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

• URL: http://arxiv.org/abs/2605.14527v1
• Date: Thu, 14 May 2026 08:10:42 GMT
• Title: Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows
• Abstract summary: We propose Lang2MLIP, a multi-agent framework that takes natural-language input and formulates end-to-end MLIP development.<n>At each step, a decision-making agent observes the current dataset, model, evaluation results, and execution log, and then automatically selects an appropriate action to improve the model.<n>We evaluate this approach on a solid electrolyte interphase (SEI) system with multiple components and interfaces.
• Score: 2.1149232897113497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developing machine learning interatomic potentials (MLIPs) for complex materials systems remains challenging because it requires expertise in atomistic simulations, machine learning, and workflow design, as well as iterative active learning procedures. Existing automated pipelines typically assume a fixed sequence of stages or depend on domain experts, which limits their adaptability to heterogeneous materials systems where the optimal curriculum is not known in advance. To lower the barrier to developing MLIPs for non-experts, we propose Lang2MLIP, a multi-agent framework that takes natural-language input and formulates end-to-end MLIP development as a sequential decision-making problem solved by large language models (LLMs). At each step, a decision-making agent observes the current dataset, model, evaluation results, and execution log, and then automatically selects an appropriate action to improve the model. This removes the need for a predefined pipeline and enables the agent to self-correct by revisiting earlier subsystems when new failures arise. We evaluate this approach on a solid electrolyte interphase (SEI) system with multiple components and interfaces. These results suggest that LLM-based multi-agent systems are a promising direction for automating MLIP development and making it more accessible to non-experts.

Related papers

• FormuEvo: LLM-Guided Evolution for Discovering Solver-Efficient Mixed-Integer Programming Formulations [66.32852384262038]
  Mixed-integer programming (MIP) lies at the core of operations research and industrial optimization.<n>We propose FormuEvo, an evolutionary framework for automated discovery of solver-efficient MIP formulations.<n>FormuEvo frames MIP formulation design as evolutionary optimization over the symbolic space of MIP formulations.
  arXiv  Detail & Related papers  (2026-08-24T15:01:28Z)
• DoctorAgents: an agentic framework to iteratively refine AutoML pipeline for small clinical temporal data [13.533048301319218]
  We propose DoctorAgents, an agentic AI framework that autonomously constructs and optimize end-to-end machine learning pipelines.<n>DoctorAgents backpropagates natural-language feedback through textual gradient descent to perform targeted updates without exhaustive search.<n>Experiments show that DoctorAgents consistently outperforms established AutoML baselines while producing more interpretable task-specific representations.
  arXiv  Detail & Related papers  (2026-08-05T19:52:55Z)
• AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems [82.01232437066487]
  AgentX is a production-deployed multi-agent system that restructures this production function.<n>It autonomously generates, implements, evaluates, and learns from recommendation experiments.<n>AgentX orchestrates four tightly coupled stages in a closed loop.
  arXiv  Detail & Related papers  (2026-06-25T10:42:28Z)
• AutoSurrogate: An LLM-Driven Multi-Agent Framework for Autonomous Construction of Deep Learning Surrogate Models in Subsurface Flow [5.022047193471448]
  AutoSurrogate is a large-language-model-driven multi-agent framework for building deep learning surrogates for subsurface flow problems.<n>It enables practitioners without machine learning expertise to build high-quality surrogates through natural-language instructions.<n>We demonstrate the utility of AutoSurrogate on a 3D geological carbon storage modeling task, mapping permeability fields to pressure and CO$$ saturation fields over 31 timesteps.
  arXiv  Detail & Related papers  (2026-04-13T18:36:02Z)
• Mimosa Framework: Toward Evolving Multi-Agent Systems for Scientific Research [2.4251063179584436]
  Mimosa is an evolving multi-agent framework that automatically synthesizes task-specific multi-agents and iteratively refines them.<n>Released as a fully open-source platform, Mimosa aims to provide an open foundation for community-driven ASR.
  arXiv  Detail & Related papers  (2026-03-30T20:35:57Z)
• ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks [62.031889234230725]
  6G networks rely on complex cross-layer optimization.<n> manually translating high-level intents into mathematical formulations remains a bottleneck.<n>We present ComAgent, a multi-LLM agentic AI framework.
  arXiv  Detail & Related papers  (2026-01-27T13:43:59Z)
• Large Language Model Agent for User-friendly Chemical Process Simulations [0.0]
  A large language model (LLM) agent is integrated with AVEVA Process Model Protocol (MCP), allowing natural language simulations.<n>Two case studies assess the framework across different task complexities and interaction modes.<n>The framework benefits both educational purposes, by translating technical concepts and demonstrating, and experienced practitioners by automating data extraction, speeding routine tasks, and supporting.<n>While current limitations such as oversimplification, calculation errors, and technical hiccups mean expert oversight is still needed, the framework suggests LLM-based agents can become valuable collaborators.
  arXiv  Detail & Related papers  (2026-01-15T12:18:45Z)
• An Agentic Framework for Autonomous Materials Computation [70.24472585135929]
  Large Language Models (LLMs) have emerged as powerful tools for accelerating scientific discovery.<n>Recent advances integrate LLMs into agentic frameworks, enabling retrieval, reasoning, and tool use for complex scientific experiments.<n>Here, we present a domain-specialized agent designed for reliable automation of first-principles materials computations.
  arXiv  Detail & Related papers  (2025-12-22T15:03:57Z)
• SelfAI: Building a Self-Training AI System with LLM Agents [79.10991818561907]
  SelfAI is a general multi-agent platform that combines a User Agent for translating high-level research objectives into standardized experimental configurations.<n>An Experiment Manager orchestrates parallel, fault-tolerant training across heterogeneous hardware while maintaining a structured knowledge base for continuous feedback.<n>Across regression, computer vision, scientific computing, medical imaging, and drug discovery benchmarks, SelfAI consistently achieves strong performance and reduces redundant trials.
  arXiv  Detail & Related papers  (2025-11-29T09:18:39Z)
• Sample-Efficient Online Learning in LM Agents via Hindsight Trajectory Rewriting [92.57796055887995]
  We introduce ECHO, a prompting framework that adapts hindsight experience replay from reinforcement learning for language model agents.<n> ECHO generates optimized trajectories for alternative goals that could have been achieved during failed attempts.<n>We evaluate ECHO on stateful versions of XMiniGrid, a text-based navigation and planning benchmark, and PeopleJoinQA, a collaborative information-gathering enterprise simulation.
  arXiv  Detail & Related papers  (2025-10-11T18:11:09Z)
• Agentic Systems in Radiology: Design, Applications, Evaluation, and Challenges [13.53016942028838]
  Large language models (LLMs) are capable of using natural language to integrate information, follow instructions, and perform forms of "reasoning" and planning.<n>With its multimodal data streams and orchestrated spanning multiple systems, radiology is uniquely suited to benefit from agents that can adapt to context and automate repetitive yet complex tasks.<n>This review examines the design of such LLM agentic systems, highlights key applications, discusses evaluation methods for planning and tool use, and outlines challenges such as error cascades, tool-use efficiency, and health IT integration.
  arXiv  Detail & Related papers  (2025-10-10T13:56:27Z)
• AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML [56.565200973244146]
  Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline.<n>Recent works have started exploiting large language models (LLM) to lessen such burden.<n>This paper proposes AutoML-Agent, a novel multi-agent framework tailored for full-pipeline AutoML.
  arXiv  Detail & Related papers  (2024-10-03T20:01:09Z)

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.