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Updated: Jun 6, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Data Generation for Machine Learning Interatomic Potentials and Beyond.

Maksim Kulichenko¹, Benjamin Nebgen¹, Nicholas Lubbers²

¹Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.

Chemical Reviews

|November 21, 2024

Summary

This summary is machine-generated.

High-quality training data is crucial for reliable machine learning models in chemistry. This review explores methods for creating effective datasets to improve model performance and applicability.

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Area of Science:

Data-driven chemistry
Computational chemistry
Materials science

Background:

Machine learning models are revolutionizing molecular property prediction.
ML-based interatomic potentials (MLIPs) enable accurate atomic-level simulations.
Training data quality is the primary factor for MLIP reliability.

Purpose of the Study:

To review essential components and integrity of training data for MLIPs.
To discuss methods for ensuring model extensibility and transferability.
To highlight strategies for constructing domain-specific training sets.

Main Methods:

Active learning strategies and implementations.
Uncertainty quantification for atomistic data acquisition.
Data acquisition using modified and surrogate potential energy surfaces.
Role of atomistic data samplers.

Main Results:

Active learning and uncertainty quantification enhance data acquisition.
Diverse structures are generated using specialized data samplers.
Novel methods improve training data diversity.
Publicly available datasets covering key chemical spaces are listed.

Conclusions:

Effective training data construction is vital for advancing data-driven chemistry.
Methods discussed improve the reliability and applicability of MLIPs.
The review provides a guide for researchers in building robust ML models.