arXiv subjects

Panchapakesan Ganesh

Publications and source records attributed to Panchapakesan Ganesh.

At least 19 recordsLinked to original sources

arXiv2026

Origin of Flat Bands and Role of Electron Correlation in Lutetium Hydrides

Lutetium hydrides (LuH$_x$, $1.75 \leq x \leq 3$) form a diverse series of phases, several of which superconduct under pressure. Characterizing their electronic properties has remained challenging owing to a high propensity for hydrogen defect formation, and recent angle-resolved photoemission (ARPES) measurements reveal puzzling flat-band regions that position these materials as candidates where superconductivity and flat-band physics may intersect. Here, by combining density functional theory, dynamical mean-field theory, and the constrained random-phase approximation, we uncover the microscopic origin and correlation nature of these flat bands. Across all compositions, the screened on-site Coulomb interaction is larger for H-s states than for Lu-$d$ states due to compact hydrogen orbitals. Nevertheless, these systems remain weakly correlated metals: the nearly filled H-$s$ shell admits little charge fluctuation, so its large interaction acts as a static level shift rather than a source of correlation. Although hydrogen primarily occupies tetrahedral sites at $x=2$, we discover that anti-site defects--where hydrogens occupy slightly unfavorable octahedral sites--generate both the ARPES flat-band features and the low-energy optical absorption peak, attesting to the usual defective nature of such materials in experimental samples. We further find that correlation strength is governed primarily by hydrogen orbital filling at these sites rather than the interaction magnitude itself. Consequently, we identify hydrogen orbital filling as the fundamental organizing principle dictating correlation and low-energy flat-band physics in lutetium hydrides.

cond-mat.supr-con↗

arXiv2026

Towards "on-demand" van der Waals epitaxy with adaptive ensemble sampling atomistic workflows

Traditional approaches to achieve targeted epitaxial growth involves exploring a vast parameter space of thermodynamical and kinetic drivers (e.g., temperature, pressure, chemical potential etc). This tedious and time-consuming approach becomes particularly cumbersome to accelerate synthesis and characterization of novel materials with complex dependencies on local chemical environment, temperature and lattice-strains, specifically nanoscale heterostructures of layered 2D materials. We combine the strength of next generation supercomputers at the extreme scale, machine learning and classical molecular dynamics simulations within an adaptive real time closed-loop virtual environment steered by Bayesian optimization to enable asynchronous ensemble sampling of the synthesis space, and apply it to the recrystallization phenomena of amorphous transition-metal dichalcogenide (TMDC) bilayer to form stack moiré heterostructures under various growth parameters. We show that such asynchronous ensemble sampling frameworks for materials simulations can be promising towards achieving on-demand epitaxy of van der Waals stacked moiré devices, paving the way towards a robust autonomous materials synthesis pipeline to enable unprecedented discovery of new functionalities.

cond-mat.mtrl-sci↗

arXiv2026

Nonmagnetic Ground State of Rutile RuO$_2$ from Diffusion Quantum Monte Carlo

Rutile RuO$_2$ has been proposed as an altermagnet, but its bulk magnetic ground state is still under debate because density-functional calculations give conflicting predictions. Using fixed-node diffusion quantum Monte Carlo, we find that stoichiometric bulk RuO$_2$ is nonmagnetic in the pristine structure, lying 23(9) meV per formula unit below the lowest antiferromagnetic state considered. A 3$\%$ compressive strain instead stabilizes antiferromagnetism, placing RuO$_2$ near a strain-tunable magnetic instability and helping reconcile apparently conflicting experimental reports.

cond-mat.mtrl-sci↗

arXiv2025

A many-body characterization of the fundamental gap in monolayer CrI$_3$

The many-body fixed-node and fixed-phase spin-orbit Diffusion Monte Carlo (DMC) methods are applied to accurately predict the fundamental gap of monolayer CrI$_3$ - the first experimentally-realized 2D material with intrinsic magnetism. The fundamental gap obtained, 2.9(1)~eV, agrees well with the highest peak in optical spectroscopy measurements and a previous $GW$ result. We numerically show that as expected in DMC the same value of the fundamental gap is obtained in the thermodynamic limit using both neutral promotions and the standard quasiparticle definition of the gap based on the ionization potential and electron affinity. Additional analysis of the differences between density matrices formed in different bases using configuration interaction calculations explains why a single-reference trial wave function can produce an accurate excitation. We find that accounting for electron correlation is more crucial than accounting for spin-orbit effects in determining the fundamental gap. These results highlight how DMC can be used to benchmark 2D material physics and emphasize the importance of using beyond-DFT methods for studying 2D materials.

cond-mat.mtrl-sci↗

arXiv2025

Electronic Structure Guided Inverse Design Using Generative Models

The electronic structure of a material fundamentally determines its underlying physical, and by extension, its functional properties. Consequently, the ability to identify or generate materials with desired electronic properties would enable the design of tailored functional materials. Traditional approaches relying on human intuition or exhaustive computational screening of known materials remain inefficient and resource-prohibitive for this task. Here, we introduce DOSMatGen, the first instance of a machine learning method which generates crystal structures that match a given desired electronic density of states. DOSMatGen is an E(3)-equivariant joint diffusion framework, and utilizes classifier-free guidance to accurately condition the generated materials on the density of states. Our experiments find this approach can successfully yield materials which are both stable and match closely with the desired density of states. Furthermore, this method is highly flexible and allows for finely controlled generation which can target specific templates or even individual sites within a material. This method enables a more physics-driven approach to designing new materials for applications including catalysts, photovoltaics, and superconductors.

cond-mat.mtrl-sci↗

arXiv2025

Simulation of 24,000 Electrons Dynamics: Real-Time Time-Dependent Density Functional Theory (TDDFT) with the Real-Space Multigrids (RMG)

We present the theory, implementation, and benchmarking of a real-time time-dependent density functional theory (RT-TDDFT) module within the RMG code, designed to simulate the electronic response of molecular systems to external perturbations. Our method offers insights into non-equilibrium dynamics and excited states across a diverse range of systems, from small organic molecules to large metallic nanoparticles. Benchmarking results demonstrate excellent agreement with established TDDFT implementations and showcase the superior stability of our time-integration algorithm, enabling long-term simulations with minimal energy drift. The scalability and efficiency of RMG on massively parallel architectures allow for simulations of complex systems, such as plasmonic nanoparticles with thousands of atoms. Future extensions, including nuclear and spin dynamics, will broaden the applicability of this RT-TDDFT implementation, providing a powerful toolset for studies of photoactive materials, nanoscale devices, and other systems where real-time electronic dynamics is essential.

physics.comp-ph↗

arXiv2025

A Panoramic View of MXenes via a New Design Strategy

Two-dimensional (2D) transition metal carbides and nitrides, known as MXenes, possess unique physical and chemical properties, enabling diverse applications in fields ranging from energy storage to communication, catalysis, sensing, healthcare, and beyond. The transition metal and nonmetallic atoms in MXenes can exhibit distinct coordination environments, potentially leading to a wide variety of 2D phases. Despite extensive research and significant advancements, a fundamental understanding of MXenes' phase diversity and its relationship with their hierarchical precursors, including intermediate MAX phases and parent bulk phases, remains limited. Using high-throughput modeling based on first-principles density functional theory, we unveil a wide range of MXenes and comprehensively evaluate their relative stabilities across a large chemical space. The key lies in considering both octahedral and trigonal prismatic coordination environments characteristic of various bulk phases. Through this comprehensive structural library of MXenes, we uncover a close alignment between the phase stability of MXenes and that of their hierarchical 3D counterparts. Building on this, we demonstrate a new design strategy where the atomic coordination environments in parent bulk phases can serve as reliable predictors for the design of MXenes, reducing reliance on intermediate MAX phases. Our study significantly expands the landscape of MXenes, at least doubling the number of possible structures.

cond-mat.mtrl-sci↗

arXiv2024

Correlation Effects on Coupled Electronic and Structural Properties of Doped Rare-Earth Trihydrides

Rare-earth trihydrides ($R$H$_3$) exhibit intriguing coupled electronic and structural properties as a function of doping, hydrogen vacancies, and thermodynamic conditions. Theoretical studies of these materials typically rely on density functional theory (DFT), including the use of small supercells that may underestimate strong correlation effects and structural distortions which in turn may influence their metallicity. Here, we elucidate the roles of lattice distortions and correlation effects on the electronic properties of pristine and doped $R$H$_3$ by adopting DFT+U and Quantum Monte Carlo (QMC) methods. Linear-response constrained DFT (LR-cDFT) methods find Hubbard U $\approx 2$ eV for $R_d$ orbitals and U$\approx 6$ eV for H$_s$/N$_p$ orbitals. The small U on Lu$_d$ orbitals is consistent with QMC calculations on LuH$_3$ and LuH$_{2.875}$N$_{0.125}$. In pure face-centered-cubic (FCC) $R$H$_3$ ($R$=Lu,Y), neither DFT nor DFT+U with the self-consistently determined U is enough to create a band gap, however a supercell with hydrogen distortions creates a small gap whose magnitude increases when performing DFT+U with self-consistently determined U values. Correlation effects, in turn, have a moderate influence on the coupled structural and electronic properties of doped RH$_3$ compounds and may be important when considering the competition between structural distortions and superconductivity.

cond-mat.str-el↗

arXiv2024

AI for Manufacturing and Healthcare: a chemistry and engineering perspective

Artificial Intelligence (AI) approaches are increasingly being applied to more and more domains of Science, Engineering, Chemistry, and Industries to not only improve efficiencies and enhance productivity, but also enable new capabilities. The new opportunities range from automated molecule design and screening, properties prediction, gaining insights of chemical reactions, to computer-aided design, predictive maintenance of systems, robotics, and autonomous vehicles. This review focuses on the new applications of AI in manufacturing and healthcare. For the Manufacturing Industries, we focus on AI and algorithms for (1) Battery, (2) Flow Chemistry, (3) Additive Manufacturing, (4) Sensors, and (5) Machine Vision. For Healthcare applications, we focus on: (1) Medical Vision (2) Diagnosis, (3) Protein Design, and (4) Drug Discovery. In the end, related topics are discussed, including physics integrated machine learning, model explainability, security, and governance during model deployment.

cond-mat.mtrl-sci↗

arXiv2024

JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods

Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with both perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard

cond-mat.mtrl-sci↗

arXiv2024

Enhanced Twist-Averaging Technique for Magnetic Metals: Applications using Quantum Monte Carlo

We propose an improved twist-averaging scheme for quantum Monte Carlo methods that use converged Kohn-Sham or Hartree-Fock orbitals as the reference. This twist-averaging technique is tailored to sample the Brillouin zone of magnetic metals, although it naturally extends to nonmagnetic conducting systems. The proposed scheme aims to reproduce the reference magnetization and achieves charge neutrality by construction, thus avoiding the large energy fluctuations and the postprocessing needed to correct the energies. It shows the most robust convergence of total energy and magnetism to the thermodynamic limit when compared to four other twist-averaging schemes. Diffusion Monte Carlo applications are shown on nonmagnetic Al and ferromagnetic $α$-Fe. The cohesive energy of Al in the thermodynamic limit shows an excellent agreement with the experimental result. Furthermore, the magnetic moments in $α$-Fe exhibit rapid convergence with an increasing number of twists.

physics.comp-ph↗

arXiv2023

A first-principles study of bilayer 1T'-WTe2/CrI3: A candidate topological spin filter

The ability to manipulate electronic spin channels in 2D materials is crucial for realizing next-generation spintronics. Spin filters are spintronic components that polarize spins using external fields or material properties like magnetism. Recently, topological protection from backscattering has emerged as an enticing feature through which to enhance the robustness of 2D spin filters. In this work, we propose and then characterize one of the first 2D topological spin filters: bilayer CrI3/1T'-WTe2 (BLCW). To do so, we use a combination of DFT, maximally localized Wannier functions, and quantum transport simulations to demonstrate that the BLCW satisfies the principal criteria for being a topological spin filter; namely that it is gapless, exhibits spin-polarized charge transfer (SPCT) from WTe2 to CrI3 that renders the BLCW metallic, and has a topological boundary which retains the edge conductance of monolayer (ML) 1T'-WTe2. We observe that the atomic magnetic moments on Cr from DFT are approximately 3.2 mB/Cr in the BL compared to 2.9 mB/Cr with small negative ferromagnetic (FM) moments induced on the W atoms in freestanding ML CrI3. Subtracting the charge/spin densities of the constituent ML's from those of the BLCW further reveals SOC-enhanced SPCT from WTe2 to CrI3. We find that the BLCW is topologically trivial by showing that its Chern number is zero. Lastly, we show that interfacial scattering at the boundary between the terraced materials does not remove WTe2's edge conductance. This evidence indicates that BLCW is gapless, magnetic, and topologically trivial, meaning that a terraced WTe2/CrI3 BL heterostructure in which only a portion of a WTe2 ML is topped with CrI3 is a promising candidate for a 2D topological spin filter. Our results further suggest that 1D chiral edge states may be realized by stacking FM ML's, like CrI3, atop 2D nonmagnetic Weyl semimetals like 1T'-WTe2.

cond-mat.mes-hall↗

arXiv2023

The role of electron correlations in the electronic structure of putative Chern magnet TbMn$_6$Sn$_6$

A member of the RMn$_6$Sn$_6$ rare-earth family materials, TbMn$_6$Sn$_6$, recently showed experimental signatures of the realization of a quantum-limit Chern magnet. In this work, we use quantum Monte Carlo (QMC) and density functional theory with Hubbard $U$ (DFT$+U$) calculations to examine the electronic structure of TbMn$_6$Sn$_6$. To do so, we optimize accurate, correlation-consistent pseudopotentials for Tb and Sn using coupled-cluster and configuration-interaction (CI) methods. We find that DFT$+U$ and single-reference QMC calculations suffer from the same overestimation of the magnetic moments as meta-GGA and hybrid density functional approximations. Our findings point to the need for improved orbitals/wavefunctions for this class of materials, such as natural orbitals from CI, or for the inclusion of multi-reference effects that capture the static correlations for an accurate prediction of magnetic properties. DFT$+U$ with Mn magnetic moments adjusted to experiment predict the Dirac crossing in bulk to be close to the Fermi level, within $\sim 120$ meV, in agreement with the experiments. Our non-stoichiometric slab calculations show that the Dirac crossing approaches even closer to the Fermi level, suggesting the possible realization of Chern magnetism in this limit.

cond-mat.str-el↗

arXiv2023

A new generation of effective core potentials: selected Lanthanides and heavy elements

We construct correlation-consistent effective core potentials (ccECPs) for a selected set of heavy atoms and f-elements that are of significant current interest in materials and chemical applications, including Y, Zr, Nb, Rh, Ta, Re, Pt, Gd, and Tb. As customary, ccECPs consist of spin-orbit averaged relativistic effective potential (AREP) and effective spin-orbit (SO) terms. For the AREP part, our constructions are carried out within a relativistic coupled-cluster framework while also taking into objective function one-particle characteristics for improved convergence in optimizations. The transferability is adjusted using binding curves of hydride and oxide molecules. We address the difficulties encountered with f-elements, such as the presence of large cores and multiple near-degeneracies of excited levels. For these elements, we construct ccECPs with core-valence partitioning that includes 4f-subshell in the valence space. The developed ccECPs achieve an excellent balance between accuracy, size of the valence space, and transferability and are also suitable to be used in plane wave codes with reasonable energy cutoffs.

cond-mat.mtrl-sci↗

arXiv2023

Emergent magnetism with continuous control in the ultrahigh conductivity layered oxide PdCoO2

The current challenge to realizing continuously tunable magnetism lies in our inability to systematically change properties such as valence, spin, and orbital degrees of freedom as well as crystallographic geometry. Here, we demonstrate that ferromagnetism can be externally turned on with the application of low-energy helium implantation and subsequently erased and returned to the pristine state via annealing. This high level of continuous control is made possible by targeting magnetic metastability in the ultra-high conductivity, non-magnetic layered oxide PdCoO2 where local lattice distortions generated by helium implantation induce emergence of a net moment on the surrounding transition metal octahedral sites. These highly-localized moments communicate through the itinerant metal states which triggers the onset of percolated long-range ferromagnetism. The ability to continuously tune competing interactions enables tailoring precise magnetic and magnetotransport responses in an ultra-high conductivity film and will be critical to applications across spintronics.

cond-mat.mtrl-sci↗

arXiv2023

DFT+U and Quantum Monte Carlo study of electronic and optical properties of AgNiO$_2$ and AgNi$_{1-x}$Co$_{x}$O$_2$ delafossite

As the only semimetallic $d^{10}$-based delafossite, AgNiO$_2$ has received a great deal of attention due to both its unique semimetallicity and its antiferromagnetism in the NiO$_2$ layer that is coupled with a lattice distortion. In contrast, other delafossites such as AgCoO$_2$ are insulating. Here we study how the electronic structure of AgNi$_{1-x}$Co$_{x}$O$_2$ alloys vary with Ni/Co concentration, in order to investigate the electronic properties and phase stability of the intermetallics. While the electronic and magnetic structure of delafossites have been studied using Density Functional Theory (DFT), earlier studies have not included corrections for strong on-site Coulomb interactions. In order to treat these interactions accurately, in this study we use Quantum Monte Carlo (QMC) simulations to obtain accurate estimates for the electronic and magnetic properties of AgNiO$_2$. By comparison to DFT results we show that these electron correlations are critical to account for. We show that Co doping on the magnetic Ni sites results in a metal-insulator transition near $x\sim 0.33$, and reentrant behavior near $x\sim 0.66$

cond-mat.mtrl-sci↗

arXiv2023

Metastability and topology in the magnetic topological insulator MnBi$_{2}$Te$_{4}$

We study the effect of stacking faults on the topological properties of the magnetic topological insulator MnBi$_{2}$Te$_{4}$ (MBT) using density functional theory calculations and the Hubbard $U$ being tuned with many-body diffusion Monte Carlo techniques. We show that a modest deviation from the equilibrium interlayer distance leads to a topological phase transition from a non-trivial to a trivial topology, suggesting that tuning the interlayer coupling by adjusting the interlayer distance alone can lead to different topological phases. Interestingly, due to the locally increased interlayer distance of the top layer, a metastable stacking fault in MBT leads to a nearly gapless state at the topmost layer due to charge redistribution as the topmost layer recedes. We further find evidence of spin-momentum locking in the surface state along with a weak preservation of the band inversion in the near gapless state, which is indicative of the non-trivial topological surface states for the metastable stacking fault. Our findings provide a possible explanation for reconciling the long-standing puzzle of gapped and gapless states on MBT surfaces.

cond-mat.mtrl-sci↗

arXiv2023

Bridging Theory with Experiment: Digital Twins and Deep Learning Segmentation of Defects in Monolayer MX2 Phases

Developing methods to understand and control defect formation in nanomaterials offers a promising route for materials discovery. Monolayer MX2 phases represent a particularly compelling case for defect engineering of nanomaterials due to the large variability in their physical properties as different defects are introduced into their structure. However, effective identification and quantification of defects remains a challenge even as high-throughput scanning tunneling electron microscopy (STEM) methods improve. This study highlights the benefits of employing first principles calculations to produce digital twins for training deep learning segmentation models for defect identification in monolayer MX2 phases. Around 600 defect structures were obtained using density functional theory calculations, with each monolayer MX2 structure being subjected to multislice simulations for the purpose of generating the digital twins. Several deep learning segmentation architectures were trained on this dataset, and their performances evaluated under a variety of conditions such as recognizing defects in the presence of unidentified impurities, beam damage, grain boundaries, and with reduced image quality from low electron doses. This digital twin approach allows benchmarking different deep learning architectures on a theory dataset, which enables the study of defect classification under a broad array of finely controlled conditions. It thus opens the door to resolving the underpinning physical reasons for model shortcomings, and potentially chart paths forward for automated discovery of materials defect phases in experiments.

cond-mat.mtrl-sci↗