Article
Chemistry, Multidisciplinary
Ruslan D. Yamaletdinov, Yaroslav A. Nikiforov, Lyubov G. Bulusheva, Alexander V. Okotrub
Summary: This paper introduces a successful approach for generating partially fluorinated graphene structures and identifies general structural patterns and conditions for achievement through data analysis. Additionally, a GenCF code is provided to facilitate further research on fluorinated graphene.
Article
Materials Science, Ceramics
Ruishi Xie, Zhicheng Guo, Xuhai Li, Haifeng Liu, Hongjuan Sun, Baogang Guo, Qinghua Liu, Yuanli Li
Summary: In this study, a simple and effective scheme was designed to fabricate strained BiVO4/ZnS nanostructures, leading to noticeable increases in band gap energies under tensile strain and tunable dual emission behavior. Experimental results also confirmed that lattice strain significantly changes the electron band structure of the nanostructures.
CERAMICS INTERNATIONAL
(2021)
Article
Physics, Multidisciplinary
Yimeng Wang, Jonah Herzog-Arbeitman, G. William Burg, Jihang Zhu, Kenji Watanabe, Takashi Taniguchi, Allan H. MacDonald, B. Andrei Bernevig, Emanuel Tutuc
Summary: The discovery that twisted double bilayer graphene can exhibit both metallic edge transport and insulating bulk properties may be a signature of the predicted topological phase. Further characterization of the edge transport is necessary to confirm this finding.
Article
Nanoscience & Nanotechnology
N. Benlakhouy, A. El Mouhafid, A. Jellal
Summary: The study investigates transport properties through a rectangular potential barrier in AB-stacked bilayer graphene gapped by dielectric layers. The results show different transmission characteristics and influencing factors under different models. Introducing inter-layer bias and considering different band models can affect the intensity and mode of transmission.
PHYSICA E-LOW-DIMENSIONAL SYSTEMS & NANOSTRUCTURES
(2021)
Article
Nanoscience & Nanotechnology
Tariq Ahmad, Kefayat Ullah, Amin Ur Rashid, Salah Uddin
Summary: This study investigates the band structure of ABA trilayer graphene in a one-dimensional superlattice formed by a periodic potential. The results show that additional Dirac points can be generated by altering the potential parameters, which is significant for designing ABA trilayer graphene-based devices.
PHYSICA E-LOW-DIMENSIONAL SYSTEMS & NANOSTRUCTURES
(2023)
Article
Materials Science, Multidisciplinary
S. Azar Oliaei Motlagh, Vadym Apalkov
Summary: The interaction between graphene quantum dots (GQDs) and ultrashort strong optical pulses was studied theoretically. The absorbance of GQDs has a highly nonlinear dependence on the field amplitude, showing different relationships at small and large-field amplitudes. The absorbance also has a maximum value depending on the size of the dot as the field amplitude changes.
Article
Optics
Yu Hao, Liwei Wang, Baohua Zhu, Yimin Zhang, Yuzong Gu
Summary: In this study, the NLO properties of reduced graphene oxide (rGO) were regulated using a functionalizing strategy with quantum dots (QDs) of gold nanospheres and Au@CdS core-shells. The saturation absorption of rGO weakened with the addition of gold nanospheres, switching to reverse saturation absorption with Au@CdS core-shells. The strength of reverse saturation absorption was tunable with the size of Au@CdS core-shells, while the nonlinear susceptibility was enhanced when combined with Au@CdS core-shells compared to complex gold. The regulation effect was explained by proposing a charge transfer mechanism.
Review
Materials Science, Multidisciplinary
Wenjing Miao, Li Wang, Xijiao Mu, Jingang Wang
Summary: Quasi-one-dimensional graphene nanoribbons have atomically accurate tunability of electronic properties, showing novel changes in optical and optoelectronic properties. With strong potential applications in the micro-nano optoelectronics field, GNRs are crucial for the development of microelectronic optoelectronic devices.
JOURNAL OF MATERIALS CHEMISTRY C
(2021)
Article
Nanoscience & Nanotechnology
Wanfu Shao, Dongqing Liu, Taishan Cao, Haifeng Cheng, Jiacai Kuang, Yingjun Deng, Wei Xie
Summary: The novel water-based superhydrophobic coating prepared using PTFE doped with graphene showed excellent comprehensive properties, including good superhydrophobicity and anti-corrosion ability. The coating achieved a water static contact angle of 153 degrees under optimized process conditions, and maintained high contact angles even after multiple wear and water impact tests.
ADVANCED COMPOSITES AND HYBRID MATERIALS
(2021)
Article
Materials Science, Multidisciplinary
Yingchao Liu, Jinlong Ren, Decheng Kong, Guangcun Shan, Kunpeng Dou
Summary: The edge effect is a detrimental factor preventing superlubricity. By simulating graphene nanoflakes, it was found that the edge pinning effect caused by dimerization significantly affects the tribological properties, while the edge contribution to friction is lattice orientation dependent and suppressed in aligned contact. Strain engineering and edge fluorination are potential methods to eliminate undesirable edge effects.
MATERIALS TODAY PHYSICS
(2023)
Article
Chemistry, Physical
Ningning Xuan, Aozhen Xie, Bing Liu, Zhengzong Sun
Summary: Bilayer graphene (BLG) has attracted significant research interest for its tunable physical properties dependent on twisted angles and interlayer interaction. This article focuses on the study of BLG single crystals with representative twisted angles of approximately 0 degrees and approximately 30 degrees, grown by chemical vapor deposition (CVD). The surface potentials of pristine BLG single crystals indicate that the surface potential difference between single layer graphene (SLG) and BLG is lower for approximately 0 degrees compared to approximately 30 degrees. Additionally, reversible tuning of the electrical coupling and properties of BLG is achieved through diazonium salts reaction and nitrogen doping, resulting in a wide range of surface potential tuning from 0 to 50 mV.
Article
Multidisciplinary Sciences
Farzaneh Shayeganfar, Rouzbeh Shahsavari
Summary: The interfacial encoded properties of polymer adlayers adsorbed on surfaces like graphene and silicon dioxide serve as a scaffold for creating new materials and designing molecular devices. By combining electronic structure computation and big data mining, hybrid polymers can be designed based on assembly on substrate, offering unique directions for the fabrication of 1D/2D polymers using only a small number of simple molecular building blocks.
SCIENTIFIC REPORTS
(2021)
Review
Construction & Building Technology
Ruiyu Zhang, Xin Yu, Qiwu Yang, Gan Cui, Zili Li
Summary: Graphene has attracted attention as an anti-corrosion material due to its unique two-dimensional nanostructure and superior physicochemical properties. Research on graphene-based coatings focuses on the structure-property relationship and anti-corrosion mechanisms, using experimental and computational methods to reveal the mechanisms.
CONSTRUCTION AND BUILDING MATERIALS
(2021)
Article
Physics, Multidisciplinary
J. N. Teixeira Rabelo
Summary: This paper calculates the lattice relaxation, change of atomic vibrations, and excess energy near the armchair and zigzag edges of a flat graphene monolayer using the unsymmetrized self-consistent field theory of strongly anharmonic solids in the lowest order of anharmonicity. A comparison is made between these properties in each type of edge.
Article
Chemistry, Multidisciplinary
Hongyue Jing, Hyeonwoo Yeo, Benzheng Lyu, Junga Ryou, Seunghyuk Choi, Jin-Hong Park, Byoung Hun Lee, Yong-Hoon Kim, Sungjoo Lee
Summary: The paper demonstrates the chemical modification of Ti3C2Tx MXene via diazonium covalent chemistry and the subsequent effects on the electrical properties of MXene. The work function of functionalized MXene can be modulated by adjusting the concentration of the diazonium salt solution, with an adjustable range of around 0.6 eV. The controlled modification of surface groups in Ti3C2Tx may imbue Ti3C2Tx with favorable electronic behaviors, showing prospects for electronic field applications.
Article
Chemistry, Multidisciplinary
Yongtao Liu, Kyle P. Kelley, Rama K. Vasudevan, Wanlin Zhu, John Hayden, Jon-Paul Maria, Hiroshi Funakubo, Maxim A. Ziatdinov, Susan Trolier-McKinstry, Sergei Kalinin
Summary: An automated experiment is developed to investigate structural, chemical, and functional behaviors in complex materials and uncover the primary physical mechanisms that control device function. By exploring non-linear electromechanical responses in piezoresponse force microscopy (PFM), it is discovered that different materials exhibit different non-linear behavior patterns, and automated experiments have the potential to distinguish between competing physical mechanisms.
Article
Chemistry, Multidisciplinary
Yongtao Liu, Jonghee Yang, Benjamin J. Lawrie, Kyle P. Kelley, Maxim Ziatdinov, Sergei V. Kalinin, Mahshid Ahmadi
Summary: The increasing photovoltaic efficiency and stability of metal halide perovskites (MHPs) are attributed to the improvement in understanding the microstructure of polycrystalline MHP thin films. A workflow combining conductive atomic force microscopy (AFM) measurement with a machine learning (ML) algorithm was designed to systematically investigate the grain boundaries in MHPs. This approach revealed that the properties of grain boundaries play critical roles in MHP stability.
Article
Chemistry, Physical
Yongtao Liu, Jonghee Yang, Rama K. Vasudevan, Kyle P. Kelley, Maxim Ziatdinov, Sergei Kalinin, Mahshid Ahmadi
Summary: We demonstrate an active machine learning framework for driving an automated scanning probe microscope (SPM) to discover the microstructures responsible for specific aspects of transport behavior in metal halide perovskites (MHPs). This approach allows the microscope to discover the microstructural elements that maximize the onset of conduction, hysteresis, or any other characteristic derived from a set of current-voltage spectra. It provides new opportunities for exploring the origins of materials functionality in complex materials by SPM and can be integrated with other characterization techniques.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2023)
Review
Materials Science, Multidisciplinary
Jonghee Yang, Sergei V. Kalinin, Ekin D. Cubuk, Maxim Ziatdinov, Mahshid Ahmadi
Summary: Low-dimensional hybrid perovskites combine the physical functionalities of inorganic materials and complexity of organic molecules to form self-organized complex structures. These materials offer high-performance optoelectronics and versatile applications, and can be produced cost-effectively.
Article
Chemistry, Multidisciplinary
Muammer Y. Yaman, Sergei V. Kalinin, Kathryn N. Guye, David S. Ginger, Maxim Ziatdinov
Summary: The application of machine learning is demonstrated for rapidly and accurately extracting plasmonic particles cluster geometries from hyperspectral image data using a dual variational autoencoder (dual-VAE). This approach shares information between the latent spaces of two VAEs, one handling particle shape data and the other handling spectral data, while enforcing a common encoding for shape-spectra pairs. The results show that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, allowing for accurate prediction of the geometry of multiparticle assemblies below the diffraction limit using hyperspectral darkfield microscopy in an automated manner.
Article
Chemistry, Multidisciplinary
Yuya Tanaka, Yeana Bae, Fumiya Ogasawara, Keita Suzuki, Shuji Kobayashi, Satoshi Kaneko, Shintaro Fujii, Tomoaki Nishino, Munetaka Akita
Summary: Precise control of molecule-electrode interface is achieved by designing ruthenium acetylide molecular wires with long-legged phosphine ligands, allowing for sterically controlled molecule-electrode interface. The observation of sharpened Raman signals for acetylene stretching in the self-assembled monolayers suggests the formation of uniform SAMs due to steric hindrance. Scanning tunneling microscope break-junction study reveals narrow conductance features, indicating a uniform molecular junction and effective electronic interactions unique to the long-legged derivatives.
ADVANCED MATERIALS INTERFACES
(2023)
Article
Chemistry, Physical
Yongtao Liu, Rama K. K. Vasudevan, Kyle P. Kelley, Hiroshi Funakubo, Maxim Ziatdinov, Sergei V. V. Kalinin
Summary: We developed automated experiment workflows for identifying the best predictive channel in spectroscopic measurements. The approach combines ensembled deep kernel learning for probabilistic predictions and reinforcement learning for channel selection. The implementation in multimodal imaging of piezoresponse force microscopy (PFM) showed that the amplitude is the best predictive channel for polarization-voltage and frequency-voltage hysteresis loop areas. This workflow and code can be applied to other multimodal imaging and local characterization methods.
NPJ COMPUTATIONAL MATERIALS
(2023)
Article
Computer Science, Artificial Intelligence
Yongtao Liu, Anna N. Morozovska, Eugene A. Eliseev, Kyle P. Kelley, Rama Vasudevan, Maxim Ziatdinov, Sergei V. Kalinin
Summary: Using hypothesis-learning-driven automated scanning probe microscopy (SPM), this study investigates the bias-induced transformations in various devices and materials. It is crucial to understand these mechanisms on the nanometer scale with a wide range of control parameters, which is experimentally challenging. The hypothesis-driven SPM autonomously identifies the mechanisms of bias-induced domain switching and reveals the importance of kinetic control.
Article
Computer Science, Artificial Intelligence
Sergei Kalinin, Rama Vasudevan, Yongtao Liu, Ayana Ghosh, Kevin Roccapriore, Maxim Ziatdinov
Summary: Microscopy provides an ideal experimental environment for the development and deployment of active Bayesian and reinforcement learning methods. By utilizing domain-specific deployable algorithms and static datasets, machine learning methods can be applied to microscopy and chemical imaging, accelerating real-world ML applications and scientific progress.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Arpan Biswas, Rama Vasudevan, Maxim Ziatdinov, Sergei Kalinin
Summary: Unsupervised and semi-supervised ML methods like VAE are widely used in physics, chemistry, and materials sciences for disentangling representations and finding latent manifolds in complex experimental data. This study explores a latent Bayesian optimization approach for hyperparameter trajectory optimization in unsupervised and semi-supervised ML, demonstrated by joint-VAE with rotational invariances. The method is applied to finding joint discrete and continuous rotationally invariant representations in the MNIST database and a plasmonic nanoparticles material system.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
S. V. Venkatakrishnan, Chris M. Fancher, Maxim Ziatdinov, Rama Vasudevan, Kyle Saleeby, James Haley, Dunji Yu, Ke An, Alex Plotkowski
Summary: Neutron diffraction is a useful technique for mapping residual strains in dense metal objects. In this paper, the authors propose an object adaptive sampling strategy to measure the significant points first and predict the next most informative positions to measure. They demonstrate the real-time measure-infer-predict workflow on additively manufactured steel parts, leading to faster strain mapping with useful real-time feedback.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Arpan Biswas, Maxim Ziatdinov, Sergei Kalinin
Summary: This paper presents a physics augmented machine learning method that combines variational autoencoders and a physics driven loss function to extract meaningful information from large volumes of imaging data.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, Helia Zandi, Debangshu Mukherjee, Maxim Ziatdinov, Craig Bridges
Summary: This article presents a framework for developing network-level cyber solutions that support remote control of science instruments and data collection. The framework is based on provisioning separate data and control connections and developing software modules using Python wrappers and Pyro server-client codes. The framework is demonstrated through automated measurement transfers and remote control operations in a microscopy use case, and it is currently being refined and applied to science workflows in autonomous chemistry laboratories and smart energy grid simulations.
2023 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING, SMARTCOMP
(2023)
Article
Automation & Control Systems
Sheryl Sanchez, Yongtao Liu, Jonghee Yang, Sergei V. Kalinin, Maxim Ziatdinov, Mahshid Ahmadi
Summary: In recent years, laboratory automation and high-throughput synthesis and characterization have become increasingly important in the research community. To effectively analyze the large datasets and extract system properties, suitable machine learning techniques, such as the variational autoencoder (VAE) approach, are needed. This study explores the binary library of metal halide perovskite microcrystals using low-dimensional latent representations of photoluminescence spectra. The combination of translationally invariant variational autoencoders (tVAEs) and conditional autoencoders (cVAEs) allows for a deeper understanding of the underlying mechanisms within the data.
ADVANCED INTELLIGENT SYSTEMS
(2023)
Review
Multidisciplinary Sciences
Sergei V. Kalinin, Colin Ophus, Paul M. Voyles, Rolf Erni, Demie Kepaptsoglou, Vincenzo Grillo, Andrew R. Lupini, Mark P. Oxley, Eric Schwenker, Maria K. Y. Chan, Joanne Etheridge, Xiang Li, Grace G. D. Han, Maxim Ziatdinov, Naoya Shibata, Stephen J. Pennycook
Summary: This article discusses the combination of STEM technology with machine learning methods, explores STEM imaging methods and data quantification, and their application opportunities in the study of complex materials' chemistry and physics. The article also mentions the infrastructure requirements for the broad application of ML methods and discusses the potential application of ML in experiment automation and novel scanning modes.
NATURE REVIEWS METHODS PRIMERS
(2022)
