Δ-machine learning for potential energy surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) level of theory

Hierarchical machine learning of potential energy surfaces

(2020) Pavlo O. Dral et al.   JOURNAL OF CHEMICAL PHYSICS

Efficient Generation of Permutationally Invariant Potential Energy Surfaces for Large Molecules

(2020) Riccardo Conte et al.   Journal of Chemical Theory and Computation

Reactive Dynamics and Spectroscopy of Hydrogen Transfer from Neural Network-Based Reactive Potential Energy Surfaces

(2020) Silvan Käser et al.   NEW JOURNAL OF PHYSICS

Permutationally invariant polynomial potential energy surfaces for tropolone and H and D atom tunneling dynamics

(2020) Paul Houston et al.   JOURNAL OF CHEMICAL PHYSICS

Accurate Many-Body Repulsive Potentials for Density-Functional Tight Binding from Deep Tensor Neural Networks

(2020) Martin Stöhr et al.   Journal of Physical Chemistry Letters

Full-dimensional, ab initio potential energy surface for glycine with characterization of stationary points and zero-point energy calculations by means of diffusion Monte Carlo and semiclassical dynamics

(2020) Riccardo Conte et al.   JOURNAL OF CHEMICAL PHYSICS

Using Gradients in Permutationally Invariant Polynomial Potential fitting: A Demonstration for CH4 Using as Few as 100 Configurations.

(2019) Apurba Nandi et al.   Journal of Chemical Theory and Computation

Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces

(2019) Huziel E. Sauceda et al.   JOURNAL OF CHEMICAL PHYSICS

A fragmented, permutationally invariant polynomial approach for potential energy surfaces of large molecules: Application to N-methyl acetamide

(2019) Chen Qu et al.   JOURNAL OF CHEMICAL PHYSICS

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

(2019) Justin S. Smith et al.   Nature Communications

Full and fragmented permutationally invariant polynomial potential energy surfaces for trans and cis N-methyl acetamide and isomerization saddle points

(2019) Apurba Nandi et al.   JOURNAL OF CHEMICAL PHYSICS

Permutationally Invariant Potential Energy Surfaces

(2018) Chen Qu et al.   Annual Review of Physical Chemistry

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

Boosting quantum machine learning models with multi-level combination technique: Pople diagrams revisited

(2018) Peter Zaspel et al.   Journal of Chemical Theory and Computation

Ab Initio Potential for H3O+ → H+ + H2O: A Step to a Many-Body Representation of the Hydrated Proton?

(2016) Qi Yu et al.   Journal of Chemical Theory and Computation

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

(2015) Raghunathan Ramakrishnan et al.   Journal of Chemical Theory and Computation

High-dimensional ab initio potential energy surfaces for reaction dynamics calculations

(2011) Joel M. Bowman et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

A Survey on Transfer Learning

(2009) Sinno Jialin Pan et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Permutationally invariant potential energy surfaces in high dimensionality

(2009) Bastiaan J. Braams et al.   INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY