arXiv subjects

Naihua Miao

Publications and source records attributed to Naihua Miao.

7 recordsLinked to original sources

arXiv2026

ALKEMIE Agent: an autonomous platform for computational materials design

Despite the powerful multi-scale modeling methods and high-throughput infrastructures established in the materials community, real material computation workflows remain fragmented and heavily manual, requiring researchers to constantly bridge software tools, data analysis, and intermediate decisions. This growing gap between methodological capability and practical execution highlights the need for a new kind of autonomous computational framework, one that can coordinate tools, knowledge, and workflows in a more unified and adaptive way. Here, we introduce ALKEMIE Agent, an agentic platform in which retrieval-augmented generation, a materials-computation knowledge base, registered skills, database-supported provenance, AI-assisted structure modeling, bounded task execution, tool-calling iteration, and error-diagnostic assistance are integrated within a traceable control loop. The capabilities of ALKEMIE Agent are demonstrated through applications including materials recommendation, structure modeling, phonon calculations, machine-learned interatomic potential training, LAMMPS simulations, Ab Initio Monte Carlo (AIMC) sampling, and active-learning-based materials screening. Finally, we outline the future directions and challenges for the development of agentic platforms for computational materials design.

cond-mat.mtrl-sci↗

arXiv2026

Hidden ordered compound-layer and its tailoring of the electronic/optical property in Ge2Sb2SexTe5-x alloys

Ge2Sb2SexTe5-x (GSST) alloys represent an emerging class of phase-change materials for integrated photonics. However, the microscopic origins underlying their superior performance compared to the parent compound Ge2Sb2Te5 remain elusive. By using atomic simulations, this work elucidates that the thermal stability and low optical loss of GSST are fundamentally governed by the formation of an in-layer compound-like structure with SeTe2 or Se2Te stoichiometry depending on the Se content, contrasting to the previously believed pure-element-layered model where Se and Te atoms occupy separate layers inside GSST. The newly identified compound-layered structures maintaining stability at temperature above 370 K, yield an enlarged bandgap, weakened antibonding character, and more importantly, a moderate refractive index as well as decreased extinction coefficient which align better with the experiment compared to the previously believed model. The present findings not only help bridge the long-standing theory-experiment gap regarding the optical properties of GSST by redefining its atomic structure, but also establish local chemical ordering as a critical materials design principle for high-performance photonics.

cond-mat.mtrl-sci↗

arXiv2024

Deep Learning Models for Colloidal Nanocrystal Synthesis

Colloidal synthesis of nanocrystals usually includes complex chemical reactions and multi-step crystallization processes. Despite the great success in the past 30 years, it remains challenging to clarify the correlations between synthetic parameters of chemical reaction and physical properties of nanocrystals. Here, we developed a deep learning-based nanocrystal synthesis model that correlates synthetic parameters with the final size and shape of target nanocrystals, using a dataset of 3500 recipes covering 348 distinct nanocrystal compositions. The size and shape labels were obtained from transmission electron microscope images using a segmentation model trained with a semi-supervised algorithm on a dataset comprising 1.2 million nanocrystals. By applying the reaction intermediate-based data augmentation method and elaborated descriptors, the synthesis model was able to predict nanocrystal's size with a mean absolute error of 1.39 nm, while reaching an 89% average accuracy for shape classification. The synthesis model shows knowledge transfer capabilities across different nanocrystals with inputs of new recipes. With that, the influence of chemicals on the final size of nanocrystals was further evaluated, revealing the importance order of nanocrystal composition, precursor or ligand, and solvent. Overall, the deep learning-based nanocrystal synthesis model offers a powerful tool to expedite the development of high-quality nanocrystals.

cond-mat.mtrl-sci↗

arXiv2016

Insight into the Role of Oxygen in Phase-Change Material GeTe

Oxygen is widely used to tune the performance of chalcogenide phase-change materials in the usage of phase-Change random access memory (PCRAM) which is considered as the most promising next-generation non-volatile memory. However, the microscopic role of oxygen in the write-erase process, i.e., the reversible phase transition between crystalline and amorphous state of phase-change materials is not clear yet. Using oxygen doped GeTe as an example, this work unravels the role of oxygen at the atomic scale by means of ab initio total energy calculations and ab initio molecular dynamics simulations. Our main finding is that after the amorphization and the subsequent re-crystallization process simulated by ab initio molecular dynamics, oxygen will drag one Ge atom out of its lattice site and both atoms stay in the interstitial region near the Te vacancy that was originally occupied by the oxygen, forming a "dumbbell-like" defect (O-VTe-Ge), which is in sharp contrast to the results of ab initio total energy calculations at 0 K showing that the oxygen prefers to substitute Te in crystalline GeTe. This specific defect configuration is found to be responsible for the slower crystallization speed and hence the improved data retention of oxygen doped GeTe as reported in recent experimental work. Moreover, we find that the oxygen will increase the effective mass of the carrier and thus increases the resistivity of GeTe. Our results unravel the microscopic mechanism of the oxygen-doping optimization of phase-change material GeTe, and the present reported mechanism can be applied to other oxygen doped ternary chalcogenide phase-change materials.

cond-mat.mtrl-sci↗

arXiv2015

Theoretical investigation on the transition metal borides with Ta3B4-type structure: a class of hard and refractory materials

Based on density functional theory, we have systematically studied the structural stability, mechanical properties and chemical bonding of the transition metal borides M3B4 (M=Ti, V, Cr, Zr, Nb, Mo, Hf, Ta, and W) for the first time. All the present studied M3B4 have been demonstrated to be thermodynamically and mechanically stable. The bulk modulus, shear modulus, Young's modulus, Poisson's ratio, microhardness, Debye temperature and anisotropy have been derived for ideal polycrystalline M3B4 aggregates. In addition, the relationship between Debye temperature and microhardness has been discussed for these isostructral M3B4. Furthermore, the results of the Cauchy pressure, the ratio of bulk modulus to shear modulus, and Poisson's ratio suggest that the valence electrons of transition metals play an important role in the ductility of M3B4. The calculated total density of states for M3B4 indicates that all these borides display a metallic conductivity. By analyzing the electron localization function, we show that the improvement of the ductility in these M3B4 might attribute to the decrease of their angular bonding character.

cond-mat.mtrl-sci↗

arXiv2015

Mechanical properties and electronic structure of the incompressible rhenium carbides and nitrides: A first-principles study

By means of first-principles calculations, the structural stability, mechanical properties and electronic structure of the newly synthesized incompressible Re2C, Re2N, Re3N and an analogous compound Re3C have been investigated. Our results agree well with the available experimental and theoretical data. The proposed Re3C is shown to be energetically, mechanically and dynamically stable and also incompressible. Furthermore, it is suggested that the incompressibility of these compounds is originated from the strong covalent bonding character with the hybridization of 5d orbital of Re and the 2p orbital of C or N, and a zigzag topology of interconnected bonds, e.g., Re-Re, Re-C or Re-N bonding.

cond-mat.mtrl-sci↗

arXiv2015

First-principles study of the lattice dynamical properties of strontium ruthenate

By means of first-principles calculations, various properties of SrRuO3 are investigated, focusing on its lattice dynamical properties. Despite having a Goldschmidt tolerance factor very close to 1, the phonon dispersion curves of the high-temperature cubic phase of SrRuO3 show strong antiferrodistortive instabilities. The energetics of metastable phases with different tilt patterns are discussed, concluding that the coupling of oxygen-rotation modes with anti-polar Sr motion plays a key role in stabilizing the Pnma phase with respect to alternative rotation patterns. This not only explains the ground state of SrRuO3 but also contributes to rationalize why most ABO3 perovskites exhibit an orthorhombic ground state. The zone-center phonon modes of the Pnma phase have been computed, from which we propose partial reassignment of available experimental data. The full dispersion curves have also been obtained, constituting benchmark results for the interpretation of future measurements and providing access to thermodynamical properties.

cond-mat.mtrl-sci↗