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

214 citations · 455 across the 37 of their papers we have counts for

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21 papers · 1 filter

cs.LG2026

Dynamic language model representations for multi-objective reaction optimisation

Joshua W. Sin, David Ming Segura, Bojana Ranković +8

Optimising chemical reactions across multiple objectives, such as yield, selectivity, and safety, is central to chemical synthesis, and model-driven approaches depend critically on…

cs.LG2026

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language

David Ming Segura, Jeremy Goumaz, Joshua W. Sin +2

Transformer models have revolutionized natural language processing (NLP), and text-based molecular representations like SMILES have successfully extended these architectures to che…

cs.LG2026

Sample Efficient Generative Optimization for Molecular Design

Sarina Kopf, Cristina Nevado, Philippe Schwaller

Molecular optimization in drug discovery, materials design, and catalysis requires searching vast chemical spaces under tight evaluation budgets, since high-fidelity oracles and ex…

cs.LG2026

MiST: Understanding the Role of Mid-Stage Scientific Training in Developing Chemical Reasoning Models

Andres M Bran, Tong Xie, Shai Pranesh +9

Large Language Models can develop reasoning capabilities through online fine-tuning with rule-based rewards. However, recent studies reveal a critical constraint: reinforcement lea…

cs.LG20251 cited

Teaching Language Models Mechanistic Explainability Through MechSMILES

Théo A. Neukomm, Zlatko Jončev, Philippe Schwaller

Chemical reaction mechanisms are the foundation of how chemists evaluate reactivity and feasibility, yet current Computer-Assisted Synthesis Planning (CASP) systems operate without…

cs.LG2025

Lookup multivariate Kolmogorov-Arnold Networks

Sergey Pozdnyakov, Philippe Schwaller

High-dimensional linear mappings, or linear layers, dominate both the parameter count and the computational cost of most modern deep-learning models. We introduce a general-purpose…