7 research outputs found
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Thermodynamics of Interacting Phonons
Many thermodynamic properties of materials can be attributed to phonons and their interactions, also known as the Taylor series of the Born-Oppenheimer (BO) energy surface. In this the- sis, we present several novel approaches to computing phonons and their interactions, as well as implementations to predict thermodynamic properties of materials from phonons and phonon interactions. First, we implemented the symmetry analysis technique that allows us to write the Taylor series of the BO energy surface for a material at arbitrary order N using the space group irreducible derivatives, guaranteeing the symmetry of the crystal by construction.
Second, we derived the minimum supercell multiplicity equation with which we can compute the smallest possible supercells that can accommodate N given wave vectors, greatly improving the computational efficiency for finite displacements calculations. Third, we implemented 2 branches of finite displacements methodologies, lone irreducible derivatives (LID) and bundled irreducible derivatives (BID), with the former sacrificing efficiency for accuracy and the latter emphasizing on using the least amount of calculations to extract all irreducible derivatives. Additionally, we implemented algorithms to predict materials properties including GrĂĽneisen parameters, phonon linewidth, phonon frequency shift and thermal conductivity using our space group irreducible derivatives. We applied our methods on a wide range of materials, and the comparison against literature demonstrated massive gain on efficiency while maintaining high quality results
Validating First-Principles Phonon Lifetimes via Inelastic Neutron Scattering
Phonon lifetimes are a key component of quasiparticle theories of transport,
yet first-principles lifetimes are rarely directly compared to inelastic
neutron scattering (INS) results. Existing comparisons show discrepancies even
at temperatures where perturbation theory is expected to be reliable. In this
work, we demonstrate that the reciprocal space voxel (-voxel), which is the
finite region in reciprocal space required in INS data analysis, must be
explicitly accounted for within theory in order to draw a meaningful
comparison. We demonstrate accurate predictions of peak widths of the
scattering function when accounting for the -voxel in CaF and ThO.
Passing this test implies high fidelity of the phonon interactions and the
approximations used to compute the Green's function, serving as critical
benchmark of theory, and indicating that other material properties should be
accurately predicted; which we demonstrate for thermal conductivity
Enhancing SPARQL Query Generation for Knowledge Base Question Answering Systems by Learning to Correct Triplets
Generating SPARQL queries from natural language questions is challenging in Knowledge Base Question Answering (KBQA) systems. The current state-of-the-art models heavily rely on fine-tuning pretrained models such as T5. However, these methods still encounter critical issues such as triple-flip errors (e.g., (subject, relation, object) is predicted as (object, relation, subject)). To address this limitation, we introduce TSET (Triplet Structure Enhanced T5), a model with a novel pretraining stage positioned between the initial T5 pretraining and the fine-tuning for the Text-to-SPARQL task. In this intermediary stage, we introduce a new objective called Triplet Structure Correction (TSC) to train the model on a SPARQL corpus derived from Wikidata. This objective aims to deepen the model’s understanding of the order of triplets. After this specialized pretraining, the model undergoes fine-tuning for SPARQL query generation, augmenting its query-generation capabilities. We also propose a method named “semantic transformation” to fortify the model’s grasp of SPARQL syntax and semantics without compromising the pre-trained weights of T5. Experimental results demonstrate that our proposed TSET outperforms existing methods on three well-established KBQA datasets: LC-QuAD 2.0, QALD-9 plus, and QALD-10, establishing a new state-of-the-art performance (95.0% F1 and 93.1% QM on LC-QuAD 2.0, 75.85% F1 and 61.76% QM on QALD-9 plus, 51.37% F1 and 40.05% QM on QALD-10)