Recent advances and applications of machine learning in experimental solid mechanics: A review

H Jin, E Zhang, HD Espinosa - Applied …, 2023 - asmedigitalcollection.asme.org
For many decades, experimental solid mechanics has played a crucial role in characterizing
and understanding the mechanical properties of natural and novel artificial materials …

Recent advances in the mechanics of 2D materials

G Wang, H Hou, Y Yan, R Jagatramka… - … Journal of Extreme …, 2023 - iopscience.iop.org
The exceptional physical properties and unique layered structure of two-dimensional (2D)
materials have made this class of materials great candidates for applications in electronics …

CHARMM-GUI nanomaterial modeler for modeling and simulation of nanomaterial systems

YK Choi, NR Kern, S Kim, K Kanhaiya… - Journal of chemical …, 2021 - ACS Publications
Molecular modeling and simulation are invaluable tools for nanoscience that predict
mechanical, physicochemical, and thermodynamic properties of nanomaterials and provide …

Strain driven anomalous anisotropic enhancement in the thermoelectric performance of monolayer MoS2

S Chaudhuri, A Bhattacharya, AK Das, GP Das… - Applied Surface …, 2023 - Elsevier
First principles density functional theory based calculations have been performed to
investigate the strain and temperature induced tunability of the thermoelectric properties of …

Information geometry for multiparameter models: New perspectives on the origin of simplicity

KN Quinn, MC Abbott, MK Transtrum… - Reports on Progress …, 2022 - iopscience.iop.org
Complex models in physics, biology, economics, and engineering are often ill-determined or
sloppy: their multiple parameters can vary over wide ranges without significant changes in …

Thermomechanical properties of transition metal dichalcogenides predicted by a machine learning parameterized force field

MSMM Ali, H Nguyen, JT Paci, Y Zhang… - Nano …, 2024 - ACS Publications
The mechanical and thermal properties of transition metal dichalcogenides (TMDs) are
directly relevant to their applications in electronics, thermoelectric devices, and heat …

Machine-learned interatomic potentials for transition metal dichalcogenide Mo1−xWxS2−2ySe2y alloys

A Siddiqui, NDM Hine - npj Computational Materials, 2024 - nature.com
Abstract Machine Learned Interatomic Potentials (MLIPs) combine the predictive power of
Density Functional Theory (DFT) with the speed and scaling of interatomic potentials …

Multi-objective parametrization of interatomic potentials for large deformation pathways and fracture of two-dimensional materials

X Zhang, H Nguyen, JT Paci… - npj Computational …, 2021 - nature.com
This investigation presents a generally applicable framework for parameterizing interatomic
potentials to accurately capture large deformation pathways. It incorporates a multi-objective …

Automated determination of grain boundary energy and potential-dependence using the OpenKIM framework

B Waters, DS Karls, I Nikiforov, RS Elliott… - Computational Materials …, 2023 - Elsevier
We present a systematic methodology, built within the Open Knowledgebase of Interatomic
Models (OpenKIM) framework (https://openkim. org), for quantifying properties of grain …

Bending and twisting rigidities of 2D materials

SS Vel, SR Maalouf - International Journal of Mechanical Sciences, 2024 - Elsevier
A novel torus-based surface parametrization is proposed and used to evaluate the bending
and twisting rigidities of 2D materials of arbitrary symmetries. A generalized constitutive …