Improved decision making with similarity based machine learning: Applications in chemistry
arXiv:2205.05633 · doi:10.1088/2632-2153/ad0fa3
Abstract
Despite the fundamental progress in autonomous molecular and materials discovery, data scarcity throughout chemical compound space still severely hampers the use of modern ready-made machine learning models as they rely heavily on the paradigm, 'the bigger the data the better'. Presenting similarity based machine learning (SML), we show an approach to select data and train a model on-the-fly for specific queries, enabling decision making in data scarce scenarios in chemistry. By solely relying on query and training data proximity to choose training points, only a fraction of data is necessary to converge to competitive performance. After introducing SML for the harmonic oscillator and the Rosenbrock function, we describe applications to scarce data scenarios in chemistry which include quantum mechanics based molecular design and organic synthesis planning. Finally, we derive a relationship between the intrinsic dimensionality and volume of feature space, governing the overall model accuracy.
References in corpus (13)
- Machine Learning Unifies the Modelling of Materials and Molecules
- Towards self-driving laboratories: The central role of density functional theory in the AI age
- Equivariant Diffusion for Molecule Generation in 3D
- Bayesian optimization with known experimental and design constraints for chemistry applications
- EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
- Torsional Diffusion for Molecular Conformer Generation
- High-throughput property-driven generative design of functional organic molecules
- Emergent autonomous scientific research capabilities of large language models
- Machine Learning of Free Energies in Chemical Compound Space Using Ensemble Representations: Reaching Experimental Uncertainty for Solvation
- Exploring the robust extrapolation of high-dimensional machine learning potentials
- Geometric Latent Diffusion Models for 3D Molecule Generation
- Reducing Training Data Needs with Minimal Multilevel Machine Learning (M3L)
- Intrinsic dimension estimation for discrete metrics