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SCIAGENTS: AUTOMATING SCIENTIFIC DISCOVERY THROUGH MULTI-AGENT INTELLIGENT GRAPH REASONING

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arXiv 2024年

作者： Ghafarollahi, Alireza Buehler, Markus J. Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States Center for Computational Science and Engineering Schwarzman College of Computing Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States

A key challenge in artificial intelligence is the creation of systems capable of autonomously advancing scientific understanding by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data. In this work, we present SciAgents, an approach that leverages three core concepts: (1) the use of large-scale ontological knowledge graphs to organize and interconnect diverse scientific concepts, (2) a suite of large language models (LLMs) and data retrieval tools, and (3) multi-agent systems with in-situ learning capabilities. Applied to biologically inspired materials, SciAgents reveals hidden interdisciplinary relationships that were previously considered unrelated, achieving a scale, precision, and exploratory power that surpasses traditional human-driven research methods. The framework autonomously generates and refines research hypotheses, elucidating underlying mechanisms, design principles, and unexpected material properties. By integrating these capabilities in a modular fashion, the intelligent system yields material discoveries, critique and improve existing hypotheses, retrieve up-to-date data about existing research, and highlights their strengths and limitations. Our case studies demonstrate scalable capabilities to combine generative AI, ontological representations, and multi-agent modeling, harnessing a ‘swarm of intelligence’ similar to biological systems. This provides new avenues for materials discovery and accelerates the development of advanced materials by unlocking Nature’s design principles. © 2024, CC BY-NC-ND.

关键词： Ontology

AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence

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arXiv 2024年

The design of new alloys is a multi-scale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning (ML) can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges. Here, we overcome these limitations by leveraging the distinct capabilities of multiple AI agents that collaborate autonomously within a dynamic environment to solve complex materials design tasks. The proposed physics-aware generative AI platform, AtomAgents, synergizes the intelligence of large language models (LLM) the dynamic collaboration among AI agents with expertise in various domains, including knowledge retrieval, multi-modal data integration, physics-based simulations, and comprehensive results analysis across modalities that includes numerical data and images of physical simulation results. The concerted effort of the multi-agent system allows for addressing complex materials design problems, as demonstrated by examples that include autonomously designing metallic alloys with enhanced properties compared to their pure counterparts. Our results enable accurate prediction of key characteristics across alloys and highlight the crucial role of solid solution alloying to steer the development of advanced metallic alloys. Our framework enhances the efficiency of complex multi-objective design tasks and opens new avenues in fields such as biomedical materials engineering, renewable energy, and environmental sustainability. © 2024, CC BY-NC-ND.

关键词： Multi agent systems

Attention Based Generative Adversarial Networks to Augment Image Data for Rapid Training

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Attention Based Generative Adversarial Networks to Augment I...

International Conference on Parallel Distributed and Grid computing (PDGC)

作者： Vijayakumar Adaickalam Sadagopan Selvarasu Gowthul Alam M A Suresh Kumar S Annamalai Sangeetha N School of Computer Science and Engineering and Information Presidency University Bangalore Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai School of Computer Science and Engineering JAIN (Deemed-to-be University) Bangalore Department of AI & ML City Engineering College Bangalore

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In this paper, we develop an Attention based Generative Adversarial Networks (AGAN) to augment image data for the purpose of robust training for efficient processing of the hyper spectral imaging. The AGAN model enables generation of images from the sample images that helps in training the classifier and in this study a fundamental classifier namely a convolutional neural network is used. A robust training is conducted to test the accuracy of detecting the instances effectively using the dataset. The simulation shows that the proposed AGAN-CNN attains improved accuracy after robust training than the existing methods.

关键词： Training Accuracy Training data Imaging Production Computer architecture Generative adversarial networks Grid computing Convolutional neural networks Hyperspectral imaging

Machine learning for materials science: Barriers to broader adoption

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Matter 2023年第5期6卷 1320-1323页

作者： Boyce, Brad Dingreville, Remi Desai, Saaketh Walker, Elise Shilt, Troy Bassett, Kimberly L. Wixom, Ryan R. Stebner, Aaron P. Arroyave, Raymundo Hattrick-Simpers, Jason Warren, James A. Center for Integrated Nanotechnologies Sandia National Laboratories AlbuquerqueNM87123 United States Center for Computing Research Sandia National Laboratories AlbuquerqueNM87123 United States Woodruff School of Mechanical Engineering School of Materials Science and Engineering Georgia Institute of Technology AtlantaGA30312 United States Department of Materials Science and Engineering. Texas A&M University College StationTX77843 United States Materials Science & Engineering University of Toronto TorontoON M5S 3E4 Canada National Institute of Standards and Technology GaithersburgMD20899 United States

Machine learning is on a bit of a tear right now, with advances that are infiltrating nearly every aspect of our lives. In the domain of materials science, this wave seems to be growing into a tsunami. Yet, there are still real hurdles that we face to maximize its benefit. This Matter of Opinion, crafted as a result of a workshop hosted by researchers at Sandia National Laboratories and attended by a cadre of luminaries, briefly summarizes our perspective on these barriers. By recognizing these problems in a community forum, we can share the burden of their resolution together with a common purpose and coordinated effort. © 2023 Elsevier Inc.

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Rapid and Automated Alloy Design with Graph Neural Network-Powered LLM-Driven Multi-Agent Systems

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arXiv 2024年

A multi-agent AI model is used to automate the discovery of new metallic alloys, integrating multimodal data and external knowledge including insights from physics via atomistic simulations. Our multi-agent system features three key components: (a) a suite of LLMs responsible for tasks such as reasoning and planning, (b) a group of AI agents with distinct roles and expertise that dynamically collaborate, and (c) a newly developed graph neural network (GNN) model for rapid retrieval of key physical properties. A set of LLM-driven AI agents collaborate to automate the exploration of the vast design space of MPEAs, guided by predictions from the GNN. We focus on the NbMoTa family of body-centered cubic (bcc) alloys, modeled using an ML-based interatomic potential, and target two key properties: the Peierls barrier and solute/screw dislocation interaction energy. Our GNN model accurately predicts these atomic-scale properties, providing a faster alternative to costly brute-force calculations and reducing the computational burden on multi-agent systems for physics retrieval. This AI system revolutionizes materials discovery by reducing reliance on human expertise and overcoming the limitations of direct all-atom simulations. By synergizing the predictive power of GNNs with the dynamic collaboration of LLM-based agents, the system autonomously navigates vast alloy design spaces, identifying trends in atomic-scale material properties and predicting macro-scale mechanical strength, as demonstrated by several computational experiments. This approach accelerates the discovery of advanced alloys and holds promise for broader applications in other complex systems, marking a significant step forward in automated materials design. © 2024, CC BY-NC-ND.

关键词： Graph neural networks

How to Get Investment Grade Rating in the Age of Explainable Ai?

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SSRN

SSRN 2022年

作者： Makwana, Ravi Bhatt, Dhruvil Delwadia, Kirtan Shah, Agam Chaudhury, Bhaskar Group in Computational Science and HPC DA-IICT Gandhinagar India Financial Services Innovation Lab Scheller College of Business Georgia Institute of Technology AtlantaGA United States School of Computational Science & Engineering College of Computing Georgia Institute of Technology AtlantaGA United States

Corporate credit ratings, issued by specialist agencies, provide an assessment about the credit worthiness of a company and acts as a pivotal financial indication to potential investors. It helps provide investors with a concrete idea about the risk associated with the company’s credit investment returns. Every company aims to attain a good credit rating for seeking more investment and lower debt interest rates. Almost all credit rating agencies have a unique rating scale, broadly divided into either investment class or junk class. Since credit rating agencies scrutinize numerous factors to assign rating, it is difficult for a company to construct a simple and comprehensive set of rules for improvising their credit rating. In this paper, we use explainable AI in the form of decision trees to establish an empirical rule on financial ratios to achieve investment class rating. Further, we explore the temporal aspect of the problem by finding the ideal time window for training data. Currently, limited amount of data is openly available for conducting temporal analysis, and hence a primary focus has been laid on creating a large and quality curated dataset. We show that our proposed empirical rule yields a significantly high precision value, offering a valuable and feasible path that companies can follow to gain a better credit rating. © 2022, The Authors. All rights reserved.

关键词： Decision trees

PROTAGENTS: PROTEIN DISCOVERY VIA LARGE LANGUAGE MODEL MULTI-AGENT COLLABORATIONS COMBINING PHYSICS AND MACHINE LEARNING

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arXiv 2024年

Designing de novo proteins beyond those found in nature holds significant promise for advancements in both scientific and engineering applications. Current methodologies for protein design often rely on AI-based models, such as surrogate models that address end-to-end problems by linking protein structure to material properties or vice versa. However, these models frequently focus on specific material objectives or structural properties, limiting their flexibility when incorporating out-of-domain knowledge into the design process or comprehensive data analysis is required. In this study, we introduce ProtAgents, a platform for de novo protein design based on Large Language Models (LLMs), where multiple AI agents with distinct capabilities collaboratively address complex tasks within a dynamic environment. The versatility in agent development allows for expertise in diverse domains, including knowledge retrieval, protein structure analysis, physics-based simulations, and results analysis. The dynamic collaboration between agents, empowered by LLMs, provides a versatile approach to tackling protein design and analysis problems, as demonstrated through diverse examples in this study. The problems of interest encompass designing new proteins, analyzing protein structures and obtaining new first-principles data – natural vibrational frequencies – via physics simulations. The concerted effort of the system allows for powerful automated and synergistic design of de novo proteins with targeted mechanical properties. The flexibility in designing the agents, on one hand, and their capacity in autonomous collaboration through the dynamic LLM-based multi-agent environment on the other hand, unleashes great potentials of LLMs in addressing multi-objective materials problems and opens up new avenues for autonomous materials discovery and design. © 2024, CC BY-NC-ND.

关键词： Proteins

Multi-Verse Optimization for Node Localization in Wireless Sensor Networks

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Multi-Verse Optimization for Node Localization in Wireless S...

Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： B. Pitchaimanickam P. Muthuvel R. Rajasekar Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu Department of ECE Saveetha Engineering College Thandalam Chennai Department of CSE School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

Wireless Sensor Networks (WSN) are focused on increasing attention to develop their potential applications in healthcare, agriculture, object tracking, and energy management. Sensor nodes are placed in random locations. The information was gathered from the unknown positions of the sensor nodes, and this information may be useless for the applications. The accurate position of the node is required for more sensitive applications of WSN. This work proposes multi-verse optimization as a method for locating unknown nodes. Particle swarm optimization and firefly algorithms are contrasted with the suggested method. The experimental findings demonstrate that, compared to alternative algorithms, the suggested technique yields superior outcomes.

关键词： Location awareness Wireless sensor networks Accuracy Medical services Agriculture Information and communication technology Object tracking Particle swarm optimization Energy management Optimization

Editorial:Special Topic on Federated Learning for IoT and Edge computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.