Search:
Match:
6 results

Analysis

This paper investigates the vapor-solid-solid growth mechanism of single-walled carbon nanotubes (SWCNTs) using molecular dynamics simulations. It focuses on the role of rhenium nanoparticles as catalysts, exploring carbon transport, edge structure formation, and the influence of temperature on growth. The study provides insights into the kinetics and interface structure of this growth method, which is crucial for controlling the chirality and properties of SWCNTs. The use of a neuroevolution machine-learning interatomic potential allows for microsecond-scale simulations, providing detailed information about the growth process.
Reference

Carbon transport is dominated by facet-dependent surface diffusion, bounding sustainable supply on a 2.0 nm particle to ~44 carbon atoms per μs on the slow (10̄11) facet.

Analysis

This paper introduces a novel application of the NeuroEvolution of Augmenting Topologies (NEAT) algorithm within a deep-learning framework for designing chiral metasurfaces. The key contribution is the automated evolution of neural network architectures, eliminating the need for manual tuning and potentially improving performance and resource efficiency compared to traditional methods. The research focuses on optimizing the design of these metasurfaces, which is a challenging problem in nanophotonics due to the complex relationship between geometry and optical properties. The use of NEAT allows for the creation of task-specific architectures, leading to improved predictive accuracy and generalization. The paper also highlights the potential for transfer learning between simulated and experimental data, which is crucial for practical applications. This work demonstrates a scalable path towards automated photonic design and agentic AI.
Reference

NEAT autonomously evolves both network topology and connection weights, enabling task-specific architectures without manual tuning.

Research#llm📝 BlogAnalyzed: Dec 26, 2025 13:44

NOMA: Neural Networks That Reallocate Themselves During Training

Published:Dec 26, 2025 13:40
1 min read
r/MachineLearning

Analysis

This article discusses NOMA, a novel systems language and compiler designed for neural networks. Its key innovation lies in implementing reverse-mode autodiff as a compiler pass, enabling dynamic network topology changes during training without the overhead of rebuilding model objects. This approach allows for more flexible and efficient training, particularly in scenarios involving dynamic capacity adjustment, pruning, or neuroevolution. The ability to preserve optimizer state across growth events is a significant advantage. The author highlights the contrast with typical Python frameworks like PyTorch and TensorFlow, where such changes require significant code restructuring. The provided example demonstrates the potential for creating more adaptable and efficient neural network training pipelines.
Reference

In NOMA, a network is treated as a managed memory buffer. Growing capacity is a language primitive.

Research#AI📝 BlogAnalyzed: Dec 29, 2025 17:27

Risto Miikkulainen: Neuroevolution and Evolutionary Computation

Published:Apr 19, 2021 05:08
1 min read
Lex Fridman Podcast

Analysis

This article summarizes a podcast episode featuring Risto Miikkulainen, a computer scientist specializing in neuroevolution and evolutionary computation. The episode, hosted by Lex Fridman, covers a wide range of topics related to AI, including the evolution of intelligent life, the potential for AI discoveries, and the workings of evolutionary computation. The discussion also touches upon related areas such as Neuralink, Tesla Autopilot, and the intersection of language and vision. The article provides timestamps for different segments of the podcast, allowing listeners to easily navigate to specific topics of interest. The inclusion of sponsor information and links to the guest's website and podcast platforms further enhances the article's utility.
Reference

The episode covers a wide range of topics related to AI, including the evolution of intelligent life, the potential for AI discoveries, and the workings of evolutionary computation.

Analysis

This article discusses neuroevolution, a method of evolving neural network architectures using genetic algorithms. It features an interview with Kenneth Stanley, a leading researcher in this field. The conversation covers Stanley's work, including the Neuroevolution of Augmenting Topologies (NEAT) paper, HyperNEAT, and novelty search. The article highlights the potential of neuroevolution to create more complex and human-like neural networks, as well as approaches that prioritize novel behaviors over predefined objectives. The discussion also touches upon the relationship between biology and computation, and Stanley's other projects.
Reference

The article doesn't contain a specific quote to extract.

Research#Neural Networks👥 CommunityAnalyzed: Jan 10, 2026 17:28

Interactive Playground for Neural Network Evolution with Backpropagation and NEAT

Published:May 14, 2016 13:28
1 min read
Hacker News

Analysis

The article likely discusses a project that combines neural network evolution techniques (e.g., NEAT) with backpropagation. This can be significant because it explores innovative approaches to designing and training neural networks.
Reference

The article is about a 'Show HN' on Hacker News, indicating a project presentation.