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

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

Submission Track: Short Paper

Submission Category: AI-Guided Design

Keywords: diffusion model, vaskas

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

AI4Mat Journal Track: Yes

Submission Number: 89