Accelerating inverse crystal structure prediction by machine learning:A case study of carbon allotropes

Based on structure prediction method,the machine learning method is used instead of the density functional theory(DFT)method to predict the material properties,thereby accelerating the material search process.In this paper,we established a data set of carbon materials by high-throughput calculation with available carbon structures obtained from the Samara Carbon Allotrope Database.We then trained a machine learning(ML)model that specifically predicts the elastic modulus(bulk modulus,shear modulus,and the Young's modulus)and confirmed that the accuracy is better than that of AFLOW-ML in predicting the elastic modulus of a carbon allotrope.We further combined our ML model with the CALYPSO code to search for new carbon structures with a high Young's modulus.A new carbon allotrope not included in the Samara Carbon Allotrope Database,named Cmcm-C24,which exhibits a hardness greater than 80 GPa,was firstly revealed.The Cmcm-C24 phase was identified as a semiconductor with a direct bandgap.The structural stability,elastic modulus,and electronic properties of the new carbon allotrope were systematically studied,and the obtained results demonstrate the feasibility of ML methods accelerating the material search process.

Crystal structure prediction in the context of inverse materials design

Inverse materials design tackles the challenge of finding materials with desired properties, tailored to specific applications, by combining atomistic simulations and optimization methods. The search for optimal materials requires one to survey large spaces of candidate solids. These spaces of materials can encompass both known and hypothetical compounds. When hypothetical compounds are explored, it becomes crucial to determine which ones are stable（and can be synthesized） and which are not. Crystal structure prediction is a necessary step for assessing theoretically the stability of a hypothetical material and, therefore, is a crucial step in inverse materials design protocols. Here, we describe how biologically-inspired global optimization methods can efficiently predict the stable crystal structure of solids. Specifically,we discuss the application of genetic algorithms to search for optimal atom configurations in systems in which the underlying lattice is given,and of evolutionary algorithms to address the general lattice-type prediction problem.

Computational discovery of energy materials in the era of big data and machine learning:A critical review

The discovery of novel materials with desired properties is essential to the advancements of energy-related technologies.Despite the rapid development of computational infrastructures and theoretical approaches,progress so far has been limited by the empirical and serial nature of experimental work.Fortunately,the situation is changing thanks to the maturation of theoretical tools such as density functional theory,high-throughput screening,crystal structure prediction,and emerging approaches based on machine learning.Together these recent innovations in computational chemistry,data informatics,and machine learning have acted as catalysts for revolutionizing material design and hopefully will lead to faster kinetics in the development of energy-related industries.In this report,recent advances in material discovery methods are reviewed for energy devices.Three paradigms based on empiricism-driven experiments,database-driven high-throughput screening,and data informatics-driven machine learning are discussed critically.Key methodological advancements involved are reviewed including high-throughput screening,crystal structure prediction,and generative models for target material design.Their applications in energy-related devices such as batteries,catalysts,and photovoltaics are selectively showcased.

High-pressure-activated carbon tetrachloride decomposition

The pressure-induced molecular dissociation as one of the fundamental problems in physical sciences has aroused many theoretical and experimental studies. Here, using a newly developed particle swarm optimization algorithm, we investigate the high-pressure-induced molecular dissociation. The results show that the carbon tetrachloride （CC14） is unstable and dissociates into C2C16 and C12 under approximately 120 GPa and more. The dissociation is confirmed by the lattice dynamic calculations and electronic structure of the Pa3 structure with pressure evolution. The dissociation pressure is far larger than that in the case of high temperature, indicating that the temperature effectively reduces the activation barrier of the dissociation reaction of CC14. This research improves the understanding of the dissociation reactions of CC14 and other halogen compounds under high pressures.

Pressure-induced phase transition in transition metal trifluorides

As a fundamental thermodynamic variable, pressure can alter the bonding patterns and drive phase transitions leading to the creation of new high-pressure phases with exotic properties that are inaccessible at ambient pressure. Using the swarm intelligence structural prediction method, the phase transition of TiF_(3), from R-3c to the Pnma phase, was predicted at high pressure, accompanied by the destruction of TiF_6 octahedra and formation of TiF_8 square antiprismatic units. The Pnma phase of TiF_(3), formed using the laser-heated diamond-anvil-cell technique was confirmed via high-pressure x-ray diffraction experiments. Furthermore, the in situ electrical measurements indicate that the newly found Pnma phase has a semiconducting character, which is also consistent with the electronic band structure calculations. Finally, it was shown that this pressure-induced phase transition is a general phenomenon in ScF_(3), VF_(3), CrF_(3), and MnF_(3), offering valuable insights into the high-pressure phases of transition metal trifluorides.

Identifying Hidden Li–Si–O Phases for Lithium-Ion Batteries via First-Principle Thermodynamic Calculations

SiO–based materials are promising alloys and conversion-type anode materials for lithium-ion batteries and are recently found to be excellent dendrite-proof layers for lithium-metal batteries.However,only a small fraction of the Li–Si–O compositional space has been reported,significantly impeding the understanding of the phase transition mechanisms and the rational design of these materials both as anodes and as protection layers for lithium-metal anodes.Herein,we identify three new thermodynamically stable phases within the Li–Si–O ternary system(Li_(2)SiO_(5),Li_(4)SiO_(6),and Li_(4)SiO_(8))in addition to the existing records via first-principle calculations.The electronic structure simulation shows that Li_(2)SiO_(5)and Li_(4)SiO_(8)phases are metallic in nature,ensuring high electronic conductivity required as electrodes.Moduli calculations demonstrate that the mechanical strength of Li–Si–O phases is much higher than that of lithium metal.The diffusion barriers of interstitial Li range from 0.1 to 0.6 eV and the interstitial Li hopping serves as the dominating diffusion mechanism in the Li–Si–O ternary systems compared with vacancy diffusion.These findings provide a new strategy for future discovery of improved alloying anodes for lithium-ion batteries and offer important insight towards the understanding of the phase transformation mechanism of alloy-type protection layers on lithium-metal anodes.

High-pressure phases of Weyl semimetals NbP, NbAs, TaP,and TaAs

In this study, we used the crystal structure search method and first-principles calculations to systematically explore the highpressure phase diagrams of the TaAs family （NbP, NbAs, TaP, and TaAs）. Our calculation results show that NbAs and TaAs have similar phase diagrams, the same structural phase transition sequence I41md→Pδm2→}P21/c→Pm3m, and slightly different transition pressures. The phase transition sequence of NbP and TaP differs somewhat from that of NbAs and TaAs, in which new structures emerge, such as the Cmcm structure in NbP and the Pmmn structure in TaP. Interestingly, we found that in the electronic structure of the high-pressure phase Pδm2-NbAs, there are coexisting Weyl points and triple degenerate points, similar to those found in high-pressure Pδm2-TaAs.

Stabilization of S_(3)O_(4) at high pressure:implications for the sulfur-excess paradox

The amount of sulfur in SO2 discharged in volcanic eruptions exceeds that available for degassing from the erupted magma.This geological conun drum,known as the"sulfur excess",has been the subject of considerable interests but remains an open question.Here,in a systematic computational investigation of sulfur-oxygen compounds under pressure,a hitherto unknown S_(3)O_(4) compound containing a mixture of sulfur oxidation states+11 and+IV is predicted to be stable at pressures above 79 GPa.We speculate that S_(3)O_(4) may be produced via redox reactions involving subducted S-bearing minerals(e.g.,sulfates and sulfides)with iron and goethite under high-pressure conditions of the deep lower mantle,decomposing to SO2 and S at shallow depths.S_(3)O_(4) may thus be a key intermediate in promoting decomposition of sulfates to release SO2,offering an alter native source of excess sulfur released during explosive eruptions.These findings provide a possible resolution of the"excess sulfur degassing"paradox and a viable mechanism for the exchange of S between Earth's surface and the lower mantle in the deep sulfur cycle.

Accelerating the detection of unfeasible hypothetical zeolites via symmetric local interatomic distance criteria

In silico prediction of potential synthetic targets is the prerequisite for function-led discovery of new zeolites. Millions of hypothetical zeolitic structures have been predicted via various computational methods, but most of them are experimentally inaccessible under conventional synthetic conditions.Screening out unfeasible structures is crucial for the selection of synthetic targets with desired functions.The local interatomic distance（LID） criteria are a set of structure rules strictly obeyed by all existing zeolite framework types. Using these criteria, many unfeasible hypothetical structures have been detected. However, to calculate their LIDs, all hypothetical structures need to be fully optimized without symmetry constraints. When evaluating a large number of hypothetical structures, such calculations may become too computationally expensive due to the forbiddingly high degree of freedom. Here, we propose calculating LIDs among structures optimized with symmetry constraints and using them as new structure evaluation criteria, i.e., the LIDsymcriteria, to screen out unfeasible hypothetical structures. We find that the LIDsymcriteria can detect unfeasible structures as many as the original non-symmetric LID criteria do, yet require at least one order of magnitude less computation at the initial geometry optimization stage.