Here we introduce the LASP code, which is designed for large‐scale atomistic simulation of
complex materials with neural network (NN) potential. The software architecture and …

Recent advances in the potential energy landscapes approach are highlighted, including
both theoretical and computational contributions. Treating the high dimensionality of …

We describe a flexible and broadly applicable energy refinement method,“nebterpolation,”
for identifying and characterizing the reaction events in a molecular dynamics (MD) …

Machine learning techniques are being increasingly used as flexible non-linear fitting and
prediction tools in the physical sciences. Fitting functions that exhibit multiple solutions as …

Rapid advances in quantum computing have opened up new opportunities for solving the
central electronic structure problem in computational chemistry. In the noisy intermediate …

The potential energy landscape perspective provides both a conceptual and a
computational framework for predicting, understanding and designing molecular properties …

This report advances the hypothesis that multifunctional systems may be associated with
multifunnel potential and free energy landscapes, with particular focus on biomolecules. It …

We implemented a gradient-based algorithm for transition state search which uses Gaussian
process regression. Besides a description of the algorithm, we provide a method to find the …

The design of novel materials requires a theoretical understanding of dynamical processes
in the solid state, including polymorphic transitions and associated pathways. The …

Nucleation is one of the most common physical phenomena in physical, chemical, biological
and materials sciences. Owing to the complex multiscale nature of various nucleation events …