arXiv subjects

Bin Shao

Publications and source records attributed to Bin Shao.

At least 37 records · Page 2Linked to original sources

arXiv2024

Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning

Orbital-free density functional theory (OFDFT) is a quantum chemistry formulation that has a lower cost scaling than the prevailing Kohn-Sham DFT, which is increasingly desired for contemporary molecular research. However, its accuracy is limited by the kinetic energy density functional, which is notoriously hard to approximate for non-periodic molecular systems. Here we propose M-OFDFT, an OFDFT approach capable of solving molecular systems using a deep learning functional model. We build the essential non-locality into the model, which is made affordable by the concise density representation as expansion coefficients under an atomic basis. With techniques to address unconventional learning challenges therein, M-OFDFT achieves a comparable accuracy with Kohn-Sham DFT on a wide range of molecules untouched by OFDFT before. More attractively, M-OFDFT extrapolates well to molecules much larger than those seen in training, which unleashes the appealing scaling of OFDFT for studying large molecules including proteins, representing an advancement of the accuracy-efficiency trade-off frontier in quantum chemistry.

stat.ML↗

arXiv2023

Orthogonality catastrophe and quantum speed limit for dynamical quantum phase transition

We investigate the orthogonality catastrophe and quantum speed limit in the Creutz model for dynamical quantum phase transitions. We demonstrate that exact zeros of the Loschmidt echo can exist in finite-size systems for specific discrete values. We highlight the role of the zero-energy mode when analyzing quench dynamics near the critical point. We also examine the behavior of the time for the first exact zeros of the Loschmidt echo and the corresponding quantum speed limit time as the system size increases. While the bound is not tight, it can be attributed to the scaling properties of the band gap and energy variance with respect to system size. As such, we establish a relation between the orthogonality catastrophe and quantum speed limit by referencing the full form of the Loschmidt echo. Significantly, we find the possibility of using the quantum speed limit to detect the critical point of a static quantum phase transition, along with a decrease in the amplitude of noise induced quantum speed limit.

quant-ph↗

arXiv2023

ViSNet: an equivariant geometry-enhanced graph neural network with vector-scalar interactive message passing for molecules

Geometric deep learning has been revolutionizing the molecular modeling field. Despite the state-of-the-art neural network models are approaching ab initio accuracy for molecular property prediction, their applications, such as drug discovery and molecular dynamics (MD) simulation, have been hindered by insufficient utilization of geometric information and high computational costs. Here we propose an equivariant geometry-enhanced graph neural network called ViSNet, which elegantly extracts geometric features and efficiently models molecular structures with low computational costs. Our proposed ViSNet outperforms state-of-the-art approaches on multiple MD benchmarks, including MD17, revised MD17 and MD22, and achieves excellent chemical property prediction on QM9 and Molecule3D datasets. Additionally, ViSNet achieved the top winners of PCQM4Mv2 track in the OGB-LCS@NeurIPS2022 competition. Furthermore, through a series of simulations and case studies, ViSNet can efficiently explore the conformational space and provide reasonable interpretability to map geometric representations to molecular structures.

physics.chem-ph↗

arXiv2023

An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022

In the technical report, we provide our solution for OGB-LSC 2022 Graph Regression Task. The target of this task is to predict the quantum chemical property, HOMO-LUMO gap for a given molecule on PCQM4Mv2 dataset. In the competition, we designed two kinds of models: Transformer-M-ViSNet which is an geometry-enhanced graph neural network for fully connected molecular graphs and Pretrained-3D-ViSNet which is a pretrained ViSNet by distilling geomeotric information from optimized structures. With an ensemble of 22 models, ViSNet Team achieved the MAE of 0.0723 eV on the test-challenge set, dramatically reducing the error by 39.75% compared with the best method in the last year competition.

cs.LG↗

arXiv2022

SE(3) Equivariant Graph Neural Networks with Complete Local Frames

Group equivariance (e.g. SE(3) equivariance) is a critical physical symmetry in science, from classical and quantum physics to computational biology. It enables robust and accurate prediction under arbitrary reference transformations. In light of this, great efforts have been put on encoding this symmetry into deep neural networks, which has been shown to improve the generalization performance and data efficiency for downstream tasks. Constructing an equivariant neural network generally brings high computational costs to ensure expressiveness. Therefore, how to better trade-off the expressiveness and computational efficiency plays a core role in the design of the equivariant deep learning models. In this paper, we propose a framework to construct SE(3) equivariant graph neural networks that can approximate the geometric quantities efficiently. Inspired by differential geometry and physics, we introduce equivariant local complete frames to graph neural networks, such that tensor information at given orders can be projected onto the frames. The local frame is constructed to form an orthonormal basis that avoids direction degeneration and ensure completeness. Since the frames are built only by cross product operations, our method is computationally efficient. We evaluate our method on two tasks: Newton mechanics modeling and equilibrium molecule conformation generation. Extensive experimental results demonstrate that our model achieves the best or competitive performance in two types of datasets.

cs.CE↗

arXiv2022

Relevance between Information scrambling and quantum Darwinism

Quantum system interacting with environment can induce redundant encoding of the information of system into a multipartite environment, which is the essence of quantum Darwinism. At the same time, environment may scramble the initially localized information about the system. We mainly investigate the relevance between information scrambling in environment and the emergence of quantum Darwinism. First, we generally identify that when the system shows a Darwinistic behavior system information that is initially localized in the environment is not scrambled, while when Darwinism disappears scrambling occurs.We then verify our result through a collision model where the system, consisting of one or two qubits, interacts with an ensemble of environmental ancillas.Moreover, dependent on the nature of system-environment interactions, our results also shows that the single qubit and two-qubit systems behave differently for the emergence of QD and the scrambling, but the above relevance between them remains valid.

quant-ph↗

arXiv2022

Quantum information scrambling in non-Markovian open quantum systems

In this paper we investigate the dynamics of a spin chain whose two end spins interact with two independent non-Markovian baths by using the non-Markovian quantum state diffusion (QSD) equation approach. Specifically two issues about quantum information scrambling in open quantum system are addressed. The first issue is that tripartite mutual information (TMI) can quantify information scrambling properly via its negative value in closed system, whether it is still suitable to indicate quantum scrambling in open quantum system. However we find that negative TMI is not an suitable quantifier of information scrambling in open quantum system in some cases while negative tripartite logarithmic negativity (TLN) is more appropriate. The second one is that up to now almost all the open quantum system effects on information scrambling reported were focus on the Markovian environment, while the effect of non-Markovian environment on information scrambling is still elusive. Significantly our results show that the memory effect of environment will be beneficial to the emergence of quantum information scrambling. Moreover, it is found that environment is generally detrimental for information scrambling in a long time, while in some cases it will be helpful for information scrambling in a short time.

quant-ph↗

arXiv2022

Generating Two-dimensional Ferromagnetic Charge Density Waves via External Fields

Two-dimensional (2D) ferromagnetic charge density wave (CDW), an exotic quantum state for exploring the intertwining effect between correlated charge and spin orders in 2D limit, has not been discovered in the experiments yet. Here, we propose a feasible strategy to realize 2D ferromagnetic CDWs under external fields, which is demonstrated in monolayer VSe$_2$ using first-principles calculations. Under external tensile strain, two novel ferromagnetic CDWs ($\sqrt{3}$$\times$$\sqrt{3}$ and 2$\times2\sqrt{3}$ CDWs) can be generated, accompanied by distinguishable lattice reconstructions of magnetic V atoms. Remarkably, because the driving forces for generating these two ferromagnetic CDWs are strongly spin-dependent, fundamentally different from that in conventional CDWs, the $\sqrt{3}$$\times$$\sqrt{3}$ and 2$\times2\sqrt{3}$ CDWs can exhibit two dramatically different half-metallic phases under a large strain range, along with either a flat band or a Dirac cone around Fermi level. Our proposed strategy and material demonstration may open a door to generate and manipulate correlation effect between collective charge and spin orders via external fields.

cond-mat.mtrl-sci↗

arXiv2022

Multi-View Substructure Learning for Drug-Drug Interaction Prediction

Drug-drug interaction (DDI) prediction provides a drug combination strategy for systemically effective treatment. Previous studies usually model drug information constrained on a single view such as the drug itself, leading to incomplete and noisy information, which limits the accuracy of DDI prediction. In this work, we propose a novel multi- view drug substructure network for DDI prediction (MSN-DDI), which learns chemical substructures from both the representations of the single drug (intra-view) and the drug pair (inter-view) simultaneously and utilizes the substructures to update the drug representation iteratively. Comprehensive evaluations demonstrate that MSN-DDI has almost solved DDI prediction for existing drugs by achieving a relatively improved accuracy of 19.32% and an over 99% accuracy under the transductive setting. More importantly, MSN-DDI exhibits better generalization ability to unseen drugs with a relatively improved accuracy of 7.07% under more challenging inductive scenarios. Finally, MSN-DDI improves prediction performance for real-world DDI applications to new drugs.

q-bio.BM↗

arXiv2022

Engineering Interlayer Hybridization in Energy Space via Dipolar Overlayers

The interlayer hybridization (IH) of van der Waals (vdW) materials is thought to be mostly associated with the unignorable interlayer overlaps of wavefunctions ($t$) in real space. Here, we develop a more fundamental understanding of IH by introducing a new physical quantity, the IH admixture ratio $α$. Consequently, an exotic strategy of IH engineering in energy space can be proposed, i.e., instead of changing t as commonly used, $α$ can be effectively tuned in energy space by changing the onsite energy difference ($2Δ$) between neighboring-layer states. In practice, this is feasible via reshaping the electrostatic potential of the surface by deposing a dipolar overlayer, e.g., crystalline ice. Our first-principles calculations unveil that IH engineering via adjusting $2Δ$ can greatly tune interlayer optical transitions in transition-metal dichalcogenide bilayers, switch different types of Dirac surface states in Bi$_2$Se$_3$ thin films, and control magnetic phase transition of charge density waves in 1H/1T-TaS$_2$ bilayers, opening new opportunities to govern the fundamental optoelectronic, topological, and magnetic properties of vdW systems beyond the traditional interlayer-distance or twisting engineering.

cond-mat.mtrl-sci↗

arXiv2021

Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model

Understanding protein sequences is vital and urgent for biology, healthcare, and medicine. Labeling approaches are expensive yet time-consuming, while the amount of unlabeled data is increasing quite faster than that of the labeled data due to low-cost, high-throughput sequencing methods. In order to extract knowledge from these unlabeled data, representation learning is of significant value for protein-related tasks and has great potential for helping us learn more about protein functions and structures. The key problem in the protein sequence representation learning is to capture the co-evolutionary information reflected by the inter-residue co-variation in the sequences. Instead of leveraging multiple sequence alignment as is usually done, we propose a novel method to capture this information directly by pre-training via a dedicated language model, i.e., Pairwise Masked Language Model (PMLM). In a conventional masked language model, the masked tokens are modeled by conditioning on the unmasked tokens only, but processed independently to each other. However, our proposed PMLM takes the dependency among masked tokens into consideration, i.e., the probability of a token pair is not equal to the product of the probability of the two tokens. By applying this model, the pre-trained encoder is able to generate a better representation for protein sequences. Our result shows that the proposed method can effectively capture the inter-residue correlations and improves the performance of contact prediction by up to 9% compared to the MLM baseline under the same setting. The proposed model also significantly outperforms the MSA baseline by more than 7% on the TAPE contact prediction benchmark when pre-trained on a subset of the sequence database which the MSA is generated from, revealing the potential of the sequence pre-training method to surpass MSA based methods in general.

cs.CL↗

arXiv2021

Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer

The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery. Although recent deep learning-based approaches achieved better performance than molecular docking, existing models often neglect certain aspects of the intermolecular information, hindering the performance of prediction. We recognize this problem and propose a novel approach named Intermolecular Graph Transformer (IGT) that employs a dedicated attention mechanism to model intermolecular information with a three-way Transformer-based architecture. IGT outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively, and shows superior generalization ability to unseen receptor proteins. Furthermore, IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses.

cs.LG↗

arXiv2021

Exploring the Regulatory Function of the N-terminal Domain of SARS-CoV-2 Spike Protein Through Molecular Dynamics Simulation

SARS-CoV-2 is what has caused the COVID-19 pandemic. Early viral infection is mediated by the SARS-CoV-2 homo-trimeric Spike (S) protein with its receptor binding domains (RBDs) in the receptor-accessible state. We performed molecular dynamics simulation on the S protein with a focus on the function of its N-terminal domains (NTDs). Our study reveals that the NTD acts as a "wedge" and plays a crucial regulatory role in the conformational changes of the S protein. The complete RBD structural transition is allowed only when the neighboring NTD that typically prohibits the RBD's movements as a wedge detaches and swings away. Based on this NTD "wedge" model, we propose that the NTD-RBD interface should be a potential drug target.

q-bio.BM↗

arXiv2020

Effect of inter-system coupling on heat transport in a microscopic collision model

In this paper we consider a bipartite system composed of two subsystems each coupled to its own thermal environment. Based on a collision model, we mainly study whether the approximation (i.e., the inter-system interaction is ignored when modeling the system-environment coupling) is valid or not. We also address the problem of heat transport unitedly for both conventional energy-preserving system-environment interactions and non-energy preserving system-environment interactions. For the former interaction, as the inter-system interaction strength increases, at first this approximation gets worse as expected, but then counterintuitively gets better even for a stronger inter-system coupling. For the latter interaction with asymmetry, this approximation gets progressively worse. In this case we realize a perfect thermal rectification, and we can not find apparent rectification effect for the former interaction. Finally and more importantly, our results show that whether this approximation is valid or not is closely related to the quantum correlations between the subsystems, i.e., the weaker the quantum correlations, the more justified the approximation and vice versa.

quant-ph↗

arXiv2020

Realization of Semiconducting Layered Multiferroic Heterojunctions via Asymmetrical Magnetoelectric Coupling

Two-dimensional (2D) semiconducting multiferroics that can effectively couple magnetic and polarization (P) orders have great interest for both fundamental research and technological applications in nanoscale, which are, however, rare in nature. In this study, we propose a general mechanism to realize semiconducting 2D multiferroics via vdW heterojunction engineering, as demonstrated in a typical heterostructure consisting of magnetic bilayer CrI3 (bi-CrI3) and ferroelectric monolayer In2Se3. Interestingly, the novel indirect orbital coupling between Se 4p and Cr 3d orbitals, intermediated by the interfacial I 5p orbitals, are switchable in the opposite P configurations, resulting in an unexpected mechanism of strong asymmetrical magnetoelectric coupling. Therefore, along with the noticeable ferroelectric energy barrier induced by In2Se3, the realization of opposite magnetic orders in opposite P configurations can eventually result in the novel multiferroicity in bi-CrI3/In2Se3. Finally, we demonstrate that our mechanism can generally be applied to design other vdW multiferroics even with tunable layer thickness.

cond-mat.mtrl-sci↗

arXiv2020

Defending Model Inversion and Membership Inference Attacks via Prediction Purification

Neural networks are susceptible to data inference attacks such as the model inversion attack and the membership inference attack, where the attacker could infer the reconstruction and the membership of a data sample from the confidence scores predicted by the target classifier. In this paper, we propose a unified approach, namely purification framework, to defend data inference attacks. It purifies the confidence score vectors predicted by the target classifier by reducing their dispersion. The purifier can be further specialized in defending a particular attack via adversarial learning. We evaluate our approach on benchmark datasets and classifiers. We show that when the purifier is dedicated to one attack, it naturally defends the other one, which empirically demonstrates the connection between the two attacks. The purifier can effectively defend both attacks. For example, it can reduce the membership inference accuracy by up to 15% and increase the model inversion error by a factor of up to 4. Besides, it incurs less than 0.4% classification accuracy drop and less than 5.5% distortion to the confidence scores.

cs.CR↗

arXiv2020

SEEK: Segmented Embedding of Knowledge Graphs

In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering. However, existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them still far from satisfactory. To mitigate this problem, we propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. Our framework focuses on the design of scoring functions and highlights two critical characteristics: 1) facilitating sufficient feature interactions; 2) preserving both symmetry and antisymmetry properties of relations. It is noteworthy that owing to the general and elegant design of scoring functions, our framework can incorporate many famous existing methods as special cases. Moreover, extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework. Source codes and data can be found at \url{https://github.com/Wentao-Xu/SEEK}.

cs.AI↗

arXiv2020

Statistical and strict momentum conservation

Arguments about the conservation laws of energy and momentum in the micro-world being statistical or strict began in 1924, and conflicting viewpoints remain today. The former is mainly supported theoretically, but the latter has been proved by many experiments. Here we explain that in principle, the strict conservation law of momentum always holds in the entangled state form of the momentum eigenstates of a closed composite system with interactions among subsystems, by expanding the total wave function to the sum of the products of momentum eigenstates of subsystems. Common scenario is that one of the two subsystems of a composite system is large or strong, its state remains approximately unchanged in a short time and the entangled state can be approximately written as a product state, which can be easily deduced from the paper of Haroche's group in 2001. The considered micro-subsystem can be approximately represented as the superposition of its different momentum eigenstates; therefore, the approximation can be used to explain why the law holds statistically for the subsystem and for any single particle due to neglecting the interactions with the large subsystem or the environment. So the two momentum conservation laws reasonably hold without conflicts.

physics.gen-ph↗