Towards a Hybrid Human-Computer Scientific Information Extraction Pipeline. - Roselyne B. Tchoua; Kyle Chard; Debra J. Audus; Logan T. Ward; Joshua Lequieu; Juan J. de Pablo; Ian T. Foster (eScience, 2017)

Profiling and Predicting Application Performance on the Cloud. - Matt Baughman; Ryan Chard; Logan T. Ward; Jason Pitt; Kyle Chard; Ian T. Foster (UCC, 2018)

Active Learning Yields Better Training Data for Scientific Named Entity Recognition. - Roselyne Tchoua; Aswathy Ajith; Zhi Hong; Logan T. Ward; Kyle Chard; Debra Audus; Shrayesh Patel; Juan de Pablo; Ian T. Foster (eScience, 2019)

Creating Training Data for Scientific Named Entity Recognition with Minimal Human Effort. - Roselyne B. Tchoua; Aswathy Ajith; Zhi Hong; Logan T. Ward; Kyle Chard; Alexander Belikov 003; Debra J. Audus; Shrayesh Patel; Juan J. de Pablo; Ian T. Foster (ICCS (1), 2019)

DLHub: Model and Data Serving for Science. - Ryan Chard; Zhuozhao Li; Kyle Chard; Logan T. Ward; Yadu N. Babuji; Anna Woodard; Steven Tuecke; Ben Blaiszik; Michael J. Franklin; Ian T. Foster (IPDPS, 2019)

IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery. - Dipendra Jha; Logan T. Ward; Zijiang Yang 008; Christopher Wolverton; Ian T. Foster; Wei-keng Liao; Alok N. Choudhary; Ankit Agrawal 001 (KDD, 2019)

Serverless Workflows for Indexing Large Scientific Data. - Tyler J. Skluzacek; Ryan Chard; Ryan Wong 002; Zhuozhao Li; Yadu N. Babuji; Logan T. Ward; Ben Blaiszik; Kyle Chard; Ian T. Foster (WOSC@Middleware, 2019)

Publishing and Serving Machine Learning Models with DLHub. - Ryan Chard; Logan T. Ward; Zhuozhao Li; Yadu N. Babuji; Anna Woodard; Steven Tuecke; Kyle Chard; Ben Blaiszik; Ian T. Foster (PEARC, 2019)

HydroNet: Benchmark Tasks for Preserving Intermolecular Interactions and Structural Motifs in Predictive and Generative Models for Molecular Data. - Sutanay Choudhury; Jenna A. Bilbrey; Logan T. Ward; Sotiris S. Xantheas; Ian T. Foster; Joseph P. Heindel; Ben Blaiszik; Marcus E. Schwarting (CoRR, 2020)

Proxima: accelerating the integration of machine learning in atomistic simulations. - Yuliana Zamora; Logan T. Ward; Ganesh Sivaraman; Ian T. Foster; Henry Hoffmann (ICS, 2021)

Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing. - Logan T. Ward; Ganesh Sivaraman; J. Gregory Pauloski; Yadu N. Babuji; Ryan Chard; Naveen Dandu; Paul C. Redfern; Rajeev S. Assary; Kyle Chard; Larry A. Curtiss; Rajeev Thakur; Ian T. Foster (MLHPC@SC, 2021)

Not All Tasks Are Created Equal: Adaptive Resource Allocation for Heterogeneous Tasks in Dynamic Workflows. - Thanh Son Phung; Logan T. Ward; Kyle Chard; Douglas Thain (WORKS, 2021)

AI- and HPC-enabled Lead Generation for SARS-CoV-2: Models and Processes to Extract Druglike Molecules Contained in Natural Language Text. - Zhi Hong; J. Gregory Pauloski; Logan T. Ward; Kyle Chard; Ben Blaiszik; Ian T. Foster (CoRR, 2021)

Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19. - Logan T. Ward; Jenna A. Bilbrey; Sutanay Choudhury; Neeraj Kumar; Ganesh Sivaraman (CoRR, 2021)

Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates. - Jenna A. Bilbrey; Logan T. Ward; Sutanay Choudhury; Neeraj Kumar; Ganesh Sivaraman (CoRR, 2021)

Co-design Center for Exascale Machine Learning Technologies (ExaLearn). - Francis J. Alexander; James A. Ang; Jenna A. Bilbrey; Jan Balewski; Tiernan Casey; Ryan Chard; Jong Choi 001; Sutanay Choudhury; Bert J. Debusschere; Anthony M. DeGennaro; Nikoli Dryden; J. Austin Ellis; et al. (Int. J. High Perform. Comput. Appl., 2021)

DLHub: Simplifying publication, discovery, and use of machine learning models in science. - Zhuozhao Li; Ryan Chard; Logan T. Ward; Kyle Chard; Tyler J. Skluzacek; Yadu N. Babuji; Anna Woodard; Steven Tuecke; Ben Blaiszik; Michael J. Franklin; Ian T. Foster (J. Parallel Distributed Comput., 2021)

RADICAL-Pilot and Parsl: Executing Heterogeneous Workflows on HPC Platforms. - Aymen Alsaadi; Logan T. Ward; André Merzky; Kyle Chard; Ian T. Foster; Shantenu Jha; Matteo Turilli (WORKS@SC, 2022)

Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision. - Nathaniel Hudson; J. Gregory Pauloski; Matt Baughman; Alok Kamatar; Mansi Sakarvadia; Logan T. Ward; Ryan Chard; André Bauer 001; Maksim Levental; Wenyi Wang; Will Engler; Owen Price Skelly; et al. (BDCAT, 2023)

Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources. - Logan T. Ward; J. Gregory Pauloski; Valérie Hayot-Sasson; Ryan Chard; Yadu N. Babuji; Ganesh Sivaraman; Sutanay Choudhury; Kyle Chard; Rajeev Thakur; Ian T. Foster (IPDPS Workshops, 2023)

Fine-grained accelerator partitioning for Machine Learning and Scientific Computing in Function as a Service Platform. - Aditya Dhakal; Philipp Raith; Logan T. Ward; Rolando P. Hong Enriquez; Gourav Rattihalli; Kyle Chard; Ian T. Foster; Dejan S. Milojicic (SC Workshops, 2023)

Accelerating Communications in Federated Applications with Transparent Object Proxies. - J. Gregory Pauloski; Valérie Hayot-Sasson; Logan T. Ward; Nathaniel Hudson; Charlie Sabino; Matt Baughman; Kyle Chard; Ian T. Foster (SC, 2023)

14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon. - Kevin Maik Jablonka; Qianxiang Ai; Alexander Al-Feghali; Shruti Badhwar; Joshua D. Bocarsly; Andres M. Bran; Stefan Bringuier; L. Catherine Brinson; Kamal Choudhary; Defne Circi; Sam Cox; Wibe A. de Jong; et al. (CoRR, 2023)

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. - Shuaiwen Leon Song; Bonnie Kruft; Minjia Zhang; Conglong Li; Shiyang Chen 004; Chengming Zhang 006; Masahiro Tanaka; Xiaoxia Wu; Jeff Rasley; Ammar Ahmad Awan; Connor Holmes; Martin Cai; et al. (CoRR, 2023)

Accelerating Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine Learning. - Logan T. Ward; Ben Blaiszik; Cheng-Wei Lee 006; Troy Martin; Ian T. Foster; André Schleife (CoRR, 2023)

GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics. - Maxim Zvyagin; Alexander Brace; Kyle Hippe; Yuntian Deng; Bin Zhang; Cindy Orozco Bohorquez; Austin Clyde; Bharat Kale; Danilo Perez-Rivera; Heng Ma; Carla M. Mann; Michael W. Irvin; et al. (Int. J. High Perform. Comput. Appl., 2023)

Accelerating Function-Centric Applications by Discovering, Distributing, and Retaining Reusable Context in Workflow Systems. - Thanh Son Phung; Colin Thomas; Logan T. Ward; Kyle Chard; Douglas Thain (HPDC, 2024)

MProt-DPO: Breaking the ExaFLOPS Barrier for Multimodal Protein Design Workflows with Direct Preference Optimization. - Gautham Dharuman; Kyle Hippe; Alexander Brace; Sam Foreman; Väinö Hatanpää; Varuni Katti Sastry; Huihuo Zheng; Logan T. Ward; Servesh Muralidharan; Archit Vasan; Bharat Kale; Carla M. Mann; et al. (SC, 2024)

Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows. - Rafael Ferreira da Silva; Deborah Bard; Kyle Chard; Shaun De Witt; Ian T. Foster; Tom Gibbs; Carole A. Goble; William F. Godoy; Johan Gustafsson 001; Utz-Uwe Haus; Stephen Hudson; Shantenu Jha; et al. (CoRR, 2024)

Foundry-ML (latest). - K. J. Schmidt; Aristana Scourtas; Logan T. Ward; Steve Wangen; Marcus Schwarting; Isaac Darling; Ethan Truelove; Aadit Ambadkar; Ribhav Bose; Zoa Katok; Jingrui Wei; Xiangguo Li; et al. (Zenodo, 2024)

Foundry-ML - Software and Services to Simplify Access to Machine Learning Datasets in Materials Science. - K. J. Schmidt; Aristana Scourtas; Logan T. Ward; Steve Wangen; Marcus Schwarting; Isaac Darling; Ethan Truelove; Aadit Ambadkar; Ribhav Bose; Zoa Katok; Jingrui Wei; Xiangguo Li; et al. (Zenodo, 2024)

Foundry-ML - Software and Services to Simplify Access to Machine Learning Datasets in Materials Science. - K. J. Schmidt; Aristana Scourtas; Logan T. Ward; Steve Wangen; Marcus Schwarting; Isaac Darling; Ethan Truelove; Aadit Ambadkar; Ribhav Bose; Zoa Katok; Jingrui Wei; Xiangguo Li; et al. (Zenodo, 2024)

Foundry-ML - Software and Services to Simplify Access to Machine Learning Datasets in Materials Science. - K. J. Schmidt; Aristana Scourtas; Logan T. Ward; Steve Wangen; Marcus Schwarting; Isaac Darling; Ethan Truelove; Aadit Ambadkar; Ribhav Bose; Zoa Katok; Jingrui Wei; Xiangguo Li; et al. (Zenodo, 2024)

Foundry-ML - Software and Services to Simplify Access to Machine Learning Datasets in Materials Science. - K. J. Schmidt; Aristana Scourtas; Logan T. Ward; Steve Wangen; Marcus Schwarting; Isaac Darling; Ethan Truelove; Aadit Ambadkar; Ribhav Bose; Zoa Katok; Jingrui Wei; Xiangguo Li; et al. (J. Open Source Softw., 2024)

Emin: A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability. - Andrew S. Lee; Sarah Elliott; Hassan Harb; Logan T. Ward; Ian T. Foster; Larry A. Curtiss; Rajeev S. Assary (J. Chem. Inf. Model., 2024)

Will It Blend? Mixing Numerical and Machine-Learned Physics Quantities for Accurate on-the-Fly Surrogate Modeling. - Michael Tynes; Kyle Chard; Ian T. Foster; Logan T. Ward (ICCS (3), 2025)

Employing artificial intelligence to steer exascale workflows with colmena. - Logan T. Ward; J. Gregory Pauloski; Valérie Hayot-Sasson; Yadu N. Babuji; Alexander Brace; Ryan Chard; Kyle Chard; Rajeev Thakur; Ian T. Foster (Int. J. High Perform. Comput. Appl., 2025)

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow. - Xiaoli Yan; Nathaniel Hudson; Hyun Park; Daniel Grzenda; J. Gregory Pauloski; Marcus Schwarting; Haochen Pan; Hassan Harb; Sam Foreman; Chris Knight 001; Tom Gibbs; Kyle Chard; et al. (CoRR, 2025)

Object Proxy Patterns for Accelerating Distributed Applications. - J. Gregory Pauloski; Valérie Hayot-Sasson; Logan T. Ward; Alexander Brace; André Bauer 001; Kyle Chard; Ian T. Foster (IEEE Trans. Parallel Distributed Syst., 2025)

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems. - Bang Liu; Xinfeng Li; Jiayi Zhang; Jinlin Wang; Tanjin He; Sirui Hong; Hongzhang Liu; Shaokun Zhang; Kaitao Song; Kunlun Zhu; Yuheng Cheng; Suyuchen Wang; et al. (CoRR, 2025)