activity
20132026
most citedGeneral-purpose machine-learned potential for 16 elemental metals and their alloys

203 citations · 380 across the 22 of their papers we have counts for

collaborators

23 papers

quant-ph2026

Memory--Transport Competition in Physical Two-Bath Quantum Reset Dynamics

Jishad Kumar, Achilleas Lazarides, Tapio Ala-Nissila

Coherence preservation and environmental renewal offer competing ways of controlling quantum memory and transport. We show that a seemingly natural protocol which exactly replaces…

quant-ph2026

The ebbs and flows of quantum learning and sensing

Matias Karjula, Teemu Ojanen, Tapio Ala-Nissila +1

What is the relation between subsystem quantum complexity and the emergence of computationally useful structure? We address this by studying a family of minimally tunable postvaria…

cond-mat.mtrl-sci2026

Dislocation-loop formation is a first-order phase transition

Xiaoya Chang, Arsalan Hashemi, Nima Ghafari Cherati +3

Dislocation loops are the elementary product of radiation damage in crystals, limiting reactor-component lifetimes, power-electronics reliability and the coherence of solid-state q…

physics.comp-ph2026

Machine-learning-driven kinetic discovery of carbon interstitial color centers in diamond

Xiaoya Chang, Arsalan Hashemi, Nima Ghafari Cherati +3

Diamond hosts optically active point defects central to quantum technologies, yet the carbon self-interstitials introduced during growth and irradiation compete with them and form…

physics.comp-ph2026

TorchNEP: Ultra-Efficient and Accurate Training of Neuroevolution Potentials

Yong-Chao Wu, Xiaoya Chang, Tero Mäkinen +5

Neuroevolution Potential (NEP) is one of the most efficient machine-learned interatomic potential frameworks for large-scale atomistic simulations. However, its original training s…

quant-ph2026

Thermodynamics of Coherence-Selective Quantum Reset Protocols

Jishad Kumar, Achilleas Lazarides, Tapio Ala-Nissila

We develop an exact theory of coherence-selective stroboscopic resetting for quadratic open quantum systems within the single-particle density-matrix formalism. We focus on the sur…

quant-ph2026

Coherence-Controlled Quantum Zeno Dynamics from Exact Reset Maps

Jishad Kumar, Achilleas Lazarides, Tapio Ala-Nissila

We develop an exact framework for quantum Zeno and anti-Zeno dynamics in a broad class of open systems, whose microscopic Hamiltonians are quadratic in bosonic or fermionic operato…

quant-ph2026

Interaction-Enabled Hartree Fixed Points in Fermionic Resetting Dynamics

Jishad Kumar, Achilleas Lazarides, Tapio Ala-Nissila

In resetting dynamics, a system is repeatedly coupled to and decoupled from ancillary degrees of freedom that are reinitialized between interactions. This provides a versatile rout…

cond-mat.mtrl-sci2026

Nine-element machine-learned interatomic potentials for multiphase refractory alloys

Jesper Byggmästar, Tiago Lopes, Zheyong Fan +1

New refractory alloys are being continuously designed and characterised for applications requiring good high-temperature mechanical properties and stability. Computational design f…

cond-mat.mtrl-sci2025

Stabilisation of hBN/SiC Heterostructures with Vacancies and Transition-Metal Atoms

Arsalan Hashemi, Nima Ghafari Cherati, Sadegh Ghaderzadeh +3

When two-dimensional atomic layers of different materials are brought into close proximity to form van der Waals (vdW) heterostructures, interactions between adjacent layers signif…

quant-ph2025★ 1 cited

Optimal quantum reservoir learning in proximity to universality

Moein N. Ivaki, Matias Karjula, Tapio Ala-Nissila

The study of the boundary between classically simulable and computationally complex quantum dynamics is fundamental to understanding which physical resources may enable enhanced in…

cond-mat.stat-mech2025★ 1 cited

Timescales for stochastic barrier crossing: inferring the potential from nonequilibrium data

A. J. Archer, T. Ala-Nissila, T. J. W. Honour +1

Kramers' rate theory forms a cornerstone for thermally activated barrier crossing. However, its reliance on equilibrium quantities excludes analysis of nonequilibrium dynamics at e…

quant-ph2025

Dynamical learning and quantum memory with non-Hermitian many-body systems

Moein N. Ivaki, Austin J. Szuminsky, Achilleas Lazarides +3

Non-Hermitian (NH) systems provide a fertile platform for quantum technologies, owing in part to their distinct dynamical phases. These systems can be characterized by the preserva…

quant-ph2025

State-Based Quantum Simulation: Releasing the Powers of Quantum States and Copies

S. Alipour, A. T. Rezakhani, Alireza Tavanfar +2

Quantum computing employs controllable interactions to perform sequences of logical gates and entire algorithms on quantum registers. This paradigm has been widely explored, e.g.,…

cond-mat.mtrl-sci2025★ 22 cited

NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements

Ting Liang, Ke Xu, Eric Lindgren +16

While machine-learned interatomic potentials offer near-quantum-mechanical accuracy for atomistic simulations, many are material-specific or computationally intensive, limiting the…

physics.chem-ph2024

On-the-fly reparametrization of pairwise dispersion interactions for accurate and efficient molecular dynamics: Phase diagram of white phosphorus

Heikki Muhli, Tapio Ala-Nissila, Miguel A. Caro

Accurate estimation of the contribution from dispersion interactions to the total energy is important for many molecular systems and low-dimensional solids. In this work we demonst…

cond-mat.mtrl-sci2024

Boron Isotope Effects on Raman Scattering in Bulk BN, BP, and BAs: A Density-Functional Theory Study

Nima Ghafari Cherati, I. Abdolhosseini Sarsari, Arsalan Hashemi +1

For many materials, Raman spectra are intricately structured and provide valuable information about compositional stoichiometry and crystal quality. Here we use density-functional…

quant-ph2024★ 8 cited

Quantum reservoir computing on random regular graphs

Moein N. Ivaki, Achilleas Lazarides, Tapio Ala-Nissila

Quantum reservoir computing (QRC) is a low-complexity learning paradigm that combines the inherent dynamics of input-driven many-body quantum systems with classical learning techni…

cond-mat.mtrl-sci2024★ 2 cited

Density dependence of thermal conductivity in nanoporous and amorphous carbon with machine-learned molecular dynamics

Yanzhou Wang, Zheyong Fan, Ping Qian +2

Disordered forms of carbon are an important class of materials for applications such as thermal management. However, a comprehensive theoretical understanding of the structural dep…

cond-mat.mtrl-sci2023★ 203 cited

General-purpose machine-learned potential for 16 elemental metals and their alloys

Keke Song, Rui Zhao, Jiahui Liu +25

Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicabil…

quant-ph2019★ 10 cited

Mutual Information is not a Reliable Measure for Variations in Total Correlations

S. Alipour, S. Tuohino, A. T. Rezakhani +1

Correlations disguised in various forms underlie a host of important phenomena in classical and quantum systems, such as information and energy exchanges. The quantum mutual inform…

cond-mat.quant-gas2015★ 7 cited

Quantum Treatment for Bose-Einstein Condensation in Non-Equilibrium Systems

H. Flayac, I. G. Savenko, M. Möttönen +1

We develop an approach based on stochastic quantum trajectories for an incoherently pumped system of interacting bosons relaxing their energy in a thermal reservoir. Our approach e…

cond-mat.stat-mech2013★ 126 cited

Distribution of Entropy Production in a Single-Electron Box

J. V. Koski, T. Sagawa, O. -P. Saira +6

Recently, the fundamental laws of thermodynamics have been reconsidered for small systems. The discovery of the fluctuation relations has spurred theoretical and experimental studi…