期刊
SOFT MATTER
卷 13, 期 27, 页码 4733-4745出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/c7sm00957g
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- Princeton Institute for Computational Science and Engineering (PICSciE)
- Office of Information and Technology's High Performance Computing Center and Visualization Laboratory at Princeton University
- Princeton Center for Complex Materials (PCCM)
- U.S. National Science Foundation Materials Research Science and Engineering Center [DMR-1420541]
- U.S. National Science Foundation [CBET-1402166]
- Department of Defense, Air Force Office ofScientific Research, National Defense Science and Engineering Graduate (NDSEG) Fellowship [FA950-11-C-0028, 32 CFR 168a]
- National Science Foundation [OCI-0725070, ACI-1238993]
- state of Illinois
- National Science Foundation CAREER Award [DMR-1350008]
- Direct For Mathematical & Physical Scien
- Division Of Materials Research [1841800] Funding Source: National Science Foundation
- Directorate For Engineering
- Div Of Chem, Bioeng, Env, & Transp Sys [1402166] Funding Source: National Science Foundation
We present a machine learning technique to discover and distinguish relevant ordered structures from molecular simulation snapshots or particle tracking data. Unlike other popular methods for structural identification, our technique requires no a priori description of the target structures. Instead, we use nonlinear manifold learning to infer structural relationships between particles according to the topology of their local environment. This graph-based approach yields unbiased structural information which allows us to quantify the crystalline character of particles near defects, grain boundaries, and interfaces. We demonstrate the method by classifying particles in a simulation of colloidal crystallization, and show that our method identifies structural features that are missed by standard techniques.
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