102 citations · 183 across the 13 of their papers we have counts for
14 papers
Democratizing Atomistic Simulation Workflows for the AI Era with the Quantum Accelerator
Andrew S. Rosen, Naisargi Goyal, Brad Ayers +25
We present the Quantum Accelerator (QuAcc), an open-source workflow library for atomistic simulations with an emphasis on quantum-mechanical calculations. QuAcc provides predefined…
The convergent laboratory: when AI reasoning, autonomous experiments, high performance and quantum computing reshape chemistry
Eliu Huerta, Xiaoyun Wang, Geetika Gupta +7
This Comment emerges from TPC26 (https://tpc26.org), a conference convening leaders from academia, national laboratories, and industry who are reshaping materials science discovery…
FABRICA: Agentic CUDA-to-CSL Translation and Optimization for Wafer-Scale Systems
Yuebo Luo, Eliu Huerta, Venkatram Vishwanath +3
Porting GPU kernels across architectures requires architectural remapping, not syntax substitution. CUDA encodes decomposition, locality, and synchronization through threads, block…
Wrong but Useful: Trajectory Value Beyond Answer Correctness in Multi-Agent Messages
Chih-Hsuan Yang, Anjir Ahmed Chowdhury, Cheng-Hau Yang +7
Multi-agent reasoning systems often use agreement, confidence, or automated scores to decide which messages should shape a final answer. Such filtering assumes that a message likel…
Precise but Uncoupled: Reviewer Precision Does Not Guarantee Critique Uptake in Multi-Agent Math Reasoning
Chih-Hsuan Yang, Jingyan Jiang, Vikram Vasudevan +7
Many math- and science-oriented agent systems use hierarchical designs with specialized reviewer roles, assuming that a dedicated review stage should help turn wrong candidates int…
From Atomistic Models to Machine Learning: Predictive Design of Nanocarbons under Extreme Conditions
Xiaoli Yan, Millicent A. Firestone, Murat Keceli +2
The formation of technologically valuable nanocarbon structures under extreme conditions, such as those produced during high-explosive detonations, remains poorly understood but ho…
Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization
Marcus Schwarting, Logan Ward, Nathaniel Hudson +5
Generative AI poses both opportunities and risks for solving inverse design problems in the sciences. Generative tools provide the ability to expand and refine a search space auton…
MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow
Xiaoli Yan, Nathaniel Hudson, Hyun Park +15
We present MOFA, an open-source generative AI (GenAI) plus simulation workflow for high-throughput generation of metal-organic frameworks (MOFs) on large-scale high-performance com…
Sequence modeling of higher-order wave modes of binary black hole mergers
Victoria Tiki, Kiet Pham, Eliu Huerta
Higher-order gravitational wave modes from quasi-circular, spinning, non-precessing binary black hole mergers encode key information about these systems' nonlinear dynamics. We mod…
APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics
Hyun Park, Parth Patel, Roland Haas +1
The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics, and plays a key role in robust protein structure prediction algori…
A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture
Hyun Park, Xiaoli Yan, Ruijie Zhu +5
Metal-organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view…
End-to-end AI framework for interpretable prediction of molecular and crystal properties
Hyun Park, Ruijie Zhu, E. A. Huerta +3
We introduce an end-to-end computational framework that allows for hyperparameter optimization using the DeepHyper library, accelerated model training, and interpretable AI inferen…
Applications of physics informed neural operators
Shawn G. Rosofsky, Hani Al Majed, E. A. Huerta
We present an end-to-end framework to learn partial differential equations that brings together initial data production, selection of boundary conditions, and the use of physics-in…
Initial Data and Eccentricity Reduction Toolkit for Binary Black Hole Numerical Relativity Waveforms
Sarah Habib, Antoni Ramos-Buades, E. A. Huerta +3
The production of numerical relativity waveforms that describe quasicircular binary black hole mergers requires high-quality initial data, and an algorithm to iteratively reduce re…