Revealing Molecule-Internal Mechanisms that Control Phonon Heat Transport through Single-Molecule Junctions by a Genetic Algorithm

Measurements of the thermal conductance of single-molecule junctions have recently been reported. It is presently unclear how much the heat transport can be controlled through molecule-internal effects. The search for molecules with lowest and highest thermal conductance is complicated by the gigantic chemical space. Here, we describe a systematic search for molecules with a low or a high phononic thermal conductance using a genetic algorithm. Beyond individual structures of well-performing molecules, delivered by the genetic algorithm, we analyze patterns and identify the different physical and chemical mechanisms to suppress or enhance phonon heat flow. In detail, mechanisms revealed to reduce phonon transport are related to the choice of terminal linker blocks, substituents and corresponding mass disorder or destructive interference, meta couplings, and molecule-internal twist. For a high thermal conductance, the molecules should instead be rather uniform and chain-like. The identified mechanisms are systematically analyzed at different levels of theory, and their significance is classified. Our findings are expected to be important for the emerging field of molecular phononics.

Authors

Institutions

Publication Details

Journal
ACS Nano
Published
2025-08-31
DOI
https://doi.org/10.1021/acsnano.5c03690
Citations
2
Primary Topic
Molecular Junctions and Nanostructures
Type
article
Field-Weighted Citation Impact
1.46

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Revealing Molecule-Internal Mechanisms that Control Phonon Heat Transport through Single-Molecule Junctions by a Genetic Algorithm

Fabian Pauly, Matthias Blaschke
2 citations
ACS Nano
Molecular Junctions and Nanostructures
1.46
article

Revealing Molecule-Internal Mechanisms that Control Phonon Heat Transport through Single-Molecule Junctions by a Genetic Algorithm

Fabian Pauly, Matthias Blaschke
article en
2 citations

Abstract

Measurements of the thermal conductance of single-molecule junctions have recently been reported. It is presently unclear how much the heat transport can be controlled through molecule-internal effects. The search for molecules with lowest and highest thermal conductance is complicated by the gigantic chemical space. Here, we describe a systematic search for molecules with a low or a high phononic thermal conductance using a genetic algorithm. Beyond individual structures of well-performing molecules, delivered by the genetic algorithm, we analyze patterns and identify the different physical and chemical mechanisms to suppress or enhance phonon heat flow. In detail, mechanisms revealed to reduce phonon transport are related to the choice of terminal linker blocks, substituents and corresponding mass disorder or destructive interference, meta couplings, and molecule-internal twist. For a high thermal conductance, the molecules should instead be rather uniform and chain-like. The identified mechanisms are systematically analyzed at different levels of theory, and their significance is classified. Our findings are expected to be important for the emerging field of molecular phononics.

ACS NanoVol. 19(36)
University of Augsburg (DE), Predictive Science (United States) (US)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 15%
Molecular Junctions and Nanostructures
1.46
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Revealing Molecule-Internal Mechanisms that Control Phonon Heat Transport through Single-Molecule Junctions by a Genetic Algorithm — Fabian Pauly, Matthias Blaschke · ACS Nano (2025) | TGRS Research Map | TGRS