Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

We present a discussion of recent progress in excited‐state‐specific quantum chemistry and
quantum Monte Carlo alongside a demonstration of how a combination of methods from …

We have carried out quantum Monte Carlo (QMC) calculations of silicon crystal focusing on
the accuracy and systematic biases that affect the electronic structure characteristics. The …

We extend our hybrid linear-method/accelerated-descent variational Monte Carlo
optimization approach to excited states and investigate its efficacy in double excitations. In …

We describe a new open-source Python-based package for high accuracy correlated
electron calculations using quantum Monte Carlo (QMC) in real space: PyQMC. PyQMC …

We present unbiased, finite-variance estimators of energy derivatives for real-space
diffusion Monte Carlo calculations within the fixed-node approximation. The derivative dλ E …

The integration of deep neural networks with the variational Monte Carlo (VMC) method has
marked a substantial advancement in solving the Schrödinger equation. In this work we …

Matrix product state methods are known to be efficient for computing ground states of local,
gapped Hamiltonians, particularly in one dimension. We introduce the multi-targeted density …

When calculating the optical absorption spectra of molecular crystals from first principles, the
influence of the crystalline environment on the excitations is of significant importance. For …

Quantum Monte Carlo (QMC) methods represent a powerful family of computational
techniques for tackling complex quantum many-body problems and performing calculations …