In this study, taking two-dimensional (2D) carbon allotropes Janus-graphene and graphene as study cases, we utilize phonon Boltzmann transport equation combined with machine learning potential to thoroughly investigate the complex folding structure of pure sp2 hybridized Janus-graphene from the perspective of crystal structure, phonon modal resolved thermal transport, and atomic interactions, with the goal of identifying the underlying relationship between 2D geometry and κ. The results reveal that the folded structure in Janus-graphene causes strong symmetry breaking, significantly reduces phonon group velocities, increases phonon–phonon scattering, and ultimately leads to low κ. These findings enhance our understanding of how atomic structure folding affects thermal transport and the relationship between structure and functionalization. Janus ; Graphene ; Constant (computer programming) ; Thermal conductivity ; Materials science ; Condensed matter physics ; Conductivity ; Nanotechnology ; Physics ; Composite material ; Computer science ; Quantum mechanics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Hoon Jeong Lee | Arlene Chiu | Yida Lin | Sreyas Chintapalli | Serene Kamal | Eric Ji | Susanna M. Thon
Source: Advanced Intelligent Systems (article, 2024-09-09)
Field: Materials Science — Materials Chemistry

Predicting PbS Colloidal Quantum Dot Solar Cell Parameters Using Neural Networks Trained on Experimental Data Recent advances in machine learning (ML) have enabled predictive programs for photovoltaic characterization, optimization, and materials discovery. Despite these advances, the standard photovoltaic materials development workflow still involves manually performing multiple characterization techniques on every new device, requiring significant time and expenditures. One barrier to ML implementation is that most models reported to date are trained on computer simulated data, due to the difficulty in experimentally collecting the massive data sets needed for model training, limiting the ability to assess the limitations and validity of these methods, as well as to access new potential physical mechanisms absent in simulations. Herein, several neural networks trained on experimental data from PbS colloidal quantum dot thin‐film solar cells are introduced. These models predict multiple, complex materials properties, including carrier mobility, relative photoluminescence intensity, and electronic trap‐state density, from a single, simple measurement: illuminated current–voltage curves. The measurement system considers the spatial distribution of the materials parameters to gather and predict large amounts of data by treating an inhomogeneous device as a series of thousands of micro‐devices, a novel feature compared to existing solutions. This model can be extended to other materials and devices, accelerating development times for new optoelectronic technologies. Quantum dot ; Solar cell ; Artificial neural network ; Materials science ; Colloid ; Nanotechnology ; Optoelectronics ; Computer science ; Artificial intelligence ; Engineering ; Chemical engineering Machine Learning in Materials Science
Authors: Conghua Qi | Chengmeng Wang | Dongmei Fu | Lizhen Shao | Ke Zhou | Zhiyi Zhao
Source: Computational Materials Science (article, 2024-12-27)
Field: Materials Science — Metals and Alloys

A hybrid knowledge-guided and data-driven method for predicting low-alloy steels performance Alloy ; Materials science ; Computer science ; Metallurgy Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Non-Destructive Testing Techniques
Authors: Bryan Owens‐Baird | Volodymyr Gvozdetskyi | Arka Sarkar | Balaranjan Selvaratnam | Arthur Mar | Kirill Kovnir
Source: Chemistry of Materials (article, 2024-06-09)
Field: Materials Science — Materials Chemistry

Discovery of Ternary Antimonides A–Al–Sb (A = Rb or Cs) with Desired Structural Motifs Guided by Machine Learning Specific structural motifs in inorganic solids are often related to their targeted physical properties. For many classes of solids, such as Zintl phases and polar intermetallics, the crystal structures are diverse and not easy to predict. Various antimonides that are potential thermoelectric materials were proposed to be synthesizable on the basis of their estimated formation energies. Their structures were broadly classified as clathrate, channel, layered, or network through a machine learning model trained on existing ternary phases and features based on elemental properties using the sure independence screening and sparsifying operator algorithm. Through experimental validation, three new ternary antimonides were synthesized and confirmed to form layered structures: tetragonal RbAlSb2 and CsAlSb2, which are isopointal but not isotypic to LiBSi2; and monoclinic Rb2Al2Sb3, which adopts the Na2Al2Sb3-type structure. Reinvestigation of the related compound Cs2In2Sb3 revealed a low thermal conductivity and p-type semiconducting behavior. Ternary operation ; Tetragonal crystal system ; Intermetallic ; Monoclinic crystal system ; Materials science ; Crystal structure ; Thermoelectric effect ; Crystal structure prediction ; Crystallography ; Chemistry ; Computer science ; Thermodynamics ; Physics ; Metallurgy Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films