The climbing-image nudged elastic band (CI-NEB) method serves as an indispensable tool
for computational chemists, offering insight into minimum-energy reaction paths (MEPs) by …

Reconstructing force fields (FFs) from atomistic simulation data is a challenge since accurate
data can be highly expensive. Here, machine learning (ML) models can help to be data …

We present a novel approach for systematically exploring the conformational space of small
molecules with multiple internal torsions. Identifying unique conformers through a systematic …

Gaussian process (GP) regression has been recently developed as an effective method in
molecular geometry optimization. The prior mean function is one of the crucial parts of the …

We present a molecular geometry optimization algorithm based on the gradient-enhanced
universal kriging (GEUK) formalism with ab initio prior mean functions, which incorporates …

First-principle molecular structural determination is critical in many aspects of computational
modeling, and yet, the precise determination of a local minimum for a large-sized organic …

Geometry optimization is a crucial step in computational chemistry, and the efficiency of
optimization algorithms plays a pivotal role in reducing computational costs. In this study, we …

Recent work has demonstrated the promise of using machine-learned surrogates, in
particular, Gaussian process (GP) surrogates, in reducing the number of electronic structure …

This paper focuses on the development of a surrogate model to predict the macroscopic
elastic properties of polymer composites doped with spherical particles. To this aim, a …