| dc.contributor | 應物所 | |
| dc.creator (作者) | 林瑜琤 | |
| dc.date (日期) | 2019-01 | |
| dc.date.accessioned | 29-May-2025 11:57:09 (UTC+8) | - |
| dc.date.available | 29-May-2025 11:57:09 (UTC+8) | - |
| dc.date.issued (上傳時間) | 29-May-2025 11:57:09 (UTC+8) | - |
| dc.identifier.uri (URI) | https://ah.lib.nccu.edu.tw/item?item_id=176705 | - |
| dc.description.abstract (摘要) | 本計畫將發展針對無序、具挫折性的二維系統設計的數值重整化群法及其張量網路版本，用以探討量子自旋液態。利用機器學習方法，我們計畫將原適用基態的重整化群法擴充至可探討激發態的方法。我們試圖瞭解是否實驗觀察到的挫折性海森堡反鐵磁物質的自旋液態為所謂無序誘導的液態。 | |
| dc.description.abstract (摘要) | In this proposed project we plan to develop real-space renormaization group (RG) methods and tensor network RG to investigate quantum spin liquids in disordered and frustrated magnets. With the aid of machine-learning approaches we will generalize the ground-state RG method to explore energy excitations and dynamical phase diagrams. We seek to clarify whether experimentally observed spin-liquid states in triangular organic salts and kagome herbertsmithite are in fact disorder-induced spin liquids. | |
| dc.format.extent | 116 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | 科技部, MOST106-2112-M004-001, 106.08-107.07 | |
| dc.title (題名) | 以機器學習演算法探索磁性物質之液態與玻璃態 | |
| dc.title (題名) | Probing Liquidity and Glassiness in Magnetism Using Machine Learning | |
| dc.type (資料類型) | report | |
