22 citations · 107 across the 37 of their papers we have counts for
39 papers
Networked Intelligence: Active Shared Context Graphs for Human-AI Team Science
Sutanay Choudhury, Jeffrey J. Czajka, Lummy M. O. Monteiro +15
Most AI-for-science systems focus on scaling a single reasoning process by using better models, larger context windows, long-horizon agentic execution, or digital co-scientists wor…
Reaction-network reasoning with frontier models for experimentally confirmed catalyst-selectivity hypotheses
Sutanay Choudhury, Anwesha Banerjee, Udishnu Sanyal +7
Catalysts are essential for sustainable chemical manufacturing, yet discovering novel architectures remains a bottleneck dominated by trial-and-error experimentation and computatio…
Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE
Jesun Firoz, Franco Pellegrini, Mario Geiger +17
Chemistry Foundation Models (CFMs) that leverage Graph Neural Networks (GNNs) operating on 3D molecular graph structures are becoming indispensable tools for computational chemists…
Thinking Fast and Laterally: Multi-Agentic Approach for Reasoning about Uncertain Emerging Events
Stefan Dernbach, Alejandro Michel, Khushbu Agarwal +3
This paper introduces lateral thinking to implement System-2 reasoning capabilities in AI systems, focusing on anticipatory and causal reasoning under uncertainty. We present a fra…
ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback
Henry W. Sprueill, Carl Edwards, Khushbu Agarwal +6
The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future. We introduce an AI-guided co…
GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encoding
Stefan Dernbach, Khushbu Agarwal, Alejandro Zuniga +2
Integrating large language models (LLMs) with knowledge graphs derived from domain-specific data represents an important advancement towards more powerful and factual reasoning. As…