arXiv subjects

Xinxu Zhang

Publications and source records attributed to Xinxu Zhang.

3 recordsLinked to original sources

arXiv2024

Tokensome: Towards a Genetic Vision-Language GPT for Explainable and Cognitive Karyotyping

Automatic karyotype analysis is often defined as a visual perception task focused solely on chromosomal object-level modeling. This definition has led most existing methods to overlook componential and holistic information, significantly constraining model performance. Moreover, the lack of interpretability in current technologies hinders clinical adoption. In this paper, we introduce Tokensome, a novel vision-language model based on chromosome tokenization for explainable and cognitive karyotyping. Tokensome elevates the method from the conventional visual perception layer to the cognitive decision-making layer. This elevation enables the integration of domain knowledge and cognitive reasoning via knowledge graphs and LLMs, markedly enhancing model's explainability and facilitating abnormality detection.

cs.CV↗

arXiv2023

Functional Group Induced Transformations in Stacking and Electron Structure in Mo2CTx/NiS Heterostructures

The two-dimensional transition metal carbide/nitride family (MXenes) has garnered significant attention due to their highly customizable surface functional groups. Leveraging modern material science techniques, the customizability of MXenes can be enhanced further through the construction of associated heterostructures. As indicated by recent research, the Mo2CTx/NiS heterostructure has emerged as a promising candidate exhibiting superior physical and chemical application potential. The geometrical structure of Mo2CTx/NiS heterostructure is modeled and 6 possible configurations are validated by Density Functional Theory simulations. The variation in functional groups leads to structural changes in Mo2CTx/NiS interfaces, primarily attributed to the competition between van der Waals and covalent interactions. The presence of different functional groups results in significant band fluctuations near the Fermi level for Ni and Mo atoms, influencing the role of atoms and electron's ability to escape near the interface. This, in turn, modulates the strength of covalent interactions at the MXenes/NiS interface and alters the ease of dissociation of the MXenes/NiS complex. Notably, the Mo2CO2/NiS(P6_3/mmc) heterostructure exhibits polymorphism, signifying that two atomic arrangements can stabilize the structure. The transition process between these polymorphs is also simulated, further indicating the modulation of the electronic level of properties by a sliding operation.

cond-mat.mes-hall↗

arXiv2023

A Method to Decipher "Genome" from Interatomic Cohesion in the Exploration for a "Central Dogma" Replacement in Material Science

In the ball-stick model, interatomic cohesions are considered "sticks". But enormous details and features of the "sticks" are usually oversimplified as indexed quantities or equivocated as geometry characteristics. These indexed quantities or geometry characteristics not only limit the explanatory capability to a few chemical/physical aspects but also eliminate generativity for expected resemblance. And these limitations can be related to the information loss during the conversion. Herein, inspired by the central dogma, a framework is introduced to compact interatomic cohesions into a detailed residue-by-residue "genome" with matched encoding/decoding tools. The framework fuses the quantum mechanical aspects, auto feature extraction, nanostructures and/or simulations, and generative models. As a proof of concept, the realization introduced in this work adopted bosonic/fermionic features, an autoencoder with image recognition processes, Density Functional Theory simulations, and a thiolate-protected gold nanocluster dataset. After repetitive modeling, validating, and analysis based on 26,528 simulated interatomic images, the interatomic cohesion can be almost losslessly encoded into an 8-value-genome, and the genome encoder-decoder pair is also obtained. The model is then automatically extended into a generative model which converts any arbitrary 8-value-genome to a bond image.

cond-mat.mes-hall↗