Ab initio structure prediction methods have been nowadays widely used as powerful tools for structure searches and materials discovery. However, they are generally restricted to small systems owing to the heavy computational cost of the underlying density functional theory (DFT) calculations in structure optimizations. In this work, by combining a state-of-art machine learning (ML) potential with our in-house developed CALYPSO structure prediction method, we developed two acceleration schemes for the structure prediction of large systems, in which a ML potential is pre-constructed to fully replace DFT calculations or trained in an on-the-fly manner from scratch during the structure searches. The developed schemes have been applied to medium- and large-sized boron clusters, both of which are challenging cases for either the construction of ML potentials or extensive structure searches. Experimental structures of B36 and B40 clusters can be readily reproduced, and the putative global minimum structure for the B84 cluster is proposed, where the computational cost is substantially reduced by ∼1-2 orders of magnitude if compared with full DFT-based structure searches. Our results demonstrate a viable route for structure prediction in large systems via the combination of state-of-art structure prediction methods and ML techniques.
The
theoretical structure prediction method via quantum mechanical
atomistic simulations such as density functional theory (DFT), based
solely on chemical composition, has already become a routine tool
to determine the structures of physical and chemical systems, e.g.,
solids and clusters. However, the application of DFT to more realistic
simulations, to a large extent, is impeded because of the unfavorable
scaling of the computational cost with respect to the system size.
During recent years, the machine learning potential (MLP) method has
been rapidly rising as an accurate and efficient tool for atomistic
simulations. In this Perspective, we provide an introduction to the
basic principles and advantages of the combination of structure prediction
and MLP, as well as the challenges and opportunities associated with
this promising approach.
Boron is an intriguing element due to its electron deficiency and the ability to form multicenter bonds in allotropes and borides, exhibiting diversified structures, unique chemical bonds, and interesting properties. Using swarm-intelligence structural prediction driven by a machine learning potential, we identified a boron phase with a 24-atom cubic unit cell, called c-B 24 , consisting of a B 6 octahedron in addition to well-known B 2 pairs and B 12 icosahedra at ambient pressure. There appear unusual four-center-two-electron (4c-2e) bonds in the B 12 icosahedron, originating from the peculiar bonding pattern between the B 2 pair and B 12 icosahedron, which is in sharp contrast with the 3c-2e and 2c-2e bonds in α-B 12 . More interestingly, c-B 24 is a metal with a superconducting critical temperature of 13.8 K at ambient pressure. The predicted Vickers hardness (23.1 GPa) indicates that c-B 24 is a potential hard material. Notably, it also has a good shear/tensile resistance (48.9/29.3 GPa). Our work not only enriches the understanding of the chemical properties of boron, but also sparks efforts on trying to synthesize this particular compound, c-B 24 .
Structure prediction methods have been widely used as a state-of-the-art tool for structure searches and materials discovery, leading to many theory-driven breakthroughs on discoveries of new materials. These methods generally involve the exploration of the potential energy surfaces of materials through various structure sampling techniques and optimization algorithms in conjunction with quantum mechanical calculations. By taking advantage of the general feature of materials potential energy surface and swarm-intelligence-based global optimization algorithms, we have developed the CALYPSO method for structure prediction, which has been widely used in fields as diverse as computational physics, chemistry, and materials science. In this review, we provide the basic theory of the CALYPSO method, placing particular emphasis on the principles of its various structure dealing methods. We also survey the current challenges faced by structure prediction methods and include an outlook on the future developments of CALYPSO in the conclusions.
Controllable phase modulation and electronic structure are essential factors in the study of two-dimensional transition metal dichalcogenides due to their impact on intriguing physical properties and versatile optoelectronic applications. Here, we report the phase-controlled growth of ternary monolayer MoSe2xTe2(1−x) (0 ≤ x ≤ 1) alloys induced through in situ doping and composition tuning via molecular beam epitaxy. Our approach leverages the substitution of selenium for tellurium to lower the energy barrier of the semi-conducting 2H and semi-metallic 1T′ phase transition. The alloys’ lattice constants, Mo-3d binding energy and electronic bandgap were demonstrated to be tunable by varying the selenium composition (x), respectively. First-principles calculations agree well with our experimental results, revealing that the valence band bowing effect of the monolayer alloys is attributed to the difference in coupling between anions and cations. This work provides a new pathway for phase modulation growth and controllable electronic structure of ternary monolayer transition metal dichalcogenide alloys, which is of great significance for ohmic contact and band engineering in developing transistor device applications using two-dimensional semiconductors.
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