NSF workshop: Envisioning Future of AI and Chemistry
The Hotel at the University of Maryland
September 2026

Chemistry-first AI for data, models, and infrastructure

The workshop will be held at The Hotel at the University of Maryland in College Park, Maryland.

Workshop Overview

Venue & lodgingThe Hotel at the University of Maryland
7777 Baltimore Ave., College Park, MD 20740
Workshop sessions and participant accommodations
DatesThursday, September 3, 2026 (starting at 12:00 noon and ending with dinner by 8:00 PM) and Friday, September 4, 2026 (starting at 9:00 AM and ending by 4:00 PM)
FormatSmall, invitation-based working meeting
OrganizersPratyush Tiwary, Connor Coley, and Ryan Jorn

Chemistry provides a demanding environment for AI development: chemical data span many instruments, simulations, scales, notebooks, repositories, publications, and proprietary archives. AI methods have already accelerated chemical discovery, but the next step is to design AI for chemistry from chemistry outward rather than adapting tools built for unrelated domains.

This NSF workshop will bring chemistry, AI, data infrastructure, automation, software, publishing, and industry stakeholders together to define a shared vision and actionable pathways for coordinated data handling, methods development, and infrastructure. The long-term objective is to make experimental and computational chemical data, models, and tools more accessible, searchable, interoperable, and shareable.

The meeting is designed as a roadmap workshop rather than a research symposium. Participants will work toward concrete pilot concepts, writing responsibilities, and a community-facing report that can guide near-term action and longer-term coordination.

This workshop is supported by NSF through award number 2630156.

Invited Participants

Karl LeswingSchrödingerMachine learning and computational drug discovery
Yunrui QiuUniversity of Notre DameMolecular simulation and nonequilibrium statistical mechanics
Chad RiskoUniversity of KentuckyComputational chemistry and organic electronic materials
Yihang WangCase Western Reserve UniversityArtificial intelligence, statistical mechanics, and molecular simulation
Jules SchleinitzCalifornia Institute of TechnologySynthetic organic chemistry and computer-assisted synthesis
Philip LampkinUniversity of UtahAI-guided molecular discovery, polymer design, and catalysis
Olaf WiestUniversity of Notre DameComputational reaction mechanisms and AI-enabled molecular discovery
Huimin ZhaoUniversity of Illinois Urbana-ChampaignSynthetic biology, metabolic engineering, and AI-enabled discovery
Brett M. SavoieUniversity of Notre DameComputational chemistry, reaction discovery, and synthesis planning
Garegin PapoianDeep OriginMultiscale computational biophysics and theoretical chemistry
Dylan WalshUniversity of Wisconsin–MadisonSynthetic chemistry, catalysis, and polymer upcycling
Xuhui HuangUniversity of Wisconsin–MadisonMolecular simulation, biomolecular kinetics, and artificial intelligence
Francesco PaesaniUniversity of California, San DiegoData-driven molecular simulation and many-body interactions
John ParkhillTerray TherapeuticsChemical foundation models, molecular design, and synthesis planning
Martin BurkeUniversity of Illinois Urbana-ChampaignAutomated modular synthesis and AI-guided small-molecule discovery
Colin LamMerck & Co.Artificial intelligence and data science for drug discovery
Cyndi Qixin HeMerck & Co.Computational chemistry, machine learning, and reaction development
Rory WatermanUniversity of VermontInorganic and organometallic synthesis and catalysis
Christopher JarzynskiUniversity of MarylandNonequilibrium statistical mechanics and thermodynamics
Shane W. KrskaMerck & Co.Synthetic chemistry, catalysis, and high-throughput experimentation
Masha ElkinMITSynthetic organic chemistry, catalysis, and machine learning
Gregory VothUniversity of ChicagoMultiscale molecular simulation and coarse-grained modeling
Mary PitmanSandboxAQMolecular simulation and biologics
Daniel TaborTexas A&M UniversityMachine learning and quantum chemistry for materials discovery
Olexandr IsayevCarnegie Mellon UniversityMachine learning and interatomic potentials
Aaron DinnerUniversity of ChicagoTheoretical chemistry, statistical mechanics, and molecular simulation
Song LinStanford UniversityOrganic electrochemistry, electrocatalysis, and synthesis
Michael WebbPrinceton UniversityTheoretical chemistry, statistical mechanics, and molecular simulation
Yuanqi DuMicrosoft ResearchMachine learning, generative modeling, and molecular design
Omar ValssonUniversity of North TexasStatistical mechanics, enhanced sampling, and molecular simulation
Sutanay ChoudhuryPacific Northwest National LaboratoryArtificial intelligence for scientific discovery and molecular modeling
Bodhi VaniPrescient Design, GenentechMachine learning, enhanced sampling, and protein conformational ensembles
Tom GrahamBristol Myers SquibbExperimental chemistry and drug discovery
Michael KoerisDARPABioprocessing, microbiome medicines, and cell and gene therapies
Ramón Alain Miranda-QuintanaUniversity of FloridaQuantum and theoretical chemistry and data analysis
Aditi S. KrishnapriyanUniversity of California, BerkeleyMachine learning for the natural sciences, molecular and materials modeling, and multiscale dynamics
Samuel StantonAnthropicProbabilistic machine learning, Bayesian optimization, and AI for biological design
Santiago MiretLila SciencesMachine learning, materials informatics, and AI-enabled materials discovery
Rigoberto HernandezJohns Hopkins UniversityTheoretical and computational chemistry, chemical dynamics, and energy transport
Timothy NewhouseYale UniversityOrganic synthesis, natural product synthesis, and reaction development
Abhinendra SinghCase Western Reserve UniversityMultiscale simulation, machine learning, and constitutive modeling of materials
Jeremy MonatBenchlingSoftware engineering and cloud-based platforms for biotechnology research and development
Yu GanUniversity of Maryland, College ParkMachine learning, generative and trustworthy AI, and biomedical imaging
Sarah TegenACS PublicationsScientific publishing, open science, research integrity, and peer review
Tom AllisonNational Institute of Standards and TechnologyQuantum chemistry, molecular modeling, electrochemistry, and solvation thermodynamics
Giulia PalermoUniversity of California, Los AngelesMultiscale molecular simulation, computational biophysics, and AI for genome editing
Richard DawesNational Science FoundationTheoretical and computational chemistry, potential energy surfaces, and molecular dynamics
Lin HeNational Science FoundationChemical measurement and imaging, bioanalysis, and data-driven discovery
Larry SitaUniversity of Maryland, College ParkOrganometallic chemistry, catalyst development, and advanced polymeric materials
Garik PetrosyanDeep OriginArtificial intelligence, molecular modeling, and computational biology for therapeutic design
Yanxin LiuUniversity of Maryland, College ParkCryo-electron microscopy, molecular dynamics simulation, and structural biophysics
Wolfgang LosertUniversity of Maryland, College ParkChemical excitability, neuromorphic computing, neuroAI, and cellular phenotypes
JC GumbartGeorgia Institute of TechnologyAtomistic molecular dynamics, computational biophysics, and bacterial membrane systems

Objectives

  1. Start with the big science questions: what should AI help chemists do soon, and what should it make possible over the next decade?
  2. Figure out what data we actually need, including experimental data, simulation data, metadata, provenance, uncertainty estimates, and negative results.
  3. Ask what the models need to be good at, from better representations and generative models to benchmarks, uncertainty tools, active learning, physics-aware methods, and interpretable AI.
  4. Decide what should be shared across chemistry and what should stay specialized for particular subfields, simulations, journals, repositories, or industry settings.
  5. Leave with a few pilot ideas that people can actually act on within 1-2 years, while still pointing toward the longer 5-10 year vision.

Organizing Committee

Pratyush Tiwary Pratyush Tiwary University of Maryland, College Park Workshop chair
Connor Coley Connor Coley Massachusetts Institute of Technology Co-organizer
Ryan Jorn Ryan Jorn Wichita State University Co-organizer