With the objective to understand microscopic principles governing thermal energy flow in nanojunctions, we study phononic heat transport through metal-molecule-metal junctions using classical molecular dynamics (MD) simulations. Considering a single-molecule gold-alkanedithiol-gold junction, we first focus on aspects of method development and compare two techniques for calculating thermal conductance: (i) The Reverse Nonequilibrium MD (RNEMD) method, where heat is inputted and extracted at a constant rate from opposite metals. In this case, the thermal conductance is calculated from the nonequilibrium temperature profile that is created on the junction. (ii) The Approach-to-Equilibrium MD (AEMD) method, with the thermal conductance of the junction obtained from the equilibration dynamics of the metals. In both methods, simulations of alkane chains of growing size display an approximate length-independence of the thermal conductance, with calculated values matching computational and experimental studies. The RNEMD and AEMD methods offer different insights, and we discuss their benefits and shortcomings. Assessing the potential application of molecular junctions as thermal diodes, alkane junctions are made spatially-asymmetric by modifying their contact regions with the bulk, either by using distinct endgroups or by replacing one of the Au contacts by Ag. Anharmonicity is built into the system within the molecular force-field. We find that, while the temperature profile strongly varies (compared to the gold-alkanedithiol-gold junctions) due to these structural modifications, the thermal diode effect is inconsequential in these systems-unless one goes to very large thermal biases. This finding suggests that one should seek molecules with considerable internal anharmonic effects for developing nonlinear thermal devices.

An electrocaloric (EC) cooling device model is proposed to study the influence of material properties, operating conditions, and device design on the coefficient of performance (COP). Because the EC temperature change cannot be predicted by ferroelectric material models, a Gaussian-fit model is proposed that achieves accurate predictions for a range of materials. The COP is calculated by considering the heat transfer and work in a thermodynamic cycle that is integrated with the material model. The device model is based on the experiments of Ma et al (Science. 2017;357:1130) and includes contact and convective thermal resistances, the work to apply the electric fields, and dielectric loss heating. A copolymer and a terpolymer are analyzed. The predicted heat flux is in good agreement with the experimental data while the predicted COP is lower. This difference is attributed to an over-prediction of the work because experimental hysteresis loop data are not available. Parametric analysis reveals that: (a) the terpolymer performance is stable over a wide range of operating temperatures, while good performance of the copolymer is limited to near its Curie temperature, and (b) small contact thermal resistances allow for highfrequency operation and a large heat flux. K E Y W O R D Scoefficient of performance, ferroelectric polymer, solid-state cooling, thermal analysis

An eXtreme Gradient Boosting (XGBoost) machine learning model is built to predict the electrocaloric (EC) temperature change of a ceramic based on its composition (encoded by Magpie elemental properties), dielectric constant, Curie temperature, and characterization conditions. A dataset of 97 EC ceramics is assembled from the experimental literature. By sampling data from clusters in the feature space, the model can achieve a coefficient of determination of 0.77 and a root mean square error of 0.38 K for the test data. Feature analysis shows that the model captures known physics for effective EC materials. The Magpie features help the model to distinguish between materials, with the elemental electronegativities and ionic charges identified as key features. The model is applied to 66 ferroelectrics whose EC performance has not been characterized. Lead-free candidates with a predicted EC temperature change above 2 K at room temperature and 100 kV/cm are identified.

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Feature extraction and a neural network model are applied to predict defect types and concentrations in experimental anatase TiO2 samples. A dataset of TiO2 structures with vacancies and interstitials of oxygen and titanium is built, and the structures are relaxed using energy minimization. The features of the calculated pair distribution functions (PDFs) of these defected structures are extracted using linear methods (principal component analysis and non-negative matrix factorization) and non-linear methods (autoencoder and convolutional neural network). The extracted features are used as inputs to a neural network that maps feature weights to the concentration of each defect type. The performance of this machine learning pipeline is validated by predicting defect concentrations based on experimentally measured TiO2 PDFs and comparing the results to brute-force predictions. A physics-based initialization of the autoencoder has the highest accuracy in predicting defect concentrations. This model incorporates physical interpretability and predictability of material structures, enabling a more efficient characterization process with scattering data.