arXiv subjects

Daniel Palmer

Publications and source records attributed to Daniel Palmer.

7 recordsLinked to original sources

arXiv2025

Multi-Agent Coordination in Autonomous Vehicle Routing: A Simulation-Based Study of Communication, Memory, and Routing Loops

Multi-agent coordination is critical for next-generation autonomous vehicle (AV) systems, yet naive implementations of communication-based rerouting can lead to catastrophic performance degradation. This study investigates a fundamental problem in decentralized multi-agent navigation: routing loops, where vehicles without persistent obstacle memory become trapped in cycles of inefficient path recalculation. Through systematic simulation experiments involving 72 unique configurations across varying vehicle densities (15, 35, 55 vehicles) and obstacle frequencies (6, 20 obstacles), we demonstrate that memory-less reactive rerouting increases average travel time by up to 682% compared to baseline conditions. To address this, we introduce Object Memory Management (OMM), a lightweight mechanism enabling agents to retain and share knowledge of previously encountered obstacles. OMM operates by maintaining a distributed blacklist of blocked nodes, which each agent consults during Dijkstra-based path recalculation, effectively preventing redundant routing attempts. Our results show that OMM-enabled coordination reduces average travel time by 75.7% and wait time by 88% compared to memory-less systems, while requiring only 1.67 route recalculations per vehicle versus 9.83 in memory-less scenarios. This work provides empirical evidence that persistent, shared memory is not merely beneficial but essential for robust multi-agent coordination in dynamic environments. The findings have implications beyond autonomous vehicles, informing the design of decentralized systems in robotics, network routing, and distributed AI. We provide a comprehensive experimental analysis, including detailed scenario breakdowns, scalability assessments, and visual documentation of the routing loop phenomenon, demonstrating OMM's critical role in preventing detrimental feedback cycles in cooperative multi-agent systems.

cs.MA↗

arXiv2025

Interatomic potential development for topological insulator Bi1-xSbx and its dislocation by force-following active learning

We introduce a force following active learning algorithm that integrates density functional theory DFT with the Gaussian Approximation Potential GAP framework to develop a robust interatomic potential IP for a dislocation in a topological insulator Bi1xSbx. Starting from an initial potential IP0 trained on unit cell data from strained Bi Sb binaries our active learning approach iteratively refines the IP during a structural relaxation. In each cycle if the force error uncertainty of any atom near the dislocation core exceeds a threshold value the IPi is efficiently retrained IPi to IPi1 by incorporating DFT computed forces and energies of atoms near the high uncertainty atom. This strategy ensures that the relaxation process maintains a low force error until full convergence is achieved. Consequently the final IP here IP5 has two capabilities 1 it reproduces the relaxation pathway observed during the active learning process unlike the initial IP0 which lacks prior dislocation core knowledge and 2 it captures the lattice and elastic properties of Bi Sb binaries across a range of Sb concentrations. We also evaluate dislocation properties Peierls stresses and dislocation generation by compression to assess the performance of the trained potential IP5.

cond-mat.mtrl-sci↗

arXiv2025

Graph Neural Network for Unified Electronic and Interatomic Potentials: Strain-tunable Electronic Structures in 2D Materials

We introduce UEIPNet, an equivariant graph neural network designed to predict both interatomic potentials and tight-binding (TB) Hamiltonians for an atomic structure. The UEIPNet is trained using density functional theory calculations followed by Wannier projection to predict energies and forces as node-level targets and Wannier-projected TB matrices as edge-level targets. This enables physically consistent modeling of coupled mechanical electronic responses with near-DFT accuracy. Trained on bilayer graphene and monolayer MoS2 DFT data, UEIPNet captures key deformation-electronic effects: in twisted bilayer graphene, it reveals how interlayer spacing, in-plane strain, and out-of-plane corrugation drive isolated flat-band formation, and further shows that modulating substrate interaction strength can generate flat bands even away from the magic angle. For monolayer MoS2, the UEIPNet accurately reproduces phonon dispersions, strain-dependent band-gap evolution, and local density of states modulations under non-uniform strain. The UEIPNet offers a generalized, efficient, and scalable framework for studying deformation-electronic coupling in large-scale atomistic systems, bridging classical atomistic simulations and electronic-structure calculations.

cond-mat.mtrl-sci↗

arXiv2025

Equity, Emissions and the Inflation Reduction Act

Preowned vehicles are disproportionally purchased by low-income households, a group that has long been unable to purchase electric vehicles. Yet, low-income households would disproportionally benefit from EV adoption given the operating costs savings offered by electrification. To help realize this benefit, provisions of the 2022 Inflation Reduction Act offer preowned EV purchasing incentives. How effective might these efforts be. Leveraging data from the United States Census Bureau, the National Household Travel Survey, and the Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model, we address this question. Our findings are fourfold. First, we demonstrate that although low-income households are more likely to benefit from preowned EV purchasing incentives offered by IRA, up to 8.4 million low-income households may be ineligible owing to heterogeneity in vehicle procurement pathways. Second, we show that program ineligibility risks preventing up to 113.9 million tons in lifecycle emissions reduction benefits from being realized. Third, we find that procurement pathways depend on vehicle price. More expensive preowned vehicles are purchased directly from commercial dealers, while less expensive preowned vehicles are purchased from private sellers. These procurement pathways matter because qualification for IRA incentives necessitates purchasing solely from commercial dealers. Fourth, we demonstrate that while incentives motivating preowned vehicle purchases from commercial dealers may be effective if the vehicle is expensive, this effectiveness diminishes at higher price points. The implications of our findings on decarbonization efforts and energy policy are discussed.

econ.GN↗

arXiv2025

Graphene-hBN interlayer interactions from quantum Monte Carlo

The interaction between graphene and hexagonal boron nitride (hBN) plays a pivotal role in determining the electronic and structural properties of graphene-based devices. In this work, we employ quantum Monte Carlo (QMC) to study the interlayer interactions and stacking-fault energy (SFE) between graphene and hBN. We generated QMC energies for several rigid bilayer stacking configurations and fitted these data to the Kolmogorov-Crespi type interlayer potential (ILP) model. Our QMC-derived potential offers a more reliable alternative to conventional density functional theory methods, which are prone to errors in predicting properties in van der Waals materials. This study enables highly accurate predictions of structural and electronic properties in graphene hBN heterostructures. The resulting ILP-QMC potential is made available for further use in simulating complex systems, such as twisted bilayer graphene (TBG) on hBN.

cond-mat.mtrl-sci↗

arXiv2024

Validation Requirements for AI-based Intervention-Evaluation in Aging and Longevity Research and Practice

The field of aging and longevity research is overwhelmed by vast amounts of data, calling for the use of Artificial Intelligence (AI), including Large Language Models (LLMs), for the evaluation of geroprotective interventions. Such evaluations should be correct, useful, comprehensive, explainable, and they should consider causality, interdisciplinarity, adherence to standards, longitudinal data and known aging biology. In particular, comprehensive analyses should go beyond comparing data based on canonical biomedical databases, suggesting the use of AI to interpret changes in biomarkers and outcomes. Our requirements motivate the use of LLMs with Knowledge Graphs and dedicated workflows employing, e.g., Retrieval-Augmented Generation. While naive trust in the responses of AI tools can cause harm, adding our requirements to LLM queries can improve response quality, calling for benchmarking efforts and justifying the informed use of LLMs for advice on longevity interventions.

cs.HC↗

arXiv2024

Electric vehicle pricing and battery costs: A misaligned assumption

Although electric vehicles (EVs) are a climate friendly alternative to internal combustion engine vehicles (ICEVs), EV adoption is challenged by higher up-front procurement prices. Existing discourse attributes this price differential to high battery costs and reasons that lowering these costs will reduce EV upfront price differentials. However, other factors beyond battery price may influence prices. Leveraging data for over 400 EV models and trims sold in the United Sates between 2011-2023, we scrutinize these factors. We find that contrary to existing discourse, EV MSRP has increased over time despite declining EV battery costs. We attribute this increase to the growing accommodation of attributes that strongly influence EV prices but have long been underappreciated in mainstream discourse. Furthermore, and relevant to decarbonization efforts, we observe that continued reductions in pack-level battery costs are unlikely to deliver price parity between EVs and ICEVs. Were pack level battery costs reduced to zero, EV MSRP would decrease by $4,025, estimates that are insufficient to offset observed price differences between EVs and ICEVs. These findings warrant attention as decarbonization efforts increasingly emphasize EVs as a pathway for complying with domestic and international climate agreements.

econ.EM↗