Machine learning frontier orbital energies of nanodiamonds

2022-09-30 · Thorren Kirschbaum, Börries von Seggern, Joachim Dzubiella, Annika Bande, Frank Noé

Nanodiamonds have a wide range of applications including catalysis, sensing, tribology and biomedicine. To leverage nanodiamond design via machine learning, we introduce the new dataset ND5k, consisting of 5,089 diamondoid and nanodiamond structures and their frontier orbital energies. ND5k structures are optimized via tight-binding density functional theory (DFTB) and their frontier orbital energies are computed using density functional theory (DFT) with the PBE0 hybrid functional. We also compare recent machine learning models for predicting frontier orbital energies for similar structures as they have been trained on (interpolation on ND5k), and we test their abilities to extrapolate predictions to larger structures. For both the interpolation and extrapolation task, we find best performance using the equivariant graph neural network PaiNN. The second best results are achieved with a message passing neural network using a tailored set of atomic descriptors proposed here.

📄 PDF Abstract BibTeX arXiv:2210.07930

Code (0)

등록된 구현이 없습니다.

Tasks

Graph Neural Network

Methods 이 논문이 사용한 방법론

Graph Neural Network 설명 없음

Test 설명 없음

Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space

2022-04-21 · Lixue Cheng, Jiace Sun, Thomas F. Miller III

We introduce an unsupervised clustering algorithm to improve training efficiency and accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML). This work determines clusters via the Gaussian…

BIG-bench Machine Learning Clustering GPR regression

Many-body Expansion Based Machine Learning Models for Octahedral Transition Metal Complexes

2024-10-12 · Ralf Meyer, Daniel Benjamin Kasman Chu, Heather J. Kulik

Graph-based machine learning models for materials properties show great potential to accelerate virtual high-throughput screening of large chemical spaces. However, in their simplest forms, graph-based models do not incl…

Systematic Generalization

Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning

2019-09-04 · Lixue Cheng, Nikola B. Kovachki, Matthew Welborn, Thomas F. Miller III

Machine learning (ML) in the representation of molecular-orbital-based (MOB) features has been shown to be an accurate and transferable approach to the prediction of post-Hartree-Fock correlation energies. Previous appli…

BIG-bench Machine Learning Clustering GPR Prediction+1

Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning

2022-05-31 · Jiace Sun, Lixue Cheng, Thomas F. Miller III

This study extends the accurate and transferable molecular-orbital-based machine learning (MOB-ML) approach to modeling the contribution of electron correlation to dipole moments at the cost of Hartree-Fock computations.…

GPR

A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules

2019-01-10 · Lixue Cheng, Matthew Welborn, Anders S. Christensen, Thomas F. Miller III

We address the degree to which machine learning can be used to accurately and transferably predict post-Hartree-Fock correlation energies. Refined strategies for feature design and selection are presented, and the molecu…

BIG-bench Machine Learning Diversity