32 citations · 64 across the 16 of their papers we have counts for
17 papers
AutoSciBench: Autonomous Benchmark Generation for Evaluating Scientific Agents
Dongki Kim, Namkyeong Lee, Surag Nair +7
As agents rapidly evolve, existing benchmarks can become saturated, limiting their ability to distinguish capabilities and reveal remaining failure modes. Particularly in scientifi…
AssayBench: An Assay-Level Virtual Cell Benchmark for LLMs and Agents
Edward De Brouwer, Carl Edwards, Alexander Wu +9
Recent advances in machine learning and large-scale biological data collections have revived the prospect of building a virtual cell, a computational model of cellular behavior tha…
oMeBench: Towards Robust Benchmarking of LLMs in Organic Mechanism Elucidation and Reasoning
Ruiling Xu, Yifan Zhang, Qingyun Wang +2
Organic reaction mechanisms describe the step-wise elementary processes by which reactants transform into intermediates and products, and are fundamental to understanding chemical…
mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Molecules
Carl Edwards, Chi Han, Gawon Lee +11
Despite their ability to understand chemical knowledge, large language models (LLMs) remain limited in their capacity to propose novel molecules with desired functions (e.g., drug-…
Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation
Keqiang Yan, Xiner Li, Hongyi Ling +8
We consider the problem of crystal materials generation using language models (LMs). A key step is to convert 3D crystal structures into 1D sequences to be processed by LMs. Prior…
MolCap-Arena: A Comprehensive Captioning Benchmark on Language-Enhanced Molecular Property Prediction
Carl Edwards, Ziqing Lu, Ehsan Hajiramezanali +3
Bridging biomolecular modeling with natural language information, particularly through large language models (LLMs), has recently emerged as a promising interdisciplinary research…
Geometry Informed Tokenization of Molecules for Language Model Generation
Xiner Li, Limei Wang, Youzhi Luo +5
We consider molecule generation in 3D space using language models (LMs), which requires discrete tokenization of 3D molecular geometries. Although tokenization of molecular graphs…
GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices
Thao Nguyen, Tiara Torres-Flores, Changhyun Hwang +3
This paper presents a novel approach for predicting Power Conversion Efficiency (PCE) of Organic Photovoltaic (OPV) devices, called GLaD: synergizing molecular Graphs and Language…
L+M-24: Building a Dataset for Language + Molecules @ ACL 2024
Carl Edwards, Qingyun Wang, Lawrence Zhao +1
Language-molecule models have emerged as an exciting direction for molecular discovery and understanding. However, training these models is challenging due to the scarcity of molec…
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…
Defining a New NLP Playground
Sha Li, Chi Han, Pengfei Yu +8
The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in…
Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design
Henry W. Sprueill, Carl Edwards, Mariefel V. Olarte +3
Discovering novel catalysts requires complex reasoning involving multiple chemical properties and resultant trade-offs, leading to a combinatorial growth in the search space. While…
SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design
Carl Edwards, Aakanksha Naik, Tushar Khot +3
Predicting synergistic drug combinations can help accelerate discovery of cancer treatments, particularly therapies personalized to a patient's specific tumor via biopsied cells. I…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
Translation between Molecules and Natural Language
Carl Edwards, Tuan Lai, Kevin Ros +3
We present a self-supervised learning framework for pretraining models on a vast amount of unlabeled natural language text and molecule strings. $\textbf{MolT5…
Semi-supervised New Event Type Induction and Description via Contrastive Loss-Enforced Batch Attention
Carl Edwards, Heng Ji
Most event extraction methods have traditionally relied on an annotated set of event types. However, creating event ontologies and annotating supervised training data are expensive…
Phase-Only Beam Broadening of Contiguous Uniform Subarrayed Arrays Utilizing Three Metaheuristic Global Optimization Techniques
Barry Daniel, Carl Edwards, Adam Anderson
Radar beam broadening provides continuous coverage of a wider angular extent. While many methods have been published that address beam broadening of traditional (nonsubarrayed) arr…