activity
20172026
most cited14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon

214 citations · 490 across the 43 of their papers we have counts for

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11 papers · 1 filter

cs.AI2026

Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation

Nguyen Xuan-Vu, Octavian Susanu, Daniel Armstrong +1

Chemical reactions are fundamentally transformations in electron space, yet most machine learning approaches model them either through de novo generation of product molecules or th…

cs.AI2026

Agentic generation of verifiable rules for deterministic, self-expanding reaction classification

Daniel Armstrong, Maarten Dobbelaere, Valentas Olikauskas +4

Computer-assisted synthesis planning breaks target molecules into accessible precursors using large libraries of reaction rules that assign each transformation a deterministic, int…

cs.AI20263 cited

CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution

Xu Huang, Junwu Chen, Yuxing Fei +3

Large language model (LLM) agents currently depend on predefined tools or early-stage tool generation, limiting their adaptability and scalability to complex scientific tasks. We i…

cs.AI20251 cited

Synthelite: Chemist-aligned and feasibility-aware synthesis planning with LLMs

Nguyen Xuan-Vu, Daniel Armstrong, Milena Wehrbach +3

Computer-aided synthesis planning (CASP) has long been envisioned as a complementary tool for synthetic chemists. However, existing frameworks often lack mechanisms to allow intera…

cs.AI20251 cited

DynaMate: An Autonomous Agent for Protein-Ligand Molecular Dynamics Simulations

Salomé Guilbert, Cassandra Masschelein, Jeremy Goumaz +2

Force field-based molecular dynamics (MD) simulations are indispensable for probing the structure, dynamics, and functions of biomolecular systems, including proteins and protein-l…

cs.AI2025

Accelerating Scientific Discovery with Autonomous Goal-evolving Agents

Yuanqi Du, Botao Yu, Tianyu Liu +25

There has been unprecedented interest in developing agents that expand the boundary of scientific discovery, primarily by optimizing quantitative objective functions specified by s…