The field of data-driven chemistry is undergoing an evolution, driven by innovations in
machine learning models for predicting molecular properties and behavior. Recent strides in …

Machine learning (ML) approaches enable large-scale atomistic simulations with near-
quantum-mechanical accuracy. With the growing availability of these methods, there arises …

Spectroscopy and chemometrics, supported by computer science, have yielded promising
outcomes, as evidenced by trends observed in literature searches. However, while …

Neural networks (NNs) often assign high confidence to their predictions, even for points far
out of distribution, making uncertainty quantification (UQ) a challenge. When they are …

Data science is assuming a pivotal role in guiding reaction optimization and streamlining
experimental workloads in the evolving landscape of synthetic chemistry. A discipline-wide …

Machine learning potentials (MLPs) trained on accurate quantum chemical data can retain
the high accuracy, while inflicting little computational demands. On the downside, they need …

Machine learning potentials have become an essential tool for atomistic simulations,
yielding results close to ab initio simulations at a fraction of computational cost. With recent …

ABSTRACT A reliable uncertainty estimator is a key ingredient in the successful use of
machine-learning force fields for predictive calculations. Important considerations are …

Abstract Machine learning (ML) including Abraham descriptors from polyparameter linear
free energy relationships (pp-LFERs) has been a popular method for the adsorption …

Machine learning (ML) methods have shown promise for discovering novel catalysts but are
often restricted to specific chemical domains. Generalizable ML models require large and …