Molecules and Materials

The intrinsic challenges of molecule/material discovery stimulate the advancement of AI knowledge.

Vision

Exploring and exploiting the design spaces of molecules and materials in terms of synthesis, processes and characterization, requires access to large quantities of high-quality multimodal data, which is a bottleneck. The use of robust AI is therefore essential, as is the integration of aspects of the scientific method, where AI behaves like an expert to be reasoning to develop predictive protocols informed by current knowledge. The concept of responsibility applies in the context where models must align with values linked to the development of molecules and materials, such as eco-responsibility. This Regroupement (R6) promotes reciprocal exchanges between AI and materials science experts, as illustrated below.

Visual: Reciprocal exchanges between AI experts and molecular and materials experts.

Objectives

The main objectives will be to advance AI methods, which currently face certain challenges when put into practice in the context of molecules and materials.

Learn rich representations leveraging existing physico-chemical knowledge to predict accurately molecule/material properties, activities, and dynamics

Deep representation learning
Uncertainty quantification

Enable efficient search in large, structured spaces

Active learning
Generative models (ex: GFlowNets)

Accelerate experiments by learning through interactions

Reinforcement learning/Bandits
(Bayesian) Optimization

Challenges

To achieve the above objectives, and respond to the problems identified in connection with data in the field of molecules and materials, it is proposed to set up a process in which various cross-disciplinary tools for the themes and projects explored are developed.

The aim of these transversal tools is to federate a community around the development of common tools, useful for projects at the interface of AI, molecules and materials, as opposed to development that takes place in silos. The proposed cross-disciplinary tools are as follows:

Identifying appropriate descriptors
Candidate generation
Accelerating experiments

Anticipated Impact

Linking expertise in AI and in molecules and materials will enable the development of common cross-disciplinary tools that will have a structuring effect on the community, bridging the gap between Université de Montréal’s sectors of excellence: Digital, Artificial and Human Intelligence, Bioinnovation and Digital Health, and Materials and Processes of Tomorrow. R6 will also help train the next generation of researchers who will be familiar with concepts at the intersection of AI, molecules and materials, and who will be able to act as translators between these fields to make a coherent contribution. Linking activities with existing initiatives (PandemicStop-AI and the Institut Courtois) will maximize the leverage effect of R³AI.

Developing a new generation of antimicrobial compounds