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Extreme Learning Machine (ELM) algorithm not only has gained much attention of many scholars and researchers, but also has been widely applied in recent years especially when dealing with big data because of its better generalization performance and learning speed. The proposal of SS-ELM (semi-supervised Extreme Learning Machine) extends ELM algorithm to the area of semi-supervised learning which is an important issue of machine learning on big data. However, the original SS-ELM algorithm needs to store the data in the memory before processing it, so that it could not handle large and web-scale data sets which are of frequent appearance in the era of big data. To solve this problem, this paper firstly proposes an efficient parallel SS-ELM (PSS-ELM) algorithm on MapReduce model, adopting a series of optimizations to improve its performance. Then, a parallel approximate SS-ELM Algorithm based on MapReduce (PASS-ELM) is proposed. PASS-ELM is based on the approximate adjacent similarity matrix (AASM) algorithm, which leverages the Locality-Sensitive Hashing (LSH) scheme to calculate the approximate adjacent similarity matrix, thus greatly reducing the complexity and occupied memory. The proposed AASM algorithm is general, because the calculation of the adjacent similarity matrix is the key operation in many other machine learning algorithms. The experimental results have demonstrated that the proposed PASS-ELM algorithm can efficiently process very large-scale data sets with a good performance, without significantly impacting the accuracy of the results. [ABSTRACT FROM AUTHOR]

The extreme learning machine (ELM) has become one of the most important and popular algorithms of machine learning, because of its extremely fast training speed, good generalization, and universal approximation/classification capability. The proposal of hierarchical ELM (H-ELM) extends ELM from single hidden layer feedforward networks to multilayer perceptron, greatly strengthening the applicability of ELM. Generally speaking, during training H-ELM, large-scale datasets (DSTs) are needed. Therefore, how to make use of H-ELM framework in processing big data is worth further exploration. This paper proposes a parallel H-ELM algorithm based on Flink, which is one of the in-memory cluster computing platforms, and graphics processing units (GPUs). Several optimizations are adopted to improve the performance, such as cache-based scheme, reasonable partitioning strategy, memory mapping scheme for mapping specific Java virtual machine objects to buffers. Most importantly, our proposed framework for utilizing GPUs to accelerate Flink for big data is general. This framework can be utilized to accelerate many other variants of ELM and other machine learning algorithms. To the best of our knowledge, it is the first kind of library, which combines in-memory cluster computing with GPUs to parallelize H-ELM. The experimental results have demonstrated that our proposed GPU-accelerated parallel H-ELM named as GPH-ELM can efficiently process large-scale DSTs with good performance of speedup and scalability, leveraging the computing power of both CPUs and GPUs in the cluster. [ABSTRACT FROM AUTHOR]

Given the importance of catalysts in the chemical industry, they have been extensively investigated by experimental and numerical methods. With the development of computational algorithms and computer hardware, large-scale simulations have enabled influential studies with more atomic details reflecting microscopic mechanisms. This review provides a comprehensive summary of recent developments in molecular dynamics, including ab initio molecular dynamics and reaction force-field molecular dynamics. Recent research on both approaches to catalyst calculations is reviewed, including growth, dehydrogenation, hydrogenation, oxidation reactions, bias, and recombination of carbon materials that can guide catalyst calculations. Machine learning has attracted increasing interest in recent years, and its combination with the field of catalysts has inspired promising development approaches. Its applications in machine learning potential, catalyst design, performance prediction, structure optimization, and classification have been summarized in detail. This review hopes to shed light and perspective on ML approaches in catalysts. [ABSTRACT FROM AUTHOR]