activity
20092026
most citedIncome and Poverty in a Developing Economy

8 citations · 14 across the 9 of their papers we have counts for

collaborators

9 papers

cond-mat.mtrl-sci2026

Calculations of the Krypton Phase Diagram and Novel Plasticity

Marcin Kirsz, Asuka Iwasaki, Graeme John Ackland

The phase diagram for Kr, as represented by the Tadah! two-body potential is shown to have face-centred cubic (fcc), hexahonal close packed (hcp), and body centred cubic (bcc) regi…

cond-mat.mtrl-sci2026

Understanding the lifetime of water with dynamic network analysis: the case of CsOH.H2O

Graeme J. Ackland, Ciprian G. Pruteanu, John S. Loveday +1

We describe the atomic-level motions in caesium hydroxide monohydrate (CsOHHO), which is a chemical compound containing layers of water and hydroxide ions. At this compo…

quant-ph2026★ 1 cited

Observation of ΔJ=0 Rotational Excitation in Dense Hydrogens

Jie Feng, XiaoDi Liu, Haian Xu +3

Raman measurements performed on dense H2, D2 and H2+D2 in a wide pressure-temperature range reveal the presence of the ΔJ=0 rotational excitation. In the gas/fluid state this excit…

cond-mat.stat-mech2025

The distinction between Ice phases VII, VIII and X

Graeme J Ackland, Andreas Hermann, Kazuki Komatsu +2

Ice phases VII, VIII and X are all based on a body-centered cubic arrangement of molecules, the differences coming from molecular orientation. There is some debate as to whether th…

physics.bio-ph2025

Oscillation in the SIRS model

D. Marenduzzo, A. T. Brown, C. Miller +1

We study the SIRS epidemic model, both analytically and on a square lattice. The analytic model has two stable solutions, post outbreak/epidemic (no infected, ) and the endemi…

physics.comp-ph2025

Tadah! A Swiss Army Knife for Developing and Deployment of Machine Learning Interatomic Potentials

M. Kirsz, A. Daramola, A. Hermann +2

The Tadah! code provides a versatile platform for developing and optimizing Machine Learning Interatomic Potentials (MLIPs). By integrating composite descriptors, it allows for a n…

physics.comp-ph2024

Understanding solid nitrogen through machine learning simulation

Marcin Kirsz, Ciprian G. Pruteanu, Peter I. C. Cooke +1

We construct a fast, transferable, general purpose, machine-learning interatomic potential suitable for large-scale simulations of . The potential is trained only on high qual…

physics.comp-ph2023★ 5 cited

A comparison of different Fourier transform procedures for analysis of diffraction data from noble gas fluids

John E. Proctor, Ciprian G. Pruteanu, Benjamin Moss +4

A comparison is made between the three principal methods for analysis of neutron and X-ray diffraction data from noble gas fluids by direct Fourier transform. All three methods (st…

q-fin.GN2009★ 8 cited

Income and Poverty in a Developing Economy

Amit K Chattopadhyay, Graeme J Ackland, Sushanta K Mallick

We present a stochastic agent-based model for the distribution of personal incomes in a developing economy. We start with the assumption that incomes are determined both by individ…