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

---
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
