2026 CAP Congress / Congrès de l'ACP 2026

Session

W1-2 SYMPOSIUM: Big Data in Matter, Materials, and Beyond | Le Big Data dans la matière, les matériaux et au-delà

W1-2

24 Jun 2026, 10:15

U. Ottawa - Learning Crossroads (CRX) Building

Description

Physics has been an empirical science for centuries, but contemporary data exist in quantities that dwarf Newton’s Principia. Today, marshalling data to distill the understanding to describe emergent behaviours, invent new materials, and develop new solutions for improving technologies and human health requires fundamentally different approaches. This symposium will draw together physicists working across disciplinary borders to forge new approaches to empirical science in the 21st century.

Planned speakers include expertise in machine learning and artificial intelligence, information geometry, big data, and applications in materials, condensed matter, biophysics, and the physics of medicine. In addition, speakers include Canadians currently abroad, foreign physicists near (ish) to Ottawa or with research connections to Canada, and Canadian physicists at diverse career stages. We expect contributed talks and participation from students in condensed matter and beyond with academic and industry interests in machine learning, data science, and applied physics.

La physique est une science empirique depuis des siècles, mais les données contemporaines existent en quantités qui éclipsent les Principia de Newton. Aujourd'hui, rassembler des données pour distiller la compréhension afin de décrire les comportements émergents, inventer de nouveaux matériaux et développer de nouvelles solutions pour améliorer les technologies et la santé humaine nécessite des approches fondamentalement différentes. Ce symposium réunira des physiciens travaillant au-delà des frontières disciplinaires afin de forger de nouvelles approches de la science empirique au XXIe siècle.

Parmi les intervenants prévus figurent des experts en apprentissage automatique et en intelligence artificielle, en géométrie de l'information, en mégadonnées et en applications dans les domaines des matériaux, de la matière condensée, de la biophysique et de la physique médicale. En outre, les intervenants comprennent des Canadiens actuellement à l'étranger, des physiciens étrangers proches (ou presque) d'Ottawa ou ayant des liens de recherche avec le Canada, ainsi que des physiciens canadiens à différentes étapes de leur carrière. Nous attendons des contributions et la participation d'étudiants en matière condensée et au-delà, qui s'intéressent à l'apprentissage automatique, à la science des données et à la physique appliquée dans le milieu universitaire et industriel.

1048. Discovery and design of emergent behavior in soft materials

Erin Teich (Wellesley College)

24/06/2026, 10:15

Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delà

Invited Speaker / Conférencier(ère) invité(e)

Nature organizes itself with often startling complexity at every length scale accessible to human inquiry, resulting in a wide range of materials with varied structural and dynamical properties. An outstanding current goal of materials science is to harness the often-subtle self-organization displayed by Nature in order to design materials with tailor-made functionalities in the laboratory....

765. Discovering New Spectroscopic Signatures of Polymer Degradation through Deep Generative Modeling of Infrared Imaging Data

John Dutcher

24/06/2026, 10:45

Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delà

Invited Speaker / Conférencier(ère) invité(e)

Machine learning (ML) and artificial intelligence (AI) approaches are revolutionizing the analysis of large data sets. We are contributing to this effort by applying a deep learning approach to analyzing a very large number of infrared (IR) spectra of polymer cross-linked polyethylene (PEX) pipes that are used in household and industrial applications. This work is at the forefront of the...

860. From Optimization to Emergence: Collective Dynamics in Design and Inference

Hazhir Aliahmadi (Queens University)

24/06/2026, 11:15

Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delà

Invited Speaker / Conférencier(ère) invité(e)

Optimization has long served as the primary paradigm for inference and design across science and engineering. More recently, machine learning has shifted this picture by trading explicit structure for universality in highly over-parameterized black-box models. Though powerful, over-parameterization obscures the interpretability of individual degrees of freedom, an effect further amplified by...

533. Data-centred computational materials physics: reproducible workflows, benchmarking datasets, and high-throughput screening

Oleg Rubel (McMaster University)

24/06/2026, 11:45

Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delà

Invited Speaker / Conférencier(ère) invité(e)

The discovery of novel materials increasingly relies on first-principles electronic-structure theory, but the value of these predictions depends on their reproducibility and connection to measurable observables. A recent community effort demonstrates that standardized, automated workflows (such as AiiDA) can establish high-precision reference datasets. By cross-validating all-electron...