activity
20242026
most citedAccurate and efficient machine learning interatomic potentials for finite temperature modeling of molecular crystals

11 citations · 61 across the 11 of their papers we have counts for

collaborators

11 papers

cond-mat.mtrl-sci2026

Towards viable H storage in Ca decorated low-dimensional materials with insights from reference quantum Monte Carlo

Yasmine S. Al-Hamdani, Dario Alfè, Andrea Zen

Hydrogen technology is set to be a key energy alternative for mitigating pollution and reducing CO emissions. However, the current storage mechanism of hydrogen molecules in ca…

physics.chem-ph2026

Assessing the impact of nodal surface optimization in fixed-node diffusion Monte Carlo on non-covalent interactions

Kousuke Nakano, Benjamin X. Shi, Dario Alfè +1

Diffusion quantum Monte Carlo (DMC) and coupled cluster theory [CCSD(T)] are widely-employed benchmark methods for noncovalent interactions (NCIs). However, recent studies have rep…

cond-mat.mtrl-sci2026★ 1 cited

Efficient first-principles modeling of complex molecular crystals at sub-chemical accuracy

Benjamin X. Shi, Kristina M. Herman, Flaviano Della Pia +5

Molecules can form myriad crystalline polymorphs, each with distinct properties affecting their performance across diverse applications, from pharmaceuticals to functional material…

physics.comp-ph2025★ 11 cited

Accurate and efficient machine learning interatomic potentials for finite temperature modeling of molecular crystals

Flaviano Della Pia, Benjamin X. Shi, Venkat Kapil +3

As with many parts of the natural sciences, machine learning interatomic potentials (MLIPs) are revolutionizing the modeling of molecular crystals. However, challenges remain for t…

physics.comp-ph2025★ 7 cited

Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water-methane dimer

Flaviano Della Pia, Benjamin X. Shi, Yasmine S. Al-Hamdani +30

Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely-trusted many-body method for solving the Schrödinger equation, known for its reliable predictions of material and mole…

cond-mat.mtrl-sci2024★ 8 cited

A brief introduction to the diffusion Monte Carlo method and the fixed-node approximation

Alfonso Annarelli, Dario Alfè, Andrea Zen

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

physics.chem-ph2024★ 8 cited

An accurate and efficient framework for modelling the surface chemistry of ionic materials

Benjamin X. Shi, Andrew S. Rosen, Tobias Schäfer +4

Quantum-mechanical simulations can offer atomic-level insights into chemical processes on surfaces. This understanding is crucial for the rational design of new solid catalysts as…

physics.chem-ph2024★ 11 cited

Systematic discrepancies between reference methods for non-covalent interactions within the S66 dataset

Benjamin X. Shi, Flaviano Della Pia, Yasmine S. Al-Hamdani +3

The accurate treatment of non-covalent interactions is necessary to model a wide range of applications, from molecular crystals to surface catalysts to aqueous solutions and many m…

physics.chem-ph2024★ 9 cited

Basis set incompleteness errors in fixed-node diffusion Monte Carlo calculations on non-covalent interactions

Kousuke Nakano, Benjamin X. Shi, Dario Alfè +1

Basis set incompleteness error (BSIE) is a common source of error in quantum chemistry (QC) calculations, but it has not been comprehensively studied in fixed-node Diffusion Monte…

cond-mat.mtrl-sci2024★ 5 cited

On the increase of the melting temperature of water confined in one-dimensional nano-cavities

Flaviano Della Pia, Andrea Zen, Venkat Kapil +3

Water confined in nanoscale cavities plays a crucial role in everyday phenomena in geology and biology, as well as technological applications at the water-energy nexus. However, ev…

cond-mat.mtrl-sci2024★ 1 cited

Cooperative CO capture via oxalate formation on metal-decorated graphene

Inioluwa Christianah Popoola, Benjamin Xu Shi, Fabian Berger +5

CO capture using carbon-based materials, particularly graphene and graphene-like materials, is a promising strategy to deal with CO emissions. However, significant gaps rem…