agent/papers/items/2026-2605-14527-lang2mlip-end-to-end-language-to-machine-learning-interatomic-potential-developm.md

Paper: Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows

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type: paper title: "Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows" authors: Wenwen Li, Yuki Orimo, Nontawat Charoenphakdee year: 2026 venue: arXiv url: https://arxiv.org/abs/2605.14527 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-05-14 updated_at: 2026-05-14 status: queued relevance: high topics:

• agent-evaluation
• multi-agent
• tool-use
• workflow-agent
• world-model methods:

benchmarks:

models:

datasets:

• cs.LG
• cond-mat.mtrl-sci
• physics.comp-ph related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 17 collection_queries: autonomous-agent-llm

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: autonomous-agent-llm
• inferred topics: agent-evaluation, multi-agent, tool-use, workflow-agent, world-model
• arXiv categories: cs.LG, cond-mat.mtrl-sci, physics.comp-ph
• collection score: 17

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2605.14527