Engineering topics

Chen, Tianqi

Publications and source records attributed to Chen, Tianqi.

3 records

DOE OSTI2024

Correlation of Dimer-Linker-Induced Conformational Lock with Nonradiative Energy Loss in Organic Solar Cells

The efficiencies of dimer-based devices still lag those of their small molecule-based counterparts. This is primarily due to the considerable dihedrals in the dimer skeleton, which compromises the molecular packing, thus influencing the charge generation and nonradiative voltage loss (ΔV oc,nr ). Herein, we developed two dimeric acceptors with varied π-linkers to investigate the influence of linker-induced conformational lock on ΔV oc,nr . We find that the helically lapped O-shaped dimer delivers better intermolecular packing than the planar S-shaped one that incorporates a bulkier π-linker. However, its planar skeleton is instead more favorable for forming a compact and ordered stacking with the host acceptor in ternary blend. This possibly promotes exciton dissociation, thus reducing the nonradiative decay of excited states. Moreover, its longer exciton lifetime could offer additional charge-transfer channels. Finally, these contributions effectively minimize ΔV oc,nr to 0.195 eV, while delivering a high efficiency approaching 20% in the derived ternary device.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Regulation of Side‐Chain Symmetry for Delaying Triplet Formation and Suppressing Non‐Radiative Loss in Organic Solar Cells

Abstract The structural revolutions of non‐fullerene acceptors (NFAs) have driven continuous efficiency breakthroughs in organic solar cells (OSCs). Rational regulation of NFA structures toward efficient exciton dissociation and mitigated non‐radiative recombination is pivotal for OSCs. The incorporation of asymmetric side chains on NFAs can often achieve these goals by inducing a desirable aggregate state. However, it lacks the studies to directly correlate the side‐chain symmetry of NFAs with the exciton delocalization and triplet dynamics in OSCs. Herein, The influence of structural symmetry on the aggregate properties is systematically investigated and exciton/charge dynamics based on two developed biaxial‐conjugated NFAs with varied side‐chain symmetry. The symmetric NFA having diverse molecular packing orientations can form multiple charge transfer channels in its blend with polymer donor, which cannot be found in that comprising the asymmetric ones. Moreover, a slower rate and lower ratio of the spin‐triplet state are formed in the blend of symmetric NFA, resulting in a much lower non‐radiative voltage loss in corresponding OSCs. This study reveals the distinct advantages of symmetric NFAs in both aggregate properties and exciton/charge dynamics over those of asymmetric ones, paving the way for developing high‐performance OSCs using easier‐to‐prepare, low‐cost symmetric materials.

Chemistry↗

DOE OSTI2022

ASK: Adversarial Soft k-Nearest Neighbor Attack and Defense

K-Nearest Neighbor (kNN)-based deep learning methods have been applied to many applications due to their simplicity and geometric interpretability. However, the robustness of kNN-based deep classification models has not been thoroughly explored and kNN attack strategies are underdeveloped. In this paper, we first propose an Adversarial Soft kNN (ASK) loss for developing more effective kNN-based deep neural network attack strategies and designing better defense methods against them. Our ASK loss provides a differentiable surrogate of the expected kNN classification error. It is also interpretable as it preserves the mutual information between the perturbed input and the in-class-reference data. We use the ASK loss to design a novel attack method called the ASK-Attack (ASK-Atk), which shows superior attack efficiency and accuracy degradation relative to previous kNN attacks on hidden layers. We then derive an ASK-Defense (ASK-Def) method that optimizes the worst-case ASK training loss. Experiments on CIFAR-10 (ImageNet) show that (i) ASK-Atk achieves ≥13% (≥ 13% ) improvement in attack success rate over previous kNN attacks, and (ii) ASK-Def outperforms the conventional adversarial training method by ≥ 6.9% (≥ 3.5% ) in terms of robustness improvement. Relevant codes are available at https://github.com/wangren09/ASK .

97 MATHEMATICS AND COMPUTING↗