136 citations · 180 across the 10 of their papers we have counts for
11 papers
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…
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…
Evaluating Large Language Models in Scientific Discovery
Zhangde Song, Jieyu Lu, Yuanqi Du +53
Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge and overlook the iterative reasonin…
SynthStrategy: Extracting and Formalizing Latent Strategic Insights from LLMs in Organic Chemistry
Daniel Armstrong, Zlatko Jončev, Andres M Bran +1
Modern computer-assisted synthesis planning (CASP) systems show promises at generating chemically valid reaction steps but struggle to incorporate strategic considerations such as…
Position: Intelligent Science Laboratory Requires the Integration of Cognitive and Embodied AI
Sha Zhang, Suorong Yang, Tong Xie +18
Scientific discovery has long been constrained by human limitations in expertise, physical capability, and sleep cycles. The recent rise of AI scientists and automated laboratories…
Chemical reasoning in LLMs unlocks strategy-aware synthesis planning and reaction mechanism elucidation
Andres M Bran, Theo A Neukomm, Daniel P Armstrong +2
While automated chemical tools excel at specific tasks, they have struggled to capture the strategic thinking that characterizes expert chemical reasoning. Here we demonstrate that…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Yoel Zimmermann, Adib Bazgir, Zartashia Afzal +141
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hyb…
Holistic chemical evaluation reveals pitfalls in reaction prediction models
Victor Sabanza Gil, Andres M. Bran, Malte Franke +3
The prediction of chemical reactions has gained significant interest within the machine learning community in recent years, owing to its complexity and crucial applications in chem…
Transformers and Large Language Models for Chemistry and Drug Discovery
Andres M Bran, Philippe Schwaller
Language modeling has seen impressive progress over the last years, mainly prompted by the invention of the Transformer architecture, sparking a revolution in many fields of machin…
ChemCrow: Augmenting large-language models with chemistry tools
Andres M Bran, Sam Cox, Oliver Schilter +3
Over the last decades, excellent computational chemistry tools have been developed. Integrating them into a single platform with enhanced accessibility could help reaching their fu…