Harnessing Chemical Understanding with Machine Learning and Quantum Computing – the 5th International Symposium on Chemical Concepts from Theory and Computation (CCTC#5)

Shubin Liu — shubin@email.unc.edu

Heather Kulik

Alán Aspuru-Guzik

Clémence Corminboeuf

Garnet Chan

The Chemical Concepts from Theory and Computation (CCTC) series provides a continuing forum to explore how chemical understanding emerges from theoretical and computational frameworks. Four prior symposiums in the series were held focusing on how quantum theories—Valence Bond Theory (VBT), Molecular Orbital Theory (MOT), and Density Functional Theory (DFT)—gave rise to the conceptual language of modern chemistry. The upcoming CCTC#5 workshop, the 5th international symposium on CCTC, aims to extend this inquiry into the age of Machine Learning (ML)/Artificial Intelligence (AI) and Quantum Computing (QC).

The 2024 Nobel Prizes in Physics and Chemistry recognized AI/ML breakthroughs as inaugurating “Year 1 of AI for Science.” These advances have transformed prediction and design in chemistry, yet they also accentuate a central tension: prediction is not understanding. Chemistry thrives on conceptual scaffolds—bonding, aromaticity, acidity, reactivity—that translate abstract data into intuitive knowledge. The new ML/AI and QC paradigms offer both a challenge (black-box models, interpretability) and an opportunity
(concept discovery, hierarchy, and emergence). This proposal symposium will explore how ML/AI and QC can be harnessed to cultivate chemical understanding.

Key scientific goals include:
• Epistemology of ML and QC: understanding representation, hierarchy, and emergence in data-driven science.
• Physics of ML models: mapping latent features and order parameters to physical observables.
• Quantum hierarchies: developing conceptual modeling frameworks for quantum algorithms and hybrid ML-QC approaches.
• Concept discovery: revisiting classical chemical ideas through modern computational paradigms.
• Knowledge Forest framework: integrating ontology, epistemology, and theory to form recursive, pluralistic understanding.

This symposium will provide a platform for interdisciplinary exchange, bridging physical theory, and data-driven modeling, laying the groundwork for chemical understanding in the ML/QC era.

We wish to ensure an intimate workshop setting, with no more than 20 to 25 participants. If you are interested in attending but have not received an invitation, please contact the workshop organizer before registering.

Sessions typically run mornings and late afternoons, leaving mid-day free for informal collaboration.

Travel to Telluride can be expensive; please assess your own potential travel costs before registering.

Please book your travel plans! The Montrose Regional Airport (MTJ) is the most reliable and convenient airport for Telluride.

Telluride Science can provide lodging starting as low as $215/night during off-peak weeks and as low as $250/night during peak-season at the Mountainside Inn. These affordable lodging options are limited and available first-come, first-serve. To encourage participants to secure affordable lodging, we offer a $100 discount on lodging provided through Telluride Science that is secured before the Early Bird deadline. Additionally, we work with many lodging providers in Telluride and Mountain Village to offer discounts between 10-15% off your total lodging balance. Additional information provided with registration.

If you have more questions about lodging, reach out to Sara at sara@telluridescience.org.

Registrations may be refunded less a $25 cancellation fee. If cancelled before the lodging cancellation deadline, lodging payments are refundable less a $100 cancellation fee. It is each participant's responsibility to confirm and understand their own lodging cancellation deadline. Travel insurance is highly recommended.