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Found 4 papers, 1 papers with code
Equivariant Matrix Function Neural Networks
Graph Neural Networks (GNNs), especially message-passing neural networks (MPNNs), have emerged as powerful architectures for learning on graphs in diverse applications.
Ranked #3 on
Graph Regression
on ZINC-500k
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.
Neural networks and kernel ridge regression for excited states dynamics of CH$_2$NH$_2^+$: From single-state to multi-state representations and multi-property machine learning models
As an ultimate test for our machine learning models, we carry out excited-state dynamics simulations based on the predicted energies, forces and couplings and, thus, show the scopes and possibilities of machine learning for the treatment of electronically excited states.
Machine learning prediction errors better than DFT accuracy
We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state properties of organic molecules.
Ranked #18 on
Formation Energy
on QM9
