6 citations · 10 across the 5 of their papers we have counts for
6 papers
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models
Yu Xie, Ludwig Winkler, Lixin Sun +10
The rare-event sampling problem has long been the central limiting factor in molecular dynamics (MD), especially in biomolecular simulation. Recently, diffusion models such as BioE…
Extending the RANGE of Graph Neural Networks: Relaying Attention Nodes for Global Encoding
Alessandro Caruso, Jacopo Venturin, Lorenzo Giambagli +3
Graph Neural Networks (GNNs) are routinely used in molecular physics, social sciences, and economics to model many-body interactions in graph-like systems. However, GNNs are inhere…
Accurate nuclear quantum statistics on machine-learned classical effective potentials
Iryna Zaporozhets, Félix Musil, Venkat Kapil +1
The contribution of nuclear quantum effects (NQEs) to the properties of various hydrogen-bound systems, including biomolecules, is increasingly recognized. Despite the development…
Learning data efficient coarse-grained molecular dynamics from forces and noise
Aleksander E. P. Durumeric, Yaoyi Chen, Frank Noé +1
Machine-learned coarse-grained (MLCG) molecular dynamics is a promising option for modeling biomolecules. However, MLCG models currently require large amounts of data from referenc…
Lattice matched heterogeneous nucleation eliminate defective buried interface in halide perovskites
Paramvir Ahlawat, Cecilia Clementi, Felix Musil +1
Metal halide perovskite-based semi-conducting hetero-structures have emerged as promising electronics for solar cells, light-emitting diodes, detectors, and photo-catalysts. Perovs…