A quatum inspired neural network for geometric modeling

By conceiving physical systems as 3D many-body point clouds, geometric graph neural networks (GNNs), such as SE(3)/E(3) equivalent GNNs, have showcased promising performance. In particular, their effective message-passing mechanics make them adept at modeling molecules and crystalline materials. However, current geometric GNNs only offer a mean-field approximation of the many-body system, encapsulated within two-body message passing, thus falling short in capturing intricate relationships within these geometric graphs. To address this limitation, tensor networks, widely employed by computational physics to handle manybody systems using high-order tensors, have been introduced. Nevertheless, integrating these tensorized networks into the message-passing framework of GNNs faces scalability and symmetry conservation (e.g., permutation and rotation) challenges. In response, we introduce an innovative equivariant Matrix Product State (MPS)-based message-passing strategy, through achieving an efficient implementation of the tensor contraction operation. Our method effectively models complex many-body relationships, suppressing mean-field approximations, and captures symmetries within geometric graphs. Importantly, it seamlessly replaces the standard message-passing and layer-aggregation modules intrinsic to geometric GNNs. We empirically validate the superior accuracy of our approach on benchmark tasks, including predicting classical Newton systems and quantum tensor Hamiltonian matrices. To our knowledge, our approach represents the inaugural utilization of parameterized geometric tensor networks

Sculpting Molecules in 3D: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimization

The integration of deep learning, particularly AI-Generated Content, with high-quality data derived from ab initio calculations has emerged as a promising avenue for transforming the landscape of scientific research. However, the challenge of designing molecular drugs or materials that incorporate multi-modality prior knowledge remains a critical and complex undertaking. Specifically, achieving a practical molecular design necessitates not only meeting the diversity requirements but also addressing structural and textural constraints with various symmetries outlined by domain experts. In this article, we present an innovative approach to tackle this inverse design problem by formulating it as a multi-modality guidance generation/optimization task. Our proposed solution involves a textural-structure alignment symmetric diffusion framework for the implementation of molecular generation/optimization tasks, namely 3DToMolo. 3DToMolo aims to harmonize diverse modalities, aligning them seamlessly to produce molecular structures adhere to specified symmetric structural and textural constraints by experts in the field. Experimental trials across three guidance generation settings have shown a superior hit generation performance compared to state-of-the-art methodologies. Moreover, 3DToMolo demonstrates the capability to generate novel molecules, incorporating specified target substructures, without the need for prior knowledge. This work not only holds general significance for the advancement of deep learning methodologies but also paves the way for a transformative shift in molecular design strategies. 3DToMolo creates opportunities for a more nuanced and effective exploration of the vast chemical space, opening new frontiers in the development of molecular entities with tailored properties and functionalities

DiskANN++: Efficient Page-based Search over Isomorphic Mapped Graph Index using Query-sensitivity Entry Vertex

Given a vector dataset $\mathcal{X}$ and a query vector $\vec{x}_q$, graph-based Approximate Nearest Neighbor Search (ANNS) aims to build a graph index $G$ and approximately return vectors with minimum distances to $\vec{x}_q$ by searching over $G$. The main drawback of graph-based ANNS is that a graph index would be too large to fit into the memory especially for a large-scale $\mathcal{X}$. To solve this, a Product Quantization (PQ)-based hybrid method called DiskANN is proposed to store a low-dimensional PQ index in memory and retain a graph index in SSD, thus reducing memory overhead while ensuring a high search accuracy. However, it suffers from two I/O issues that significantly affect the overall efficiency: (1) long routing path from an entry vertex to the query's neighborhood that results in large number of I/O requests and (2) redundant I/O requests during the routing process. We propose an optimized DiskANN++ to overcome above issues. Specifically, for the first issue, we present a query-sensitive entry vertex selection strategy to replace DiskANN's static graph-central entry vertex by a dynamically determined entry vertex that is close to the query. For the second I/O issue, we present an isomorphic mapping on DiskANN's graph index to optimize the SSD layout and propose an asynchronously optimized Pagesearch based on the optimized SSD layout as an alternative to DiskANN's beamsearch. Comprehensive experimental studies on eight real-world datasets demonstrate our DiskANN++'s superiority on efficiency. We achieve a notable 1.5 X to 2.2 X improvement on QPS compared to DiskANN, given the same accuracy constraint.Comment: 15 pages including reference

NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams

Existing Graph Neural Network (GNN) training frameworks have been designed to help developers easily create performant GNN implementations. However, most existing GNN frameworks assume that the input graphs are static, but ignore that most real-world graphs are constantly evolving. Though many dynamic GNN models have emerged to learn from evolving graphs, the training process of these dynamic GNNs is dramatically different from traditional GNNs in that it captures both the spatial and temporal dependencies of graph updates. This poses new challenges for designing dynamic GNN training frameworks. First, the traditional batched training method fails to capture real-time structural evolution information. Second, the time-dependent nature makes parallel training hard to design. Third, it lacks system supports for users to efficiently implement dynamic GNNs. In this paper, we present NeutronStream, a framework for training dynamic GNN models. NeutronStream abstracts the input dynamic graph into a chronologically updated stream of events and processes the stream with an optimized sliding window to incrementally capture the spatial-temporal dependencies of events. Furthermore, NeutronStream provides a parallel execution engine to tackle the sequential event processing challenge to achieve high performance. NeutronStream also integrates a built-in graph storage structure that supports dynamic updates and provides a set of easy-to-use APIs that allow users to express their dynamic GNNs. Our experimental results demonstrate that, compared to state-of-the-art dynamic GNN implementations, NeutronStream achieves speedups ranging from 1.48X to 5.87X and an average accuracy improvement of 3.97%.Comment: 12 pages, 15 figure

Hydrostatic piezoelectric properties of [011] poled Pb(Mg 1/3Nb2/3)O3-PbTiO3 single crystals and 2-2 lamellar composites

The hydrostatic piezoelectric properties of [011] poled Pb(Mg 1/3Nb2/3)O3-PbTiO3 (PMN-PT) crystals and corresponding 2-2 crystal/epoxy composites were investigated. The crystal volume ratio and compositional dependencies of the hydrostatic charge and voltage coefficients (dh and gh) and hydrostatic figure of merit (FOM) dh x gh were determined, where large FOM value of 3.2 pm2/N with high stability as a function of hydrostatic pressure was achieved for rhombohedral crystal composites. In addition, the stress amplification effects of the face-plate and different epoxy matrixes were investigated, with maximum FOM value being on the order of 92 pm2/N, indicating that 2-2 crystal/epoxy composites are promising materials for hydrostatic applications. 2014 AIP Publishing LLC

Electroacoustic response of 1-3 piezocomposite transducers for high power applications

The electroacoustic performance of 1-3 piezoelectric composite transducers with low loss polymer filler was studied and compared to monolithic Pb(Zr,Ti)O 3 (PZT) piezoelectric transducers. The 1-3 composite transducers exhibited significantly high electromechanical coupling factor (k t ∼ 0.64) when compared to monolithic counterparts (k t ∼ 0.5), leading to the improved bandwidth and loop sensitivity, being on the order of 67% and -24.0 dB versus 44% and -24.8 dB, respectively. In addition, the acoustic output power and transmit efficiency (∼50%) were found to be comparable to the monolithic PZT transducers, demonstrating potential for broad bandwidth, high power ultrasonic transducer applications

1-3 piezoelectric composites for high-temperature transducer applications

High-temperature Pb(Zr,Ti)O3/epoxy 1-3 composites were fabricated using the dice and fill method. The epoxy filler was modified with glass spheres in order to improve the thermal reliability of the composites at elevated temperatures. Temperature-dependent dielectric and electromechanical properties of the composites were measured after ageing at 250 °C with different dwelling times. Obvious cracks were observed and the electrodes were damaged in the composite with unmodified epoxy after 200 h, leading to the failure of the composite. In contrast, composites with \u3e12 vol% glass sphere loaded epoxies were found to exhibit minimal electrical property variation after ageing for 500 h, with dielectric permittivity, piezoelectric coefficient and electromechanical coupling being on the order of 940, 310 pC N-1 and 57%, respectively. This is due to the improved thermal expansion behaviour of the modified filler. 2013 IOP Publishing Ltd

1-3 ceramic/polymer composites for high-temperature transducer applications

1-3 ceramic/polymer composites based on high Curie temperature ferroelectric ceramic BiScO3-PbTiO3 (BSPT) were fabricated using the dice and fill method. The electromechanical coupling factor k t of BSPT composite was found to be 58% at room temperature, higher than the thickness coupling factor of monolithic BSPT ∼50%. In addition, BSPT 1-3 composite exhibits high thermal stability of electromechanical properties and low dielectric loss up to 300 °C, making it a potential candidate for broad-bandwidth transducer applications at elevated temperatures. 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim