arXiv subjects

Turab Lookman

Publications and source records attributed to Turab Lookman.

At least 19 recordsLinked to original sources

arXiv2026

Four-phonon scattering and coherent heat transport in ultrawide-bandgap SrSnO3

SrSnO3 is a promising ultrawide-bandgap perovskite oxide whose thermal transport is governed by structural distortions and anharmonic lattice dynamics. Here, we investigate the lattice thermal conductivity (kL) of orthorhombic and cubic SrSnO3 within a unified first-principles framework combining self-consistent phonon renormalization, three- and four-phonon scattering, and coherent heat transport. Bonding analysis reveals a rigid Sn-O octahedral framework embedded in a weakly bonded Sr sublattice, giving rise to low-frequency vibrational modes susceptible to strong anharmonic effects. Four-phonon scattering is identified as a key mechanism limiting particle-like heat conduction, reducing the Peierls thermal conductivity by 19.4% at 300 K in the orthorhombic phase and by 52.1% at 1300 K in the cubic phase, while the coherent contribution provides a finite channel that partially compensates this reduction. We further show that the apparent agreement between three-phonon calculations and experimental thermal conductivity at room temperature is not indicative of a complete physical description. Instead, it arises from a near cancellation between four-phonon suppression of the particle-like channel and the neglected coherent contribution. This cancellation breaks down when the full temperature dependence is considered, where only the combined treatment improves agreement with the experimentally observed scaling behavior. A physically consistent description of kL therefore requires phonon renormalization, four-phonon scattering, and coherent transport to be treated on equal footing rather than inferred from three-phonon agreement at a single temperature. These results provide microscopic insight into thermal transport in ultrawide-bandgap stannate perovskites and establish a benchmark for anharmonic transport in strongly distorted oxides.

cond-mat.mtrl-sci↗

arXiv2026

LLM-driven discovery for carbon allotropes with bond-network entropy

The discovery of novel carbon allotropes with tailored thermal and mechanical properties is critical for advanced thermal management. However, exploring the vast configurational space of carbon using \textit{ab initio} calculations remains computationally prohibitive. Driven by the rich topological landscape of carbon, where the competition between $sp, sp^2,$ and $sp^3$ hybridization states dictates material performance, we establish a closed-loop AI framework to explore this complex configurational space. We introduce a hybridization entropy descriptor to guide the search beyond conventional forms. Here, we establish a closed-loop AI framework that synergizes a Large Language Model (LLM) for structural generation with a Machine Learning Potential (MLP) for accelerated evaluation. Leveraging CrystaLLM to generate candidates and an iteratively refined MLP for high-fidelity validation, we screened thousands of structures to identify several stable allotropes with exotic properties. Specifically, we report ``yne-diamond C$_{12}$'' and ``yne-hex-diamond C$_{8}$'', which exhibit extreme thermal anisotropy and ultralow in-plane shear stiffness arising from their mixed $sp$-$sp^3$ hybridization. Furthermore, we discovered a complex $sp$-$sp^2$-$sp^3$ hybridized C$_{12}$ phase that combines metallic conductivity with an anomalous negative Poisson's ratio. Notably, we identified a superhard phase (C16_3) possessing a calculated Vickers hardness (103.3 GPa) exceeding that of diamond 96 GPa). Microscopic analysis reveals that thermal transport in these materials is governed by the interplay between rigid frameworks and flexible linkers. This work expands the known carbon phase space and demonstrates the efficacy of coupling generative AI with machine learning potentials for the accelerated inverse design of functional materials.

cond-mat.mtrl-sci↗

arXiv2026

Role of octahedral tilting induced acoustic softening on limiting thermal transport in SrSnO3

Octahedral tilting is a fundamental structural distortion in perovskites, governing key phenomena such as lattice stabilizing, soft phonon dynamics, group-theoretical analysis, phase transitions, ferroelectricity, and even for tunable electronic band gap. However, its influence on lattice thermal conductivity (kL) remains poorly understood. In the archetypal perovskite SrTiO3, tilting in the low-temperature tetragonal phase is known to enhance kL by suppressing specific phonon scattering channels around 200 cm-1. Here, we investigate the thermal transport in strontium stannate (SrSnO3), another perovskite oxide that undergoes temperature-driven phase transitions, and reveal a completely opposite effect. Through a systematic study across its orthorhombic, tetragonal, and cubic phases, we demonstrate that octahedral tilting in the tetragonal phase of SrSnO3 anomalously triggers acoustic phonon softening. This softening manifests as reduced frequencies and group velocities in low-frequency (<3 THz) acoustic modes, creating a large decrease for heat transport, particularly along the c-axis. Consequently, kL is significantly suppressed, decreasing from 7.48 W m-1 K-1 to 6.06 W m-1 K-1 as the tilting angle increases by a mere 1 degree. These findings identify tilting-induced acoustic softening as a pivotal mechanism for limiting and controlling anisotropic thermal transport in SrSnO3, presenting a stark contrast to the established behavior in SrTiO3.

cond-mat.mtrl-sci↗

arXiv2026

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery

Efficient exploration of vast compositional and processing spaces remains a major challenge in accelerated materials discovery. Bayesian optimization (BO) provides a principled approach to identify optimal materials with minimal experimentation, but its adoption has been limited by implementation complexity and a lack of domain-specific tools. Here, we present Bgolearn, a versatile Python framework that brings BO to materials research through intuitive interfaces, robust algorithms, and materials-focused workflows. Bgolearn supports single- and multi-objective optimization, multiple acquisition strategies, diverse surrogate models, and uncertainty quantification, enabling effective navigation of complex design spaces. Benchmark studies show that Bgolearn reduces experimental effort by 40-60\% compared with random search, grid search, and genetic algorithms, while achieving comparable or superior solution quality. Its effectiveness is demonstrated across case studies, including the discovery of maximum-elastic-modulus triply periodic minimal surface structures, ultra-high-hardness high-entropy alloys, and high-strength, high-ductility medium-Mn steels, and is further supported by numerous publications. With a modular architecture that integrates seamlessly into existing materials workflows and a graphical interface (BgoFace) that removes programming barriers, Bgolearn establishes a practical, reliable platform for Bayesian optimization in materials science. The software is openly available at https://github.com/Bin-Cao/Bgolearn.

cond-mat.mtrl-sci↗

arXiv2026

Materials Informatics: Emergence To Autonomous Discovery In The Age Of AI

This perspective explores the evolution of materials informatics, from its foundational roots in physics and information theory to its maturation through artificial intelligence (AI). We trace the field's trajectory from early milestones to the transformative impact of the Materials Genome Initiative and the recent advent of large language models (LLMs). Rather than a mere toolkit, we present materials informatics as an evolving ecosystem, reviewing key methodologies such as Bayesian Optimization, Reinforcement Learning, and Transformers that drive inverse design and autonomous self-driving laboratories. We specifically address the practical challenges of LLM integration, comparing specialist versus generalist models and discussing solutions for uncertainty quantification. Looking forward, we assess the transition of AI from a predictive tool to a collaborative research partner. By leveraging active learning and retrieval-augmented generation (RAG), the field is moving toward a new era of autonomous materials science, increasingly characterized by "human-out-of-the-loop" discovery processes.

physics.comp-ph↗

arXiv2025

Metavalent Bonding-Induced Phonon Hardening and Giant Anharmonicity in BeO

The search for materials with intrinsically low thermal conductivity ($\kappa_L$) is critical for energy applications, yet conventional descriptors often fail to capture the complex interplay between bonding and lattice dynamics. Here, first-principles calculations are used to contrast the thermal transport in covalent zincblende (zb) and metavalent rocksalt (rs) BeO. We find that the metavalent bonding in rs-BeO enhances lattice anharmonicity, activating multi-phonon scattering channels and suppressing phonon transport. This results in an ultralow $\kappa_L$ of 24 W m$^{-1}$ K$^{-1}$ at 300 K, starkly contrasting with the zb phase (357 W m$^{-1}$ K$^{-1}$). Accurately modeling such strongly anharmonic systems requires explicit inclusion of temperature-dependent phonon renormalization and four-phonon scattering. These contributions, negligible in zb-BeO, are essential for high-precision calculations of the severely suppressed $\kappa_L$ in rs-BeO. Finally, we identify three key indicators to guide the discovery of metavalently bonded, incipient-metallic materials: (i) an NaCl-type crystal structure, (ii) large Gr\"uneisen parameters ($\textgreater$2), and (iii) a breakdown of the Lyddane-Sachs-Teller relation. These findings provide microscopic insight into thermal transport suppression by metavalent bonding and offer a predictive framework for identifying promising thermoelectrics and phase-change materials.

cond-mat.mtrl-sci↗

arXiv2025

Same-group element replacement enhances superconductivity in clathrate-like YH4

H3S, LaH10, and hydrogen-based compounds have garnered significant interest due to their high-temperature superconducting properties. However, the requirement for extremely high pressures limits their practical applications. In this study, YH4 is adopted as a base material, with partial substitution of Yttrium (Y) by Scandium (Sc), Lanthanum (La), and Zirconium (Zr). Pure YH4, stable at 120 GPa, exhibits a critical temperature (Tc) of 84-95 K. Substituting half of the Y atoms increases Tc to 124.43 K for (Y,Sc)H4 at 100 GPa but reduces it to 101.24 K for (Y,La)H4 at 120 GPa. In contrast, (Y,Zr)H4 at 200 GPa shows a further suppressed Tc of 69.55 K. The remarkable superconductivity in (Y,Sc)H4 might be related to its unique phonon dispersion without optical-acoustic gap, compressed Y-H bonds, and significant electron delocalization under pressure, collectively boosting electron-phonon interactions. Furthermore, the lowest optical phonons play a crucial role in the superconductivity of these materials. This work suggests that substituting Y with same-group metal elements is an effective strategy to enhance Tc in hydride superconductors.

cond-mat.supr-con↗

arXiv2025

PINK: physical-informed machine learning for lattice thermal conductivity

Lattice thermal conductivity ($\kappa_L$) is crucial for efficient thermal management in electronics and energy conversion technologies. Traditional methods for predicting \k{appa}L are often computationally expensive, limiting their scalability for large-scale material screening. Empirical models, such as the Slack model, offer faster alternatives but require time-consuming calculations for key parameters such as sound velocity and the Gruneisen parameter. This work presents a high-throughput framework, physical-informed kappa (PINK), which combines the predictive power of crystal graph convolutional neural networks (CGCNNs) with the physical interpretability of the Slack model to predict \k{appa}L directly from crystallographic information files (CIFs). Unlike previous approaches, PINK enables rapid, batch predictions by extracting material properties such as bulk and shear modulus from CIFs using a well-trained CGCNN model. These properties are then used to compute the necessary parameters for $\kappa_L$ calculation through a simplified physical formula. PINK was applied to a dataset of 377,221 stable materials, enabling the efficient identification of promising candidates with ultralow $\kappa_L$ values, such as Ag$_3$Te$_4$W and Ag$_3$Te$_4$Ta. The platform, accessible via a user-friendly interface, offers an unprecedented combination of speed, accuracy, and scalability, significantly accelerating material discovery for thermal management and energy conversion applications.

cond-mat.mtrl-sci↗

arXiv2024

Copper delocalization leads to ultralow thermal conductivity in chalcohalide CuBiSeCl2

Mixed anion halide-chalcogenide materials have attracted considerable attention due to their exceptional optoelectronic properties, making them promising candidates for various applications. Among these, CuBiSeCl_2 has recently been experimentally identified with remarkably low lattice thermal conductivity (k_L). In this study, we employ Wigner transport theory combined with neuroevolution machine learning potential (NEP)-assisted self-consistent phonon calculations to unravel the microscopic origins of this low k_L. Our findings reveal that the delocalization and weak bonding of copper atoms are key contributors to the strong phonon anharmonicity and wavelike tunneling (random walk diffusons). These insights deepen our understanding of the relationship between bonding characteristics, anharmonicity, delocalization, and vibrational dynamics, paving the way for the design and optimization of CuBiSeCl_2 and analogous materials for advanced phonon engineering applications.

cond-mat.mtrl-sci↗

arXiv2024

Phase transition and polar cluster behavior above Curie temperature in ferroelectric BaTi$_{0.8}$Zr$_{0.2}$O$_3$

We study the phase transition behavior of the ferroelectric BaTi$_{0.8}$Zr$_{0.2}$O$_3$ in the paraelectric region. The temperature dependencies of thermal, polar, elastic and dielectric properties indicate the presence of local structures above the paraelectric-ferroelectric transition temperature Tc = 292 K. The non-zero remnant polarization is measured up to a characteristic temperature T* ~350 K, which coincides with the temperature where the dielectric constant deviates from Curie-Weiss law. Resonant Piezoelectric Spectroscopy shows that DC field-cooling above Tc using fields smaller than the coercive field leads to an elastic response and remnant piezoelectricity below T*, which likely corresponds to the coherence temperature associated with polar nanostructures in ferroelectrics. The observed remnant effect is attributed to the reorientation of polar nanostructures above Tc.

cond-mat.mtrl-sci↗

arXiv2023

Enhanced piezoelectric response at nanoscale vortex structures in ferroelectrics

The piezoelectric response is a measure of the sensitivity of a material's polarization to stress or its strain to an applied field. Using in-operando x-ray Bragg coherent diffraction imaging, we observe that topological vortices are the source of a five-fold enhancement of the piezoelectric response near the vortex core. The vortices form where several low symmetry ferroelectric phases and phase boundaries coalesce. Unlike bulk ferroelectric solid solutions in which a large piezoelectric response is associated with coexisting phases in the proximity of the triple point, the largest responses for pure BaTiO3 at the nanoscale are in spatial regions of extremely small spontaneous polarization at vortex cores. The response decays inversely with polarization away from the vortex, analogous to the behavior in bulk ceramics as the cation compositions are varied away from the triple point. We use first-principles-based molecular dynamics to augment our observations, and our results suggest that nanoscale piezoelectric materials with large piezoelectric response can be designed within a parameter space governed by vortex cores. Our findings have implications for the development of next-generation nanoscale piezoelectric materials.

cond-mat.mtrl-sci↗

arXiv2020

Efficient Estimation of Material Property Curves and Surfaces via Active Learning

The relationship between material properties and independent variables such as temperature, external field or time, is usually represented by a curve or surface in a multi-dimensional space. Determining such a curve or surface requires a series of experiments or calculations which are often time and cost consuming. A general strategy uses an appropriate utility function to sample the space to recommend the next optimal experiment or calculation within an active learning loop. However, knowing what the optimal sampling strategy to use to minimize the number of experiments is an outstanding problem. We compare a number of strategies based on directed exploration on several materials problems of varying complexity using a Kriging based model. These include one dimensional curves such as the fatigue life curve for 304L stainless steel and the Liquidus line of the Fe-C phase diagram, surfaces such as the Hartmann 3 function in 3D space and the fitted intermolecular potential for Ar-SH, and a four dimensional data set of experimental measurements for BaTiO3 based ceramics. We also consider the effects of experimental noise on the Hartmann 3 function. We find that directed exploration guided by maximum variance provides better performance overall, converging faster across several data sets. However, for certain problems, the trade-off methods incorporating exploitation can perform at least as well, if not better than maximum variance. Thus, we discuss how the choice of the utility function depends on the distribution of the data, the model performance and uncertainties, additive noise as well as the budget.

cond-mat.mtrl-sci↗

arXiv2017

Yield in Amorphous Solids: The Ant in the Energy Landscape Labyrinth

It has recently been shown that yield in amorphous solids under oscillatory shear is a dynamical transition from asymptotically periodic to asymptotically chaotic, diffusive dynamics. However, the type and universality class of this transition are still undecided. Here we show that the diffusive behavior of the vector of coordinates of the particles comprising an amorphous solid when subject to oscillatory shear, is analogous to that of a particle diffusing in a percolating lattice, the so-called "ant in the labyrinth" problem, and that yield corresponds to a percolation transition in the lattice. We explain this as a transition in the connectivity of the energy landscape, which affects the phase-space regions accessible to the coordinate vector for a given maximal strain amplitude. This transition provides a natural explanation to the observed limit-cycles, periods larger than one and diverging time-scales at yield.

cond-mat.soft↗

arXiv2016

Inferring low-dimensional microstructure representations using convolutional neural networks

We apply recent advances in machine learning and computer vision to a central problem in materials informatics: The statistical representation of microstructural images. We use activations in a pre-trained convolutional neural network to provide a high-dimensional characterization of a set of synthetic microstructural images. Next, we use manifold learning to obtain a low-dimensional embedding of this statistical characterization. We show that the low-dimensional embedding extracts the parameters used to generate the images. According to a variety of metrics, the convolutional neural network method yields dramatically better embeddings than the analogous method derived from two-point correlations alone.

physics.comp-ph↗

arXiv2015

Long-time behavior of the $\omega \to \alpha$ transition in shocked Zirconium: Interplay of nucleation and plastic deformation

We study the thermally activated, slow conversion of the hysteretically retained $\omega$ phase into stable $\alpha$ phase in recovered samples of shocked zirconium. The $\omega$-phase decays in time following an algebraic law, unlike the predictions of the nucleation-growth framework for first order transitions, and residual volume fractions of phases and dislocation densities are related by a power law. We propose an explanation for the annealing mechanism through coupled dynamics of dislocations and phase change. We find that the long-time behavior is controlled by the interplay of dislocations, shear fluctuations, and remnant volume fractions of phases, which lead to an algebraic decay in time. For late time, thermally activated quantities such as the dislocation mobility and nucleation rate set the timescale and control the algebraic behavior, respectively. At high enough temperatures this behavior is effectively indistinguishable from standard Avrami kinetics.

cond-mat.mtrl-sci↗

arXiv2015

Asymptotic analysis of hierarchical martensitic microstructure

We consider a hierarchical nested microstructure, which also contains a point of singularity (disclination) at the origin, observed in lead orthovanadate. We show how to exactly compute the energy cost and associated displacement field within linearized elasticity by enforcing geometric compatibility of strains across interfaces of the three-phase mixture of distortions (variants) in the microstructure. We prove that the mechanical deformation is purely elastic and discuss the behavior of the system close to the origin.

math.AP↗

arXiv2013

Quasi-One-Dimensional Thermal Breakage

Breakage is generally understood in mechanical terms, yet nano-structures can rupture not only under external loads but also via thermal activation. Here we treat in a general framework the statistical mechanics of thermally induced breakage at the nano-scale for one-dimensional systems. We test it on a simple approximation and find that the probability of breakage controls distinct regimes, characterized by sharp crossovers and narrow peaks in the thermal fluctuations and specific heat. Our work provides predictions on clustering of new phases, of relevance in nano-fabrication.

cond-mat.stat-mech↗

arXiv2013

Onset of Irreversibility and Chaos in Amorphous Solids Under Periodic Shear

An important aspect of the physics of amorphous solids is the onset of irreversible behavior usually associated with yield. Here we study amorphous solids under periodic shear using quasi-static molecular dynamics simulations and observe a transition from reversible to irreversible deformation at a critical strain amplitude. We find that for small strain amplitudes the system exhibits a noisy but repetitive limit-cycle, similar to return point memory \cite{sethna1993hysteresis}. However, for large strain amplitudes the behavior becomes chaotic (shows sensitivity to initial conditions) and thus irreversible. We show that the chaotic behavior is a result of the shear band instabilities that arise for large strains and the convective displacement fields they create.

cond-mat.soft↗