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Gao, Shang

Publications and source records attributed to Gao, Shang.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Phonon excitations in Eu 2 Ir 2 O 7 probed by inelastic x-ray scattering

The study of phonon dynamics and its interplay with magnetic ordering is crucial for understanding the unique quantum phases in the pyrochlore iridates. Here, through inelastic x-ray scattering on a single crystal sample of the pyrochlore iridate Eu 2 Ir 2 O 7 , we map out the phonon excitation spectra in Eu 2 Ir 2 O 7 and compare them with the theoretical phonon spectra calculated using the density functional theory. Further, possible phonon renormalization across the magnetic long-range order transition is observed in our experiments, which is consistent with the results of the previous Raman scattering experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Cubic double perovskites host noncoplanar spin textures

Magnetic materials with noncoplanar magnetic structures can show unusual physical properties driven by nontrivial topology. Topologically-active states are often multi-q structures, which are challenging to stabilize in models and to identify in materials. Here, we use inelastic neutron-scattering experiments to show that the insulating double perovskites Ba 2 YRuO 6 and Ba 2 LuRuO 6 host a noncoplanar 3-q structure on the face-centered cubic lattice. Quantitative analysis of our neutron-scattering data reveals that these 3-q states are stabilized by biquadratic interactions. Our study identifies double perovskites as a highly promising class of materials to realize topological magnetism, elucidates the stabilization mechanism of the 3-q state in these materials, and establishes neutron spectroscopy on powder samples as a valuable technique to distinguish multi-q from single-q states, facilitating the discovery of topologically-nontrivial magnetic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Path-BigBird: An AI-Driven Transformer Approach to Classification of Cancer Pathology Reports

PURPOSE Surgical pathology reports are critical for cancer diagnosis and management. To accurately extract information about tumor characteristics from pathology reports in near real time, we explore the impact of using domain-specific transformer models that understand cancer pathology reports. METHODS We built a pathology transformer model, Path-BigBird, by using 2.7 million pathology reports from six SEER cancer registries. We then compare different variations of Path-BigBird with two less computationally intensive methods: Hierarchical Self-Attention Network (HiSAN) classification model and an offthe-shelf clinical transformer model (Clinical BigBird). We use five pathology information extraction tasks for evaluation: site, subsite, laterality, histology, and behavior. Model performance is evaluated by using macro and micro F 1 scores. RESULTS We found that Path-BigBird and Clinical BigBird outperformed the HiSAN in all tasks. Clinical BigBird performed better on the site and laterality tasks. Versions of the Path-BigBird model performed best on the two most difficult tasks: subsite (micro F 1 score of 72.53, macro F 1 score of 35.76) and histology (micro F 1 score of 80.96, macro F 1 score of 37.94). The largest performance gains over the HiSAN model were for histology, for which a Path-BigBird model increased the micro F 1 score by 1.44 points and the macro F 1 score by 3.55 points. Overall, the results suggest that a Path-BigBird model with a vocabulary derived from wellcurated and deidentified data is the best-performing model. CONCLUSION The Path-BigBird pathology transformer model improves automated information extraction from pathology reports. Although Path-BigBird outperforms Clinical BigBird and HiSAN, these less computationally expensive models still have utility when resources are constrained.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Spinon continuum in the Heisenberg quantum chain compound Sr 2 ⁢V 3 ⁢O 9

Magnetic excitations in the spin chain candidate Sr 2 ⁢V 3 ⁢O 9 have been investigated by inelastic neutron scattering on a single crystal sample. A spinon continuum with a bandwidth of ~22 meV is observed along the chain formed by alternating magnetic V 4+ and nonmagnetic V 5+ ions, which reveals the importance of the orbital degree of freedom in determining the chain axis as identified by prior electronic structure calculations. Incipient magnetic Bragg peaks due to weak ferromagnetic interchain couplings emerge when approaching the magnetic transition at T N ~ 5.3 K, while the excitations remain gapless within the instrumental resolution. Comparisons to the Bethe ansatz, density matrix renormalization group calculations, and effective field theories confirm Sr 2 ⁢V 3 ⁢O 9 as a host of weakly coupled S = 1/2 chains dominated by antiferromagnetic intrachain interactions of ~7.1(1) meV.

1-dimensional spin chains↗
DOE OSTI2023

Deep learning uncertainty quantification for clinical text classification

Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network’s confidence, in-depth analyses are needed to establish whether they are well calibrated. In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) population-based cancer registries. In particular, we introduce multiple methods for selective classification to achieve a target level of accuracy on multiple classification tasks while minimizing the rejection amount—that is, the number of electronic pathology reports for which the model’s predictions are unreliable. We evaluate the proposed methods by comparing our approach with the current in-house deep learning-based abstaining classifier. Overall, all the proposed selective classification methods effectively allow for achieving the targeted level of accuracy or higher in a trade-off analysis aimed to minimize the rejection rate. On in-distribution validation and holdout test data, with all the proposed methods, we achieve on all tasks the required target level of accuracy with a lower rejection rate than the deep abstaining classifier (DAC). Interpreting the results for the out-of-distribution test data is more complex; nevertheless, in this case as well, the rejection rate from the best among the proposed methods achieving 97% accuracy or higher is lower than the rejection rate based on the DAC. We show that although both approaches can flag those samples that should be manually reviewed and labeled by human annotators, the newly proposed methods retain a larger fraction and do so without retraining—thus offering a reduced computational cost compared with the in-house deep learning-based abstaining classifier.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Line-Graph Approach to Spiral Spin Liquids

Competition among exchange interactions is able to induce novel spin correlations on a bipartite lattice without geometrical frustration. A prototype example is the spiral spin liquid, which is a correlated paramagnetic state characterized by subdimensional degenerate propagation vectors. Here, using spectral graph theory, we show that spiral spin liquids on a bipartite lattice can be approximated by a further-neighbor model on the corresponding line-graph lattice that is nonbipartite, thus broadening the space of candidate materials that may support the spiral spin liquid phases. As examples, we examine neutron scattering experiments performed on two spinel compounds, ZnCr 2 Se 4 and CuInCr 4 Se 8 , to demonstrate the feasibility of this new approach and expose its possible limitations in experimental realizations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Language models for the prediction of SARS-CoV-2 inhibitors

The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ∼9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.

Blanchard, Andrew E.↗
DOE OSTI2022

Using ensembles and distillation to optimize the deployment of deep learning models for the classification of electronic cancer pathology reports

One of the goals of the Surveillance, Epidemiology, and End Results (SEER) program is to estimate incidence, prevalence, and mortality of all cancers. To that end, cancer registries across the country maintain a massive database of cancer pathology reports which contain rich information to understand cancer trends. However, these reports are stored in the form of unstructured text, and human annotators are required to read and extract relevant information. In this article, we show that existing deep learning models for automating information extraction from cancer pathology reports can be significantly improved by using ensemble model distillation. We found that by training multiple predictive models and transferring their knowledge to a single, low-resource model, we can reduce the number of highly confident wrong predictions. Our results show that our implemented methods could save 1000s of manual annotation hours.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Electronic structure, magnetic properties, and pairing tendencies of the copper-based honeycomb lattice Na 2 Cu 2 TeO 6

Spin-1/2 chains with alternating antiferromagnetic (AFM) and ferromagnetic (FM) couplings have attracted considerable interest due to the topological character of their spin excitations. Here, using density functional theory and density-matrix renormalization-group (DMRG) methods, we have systematically studied the dimerized chain system Na 2 Cu 2 TeO 6 with a d 9 electronic configuration. Near the Fermi level, in the nonmagnetic phase the dominant states are mainly contributed by the Cu 3d x 2 -y 2 orbitals highly hybridized with the O 2p orbitals, leading to an “effective” single-orbital low-energy model. By calculating the relevant hoping amplitudes, we explain the size and sign of the exchange interactions in Na 2 Cu 2 TeO 6 . In addition, a single-orbital Hubbard model is constructed for this dimerized chain system where the quantum fluctuations are taken into account. Both AFM and FM couplings (leading to an ↑|-|↓-|↓-↑| state) along the chain were found in our DMRG and Lanczos calculations, in agreement with density functional theory and neutron-scattering results. The hole pairing binding energy ΔE is predicted to be negative at Hubbard U~ 11eV, suggesting incipient pairing tendencies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Spiral Spin Liquid on a Honeycomb Lattice

Spiral spin liquids are correlated paramagnetic states with degenerate propagation vectors forming a continuous ring or surface in reciprocal space. On the honeycomb lattice, spiral spin liquids present a novel route to realize emergent fracton excitations, quantum spin liquids, and topological spin textures, yet experimental realizations remain elusive. Here, using neutron scattering, we show that a spiral spin liquid is realized in the van der Waals honeycomb magnet FeCl3. A continuous ring of scattering is directly observed, which indicates the emergence of an approximate U(1) symmetry in momentum space. Our work demonstrates that spiral spin liquids can be achieved in two-dimensional systems and provides a promising platform to study the fracton physics in spiral spin liquids.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Magnetic properties of Fe-substituted NiBr 2 single crystals

NiBr 2 is a multiferroic material with an incommensurate magnetic ground state that has easy-plane anisotropy. This manuscript reports how the magnetic properties evolve with Fe substitution in Ni 1-x Fe x Br 2 single crystals for 0 0.17. A particular emphasis is placed on the = 0.055 crystal, for which magnetic phase diagrams were developed on the basis of anisotropic magnetization measurements. The observed phase evolution reveals a phase transition in the x = 0.055 crystal that is not present in NiBr 2 . The data also demonstrate that the easy-plane anisotropy of NiBr is altered by Fe substitution, with a finite easy-axis anisotropy developing at even the low concentration of x = 0.055. Overall, these results demonstrate that Fe substitution enables a fine-tuning of the magnetism in NiBr 2 , especially the magnetic anisotropy, and may therefore promote complex magnetic phases.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Zoology of Multiple- Q Spin Textures in a Centrosymmetric Tetragonal Magnet with Itinerant Electrons

Magnetic skyrmion is a topologically stable particle-like swirling spin texture potentially suitable for high-density information bit, which was first observed in noncentrosymmetric magnets with Dzyaloshinskii–Moriya interaction. Recently, nanometric skyrmion has also been discovered in centrosymmetric rare-earth compounds, and the identification of their skyrmion formation mechanism and further search of nontrivial spin textures are highly demanded. Here, magnetic structures in a prototypical skyrmion-hosting centrosymmetric tetragonal magnet GdRu2Si2 is exhaustively studied by performing the resonant X-ray scattering experiments. A rich variety of double-Q magnetic structures, including the antiferroic order of meron(half-skyrmion)/anti-meron-like textures with fractional local topological charges are identified. The observed intricate magnetic phase diagram is successfully reproduced by the theoretical framework considering the four-spin interaction mediated by itinerant electrons and magnetic anisotropy. The present results will contribute to the better understanding of the novel skyrmion formation mechanism in this centrosymmetric rare-earth compound, and suggest that itinerant electrons can ubiquitously host a variety of unique multiple-Q spin orders in a simple crystal lattice system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Automating Genetic Algorithm Mutations for Molecules Using a Masked Language Model

Inspired by the evolution of biological systems, genetic algorithms have been applied to generate solutions for optimization problems in a variety of scientific and engineering disciplines. For a given problem, a suitable genome representation must be defined along with a mutation operator to generate subsequent generations. Unlike natural systems which display a variety of complex rearrangements (e.g. mobile genetic elements), mutation for genetic algorithms commonly utilizes only random point-wise changes. Furthermore, generalizing beyond point-wise mutations poses a key difficulty as useful genome rearrangements depend on the representation and problem domain. To move beyond the limitations of manually defined point-wise changes, here we propose the use of techniques from masked language models to automatically generate mutations. As a first step, common subsequences within a given population are used to generate a vocabulary. The vocabulary is then used to tokenize each genome. A masked language model is trained on the tokenized data in order to generate possible rearrangements (i.e. mutations). In order to illustrate the proposed strategy, we use string representations of molecules and use a genetic algorithm to optimize for drug-likeness and synthesizability. Finally, our results show that moving beyond random point-wise mutations accelerates genetic algorithm optimization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Spin dynamics in the skyrmion-host lacunar spinel GaV 4 S 8

Here in the lacunar spinel GaV 4 S 8 , the interplay of spin, charge, and orbital degrees of freedom produces a rich phase diagram that includes an unusual Néel-type skyrmion phase composed of molecular spins. To provide insight into the interactions underlying this complex phase diagram, we study the spin excitations in GaV 4 S 8 through inelastic neutron scattering measurements on polycrystalline and single crystal samples. Using linear spin wave theory, we describe the spin wave excitations using a model where V 4 clusters decorate an fcc lattice. The effective cluster model includes a ferromagnetic interaction and a weaker antisymmetric Dzyaloshinskii-Moriya interaction between the neighboring molecular spins. Our work clarifies the spin interactions in GaV 4 S 8 and supports the picture of interacting molecular clusters.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Class imbalance in out-of-distribution datasets: Improving the robustness of the TextCNN for the classification of rare cancer types

In the last decade, the widespread adoption of electronic health record documentation has created huge opportunities for information mining. Natural language processing (NLP) techniques using machine and deep learning are becoming increasingly widespread for information extraction tasks from unstructured clinical notes. Disparities in performance when deploying machine learning models in the real world have recently received considerable attention. In the clinical NLP domain, the robustness of convolutional neural networks (CNNs) for classifying cancer pathology reports under natural distribution shifts remains understudied. In this research, we aim to quantify and improve the performance of the CNN for text classification on out-of-distribution (OOD) datasets resulting from the natural evolution of clinical text in pathology reports. We identified class imbalance due to different prevalence of cancer types as one of the sources of performance drop and analyzed the impact of previous methods for addressing class imbalance when deploying models in real-world domains. Our results show that our novel class-specialized ensemble technique outperforms other methods for the classification of rare cancer types in terms of macro F1 scores. We also found that traditional ensemble methods perform better in top classes, leading to higher micro F1 scores. Based on our findings, we formulate a series of recommendations for other ML practitioners on how to build robust models with extremely imbalanced datasets in biomedical NLP applications.

60 APPLIED LIFE SCIENCES↗