activity
20222026
most citedDo Machine-Learning Atomic Descriptors and Order Parameters Tell the Same Story? The Case of Liquid Water

27 citations · 93 across the 6 of their papers we have counts for

collaborators

7 papers

physics.chem-ph2026

Learning the information contained in the microstructure of data distributions

Lucrezia L. Baldo, Chiara Lionello, Edward D. Donkor +2

Understanding how much and where information is nested in sets of data obtained using different parameters and metrics is a typical problem in the analysis of high-dimensional data…

cond-mat.soft2025★ 4 cited

Dynamical Heterogeneity in Supercooled Water and its Spectroscopic Fingerprints

Cesare Malosso, Edward Danquah Donkor, Stefano Baroni +1

A growing body of theoretical and experimental evidence strongly supports the existence of a second liquid-liquid critical point (LLCP) in deeply supercooled water leading to the c…

physics.chem-ph2024★ 7 cited

Aqueous Solution Chemistry In Silico and the Role of Data Driven Approaches

Debarshi Banerjee, Khatereh Azizi, Colin K. Egan +14

The use of computer simulations to study the properties of aqueous systems is, today more than ever, an active area of research. In this context, during the last decade there has b…

cond-mat.soft2024★ 26 cited

Beyond Local Structures In Critical Supercooled Water Through Unsupervised Learning

Edward Danquah Donkor, Adu Offei-Danso, Alex Rodriguez +2

The presence of a second critical point in water has been a topic of intense investigation for the last few decades. The molecular origins underlying this phenomenon are typically…

cond-mat.mtrl-sci2023★ 18 cited

ZundEig: The Structure of the Proton in Liquid Water From Unsupervised Learning

Solana Di Pino, Edward Danquah Donkor, Verónica M. Sánchez +4

The structure of the excess proton in liquid water has been the subject of lively debate from both experimental and theoretical fronts for the last century. Fluctuations of the pro…

cond-mat.quant-gas2023★ 11 cited

Data-driven discovery of statistically relevant information in quantum simulators

R. Verdel, V. Vitale, R. K. Panda +7

Quantum simulators offer powerful means to investigate strongly correlated quantum matter. However, interpreting measurement outcomes in such systems poses significant challenges.…

cond-mat.soft2022★ 27 cited

Do Machine-Learning Atomic Descriptors and Order Parameters Tell the Same Story? The Case of Liquid Water

Edward Danquah Donkor, Alessandro Laio, Ali Hassanali

Machine-learning (ML) has become a key workhorse in molecular simulations. Building an ML model in this context, involves encoding the information of chemical environments using lo…