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Structure of disordered TiO2 phases from ab initio based deep neural network simulations

Journal Article · · Physical Review Materials
 [1];  [2]
  1. Princeton Univ., NJ (United States); Princeton University
  2. Princeton Univ., NJ (United States)
Amorphous TiO2 (a-TiO2) is widely used in many fields, ranging from photo-electrochemistry to bio-engineering, hence detailed knowledge of its atomic structure is of scientific and technological interest. Here we use an ab initio-based deep neural network potential (DP) to simulate large scale atomic models of crystalline and disordered TiO2 with molecular dynamics. Our DP reproduces the structural properties of all (11) TiO2 crystalline phases, predicts the densities and structure factors of molten and amorphous TiO2 with only a few percent deviation from experiments, and describes the pressure dependence of the amorphous structure in agreement with recent observations. Furthermore, it can be extended to model additional structures and compositions and can be thus of great value in the study of TiO2-based (nano-)materials.
Research Organization:
Princeton Univ., NJ (United States); Princeton University, NJ (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division
Grant/Contract Number:
AC02-05CH11231; SC0007347; SC0019394
OSTI ID:
1701740
Alternate ID(s):
OSTI ID: 1774028
OSTI ID: 1999131
Journal Information:
Physical Review Materials, Journal Name: Physical Review Materials Journal Issue: 11 Vol. 4; ISSN 2475-9953
Publisher:
American Physical Society (APS)Copyright Statement
Country of Publication:
United States
Language:
English

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