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Timothy Liao

Publications and source records attributed to Timothy Liao.

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Insights into the long-standing controversy over sound velocities in lizardite

Serpentine minerals are abundant H-bearing phases in the upper mantle and play a central role in water transport in subduction zones. Lizardite is the low-temperature polymorph expected in cold slabs and shallow serpentinized mantle. Using an ab initio thermoelastic framework combining density-functional theory with the r2SCAN meta-GGA and the quasiharmonic approximation, we calculate its compression curve, elastic tensor, and acoustic velocities at 300 K. r2SCAN reproduces the measured compression curve and equilibrium volume to within 0.2%. Weak interlayer hydrogen bonding gives rise to pronounced axial softening, with $C_{33} \simeq 0.21C_{11}$ and $C_{44} \simeq 0.13C_{66}$ at 0 GPa. Solving the Christoffel equation over all propagation directions, we construct the velocity density of states (VelDOS), which reveals a high density of slow acoustic modes and other directional features obscured by Voigt-Reuss-Hill (VRH) aggregate averages. Reported experimental velocities preferentially sample the slower portion of this distribution rather than clustering near the VRH values, a correspondence consistent with subtle crystallographic texture or preferential orientation not resolved experimentally. Thus, the appropriate seismic-velocity endmember for lizardite depends on its orientational state, and reliance on VRH velocities alone may lead to overestimation of the degree of serpentinization when seismic observations preferentially sample slow crystallographic directions. Direction-resolved single-crystal measurements and aggregate measurements with independently quantified texture would provide direct tests of this interpretation.

cond-mat.mtrl-sci

Accelerated discovery and design of Fe-Co-Zr magnets with tunable magnetic anisotropy through machine learning and parallel computing

Rare earth (RE)-free permanent magnets, as alternative substitutes for RE-containing magnets for sustainable energy technologies and modern electronics, have attracted considerable interest. We performed a comprehensive search for new hard magnetic materials in the ternary Fe-Co-Zr space by leveraging a scalable, machine learning-assisted materials discovery framework running on GPU-enabled exascale computing resources. This framework integrates crystal graph convolutional neural network (CGCNN) machine learning (ML) method with first-principles calculations to efficiently navigate the vast composition-structure space. The efficiency and accuracy of the ML approach enable us to reveal 9 new thermodynamically stable ternary Fe-Co-Zr compounds and 81 promising low-energy metastable phases with their formation energies within 0.1 eV/atom above the convex hull. The predicted compounds span a wide range of crystal symmetries and magnetic behaviors, providing a rich platform for tuning functional properties. Based on the analysis of site-specific magnetic properties, we show that the Fe6Co17Zr6 compound obtained from our ML discovery can be further optimized by chemical doping. Chemical substitutions lead to a ternary Fe5Co18Zr6 phase with a strong anisotropy of K1 = 1.1 MJ/m3, and a stable quaternary magnetic Fe5Co16Zr6Mn4 compound.

cond-mat.mtrl-sci