mlsanityData to 7 Oct 2026

Molecular Energy Learning Using Alternative Blackbox Matrix-Matrix Multiplication Algorithm for Exact Gaussian Process

arXiv 2109.09817 ↗First posted 20 Sep 2021physics.chem-ph cs.LG physics.comp-phcited by 3

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Abstract (arXiv)

We present an application of the blackbox matrix-matrix multiplication (BBMM) algorithm to scale up the Gaussian Process (GP) training of molecular energies in the molecular-orbital based machine learning (MOB-ML) framework. An alternative implementation of BBMM (AltBBMM) is also proposed to train more efficiently (over four-fold speedup) with the same accuracy and transferability as the original BBMM implementation. The training of MOB-ML was limited to 220 molecules, and BBMM and AltBBMM scale the training of MOB-ML up by over 30 times to 6500 molecules (more than a million pair energies). The accuracy and transferability of both algorithms are examined on the benchmark datasets of organic molecules with 7 and 13 heavy atoms. These lower-scaling implementations of the GP preserve the state-of-the-art learning efficiency in the low-data regime while extending it to the large-data regime with better accuracy than other available machine learning works on molecular energies.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 6 of its 23 references that are in the map. First posted on arXiv 20 Sep 2021.

Builds on

6 of 23 references.

References · 6

  1. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 603
  2. 2013Gaussian Processes for Big Datacited by 377
  3. 2019Exact Gaussian Processes on a Million Data Pointscited by 118
  4. 2020OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Featurescited by 63
  5. 2016Preconditioning Kernel Matricescited by 32
  6. 2019A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Moleculescited by 9

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Cited by year

Citing papers per year: 2022: 3.

3 papers.

  1. 2022Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Spacecited by 3
  2. 2022Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learningcited by 1
  3. 2022Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regressioncited by 1