DeepAI AI Chat
Log In Sign Up

An Atomistic Machine Learning Package for Surface Science and Catalysis

by   Martin Hangaard Hansen, et al.
Stanford University

We present work flows and a software module for machine learning model building in surface science and heterogeneous catalysis. This includes fingerprinting atomic structures from 3D structure and/or connectivity information, it includes descriptor selection methods and benchmarks, and it includes active learning frameworks for atomic structure optimization, acceleration of screening studies and for exploration of the structure space of nano particles, which are all atomic structure problems relevant for surface science and heterogeneous catalysis. Our overall goal is to provide a repository to ease machine learning model building for catalysis, to advance the models beyond the chemical intuition of the user and to increase autonomy for exploration of chemical space.


Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions

Machine learning advances chemistry and materials science by enabling la...

Atomistic structure search using local surrogate mode

We describe a local surrogate model for use in conjunction with global s...

Compressing physical properties of atomic species for improving predictive chemistry

The answers to many unsolved problems lie in the intractable chemical sp...

Completeness of Atomic Structure Representations

Achieving a complete and symmetric description of a group of point parti...

Atomic structure generation from reconstructing structural fingerprints

Data-driven machine learning methods have the potential to dramatically ...

Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation

An active learning procedure called Deep Potential Generator (DP-GEN) is...

An Experimentally Driven Automated Machine Learned lnter-Atomic Potential for a Refractory Oxide

Understanding the structure and properties of refractory oxides are crit...