Sparks of Artificial General Intelligence(AGI) in Semiconductor Material Science: Early Explorations into the Next Frontier of Generative AI-Assisted Electron Micrograph Analysis

• URL: http://arxiv.org/abs/2409.12244v1
• Date: Tue, 17 Sep 2024 04:25:27 GMT
• Title: Sparks of Artificial General Intelligence(AGI) in Semiconductor Material Science: Early Explorations into the Next Frontier of Generative AI-Assisted Electron Micrograph Analysis
• Authors: Sakhinana Sagar Srinivas, Geethan Sannidhi, Sreeja Gangasani, Chidaksh Ravuru, Venkataramana Runkana,
• Abstract summary: We introduce a fully automated, end-to-end pipeline that leverages recent advances in Generative AI. Our approach utilizes Large MultiModal Models (LMMs) such as GPT-4V, alongside text-to-image models like DALLE-3. We integrate a GPT-4 guided Visual Question Answering (VQA) method to analyze nanomaterial images, generate synthetic nanomaterial images via DALLE-3, and employ in-context learning with few-shot prompting in GPT-4V for accurate nanomaterial identification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Characterizing materials with electron micrographs poses significant challenges for automated labeling due to the complex nature of nanomaterial structures. To address this, we introduce a fully automated, end-to-end pipeline that leverages recent advances in Generative AI. It is designed for analyzing and understanding the microstructures of semiconductor materials with effectiveness comparable to that of human experts, contributing to the pursuit of Artificial General Intelligence (AGI) in nanomaterial identification. Our approach utilizes Large MultiModal Models (LMMs) such as GPT-4V, alongside text-to-image models like DALLE-3. We integrate a GPT-4 guided Visual Question Answering (VQA) method to analyze nanomaterial images, generate synthetic nanomaterial images via DALLE-3, and employ in-context learning with few-shot prompting in GPT-4V for accurate nanomaterial identification. Our method surpasses traditional techniques by enhancing the precision of nanomaterial identification and optimizing the process for high-throughput screening.

Related papers

• Preliminary Investigations of a Multi-Faceted Robust and Synergistic Approach in Semiconductor Electron Micrograph Analysis: Integrating Vision Transformers with Large Language and Multimodal Models [0.0]
  This study introduces an innovative architecture that leverages the generative capabilities of zero-shot prompting. It fuses knowledge across image based and linguistic insights for accurate nanomaterial category prediction.
  arXiv  Detail & Related papers  (2024-08-24T16:28:00Z)
• EMCNet : Graph-Nets for Electron Micrographs Classification [0.0]
  We propose an effective end-to-end electron micrograph representation learning-based framework for nanomaterial identification. We demonstrate that our framework outperforms the popular baselines on the open-source datasets in nanomaterials-based identification tasks.
  arXiv  Detail & Related papers  (2024-08-21T02:15:26Z)
• Improving Molecular Modeling with Geometric GNNs: an Empirical Study [56.52346265722167]
  This paper focuses on the impact of different canonicalization methods, (2) graph creation strategies, and (3) auxiliary tasks, on performance, scalability and symmetry enforcement. Our findings and insights aim to guide researchers in selecting optimal modeling components for molecular modeling tasks.
  arXiv  Detail & Related papers  (2024-07-11T09:04:12Z)
• Unveiling the Potential of AI for Nanomaterial Morphology Prediction [0.0]
  This study explores the potential of AI to predict the morphology of nanoparticles within the data availability constraints. We first generated a new multi-modal dataset that is double the size of analogous studies.
  arXiv  Detail & Related papers  (2024-05-31T19:16:07Z)
• Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision [76.4345564864002]
  Next-generation multiple input multiple output (MIMO) is expected to be intelligent and scalable. We propose the concept of the generative AI agent, which is capable of generating tailored and specialized contents. We present two compelling case studies that demonstrate the effectiveness of leveraging the generative AI agent for performance analysis.
  arXiv  Detail & Related papers  (2024-04-13T02:39:36Z)
• The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) [121.42924593374127]
  We analyze the latest model, GPT-4V, to deepen the understanding of LMMs. GPT-4V's unprecedented ability in processing arbitrarily interleaved multimodal inputs makes it a powerful multimodal generalist system. GPT-4V's unique capability of understanding visual markers drawn on input images can give rise to new human-computer interaction methods.
  arXiv  Detail & Related papers  (2023-09-29T17:34:51Z)
• Extracting Structured Seed-Mediated Gold Nanorod Growth Procedures from Literature with GPT-3 [52.59930033705221]
  We present a dataset of 11,644 entities extracted from 1,137 papers, resulting in 268 papers with at least one complete seed-mediated gold nanorod growth procedure and outcome for a total of 332 complete procedures. We present a dataset of 11,644 entities extracted from 1,137 papers, resulting in papers with at least one complete seed-mediated gold nanorod growth procedure and outcome for a total of 332 complete procedures.
  arXiv  Detail & Related papers  (2023-04-26T22:21:33Z)
• Automatically Predict Material Properties with Microscopic Image Example Polymer Compatibility [94.40113383292139]
  Computer image recognition with machine learning method can make up the defects of artificial judging. We achieve automatic miscibility recognition utilizing convolution neural network and transfer learning method. The proposed method can be widely applied to the quantitative characterization of the microstructure and properties of various materials.
  arXiv  Detail & Related papers  (2023-03-22T07:51:32Z)
• Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering [0.0]
  High-performance computing has made it possible to test deep learning (DL) models with significant parameters. generative adversarial networks (GANs) have facilitated the generation of chemical compositions of inorganic materials. The use of AI to analyze the results from existing analytical instruments is also discussed.
  arXiv  Detail & Related papers  (2022-09-15T04:21:07Z)
• Functional Nanomaterials Design in the Workflow of Building Machine-Learning Models [0.0]
  Machine-learning (ML) techniques have revolutionized a host of research fields of chemical and materials science. ML provides a more comprehensive insight into combinations with molecules/materials. The key to the advances in nanomaterials discovery is how input fingerprints and output values can be linked quantitatively.
  arXiv  Detail & Related papers  (2021-08-16T05:51:03Z)
• Machine Learning in Nano-Scale Biomedical Engineering [77.75587007080894]
  We review the existing research regarding the use of machine learning in nano-scale biomedical engineering. The main challenges that can be formulated as ML problems are classified into the three main categories. For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations.
  arXiv  Detail & Related papers  (2020-08-05T15:45:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.