arXiv AI
Jun 12
EurekAgent: Agent Environment Engineering is All You Need For Autonomous Scientific Discovery
arXiv:2606. 13662v1 Announce Type: new Abstract: LLM-based agents have shown increasing potential in automating scientific discovery.
Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists
arXiv:2506. 05616v3 Announce Type: replace Abstract: We aim at designing language agents with greater autonomy for crystal materials discovery.
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arXiv AI
Sep 3
Can Coding Agents Reproduce Findings in Computational Materials Science?
The paper introduces AutoMat, a benchmark designed to test large language model (LLM) coding agents on their ability to reproduce claims from computational materials science. AutoMat presents three challenges: reconstructing underspecified procedures, navigating specialized toolchains, and assessing whether the evidence supports a claim. Experiments show that current LLM agents achieve low success rates, with the best setting reaching only 53%, and failures stem mainly from incomplete procedures, methodological deviations, and execution fragility.
arXiv AI
Jun 9
MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science
arXiv:2606. 07712v1 Announce Type: cross Abstract: Progress in AI-driven crystal materials science has so far been carried by narrow architectures purpose-built for individual tasks -- graph neural networks for property prediction, diffusion and flow-matching models for crystal generation -- each excelling within its niche yet unable to act as a shared backbone across the full spectrum of materials problems.
arXiv AI
Jun 6
AutoDFT: A Closed-Loop Multi-Agent Framework for Autonomous DFT Calculations
arXiv:2605. 26179v2 Announce Type: replace-cross Abstract: Density functional theory (DFT) serves as the basis for computational discovery in materials science and chemistry, yet each calculation demands extensive human effort: adjusting algorithms when convergence stalls, revising plans when unexpected physics emerges, and inserting steps as intermediate results reshape the problem.
arXiv AI
21h ago
MIND: AI Co-Scientist for Material Research
arXiv:2604.13699v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have enabled agentic AI systems for scientific discovery, but most approaches remain limited to textbased reason...
Hugging Face Trending Papers
Aug 5
CheMLFlow: An Open-Source Platform for Cheminformatics and Materials Informatics Applications
CheMLFlow is an open-source platform for building and executing end-to-end, high-throughput, and agentic workflows for scientific and technological applications. CheMLFlow targets a common bottleneck in scientific machine learning development, where researchers often need to assemble data acquisition, curation, representation, model training, validation, screening, interpretation, and reporting into a reproducible pipeline, even when their primary research contribution concerns only one stage.
arXiv AI
Oct 27
MatOS: An Agent Operating System for Materials Research
MatOS is an agent operating system designed to streamline materials research by organizing data, knowledge, tools, and workflows into reusable assets. It features a layered scientific asset graph, a stateful adaptive workflow that tracks and resumes tasks, and an evidence-driven asset evolution mechanism that updates assets based on execution records and user feedback. In benchmark tests, MatOS outperforms existing agents and has successfully narrowed millions of candidate structures to a few promising solid-electrolyte and halide variants, demonstrating its effectiveness in long, multistage materials studies.
arXiv AI
Sep 15
El Agente Potente: High-Throughput Agentic Atomistic Simulations
El Agente Potente is an agentic system that integrates typed execution graphs and a coding mode to facilitate machine‑learning interatomic potential (MLIP) driven atomistic simulations. Typed execution graphs offer structured, provenance‑aware workflows where large language models handle planning and routing while deterministic Python code performs scientific computation and validation. The coding agent builds customized workflows for tasks needing procedural flexibility, invoking existing Potente functions for supported calculations. The system is demonstrated across materials discovery, energy‑landscape exploration, adsorption, and catalytic reaction workflows, with benchmarks on reproducibility and LLM token cost.
arXiv Computation and Language
Oct 9
SAIL: Scientific Agentic Intelligence via a Science-Aware Loop
SAIL is an open model with 35 B total and 3 B active parameters designed for literature research, scientific coding, and multi‑step research workflows. It is developed through a science‑aware improvement loop in which agents built on frontier AI models analyze task failures, create training tasks to address capability gaps, and iterate this process across multiple development cycles. The training pipeline combines supervised fine‑tuning, specialist training, multi‑teacher on‑policy distillation, and agentic reinforcement learning, enabling SAIL to achieve competitive performance on scientific research tasks with fewer parameters than leading open‑weight models.
arXiv AI
Sep 2
Agentic programs: an emerging form of scientific software in computational materials science
The article introduces the concept of agentic programs—scientific software that blends deterministic algorithms with bounded large‑language‑model (LLM) judgment, task‑specific verification, episodic maturation, and full delegation in production. It argues that recent LLM‑based agents enable this new form of computational materials science software. The authors illustrate the idea with DeMARS, an agentic program designed to build atomistic models from experimentally measured disordered crystal structures.
arXiv AI
Aug 18
ALKEMIE Agent: an autonomous platform for computational materials design
arXiv:2608. 15776v1 Announce Type: cross Abstract: Despite the powerful multi-scale modeling methods and high-throughput infrastructures established in the materials community, real material computation workflows remain fragmented and heavily manual, requiring researchers to constantly bridge software tools, data analysis, and intermediate decisions.
arXiv Machine Learning
Oct 15
The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery
arXiv:2610.18933v1 Announce Type: cross Abstract: Modern coding agents are fundamentally reshaping the role of human input for computational research: they write code, launch in silico experiments, a...