Born-Qualified: An Autonomous Framework for Deploying Advanced Energy and Electronic Materials

• URL: http://arxiv.org/abs/2605.00639v1
• Date: Fri, 01 May 2026 13:21:25 GMT
• Title: Born-Qualified: An Autonomous Framework for Deploying Advanced Energy and Electronic Materials
• Abstract summary: "born-qualified" autonomous development embeds manufacturability, cost, and durability constraints from the outset.<n>This vision will require sustained, community-wide commitment, but the potential return on that investment is commensurate with the scale of the challenge.
• Score: 2.568084337313714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous science is transforming how we discover materials and chemical systems for advanced energy technologies. However, many initially promising systems never reach deployment. This "valley of death" stems from optimization that prioritizes laboratory metrics over industrial viability. We propose a new strategy: "born-qualified" autonomous development, which embeds manufacturability, cost, and durability constraints from the outset. This approach is enabled by four pillars, including the development of multi-objective metrics, causal models, a modular infrastructure, and embedding manufacturing in the discovery loop. Realizing this vision will require sustained, community-wide commitment, but the potential return on that investment is commensurate with the scale of the challenge.

Related papers

• A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design [71.32556809242666]
  The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains.<n>It has also raised critical concerns regarding computational costs, energy consumption, and environmental sustainability.<n>This survey aims to offer a holistic roadmap toward sustainable, scalable, and socially responsible development of large models.
  arXiv  Detail & Related papers  (2026-07-10T04:02:22Z)
• From Stacks to Circuits: A Regenerative Socio-Technical Roadmap for AI Infrastructure within Planetary Boundaries [0.0]
  Current scaling trajectories for Generative AI, typified by linear supply-side "stacks," prioritize performance density while externalizing significant thermodynamic and material costs.<n>This study proposes a Regenerative Socio-Technical roadmap that reframes artificial intelligence infrastructure as a system-of-systems governed ultimately by planetary limits.
  arXiv  Detail & Related papers  (2026-06-09T08:11:25Z)
• Perspective: Towards sustainable exploration of chemical spaces with machine learning [49.29528647472741]
  We argue that sustainable progress will rely on open data and models, reusable datasets, and domain-specific AI systems.<n>We highlight emerging strategies to enhance efficiency, including general-purpose machine learning (ML) models, multi-fidelity approaches, model distillation, and active learning.
  arXiv  Detail & Related papers  (2026-03-31T13:29:20Z)
• EmboCoach-Bench: Benchmarking AI Agents on Developing Embodied Robots [68.29056647487519]
  Embodied AI is fueled by high-fidelity simulation and large-scale data collection.<n>However, this scaling capability remains bottlenecked by a reliance on labor-intensive manual oversight.<n>We introduce textscEmboCoach-Bench, a benchmark evaluating the capacity of LLM agents to autonomously engineer embodied policies.
  arXiv  Detail & Related papers  (2026-01-29T11:33:49Z)
• Sustainable Materials Discovery in the Era of Artificial Intelligence [3.222363676081407]
  We propose to integrate upstream machine learning (ML) assisted materials discovery with downstream lifecycle assessment into a uniform ML-LCA environment.<n>The framework ML-LCA integrates five components, information extraction for building materials-environment knowledge bases, harmonized databases linking properties to sustainability metrics, multi-scale models bridging atomic properties to lifecycle impacts, ensemble prediction of manufacturing pathways with uncertainty quantification, and uncertainty-aware optimization.
  arXiv  Detail & Related papers  (2026-01-29T10:42:44Z)
• Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents [63.03252293761656]
  This paper systematically reviews the technologies, applications, and evaluation methods of industry agents based on large language models (LLMs)<n>We examine the three key technological pillars that support the advancement of agent capabilities: Memory, Planning, and Tool Use.<n>We provide an overview of the application of industry agents in real-world domains such as digital engineering, scientific discovery, embodied intelligence, collaborative business execution, and complex system simulation.
  arXiv  Detail & Related papers  (2025-10-20T12:46:55Z)
• Perovskite-R1: A Domain-Specialized LLM for Intelligent Discovery of Precursor Additives and Experimental Design [5.378023608941598]
  Perovskite solar cells (PSCs) have rapidly emerged as a leading contender in next-generation photovoltaic technologies.<n> challenges such as long-term stability, environmental sustainability, and scalable manufacturing continue to hinder their commercialization.<n>Precursor additive engineering has shown promise in addressing these issues by enhancing both the performance and durability of PSCs.<n>We introduce Perovskite-R1, a specialized large language model (LLM) with advanced reasoning capabilities tailored for the discovery and design of PSC precursor additives.
  arXiv  Detail & Related papers  (2025-07-22T07:48:32Z)
• Generative AI for Autonomous Driving: Frontiers and Opportunities [145.6465312554513]
  This survey delivers a comprehensive synthesis of the emerging role of GenAI across the autonomous driving stack.<n>We begin by distilling the principles and trade-offs of modern generative modeling, encompassing VAEs, GANs, Diffusion Models, and Large Language Models.<n>We categorize practical applications, such as synthetic data generalization, end-to-end driving strategies, high-fidelity digital twin systems, smart transportation networks, and cross-domain transfer to embodied AI.
  arXiv  Detail & Related papers  (2025-05-13T17:59:20Z)
• Power Hungry Processing: Watts Driving the Cost of AI Deployment? [74.19749699665216]
  generative, multi-purpose AI systems promise a unified approach to building machine learning (ML) models into technology. This ambition of generality'' comes at a steep cost to the environment, given the amount of energy these systems require and the amount of carbon that they emit. We measure deployment cost as the amount of energy and carbon required to perform 1,000 inferences on representative benchmark dataset using these models. We conclude with a discussion around the current trend of deploying multi-purpose generative ML systems, and caution that their utility should be more intentionally weighed against increased costs in terms of energy and emissions
  arXiv  Detail & Related papers  (2023-11-28T15:09:36Z)

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.