arXiv subjects

Hadi H. Arefi

Publications and source records attributed to Hadi H. Arefi.

5 recordsLinked to original sources

arXiv2026

Sequential replica exchange with solute tempering for atomistic modeling of supramolecular polymer structures

Predicting detailed atomistic structures of self-assembling systems remains a challenge for all-atom molecular dynamics simulations. Replica exchange with solute tempering (REST) has been used to study those systems by accelerating all monomers in a global and uniform manner. While such a global approach can in principle predict any morphology of the system, it has computational drawbacks such as inefficient replica traversal due to order-disorder transitions and the growing number of replicas with system size. To address these issues, here we propose an alternative, stepwise construction approach to modeling supramolecular polymers under the assumption of one-dimensional polymerization. Specifically, we generate polymer structures by adding new monomers one by one to the system and applying REST to the new monomers to find their optimal binding positions based on an energy-based scoring function. The monomer addition and enhanced sampling are repeated sequentially until a polymer of desired length is obtained. We test the above procedure using a model supramolecular polymer in explicit solvent, and show that it can generate a polymer structure with characteristic H-bonding patterns at reduced computational costs, while also improving the efficiency of replica traversal significantly. We thus expect that the sequential REST will be useful for modeling supramolecular polymers, particularly for cases where global REST simulations are too demanding computationally.

cond-mat.soft↗

arXiv2026

Graph Neural Networks for Fast Operator Selection in Adaptive VQE

Adaptive variational quantum algorithms like ADAPT-VQE construct tailored ansätze by iteratively selecting operators from a pool using gradient-based criteria. While this avoids oversized parameter spaces, repeatedly scanning the full pool incurs a classical cost that scales linearly with pool size-a major bottleneck for systems with long-range interactions or large operator sets. Here, we reformulate adaptive operator selection as a graph-based decision problem and introduce a graph neural network (GNN) policy that predicts the next entangling operator directly from the interaction graph and state-dependent observables. Training data are generated from exact simulations of disordered long-range spin chains, using gradient magnitudes as supervision signals. The learned policy accurately reproduces the dominant structure of the greedy gradient-based selection rule, significantly outperforming heuristics based solely on interaction strength. Integrated into a variational quantum eigensolver (VQE) workflow, this GNN-VQE approach achieves energy errors close to standard ADAPT-VQE while drastically reducing full-pool gradient evaluations. To test transferability beyond spin models, we evaluate the policy on small active-space molecular benchmarks (LiH and BeH_$2$). We find the GNN is highly effective as a shortlist generator: exact rescoring over just a few GNN-proposed candidates recovers near-oracle rollout behavior while searching only a small fraction of the pool. These results demonstrate that adaptive circuit construction contains learnable structure that can be exploited to accelerate variational quantum algorithms.

quant-ph↗

arXiv2023

The electrostatic potential of atomic nanostructures on a metal surface

The discrete and charge-separated nature of matter - electrons and nuclei - results in local electrostatic fields that are ubiquitous in nanoscale structures and are determined by their shape, material, and environment. Such fields are relevant in catalysis, nanoelectronics and quantum nanoscience, and their control will become even more important as the devices in question reach few-nanometres dimensions. Surface-averaging techniques provide only limited experimental access to these potentials at and around individual nanostructures. Here, we use scanning quantum dot microscopy to investigate how electric potentials evolve as nanostructures are built up atom by atom. We image the potential over adatoms, chains, and clusters of Ag and Au atoms on Ag(111) and quantify their surface dipole moments. By focusing on the total charge density, these data establish a new benchmark for ab initio calculations. Indeed, our density functional theory calculations not only show an impressive agreement with experiment, but also allow a deeper analysis of the mechanisms behind the dipole formation, their dependence on fundamental atomic properties and on the atomic configuration of the nanostructures. This allows us to formulate an intuitive picture of the basic mechanisms behind dipole formation, which enables better design choices for future nanoscale systems such as single atom catalysts.

cond-mat.mtrl-sci↗

arXiv2022

Disentangling the Complex Electronic Structure of an Adsorbed Nanographene: Cycloarene C108

We combine low-temperature scanning tunneling spectroscopy, CO functionalized tips and algorithmic data analysis to investigate the electronic structure of the molecular cycloarene C108 (graphene nanoring) adsorbed on a Au(111) surface. We demonstrate that CO functionalized tips enhance the visibility of molecular resonances, both in differential conductance spectra and in real-space topographic images without introducing spurious artifacts. Comparing our experimental data with ab-initio density functional theory reveals a remarkably precise agreement of the molecular orbitals and enables us to disentangle close-lying molecular states only separated by 50 meV at an energy of 2 eV below the Fermi level. We propose this combination of techniques as a promising new route for a precise characterization of complex molecules and other physical entities which have electronic resonances in the tip-sample junction.

cond-mat.mes-hall↗

arXiv2017

Self-assembly of a model supramolecular polymer studied by replica exchange with solute tempering

Conventional molecular-dynamics (cMD) simulation has a well-known limitation in accessible time and length scales, and thus various enhanced sampling techniques have been proposed to alleviate the problem. In this paper we explore the utility of replica exchange with solute tempering (REST) (i.e., a variant of Hamiltonian replica exchange methods) to simulate the self-assembly of a supramolecular polymer in explicit solvent, and compare the performance with temperature-based replica exchange MD (T-REMD) as well as cMD. As a test system, we consider a relatively simple all-atom model of supramolecular polymerization (namely, benzene-1,3,5-tricarboxamides in methylcyclohexane solvent). Our results show that both REST and T-REMD are able to predict highly ordered polymer structures with helical H-bonding patterns, in contrast to cMD which completely fails to obtain such a structure for the present model. At the same time, we have also experienced some technical challenge (i.e., aggregation-dispersion transition and the resulting bottleneck for replica traversal), which is illustrated numerically. Since the computational cost of REST scales more moderately than T-REMD, we expect that REST will be useful for studying the self-assembly of larger systems in solution with enhanced rearrangement of monomers.

physics.chem-ph↗