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Mohan Chen

Mohan Chen appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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cond-mat.mtrl-sciphysics.comp-phphysics.chem-phcond-mat.dis-nnArtificial Intelligencecond-mat.stat-mechDistributed, Parallel, and Cluster ComputingMachine LearningRobotics

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Published work

15 published item(s)

preprint2026arXiv

Topology-Driven Anti-Entanglement Control for Soft Robots

In the field of precision manufacturing in complex constrained environments, the role of soft robots is increasingly prominent, and the realization of anti-winding control based on multi-intelligent body reinforcement learning has become a research hotspot. One of the core problems at present is to coordinate multiple robots to complete the unwinding operation in a highly constrained environment. The existing distributed training framework faces some observability challenges in high-density barrier and unstable environments, resulting in poor learning results. This paper proposes a topology-driven Multi-Agent Reinforcement Learning (TD-MARL) framework to coordinate multi-robot systems to avoid entanglement. Specifically, the critical network adopts centralized learning, so that each intelligent body can perceive the strategies of other intelligent bodies by sharing the topological state, thus alleviating the training instability caused by complex interactions; eliminating the demand for communication resources between robots through distributed execution, Upgrade system reliability; the integrated topological security layer uses topological invariants to accurately assess and mitigate the risk of entanglement to avoid the strategy from falling into local difficulties. Finally, the full simulation experiments carried out in the real simulation environment show that the method is better than the current advanced deep reinforcement learning (DRL) method in terms of convergence and anti-winding effect.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for high-level QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training a ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn-Sham (DeePKS), a ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely-matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model, and then use the DeePKS model to label a much larger amount of configurations to train a ML potential. This scheme for periodic systems is implemented in a DFT package ABACUS, which is open-source and ready for use in various applications.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Disordered Hyperuniform Quasi-1D Materials

Carbon nanotubes are quasi-one-dimensional systems that possess superior transport, mechanical, optical, and chemical properties. In this work, we generalize the notion of disorder hyperuniformity, a recently discovered exotic state of matter with hidden long-range order, to quasi-one-dimensional materials. As a proof of concept, we then apply the generalized framework to quantify the density fluctuations in amorphous carbon nanotubes containing randomly distributed Stone-Wales defects. We demonstrate that all of these amorphous nanotubes are hyperuniform, i.e., the infinite-wavelength density fluctuations of these systems are completely suppressed, regardless of the diameter, rolling axis, number of rolling sheets, and defect fraction of the nanotubes. We find that these amorphous nanotubes are energetically more stable than nanotubes with periodically distributed Stone-Wales defects. Moreover, certain semiconducting defect-free carbon nanotubes become metallic as sufficiently large amounts of defects are randomly introduced. This structural study of amorphous nanotubes strengthens our fundamental understanding of these systems, and suggests possible exotic physical properties, as endowed by their disordered hyperuniformity. Our findings also shed light on the effect of dimensionality reduction on the hyperuniformity property of materials.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

DP Compress: a Model Compression Scheme for Generating Efficient Deep Potential Models

Machine-learning-based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structure schemes, these models suffer from costly computations via deep neural networks to predict the energy and atomic forces, resulting in lower running efficiency as compared to the typical empirical force fields. Herein, we report a model compression scheme for boosting the performance of the Deep Potential (DP) model, a deep learning based PES model. This scheme, we call DP Compress, is an efficient post-processing step after the training of DP models (DP Train). DP Compress combines several DP-specific compression techniques, which typically speed up DP-based molecular dynamics simulations by an order of magnitude faster, and consume an order of magnitude less memory. We demonstrate that DP Compress is sufficiently accurate by testing a variety of physical properties of Cu, H2O, and Al-Cu-Mg systems. DP Compress applies to both CPU and GPU machines and is publicly available online.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms

High-performance computing, together with a neural network model trained from data generated with first-principles methods, has greatly boosted applications of \textit{ab initio} molecular dynamics in terms of spatial and temporal scales on modern supercomputers. Previous state-of-the-art can achieve $1-2$ nanoseconds molecular dynamics simulation per day for 100-million atoms on the entire Summit supercomputer. In this paper, we have significantly reduced the memory footprint and computational time by a comprehensive approach with both algorithmic and system innovations. The neural network model is compressed by model tabulation, kernel fusion, and redundancy removal. Then optimizations such as acceleration of customized kernel, tabulation of activation function, MPI+OpenMP parallelization are implemented on GPU and ARM architectures. Testing results of the copper system show that the optimized code can scale up to the entire machine of both Fugaku and Summit, and the corresponding system size can be extended by a factor of $134$ to an unprecedented $17$ billion atoms. The strong scaling of a $13.5$-million atom copper system shows that the time-to-solution can be 7 times faster, reaching $11.2$ nanoseconds per day. This work opens the door for unprecedentedly large-scale molecular dynamics simulations based on {\it ab initio} accuracy and can be potentially utilized in studying more realistic applications such as mechanical properties of metals, semiconductor devices, batteries, etc. The optimization techniques detailed in this paper also provide insight for relevant high-performance computing applications.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Structural and Dynamic Properties of Solvated Hydroxide and Hydronium Ions in Water from Ab Initio Modeling

Predicting the asymmetric structure and dynamics of solvated hydroxide and hydronium in water has been a challenging task from ab initio molecular dynamics (AIMD). The difficulty mainly comes from a lack of accurate and efficient exchange-correlation functional in elucidating the amphiphilic nature and the ubiquitous proton transfer behaviors of the two ions. By adopting the strongly-constrained and appropriately normed (SCAN) meta-GGA functional in AIMD simulations, we systematically examine the amphiphilic properties, the solvation structures, the electronic structures, and the dynamic properties of the two water ions. In particular, we compare these results to those predicted by the PBE0-TS functional, which is an accurate yet computationally more expensive exchange-correlation functional. We demonstrate that the general-purpose SCAN functional provides a reliable choice in describing the two water ions. Specifically, in the SCAN picture of water ions, the appearance of the fourth and fifth hydrogen bonds near hydroxide stabilizes the pot-like shape solvation structure and suppresses the structural diffusion, while the hydronium stably donates three hydrogen bonds to its neighbors. As a result, hydroxide prefers the stepwise proton transfer (PT) while hydronium prefers the concerted PT behavior. Consequently, hydroxide diffuses slower than hydronium in water, which is consistent with the experiments.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Deep neural network for the dielectric response of insulators

We introduce a deep neural network to model in a symmetry preserving way the environmental dependence of the centers of the electronic charge. The model learns from ab-initio density functional theory, wherein the electronic centers are uniquely assigned by the maximally localized Wannier functions. When combined with the Deep Potential model of the atomic potential energy surface, the scheme predicts the dielectric response of insulators for trajectories inaccessible to direct ab-initio simulation. The scheme is non-perturbative and can capture the response of a mutating chemical environment. We demonstrate the approach by calculating the infrared spectra of liquid water at standard conditions, and of ice under extreme pressure, when it transforms from a molecular to an ionic crystal.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Electrical and thermal transport properties of medium-entropy SiyGeySnx alloys

Electrical and thermal transport properties of disordered materials have long been of both theoretical interest and engineering importance. As a new class of materials with an intrinsic compositional disorder, high/medium-entropy alloys (HEAs/MEAs) are being immensely studied mainly for their excellent mechanical properties. By contrast, electrical and thermal transport properties of HEAs/MEAs are less well studied. Here we investigate these two properties of silicon (Si)-germanium (Ge)-tin (Sn) MEAs, where we keep the same content of Si and Ge while increasing the content of Sn from 0 to 1/3 to tune the configurational entropy and thus the degree of compositional disorder. We predict all SiyGeySnx MEAs to be semiconductors with a wide range of bandgaps from near-infrared (0.28 eV) to visible (1.11 eV) in the light spectrum. We find that the bandgaps and effective carrier masses decrease with increasing Sn content. As a result, increasing the compositional disorder in SiyGeySnx MEAs enhances their electrical conductivity. For the thermal transport properties of SiyGeySnx MEAs, our molecular dynamics simulations show an opposite trend in the thermal conductivity of these MEAs at room temperature, which decreases with increasing compositional disorder, owing to enhanced Anderson localization and strong phonon-phonon anharmonic interactions. The enhanced electrical conductivity and weakened thermal conductivity make SiyGeySnx MEAs with high Sn content promising functional materials for thermoelectric applications. Our work demonstrates that HEAs/MEAs not only represent a new class of structural alloys but also a novel category of functional alloys with unique electrical and thermal transport properties.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning

For 35 years, {\it ab initio} molecular dynamics (AIMD) has been the method of choice for modeling complex atomistic phenomena from first principles. However, most AIMD applications are limited by computational cost to systems with thousands of atoms at most. We report that a machine learning-based simulation protocol (Deep Potential Molecular Dynamics), while retaining {\it ab initio} accuracy, can simulate more than 1 nanosecond-long trajectory of over 100 million atoms per day, using a highly optimized code (GPU DeePMD-kit) on the Summit supercomputer. Our code can efficiently scale up to the entire Summit supercomputer, attaining $91$ PFLOPS in double precision ($45.5\%$ of the peak) and {$162$/$275$ PFLOPS in mixed-single/half precision}. The great accomplishment of this work is that it opens the door to simulating unprecedented size and time scales with {\it ab initio} accuracy. It also poses new challenges to the next-generation supercomputer for a better integration of machine learning and physical modeling.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Stone-Wales Defects Preserve Hyperuniformity in Amorphous Two-Dimensional Materials

Crystalline two-dimensional (2D) materials such as graphene possess unique physical properties absent in their bulk form, enabling many novel device applications. Yet, little is known about their amorphous counterparts, which can be obtained by introducing the Stone-Wales (SW) topological defects via proton radiation. Here we provide strong numerical evidence that SW defects preserve hyperuniformity in hexagonal 2D materials, a recently discovered new state of matter characterized by vanishing normalized infinite-wavelength density fluctuations, which implies that all amorphous states of these materials are hyperuniform. Specifically, the static structure factor S(k) of these materials possesses the scaling S(k) ~ k^α for small wave number k, where 1<=α(p)<=2 is monotonically decreasing as the SW defect concentration p increases, indicating a transition from type-I to type-II hyperuniformity at p ~= 0.12 induced by the saturation of the SW defects. This hyperuniformity transition marks a structural transition from perturbed lattice structures to truly amorphous structures, and underlies the onset of strong correlation among the SW defects as well as a transition between distinct electronic transport mechanisms associated with different hyperuniformity classes.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Stabilization of highly polar BiFeO$_3$-like structure: a new interface design route for enhanced ferroelectricity in artificial perovskite superlattices

In ABO3 perovskites, oxygen octahedron rotations are common structural distortions that can promote large ferroelectricity in BiFeO3 with an R3c structure [1], but suppress ferroelectricity in CaTiO3 with a Pbnm symmetry [2]. For many CaTiO3-like perovskites, the BiFeO3 structure is a metastable phase. Here, we report the stabilization of the highly-polar BiFeO3-like phase of CaTiO3 in a BaTiO3/CaTiO3 superlattice grown on a SrTiO3 substrate. The stabilization is realized by a reconstruction of oxygen octahedron rotations at the interface from the pattern of nonpolar bulk CaTiO3 to a different pattern that is characteristic of a BiFeO3 phase. The reconstruction is interpreted through a combination of amplitude-contrast sub 0.1nm high-resolution transmission electron microscopy and first-principles theories of the structure, energetics, and polarization of the superlattice and its constituents. We further predict a number of new artificial ferroelectric materials demonstrating that nonpolar perovskites can be turned into ferroelectrics via this interface mechanism. Therefore, a large number of perovskites with the CaTiO3 structure type, which include many magnetic representatives, are now good candidates as novel highly-polar multiferroic materials [3].

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Large-scale ab initio simulations based on systematically improvable atomic basis

We present a first-principles computer code package (ABACUS) that is based on density functional theory and numerical atomic basis sets. Theoretical foundations and numerical techniques used in the code are described, with focus on the accuracy and transferability of the hierarchical atomic basis sets as generated using a scheme proposed by Chen, Guo and He [J. Phys.:Condens. Matter \textbf{22}, 445501 (2010)]. Benchmark results are presented for a variety of systems include molecules, solids, surfaces, and defects. All results show that the ABACUS package with its associated atomic basis sets is an efficient and reliable tool for simulating both small and large-scale materials.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Accelerating Atomic Orbital-based Electronic Structure Calculation via Pole Expansion and Selected Inversion

We describe how to apply the recently developed pole expansion and selected inversion (PEXSI) technique to Kohn-Sham density function theory (DFT) electronic structure calculations that are based on atomic orbital discretization. We give analytic expressions for evaluating the charge density, the total energy, the Helmholtz free energy and the atomic forces (including both the Hellman-Feynman force and the Pulay force) without using the eigenvalues and eigenvectors of the Kohn-Sham Hamiltonian. We also show how to update the chemical potential without using Kohn-Sham eigenvalues. The advantage of using PEXSI is that it has a much lower computational complexity than that associated with the matrix diagonalization procedure. We demonstrate the performance gain by comparing the timing of PEXSI with that of diagonalization on insulating and metallic nanotubes. For these quasi-1D systems, the complexity of PEXSI is linear with respect to the number of atoms. This linear scaling can be observed in our computational experiments when the number of atoms in a nanotube is larger than a few hundreds. Both the wall clock time and the memory requirement of PEXSI is modest. This makes it even possible to perform Kohn-Sham DFT calculations for 10,000-atom nanotubes with a sequential implementation of the selected inversion algorithm. We also perform an accurate geometry optimization calculation on a truncated (8,0) boron-nitride nanotube system containing 1024 atoms. Numerical results indicate that the use of PEXSI does not lead to loss of accuracy required in a practical DFT calculation.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Electronic structure interpolation via atomic orbitals

We present an efficient scheme for accurate electronic structure interpolations based on the systematically improvable optimized atomic orbitals. The atomic orbitals are generated by minimizing the spillage value between the atomic basis calculations and the converged plane wave basis calculations on some coarse $k$-point grid. They are then used to calculate the band structure of the full Brillouin zone using the linear combination of atomic orbitals (LCAO) algorithms. We find that usually 16 -- 25 orbitals per atom can give an accuracy of about 10 meV compared to the full {\it ab initio} calculations. The current scheme has several advantages over the existing interpolation schemes. The scheme is easy to implement and robust which works equally well for metallic systems and systems with complex band structures. Furthermore, the atomic orbitals have much better transferability than the Shirley's basis and Wannier functions, which is very useful for the perturbation calculations.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Systematically improvable optimized atomic basis sets for {\it ab inito} calculations

We propose a unique scheme to construct fully optimized atomic basis sets for density-functional calculations. The shapes of the radial functions are optimized by minimizing the {\it spillage} of the wave functions between the atomic orbital calculations and the converged plane wave calculations for dimer systems. The quality of the bases can be systematically improved by increasing the size of the bases within the same framework. The scheme is easy to implement and very flexible. We have done extensive tests of this scheme for wide variety of systems. The results show that the obtained atomic basis sets are very satisfactory for both accuracy and transferability.

0 citations0 reviewsNo code linkedOpen access