arXiv subjects

Deyu Lu

Publications and source records attributed to Deyu Lu.

33 records · Page 2Linked to original sources

arXiv2023

Decoding Structure-Spectrum Relationships with Physically Organized Latent Spaces

A new semi-supervised machine learning method for the discovery of structure-spectrum relationships is developed and demonstrated using the specific example of interpreting X-ray absorption near-edge structure (XANES) spectra. This method constructs a one-to-one mapping between individual structure descriptors and spectral trends. Specifically, an adversarial autoencoder is augmented with a novel rank constraint (RankAAE). The RankAAE methodology produces a continuous and interpretable latent space, where each dimension can track an individual structure descriptor. As a part of this process, the model provides a robust and quantitative measure of the structure-spectrum relationship by decoupling intertwined spectral contributions from multiple structural characteristics. This makes it ideal for spectral interpretation and the discovery of new descriptors. The capability of this procedure is showcased by considering five local structure descriptors and a database of over fifty thousand simulated XANES spectra across eight first-row transition metal oxide families. The resulting structure-spectrum relationships not only reproduce known trends in the literature, but also reveal unintuitive ones that are visually indiscernible in large data sets. The results suggest that the RankAAE methodology has great potential to assist researchers to interpret complex scientific data, test physical hypotheses, and reveal new patterns that extend scientific insight.

cond-mat.mtrl-sci↗

arXiv2022

Lightshow: a Python package for generating computational x-ray absorption spectroscopy input files

First-principles computational spectroscopy is a critical tool for interpreting experiment, performing structure refinement, and developing new physical understanding. Systematically setting up input files for different simulation codes and a diverse class of materials is a challenging task with a very high barrier-to-entry, given the complexities and nuances of each individual simulation package. This task is non-trivial even for experts in the electronic structure field and nearly formidable for non-expert researchers. Lightshow solves this problem by providing a uniform abstraction for writing computational x-ray spectroscopy input files for multiple popular codes, including FEFF, VASP, OCEAN, EXCITING and XSPECTRA. Its extendable framework will also allow the community to easily add new functions and to incorporate new simulation codes.

cond-mat.mtrl-sci↗

arXiv2021

Geometric quantum adiabatic methods for quantum chemistry

Existing quantum algorithms for quantum chemistry work well near the equilibrium geometry of molecules, but the results can become unstable when the chemical bonds are broken at large atomic distances. For any adiabatic approach, this usually leads to serious problems, such as level crossing and/or energy gap closing along the adiabatic evolution path. In this work, we propose a quantum algorithm based on adiabatic evolution to obtain molecular eigenstates and eigenenergies in quantum chemistry, which exploits a smooth geometric deformation by changing bond lengths and bond angles. Even with a simple uniform stretching of chemical bonds, this algorithm performs more stably and achieves better accuracy than our previous adiabatic method [Phys. Rev. Research 3, 013104 (2021)]. It solves the problems related to energy gap closing and level crossing along the adiabatic evolution path at large atomic distances. We demonstrate its utility in several examples, including H${}_2$O, CH${}_2$, and a chemical reaction of H${}_2$+D${}_2\rightarrow$ 2HD. Furthermore, our fidelity analysis demonstrates that even with finite bond length changes, our algorithm still achieves high fidelity with the ground state.

physics.chem-ph↗

arXiv2021

Machine learning of Kondo physics using variational autoencoders and symbolic regression

We employ variational autoencoders to extract physical insight from a dataset of one-particle Anderson impurity model spectral functions. Autoencoders are trained to find a low-dimensional, latent space representation that faithfully characterizes each element of the training set, as measured by a reconstruction error. Variational autoencoders, a probabilistic generalization of standard autoencoders, further condition the learned latent space to promote highly interpretable features. In our study, we find that the learned latent variables strongly correlate with well known, but nontrivial, parameters that characterize emergent behaviors in the Anderson impurity model. In particular, one latent variable correlates with particle-hole asymmetry, while another is in near one-to-one correspondence with the Kondo temperature, a dynamically generated low-energy scale in the impurity model. Using symbolic regression, we model this variable as a function of the known bare physical input parameters and "rediscover" the non-perturbative formula for the Kondo temperature. The machine learning pipeline we develop suggests a general purpose approach which opens opportunities to discover new domain knowledge in other physical systems.

cond-mat.str-el↗

arXiv2020

Predicting impurity spectral functions using machine learning

The Anderson Impurity Model (AIM) is a canonical model of quantum many-body physics. Here we investigate whether machine learning models, both neural networks (NN) and kernel ridge regression (KRR), can accurately predict the AIM spectral function in all of its regimes, from empty orbital, to mixed valence, to Kondo. To tackle this question, we construct two large spectral databases containing approximately 410k and 600k spectral functions of the single-channel impurity problem. We show that the NN models can accurately predict the AIM spectral function in all of its regimes, with point-wise mean absolute errors down to 0.003 in normalized units. We find that the trained NN models outperform models based on KRR and enjoy a speedup on the order of $10^5$ over traditional AIM solvers. The required size of the training set of our model can be significantly reduced using furthest point sampling in the AIM parameter space, which is important for generalizing our method to more complicated multi-channel impurity problems of relevance to predicting the properties of real materials.

cond-mat.str-el↗

arXiv2020

Probing the pathway of an ultrafast structural phase transition to illuminate the transition mechanism in Cu2S

Disentangling the primary order parameter from secondary order parameters in phase transitions is critical to the interpretation of the transition mechanisms in strongly correlated systems and quantum materials. Here we present a study of structural phase transition pathways in superionic Cu2S nanocrystals that exhibit intriguing properties. Utilizing ultrafast electron diffraction techniques sensitive in both momentum-space and the time-domain, we distinguish the dynamics of crystal symmetry breaking and lattice expansion in this system. We are able to follow the transient states along the transition pathway and so observe the dynamics of both the primary and secondary order parameters. Based on these observations we argue that the mechanism of the structural phase transition in Cu2S is dominated by the electron-phonon coupling. This mechanism advances the understanding from previous results where the focus was solely on dynamic observations of the lattice expansion.

cond-mat.str-el↗

arXiv2020

Importance of Nuclear Quantum Effects on the Hydration of Chloride Ion

Path-integral ab initio molecular dynamics (PI-AIMD) calculations have been employed to probe the nature of chloride ion solvation in aqueous solution. Nuclear quantum effects (NQEs) are shown to weaken hydrogen bonding between the chloride anion and the solvation shell of water molecules. As a consequence, the disruptive effect of the anion on the solvent water structure is significantly reduced compared to what is found in the absence of NQEs. The chloride hydration structure obtained from PI-AIMD agrees well with information extracted from neutron scattering data. Inparticular, the observed satellite peak in the hydrogen-chloride-hydrogen triple angular distribution serves as a clear signature of NQEs. The present results suggest that NQEs are likely to play acrucial role in determining the structure of saline solutions.

physics.chem-ph↗

arXiv2020

Machine-Learning X-ray Absorption Spectra to Quantitative Accuracy

The advent of massive data repositories has propelled machine learning techniques to the front lines of many scientific fields, and exploring new frontiers by leveraging the predictive power of machine learning will greatly accelerate big data-assisted discovery. In this work, we show that graph-based neural networks can be used to predict the near edge x-ray absorption structure spectra of molecules with exceptional accuracy. The predicted spectra reproduce nearly all the prominent peaks, with 90% of the predicted peak locations within 1 eV of the ground truth. Our study demonstrates that machine learning models can achieve practically the same accuracy as first-principles calculations in predicting complex physical quantities, such as spectral functions, but at a fraction of the cost.

cond-mat.mtrl-sci↗

arXiv2019

Probe Ferroelectricity by X-ray Absorption Spectroscopy in Molecular Crystal

We carry out X-ray absorption spectroscopy experiment at oxygen K-edge in croconic acid (C5H2O5) crystal as a prototype of ferroelectric organic molecular solid, whose electric polarization is generated by proton transfer. The experimental spectrum is well reproduced by the electron-hole excitation theory simulations from configuration generated by ab initio molecular dynamics simulation. When inversion symmetry is broken in ferroelectric state, the hydrogen bonding environment on the two bonded molecules become inequivalent. Such a difference is sensitively probed by the bound excitation in the pre-edge, which are strongly localized on the excited molecules. Our analysis shows that a satellite peak in the pre-edge will emerge at higher excitation energy which serves as a clear signature of ferroelectricity in the material.

physics.comp-ph↗

arXiv2019

Classification of Local Chemical Environments from X-ray Absorption Spectra using Supervised Machine Learning

X-ray absorption spectroscopy is a premier element-specific technique for materials characterization. Specifically, the x-ray absorption near-edge structure (XANES) encodes important information about the local chemical environment of an absorbing atom, including coordination number, symmetry, and oxidation state. Interpreting XANES spectra is a key step towards understanding the structural and electronic properties of materials, and as such, extracting structural and electronic descriptors from XANES spectra is akin to solving a challenging inverse problem. Existing methods rely on empirical fingerprints, which are often qualitative or semiquantitative and not transferable. In this paper, we present a machine learning-based approach, which is capable of classifying the local coordination environments of the absorbing atom from simulated K-edge XANES spectra. The machine learning classifiers can learn important spectral features in a broad energy range without human bias and once trained, can make predictions on the fly. The robustness and fidelity of the machine learning method are demonstrated by an average 86% accuracy across the wide chemical space of oxides in eight 3d transition-metal families. We found that spectral features beyond the preedge region play an important role in the local structure classification problem especially for the late 3d transition-metal elements.

cond-mat.mtrl-sci↗

arXiv2017

Molecular Polarizability of Water from the Local Dielectric Response Theory

We propose a fully ab initio theory to compute the electron density response under the perturbation in the local field. This method is based on our recently developed local dielectric response theory [Phys. Rev. B 92, 241107(R), 2015], which provides a rigorous theoretical framework to treat local electronic excitations in extended systems beyond the commonly employed dipole approximation. We have applied this method to study the electronic part of the molecular polarizability of water in ice Ih and liquid water. Our results reveal that the crystal field of the hydrogen-bond network has strong anisotropic effects, which significantly enhance the out-of-plane component and suppress the in-plane component perpendicular to the bisector direction. The contribution from the charge transfer is equally important, which increases the isotropic molecular polarizability by 5-6%. Our study provides new insights into the dielectric properties of water, which form the basis to understand the electronic excitations in water and to develop accurate polarizable force fields of water.

cond-mat.mtrl-sci↗

arXiv2017

Unveiling two types of local order in liquid water using machine learning

Machine learning methods are being explored in many areas of science, with the aim of finding solution to problems that evade traditional scientific approaches due to their complexity. In general, an order parameter capable of identifying two different phases of matter separated by a correspond- ing phase transition is constructed based on symmetry arguments. This parameter measures the degree of order as the phase transition proceeds. However, when the two distinct phases are highly disordered it is not trivial to identify broken symmetries with which to find an order parameter. This poses an excellent problem to be addressed using machine learning procedures. Room tem- perature liquid water is hypothesized to be a supercritical liquid, with fluctuations of two different molecular orders associated to two parent liquid phases, one with high density and another one with low density. The validity of this hypothesis is linked to the existence of an order parameter capable of identifying the two distinct liquid phases and their fluctuations. In this work we show how two different machine learning procedures are capable of recognizing local order in liquid water. We argue that when in order to learn relevant features from this complexity, an initial, physically motivated preparation of the available data is as important as the quality of the data set, and that machine learning can become a successful analysis tool only when coupled to high level physical information.

cond-mat.soft↗

arXiv2015

A Local Representation of the Electronic Dielectric Response Function

We present a local representation of the electronic dielectric response function, based on a spatial partition of the dielectric response into contributions from each Wannier function using a generalized density functional perturbation theory. This procedure is fully ab initio, and therefore allows us to rigorously define local metrics, such as "bond polarizability," on Wannier centers. We show that the locality of the response function is determined by the locality of three quantities: Wannier functions of the occupied manifold, the density matrix, and the Hamiltonian matrix. In systems with a gap, the bare dielectric response is exponentially localized, which supports the physical picture of the dielectric response function as a collection of interacting local response that can be captured by a tight-binding model.

cond-mat.mtrl-sci↗

arXiv2002

Representative Pathways of Excitation Migration in Photosystem I

Photosystem I is a protein-pigment complex that performs photosynthesis in plants, green algae, and cyanobacteria. It contains an aggregate of chlorophylls that absorbs light and delivers the resulting electronic excitation to the special pair of chlorophylls where the excitation energy is used for producing charge separation across the cell membrane. The seemingly random arrangement of chlorophylls in photosystem I poses the question which pathways excitation migration follows towards the special pair after absorption of light at any of its chlorophylls. We employ a master equation to describe the process of excitation migration in photosystem I, and find representative paths of excitation migration based on the mean first-passage time from each chlorophyll to the special pair. The resulting paths, beginning at each of the chlorophylls and ending at the special pair, provide a complete yet distilled picture of the excitation migration towards the special pair.

physics.bio-ph↗

arXiv2002

Robustness and Optimality of Light Harvesting in Cyanobacterial Photosystem I

As most biological species, photosynthetic lifeforms have evolved to function optimally, despite thermal disorder and with fault tolerance. It remains a challenge to understand how this is achieved. To address this challenge the function of the protein-pigment complex photosystem I (PSI) of the cyanobacterium Synechococcus elongatus is investigated theoretically. The recently obtained high resolution structure of this complex exhibits an aggregate of 96 chlorophylls that are electronically coupled to function as a light-harvesting antenna complex. This paper constructs an effective Hamiltonian for the chlorophyll aggregate to describe excitation transfer dynamics and spectral properties of PSI. For this purpose, a new kinetic expansion method, the sojourn expansion, is introduced. Our study shows that at room temperature fluctuations of site energies have little effect on the calculated excitation lifetime and quantum yield, which compare favorably with experimental results. The efficiency of the system is found to be robust against `pruning' of individual chlorophylls. An optimality of the arrangement of chlorophylls is identified through the quantum yield in comparison with an ensemble of randomly oriented chlorophylls, though, the quantum yield is seen to change only within a narrow interval in such an ensemble.

physics.bio-ph↗