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A sub-domain scheme combined with a reduced system is proposed for non-matched interface problems. A twolevel condensation scheme (TLCS) is employed for the selection of primary degrees of freedom of each sub-domain. The degrees of freedom of each domain are divided into primary, secondary and interface degrees of freedom. After the reduced system is constructed in subdomain-level, the penalty frame method, which utilizes a conventional FEM solver, is employed for assembling non-matched subdomains. The present method can construct the reduced system of each domain without coupling with adjacent subdomains. Thus it is remarkably efficient in computation time and does not require a full memory of global system. Numerical examples demonstrate that the proposed method saves computational cost effectively and provides a reduced system which predicts the accurate eigenvalues of global system. [ABSTRACT FROM AUTHOR]

An accelerated substructuring reduction procedure for the iterated improved reduced system (IIRS) method is proposed. The iterated IIRS method can be combined with a substructuring scheme to provide an efficient methodology for large-scale eigenvalue problems. Not only can it reduce eigenvalue analysis errors through successive iterations, but the accuracy of the eigenanalysis is not sensitive to the selected master degrees of freedom. In practical structural eigenproblems, reducing the number of iterations can save a great deal of computation cost. The present substructuring technique modifies the iterative form of the transformation matrices in each substructure to achieve faster convergence. Applications of the present method to two numerical examples demonstrate that the proposed method can obtain lower eigensolutions of structures more accurately and efficiently, as compared with those of the current substructuring technique. [ABSTRACT FROM AUTHOR]

Data-driven deep learning algorithms provide accurate prediction of high-level quantum-chemical molecular properties. However, their inputs must be constrained to the same quantum-chemical level of geometric relaxation as the training dataset, limiting their flexibility. Adopting alternative cost-effective conformation generative methods introduces domain-shift problems, deteriorating prediction accuracy. Here we propose a deep contrastive learning-based domain-adaptation method called Local Atomic environment Contrastive Learning (LACL). LACL learns to alleviate the disparities in distribution between the two geometric conformations by comparing different conformation-generation methods. We found that LACL forms a domain-agnostic latent space that encapsulates the semantics of an atom's local atomic environment. LACL achieves quantum-chemical accuracy while circumventing the geometric relaxation bottleneck and could enable future application scenarios such as inverse molecular engineering and large-scale screening. Our approach is also generalizable from small organic molecules to long chains of biological and pharmacological molecules., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)