Dependence of polymer thin film adhesion energy on cohesive interactions between chains

The adhesion of supported polymer thin films is predominantly influenced by the substrate-film interfacial properties. Utilizing steered molecular dynamics simulations, here we uncover that the cohesive noncovalent forces between polymer chains in the film also have a significant effect on the adhesive properties of supported film. We demonstrate that weaker interchain interactions, all else being the same, can induce higher adhesion energy within the interface. Three different adhesion regimes in the substrate–film interaction strength profile can be characterized by a nonlinear scaling relationship that earlier theoretical predictions currently do not capture. In the weak substrate–film interaction regime, the adhesion energy of the films exhibits near independence of cohesive forces, and entropic contributions to the surface free energy are consequential. In the intermediate regime, weaker film cohesive forces achieve higher adhesion energy due to the ability of polymer chains to pack more effectively in the interfacial region, thereby increasing the adhesive interaction density. In the strong interaction regime, the adhesion energy increases linearly with the adhesive interaction strength because of saturation of local packing in the interfacial region. These findings corroborate recent polymer wetting observations that have hinted on the importance of local relaxation and packing effects on interfacial properties

Vertical Semi-Federated Learning for Efficient Online Advertising

As an emerging secure learning paradigm in leveraging cross-silo private data, vertical federated learning (VFL) is expected to improve advertising models by enabling the joint learning of complementary user attributes privately owned by the advertiser and the publisher. However, the 1) restricted applicable scope to overlapped samples and 2) high system challenge of real-time federated serving have limited its application to advertising systems. In this paper, we advocate new learning setting Semi-VFL (Vertical Semi-Federated Learning) as a lightweight solution to utilize all available data (both the overlapped and non-overlapped data) that is free from federated serving. Semi-VFL is expected to perform better than single-party models and maintain a low inference cost. It's notably important to i) alleviate the absence of the passive party's feature and ii) adapt to the whole sample space to implement a good solution for Semi-VFL. Thus, we propose a carefully designed joint privileged learning framework (JPL) as an efficient implementation of Semi-VFL. Specifically, we build an inference-efficient single-party student model applicable to the whole sample space and meanwhile maintain the advantage of the federated feature extension. Novel feature imitation and ranking consistency restriction methods are proposed to extract cross-party feature correlations and maintain cross-sample-space consistency for both the overlapped and non-overlapped data. We conducted extensive experiments on real-world advertising datasets. The results show that our method achieves the best performance over baseline methods and validate its effectiveness in maintaining cross-view feature correlation

Systematic method for thermomechanically consistent coarse graining: a universal model for methacrylate-based polymers

We present a versatile systematic two-bead-per-monomer coarse-grain modeling strategy for simulating the -thermomechanical behavior of methacrylate polymers at length and time scales far exceeding atomistic -simulations. We establish generic bonded interaction parameters via Boltzmann inversion of probability distributions obtained from the common coarse-grain bead center locations of five different methacrylate polymers. Distinguishing features of each monomer side-chain group are captured using Lennard-Jones nonbonded potentials with -parameters specified to match the density and glass-transition temperature values obtained from all-atomistic simulations. The developed force field is validated using Flory–Fox scaling relationships, self-diffusion coefficients of -monomers, and modulus of elasticity for p (MMA). Our approach establishes a transferable, efficient, and accurate scale--bridging strategy for investigating the thermomechanics of copolymers, polymer blends, and nanocomposites

Theoretically Principled Federated Learning for Balancing Privacy and Utility

We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters, which facilitates the trade-off between privacy and utility. The algorithm is applicable to arbitrary privacy measurements that maps from the distortion to a real value. It can achieve personalized utility-privacy trade-off for each model parameter, on each client, at each communication round in federated learning. Such adaptive and fine-grained protection can improve the effectiveness of privacy-preserved federated learning. Theoretically, we show that gap between the utility loss of the protection hyperparameter output by our algorithm and that of the optimal protection hyperparameter is sub-linear in the total number of iterations. The sublinearity of our algorithm indicates that the average gap between the performance of our algorithm and that of the optimal performance goes to zero when the number of iterations goes to infinity. Further, we provide the convergence rate of our proposed algorithm. We conduct empirical results on benchmark datasets to verify that our method achieves better utility than the baseline methods under the same privacy budget

Achieving Lightweight Federated Advertising with Self-Supervised Split Distillation

As an emerging secure learning paradigm in leveraging cross-agency private data, vertical federated learning (VFL) is expected to improve advertising models by enabling the joint learning of complementary user attributes privately owned by the advertiser and the publisher. However, there are two key challenges in applying it to advertising systems: a) the limited scale of labeled overlapping samples, and b) the high cost of real-time cross-agency serving. In this paper, we propose a semi-supervised split distillation framework VFed-SSD to alleviate the two limitations. We identify that: i) there are massive unlabeled overlapped data available in advertising systems, and ii) we can keep a balance between model performance and inference cost by decomposing the federated model. Specifically, we develop a self-supervised task Matched Pair Detection (MPD) to exploit the vertically partitioned unlabeled data and propose the Split Knowledge Distillation (SplitKD) schema to avoid cross-agency serving. Empirical studies on three industrial datasets exhibit the effectiveness of our methods, with the median AUC over all datasets improved by 0.86% and 2.6% in the local deployment mode and the federated deployment mode respectively. Overall, our framework provides an efficient federation-enhanced solution for real-time display advertising with minimal deploying cost and significant performance lift.Comment: Accepted to the Trustworthy Federated Learning workshop of IJCAI2022 (FL-IJCAI22). 6 pages, 3 figures, 3 tables Old title: Semi-Supervised Cross-Silo Advertising with Partial Knowledge Transfe

Machine Learning Prediction of Glass Transition Temperature of Conjugated Polymers From Chemical Structure

Predicting the glass transition temperature (Tg) is of critical importance as it governs the thermomechanical performance of conjugated polymers (CPs). Here, we report a predictive modeling framework to predict Tg of CPs through the integration of machine learning (ML), molecular dynamics (MD) simulations, and experiments. With 154 Tg data collected, an ML model is developed by taking simplified “geometry” of six chemical building blocks as molecular features, where side-chain fraction, isolated rings, fused rings, and bridged rings features are identified as the dominant ones for Tg. MD simulations further unravel the fundamental roles of those chemical building blocks in dynamical heterogeneity and local mobility of CPs at a molecular level. The developed ML model is demonstrated for its capability of predicting Tg of several new high-performance solar cell materials to a good approximation. The established predictive framework facilitates the design and prediction of Tg of complex CPs, paving the way for addressing device stability issues that have hampered the field from developing stable organic electronics