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Simulating water from first principles remains a significant computational challenge due to the slow dynamics of the underlying system. Although machine-learned interatomic potentials (MLPs) can accelerate these simulations, they often fail…
Numerical calculations of lattice thermal conductivity are reported for the binary alloys NiPd and NiPt. The present work is a continuation of an earlier paper by us [PRB, 72, 214207 (2005)]which had developed a theoretical framework for…
Bulk thermal conductivity estimates based on predictions from non-equilibrium molecular dynamics (NEMD) using the so-called direct method are known to be severely under-predicted since finite simulation length-scales are unable to mimic…
Universal Machine Learning Interactomic Potentials (MLIPs) enable accelerated simulations for materials discovery. However, current research efforts fail to impactfully utilize MLIPs due to: 1. Overreliance on Density Functional Theory…
Thermodynamic uncertainty relations (TURs) are the inequalities which give lower bounds on the entropy production rate using only the mean and the variance of fluctuating currents. Since the TURs do not refer to the full details of the…
The lattice thermal conductivity of thermoelectric material CuInTe2 is predicted using classical molecular dynamics simulations, where a simple but effective Morse-type interatomic potential is constructed by fitting first-principles total…
The lattice thermal conductivity of the candidate thermoelectric material Mg$_3$Sb$_2$ is studied from first principles, with the inclusion of anharmonic, isotope, and boundary scattering processes, and via an accurate solution of the…
Ordered crystalline compounds exhibiting ultralow and glass-like thermal conductivity are both fundamentally and technologically important, where phonon quasi-particles dominate their heat transport. Understanding the microscopic mechanisms…
We present a machine-learning interatomic potential (MLIP) framework, which substantially accelerates the prediction of lattice thermal conductivity for both particle-like and wave-like thermal transport, including three-phonon and…
Prediction models of lattice thermal conductivity have wide applications in the discovery of thermoelectrics, thermal barrier coatings, and thermal management of semiconductors. kL is notoriously difficult to predict. While classic models…
Since the internal temperature is less accessible than surface temperature, there is an urgent need to develop accurate and real-time estimation algorithms for better thermal management and safety. This work presents a novel framework for…
Thermoelectric technology is an alternate way to efficiently utilize the energy by converting waste heat into electricity. Thermoelectric requires material with low thermal conductivity to improves its thermoelectric performance. In this…
In crystalline materials, low lattice thermal conductivity is often associated with strong anharmonicity, which can cause significant deviations from the expected Lorentzian lineshape of phonon spectral functions. These deviations,…
We systematically investigate multi-token prediction (MTP) capabilities within LLMs pre-trained for next-token prediction (NTP). We first show that such models inherently possess MTP capabilities via numerical marginalization over…
We investigate electronic structures and thermoelectric properties of recent synthetic half-Heusler $\mathrm{ZrNiPb}$ by using generalized gradient approximation (GGA) and GGA plus spin-orbit coupling (GGA+SOC). Calculated results show that…
We investigate the nanoscale mechanisms determining lattice thermal conductivity (LTC) of pristine and W-doped MX$_2$-M$^\prime$X$^\prime_2$ transition metal dichalcogenide heterobilayers from first principles, using the exact solution of…
Precise modeling and understanding of heat transport in the superionic phase are of great interest. Although simulations combining Green-Kubo (GK) molecular dynamics with machine-learned potentials (MLPs) stand as a promising approach,…
Machine-learned interatomic potentials enable realistic finite temperature calculations of complex materials properties with first-principles accuracy. It is not yet clear, however, how accurately they describe anharmonic properties, which…
Machine learning interatomic potentials (MLIPs) enable more efficient molecular dynamics (MD) simulations with ab initio accuracy, which have been used in various domains of physical science. However, distribution shift between training and…
We investigate the microscopic mechanism of ultralow lattice thermal conductivity ($\kappa_l$) of TlInTe$_2$ and its weak temperature dependence using a unified theory of lattice heat transport that considers contributions arising from the…