Search Results for author:
Found 5 papers, 4 papers with code
Cartesian atomic cluster expansion for machine learning interatomic potentials
Many potentials use atomic cluster expansion or equivariant message passing frameworks.
GAUCHE: A Library for Gaussian Processes in Chemistry
By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry.
BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale
We introduce a machine-learning (ML) framework for high-throughput benchmarking of diverse representations of chemical systems against datasets of materials and molecules.
Ranking the information content of distance measures
Real-world data typically contain a large number of features that are often heterogeneous in nature, relevance, and also units of measure.
Ab initio thermodynamics of liquid and solid water
Thermodynamic properties of liquid water as well as hexagonal (Ih) and cubic (Ic) ice are predicted based on density functional theory at the hybrid-functional level, rigorously taking into account quantum nuclear motion, anharmonic fluctuations and proton disorder.
Materials Science Statistical Mechanics Chemical Physics
