Equivariant conditional diffusion model for exploring the chemical space around Vaska’s complex

François Cornet*, Pratham Deshmukh, Bardi Benediktsson, Mikkel N. Schmidt, Arghya Bhowmik

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

25 Downloads (Pure)

Abstract

Generative modelling has recently emerged as a promising tool to efficiently explore the vast chemical space. In homogeneous catalysis, Transition Metal Complexes (TMCs) are ubiquitous, and finding better TMC catalysts is critical to a number of technologically relevant reactions. Evaluating reaction rates requires expensive transition state (TS) structure search, making traditional library-based screening difficult. Inverse-design of TMCs with a model capable of generating good TS guesses can lead to breakthroughs in catalytic science. We present such generative model herein. The model is an instance of an equivariant conditional diffusion model, and the key innovation lies in its specific data representation and training procedure, that allow generic databases (e.g. non-TS structures) to be leveraged at training time, while offering the desired controllability at sampling time (e.g. ability to generate TSs on demand). We demonstrate that augmenting the training database with generic (but related) data enables a practical level of performance to be reached. In a case study, our model successfully explores the chemical space around Vaska’s complex, where the property of interest is the H2-activation barrier, in two distinct settings: generation from scratch, and redesign of a specific ligand in a known TMC. In both cases, we validate a selection of novel samples with Density Functional Theory (DFT) calculations.
Original languageEnglish
Title of host publicationProceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS 2024) - AI4Mat Workshop
Number of pages10
Publication statusAccepted/In press - 2025
Event38th Conference on Neural Information Processing Systems - Vancouver, Canada
Duration: 10 Dec 202415 Dec 2024

Conference

Conference38th Conference on Neural Information Processing Systems
Country/TerritoryCanada
CityVancouver
Period10/12/202415/12/2024

Fingerprint

Dive into the research topics of 'Equivariant conditional diffusion model for exploring the chemical space around Vaska’s complex'. Together they form a unique fingerprint.

Cite this