Silicon phase transitions in nanoindentation: Advanced molecular dynamics simulations with machine learning phase recognition

Ge, Guojia; Rovaris, Fabrizio; Lanzoni, Daniele; Barbisan, Luca; Tang, Xiaobin; Miglio, Leo; Marzegalli, Anna; Scalise, Emilio; Montalenti, Francesco

doi:10.1016/j.actamat.2023.119465

Cited by 11 publications

(2 citation statements)

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“…Machine learning (ML) is becoming a critical tool in various fields of nanomaterials research, such as nanoindentation [ 317 , 318 , 319 , 320 , 321 , 322 , 323 , 324 , 325 , 326 , 327 , 328 ], nanorobotics [ 329 , 330 , 331 , 332 , 333 , 334 ], and nanosensor [ 335 , 336 , 337 , 338 , 339 , 340 ] development. Its ability to analyze and interpret complex patterns from large datasets is particularly beneficial in advancing areas like nanostructured materials analysis, nanoscale manufacturing processes, and the development of nanotechnology applications in medicine and environmental monitoring [ 327 , 341 , 342 , 343 , 344 , 345 , 346 , 347 , 348 , 349 , 350 , 351 , 352 , 353 , 354 , 355 , 356 , 357 , 358 , 359 , 360 , 361 , 362 , 363 , 364 , …”

Section: Introductionmentioning

confidence: 99%

“…Machine learning (ML) is becoming a critical tool in various fields of nanomaterials research, such as nanoindentation [317][318][319][320][321][322][323][324][325][326][327][328], nanorobotics [329][330][331][332][333][334], and nanosensor [335][336][337][338][339][340] development. Its ability to analyze and interpret complex patterns from large datasets is particularly beneficial in advancing areas like nanostructured materials analysis, nanoscale manufacturing processes, and the development of nanotechnology applications in medicine and environmental monitoring [327,. Employing unsupervised ML techniques on databases like Scopus, Web of Science, Scielo, and Google Scholar can substantially contribute to nanomaterials research [354,.…”

Section: Introductionmentioning

confidence: 99%

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A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

Gomes Souza,

Bhansali,

Pal

et al. 2024

Materials

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From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment analysis of nanocomposite literature, distinguishing itself from existing reviews through its unique computational methodology. Developed by our research group, this novel approach systematically investigates the evolution of nanocomposites, focusing on microstructural characterization, electrical properties, and mechanical behaviors. By deploying advanced Boolean search strategies within the Scopus database, we achieve a meticulous extraction and in-depth exploration of thematic content, a methodological advancement in the field. Our analysis uniquely identifies critical trends and insights concerning nanocomposite microstructure, electrical attributes, and mechanical performance. The paper goes beyond traditional textual analytics and bibliometric evaluation, offering new interpretations of data and highlighting significant collaborative efforts and influential studies within the nanocomposite domain. Our findings uncover the evolution of research language, thematic shifts, and global contributions, providing a distinct and comprehensive view of the dynamic evolution of nanocomposite research. A critical component of this study is the “State-of-the-Art and Gaps Extracted from Results and Discussions” section, which delves into the latest advancements in nanocomposite research. This section details various nanocomposite types and their properties and introduces novel interpretations of their applications, especially in nanocomposite films. By tracing historical progress and identifying emerging trends, this analysis emphasizes the significance of collaboration and influential studies in molding the field. Moreover, the “Literature Review Guided by Artificial Intelligence” section showcases an innovative AI-guided approach to nanocomposite research, a first in this domain. Focusing on articles from 2023, selected based on citation frequency, this method offers a new perspective on the interplay between nanocomposites and their electrical properties. It highlights the composition, structure, and functionality of various systems, integrating recent findings for a comprehensive overview of current knowledge. The sentiment analysis, with an average score of 0.638771, reflects a positive trend in academic discourse and an increasing recognition of the potential of nanocomposites. Our bibliometric analysis, another methodological novelty, maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While acknowledging its limitations, this study exemplifies the indispensable role of our innovative computational tools in synthesizing and understanding the extensive body of nanocomposite literature. This work not only elucidates prevailing trends but also contributes a unique perspective and novel insights, enhancing our understanding of the nanocomposite research field.

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