Rokas Elijošius
A5093642947- Chemical Reactions and Isotopes
- Scientific Computing and Data Management
- Machine Learning in Materials Science
- Spectroscopy Techniques in Biomedical and Chemical Research
Machine-learned force fields have transformed the atomistic modelling of materials by enabling simulations ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) significant computational human effort that must go into development validation potentials for each particular system interest; (ii) a general lack transferability from one chemical to next. Here, using state-of-the-art MACE architecture we introduce single general-purpose ML model,...
Generative modelling aims to accelerate the discovery of novel chemicals by directly proposing structures with desirable properties. Recently, score-based, or diffusion, generative models have significantly outperformed previous approaches. Key their success is close relationship between score and physical force, allowing use powerful equivariant neural networks. However, behaviour learnt not yet well understood. Here, we analyse training an energy-based diffusion model for molecular...