304 citations · 1.1k across the 21 of their papers we have counts for
6 papers · 1 filter
BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps
Lars L. Schaaf, Ilyes Batatia, Christoph Brunken +2
Simulating atomic-scale processes, such as protein dynamics and catalytic reactions, is crucial for advancements in biology, chemistry, and materials science. Machine learning forc…
Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies
Harveen Kaur, Flaviano Della Pia, Ilyes Batatia +6
Calculating sublimation enthalpies of molecular crystal polymorphs is relevant to a wide range of technological applications. However, predicting these quantities at first-principl…
Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
Wojciech G. Stark, Cas van der Oord, Ilyes Batatia +4
Simulations of chemical reaction probabilities in gas surface dynamics require the calculation of ensemble averages over many tens of thousands of reaction events to predict dynami…
Zero Shot Molecular Generation via Similarity Kernels
Rokas Elijošius, Fabian Zills, Ilyes Batatia +4
Generative modelling aims to accelerate the discovery of novel chemicals by directly proposing structures with desirable properties. Recently, score-based, or diffusion, generative…
Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials
Ivan Grega, Ilyes Batatia, Gábor Csányi +2
Lattices are architected metamaterials whose properties strongly depend on their geometrical design. The analogy between lattices and graphs enables the use of graph neural network…
A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang +85
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much…