arXiv subjects

Shayantan Chaudhuri

Publications and source records attributed to Shayantan Chaudhuri.

9 recordsLinked to original sources

arXiv2026

Probing (sub)nanoscale ferrons in an electron microscope

Ferrons are collective excitations of polarization fluctuations that can enable terahertz communications and quantum transduction due to long propagation lengths. Although ferrons have been experimentally demonstrated in van der Waals ferroelectrics and relaxor ferroelectrics, there is no direct (sub)nanoscale experimental evidence of ferrons in three-dimensional ferroelectrics. Here, we detect two types of ferrons, Higgs and pseudo-Goldstone, at the (sub)nanoscale in lead titanate by measuring vibrational signals due to polarization fluctuations. By harnessing momentum transfer in electron energy loss spectroscopy (EELS), we directly distinguish between soft-phonons and ferrons. We observe that the Higgs mode originates from the soft optical phonon parallel to the polar axis, whereas the pseudo-Goldstone mode originates from the soft optical phonon perpendicular to the polarization axis. Together with Landau theory, Raman spectroscopy measurements, and EELS, we observe that Higgs group velocities, in the bulk limit, are eight times greater than those of out-of-plane soft phonons and the pseudo-Goldstone ferrons have group velocities six times greater than in-plane soft phonons due to long-range dipole interactions. We further show that the domain size confinement effects lead to doubling of the respective bulk ferron group velocities, reaching up to approximately 15 km per second (almost 15 times higher than the out-of-plane soft phonon modes). Overall, this study opens a pathway to the detection of ferrons in three-dimensional ferroelectrics with domain engineering as a promising avenue for terahertz communication and transduction.

cond-mat.mtrl-sci↗

arXiv2026

From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry

Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broad set of community-developed LLM applications in an effort to identify emerging patterns in how these systems can be used across the scientific research lifecycle. We organize the projects into two complementary categories: Knowledge Infrastructure, systems that structure, retrieve, synthesize, and validate scientific information; and Action Systems, systems that execute, coordinate, or automate scientific work across computational and experimental environments. The submissions reveal a shift from single-purpose LLM tools toward integrated, multi-agent workflows that combine retrieval, reasoning, tool use, and domain-specific validation. Prominent themes include retrieval-augmented generation as grounding infrastructure, persistent structured knowledge representations, multimodal and multilingual scientific inputs, and early progress toward laboratory-integrated closed-loop systems. Together, these results suggest that LLMs are evolving from general-purpose assistants into composable infrastructure for scientific reasoning and action. This work provides a community snapshot of that transition and a practical taxonomy for understanding emerging LLM-enabled workflows in materials science and chemistry.

cond-mat.mtrl-sci↗

arXiv2025

Multiplet structure of chromium(III) dopants in wide band gap materials

Transition metal doping is commonly used for altering the properties of solid-state materials to suit applications in science and technology. Partially filled $d$-shells of transition metal atoms lead to electronic states with diverse spatial and spin symmetries. Chromium(III) cations have shown great potential for designing laser materials and, more recently, for developing spin qubits in quantum applications. They also represent an intriguing class of chemical systems with strongly correlated multi-reference excited states, due to the $d^3$ electron configuration. These states are difficult to describe accurately using single-reference quantum chemical methods such as density functional theory (DFT), the most commonly used method to study the electronic structures of solid-state systems. Recently, the periodic effective Hamiltonian of crystal field (pEHCF) method has been shown to overcome some limitations arising in the calculations of excited $d$-states. In this work, we assess the suitability of DFT and pEHCF to calculate the electronic structure and $d$-$d$ excitations of chromium(III) dopants in wide band gap host materials. The results will aid computational development of novel transition metal-doped materials and provide a deeper understanding of the complex nature of transition metal dopants in solids.

cond-mat.mtrl-sci↗

arXiv2025

Generative design of functional organic molecules for terahertz radiation detection

Plasmonic nanocavities are molecule-nanoparticle junctions that offer a promising approach to upconvert terahertz radiation into visible or near-infrared light, enabling nanoscale detection at room temperature. However, the identification of molecules with strong terahertz-to-visible frequency upconversion efficiency is limited by the availability of suitable compounds in commercial databases. Here, we employ the generative autoregressive deep neural network, G-SchNet, to perform property-driven design of novel monothiolated molecules tailored for terahertz radiation detection. To design functional organic molecules, we iteratively bias G-SchNet to drive molecular generation towards highly active and synthesizable molecules based on machine learning-based property predictors, including molecular fingerprints and state-of-the-art neural networks. We study the reliability of these property predictors for generated molecules and analyze the chemical space and properties of generated molecules to identify trends in activity. Finally, we filter generated molecules and plan retrosynthetic routes from commercially available reactants to identify promising novel compounds and their most active vibrational modes in terahertz-to-visible upconversion.

physics.chem-ph↗

arXiv2025

Challenges in the Theory and Atomistic Simulation of Metal Electrodeposition

Electrodeposition is a fundamental process in electrochemistry, and has applications in numerous industries, such as corrosion protection, decorative finishing, energy storage, catalysis, and electronics. While there is a long history of using electrodeposition, its application for controlled nanostructure growth is limited. The establishment of an atomic-scale understanding of the electrodeposition process and dynamics is crucial to enable the controlled fabrication of metal nanoparticles and other nanostructures. Significant advancements in molecular simulation capabilities and the electronic structure theory of electrified solid-liquid interfaces bring theory closer to realistic applications, but a gap remains between realistic applications, theoretical understanding of dynamics, and atomistic simulation. In this review we briefly summarize the current state-of-the-art computational techniques available for the simulation of electrodeposition and electrochemical growth on surfaces, and identify the remaining open challenges.

cond-mat.mtrl-sci↗

arXiv2023

Eat, Sleep, Code, Repeat: Tips for Early-Career Researchers in Computational Science

This article is intended as a guide for new graduate students in the field of computational science. With the increasing influx of students from diverse backgrounds joining the ever-popular field, this short guide aims to help students navigate through the various computational techniques that they are likely to encounter during their studies. These techniques span from Bash scripting and scientific programming to machine learning, among other areas. This paper is divided into ten sections, each introducing a different computational method. To enhance readability, we have adopted a casual and instructive tone, and included code snippets where relevant. Please note that due to the introductory nature of this article, it is not intended to be exhaustive; instead, we direct readers to a list of references to expand their knowledge of the techniques discussed within the paper. It is likely that this article will continue to evolve with time, and as such, we advise readers to seek the latest version. Finally, readers should note this article serves as an extension to our student-led seminar series, with additional resources and videos available at \url{https://computationaltoolkit.github.io/} for reference.

physics.comp-ph↗

arXiv2023

libMBD: A general-purpose package for scalable quantum many-body dispersion calculations

Many-body dispersion (MBD) is a powerful framework to treat van der Waals (vdW) dispersion interactions in density-functional theory and related atomistic modeling methods. Several independent implementations of MBD with varying degree of functionality exist across a number of electronic structure codes, which both limits the current users of those codes and complicates dissemination of new variants of MBD. Here, we develop and document libMBD, a library implementation of MBD that is functionally complete, efficient, easy to integrate with any electronic structure code, and already integrated in FHI-aims, DFTB+, VASP, Q-Chem, CASTEP, and Quantum ESPRESSO. libMBD is written in modern Fortran with bindings to C and Python, uses MPI/ScaLAPACK for parallelization, and implements MBD for both finite and periodic systems, with analytical gradients with respect to all input parameters. The computational cost has asymptotic cubic scaling with system size, and evaluation of gradients only changes the prefactor of the scaling law, with libMBD exhibiting strong scaling up to 256 processor cores. Other MBD properties beyond energy and gradients can be calculated with libMBD, such as the charge-density polarization, first-order Coulomb correction, the dielectric function, or the order-by-order expansion of the energy in the dipole interaction. Calculations on supramolecular complexes with MBD-corrected electronic structure methods and a meta-review of previous applications of MBD demonstrate the broad applicability of the libMBD package to treat vdW interactions.

physics.chem-ph↗

arXiv2023

Stability of Single Metal Atoms on Defective and Doped Diamond Surfaces

Polycrystalline boron-doped diamond (BDD) is widely used as a working electrode material in electrochemistry, and its properties, such as its stability, make it an appealing support material for nanostructures for electrocatalytic applications. Recent experiments have shown that electrodeposition can lead to the creation of stable small nanoclusters and even single metal adatoms on BDD surfaces. We investigate the adsorption energy and kinetic stability of single metal atoms adsorbed onto an atomistic model of BDD surfaces using density functional theory. The surface model is constructed using hybrid quantum/molecular mechanics embedding techniques and is based on an oxygen-terminated diamond (110) surface. We use the hybrid quantum mechanics/molecular mechanics method to assess the ability of different density-functional approximations to predict the adsorption structure, energy and the barrier for diffusion on pristine and defective surfaces. We find that surface defects (vacancies and surface dopants) strongly anchor metal adatoms on vacancy sites. We further investigate the thermal stability of metal adatoms, which reveals high barriers associated with lateral diffusion away from the vacancy site. The result provides an explanation for the high stability of experimentally imaged single metal adatoms on BDD and a starting point to investigate the early stages of nucleation during metal surface deposition.

cond-mat.mtrl-sci↗

arXiv2022

Long-range dispersion-inclusive machine learning potentials for structure search and optimization of hybrid organic-inorganic interfaces

The computational prediction of the structure and stability of hybrid organic-inorganic interfaces provides important insights into the measurable properties of electronic thin film devices, coatings, and catalyst surfaces and plays an important role in their rational design. However, the rich diversity of molecular configurations and the important role of long-range interactions in such systems make it difficult to use machine learning (ML) potentials to facilitate structure exploration that otherwise require computationally expensive electronic structure calculations. We present an ML approach that enables fast, yet accurate, structure optimizations by combining two different types of deep neural networks trained on high-level electronic structure data. The first model is a short-ranged interatomic ML potential trained on local energies and forces, while the second is an ML model of effective atomic volumes derived from atoms-in-molecules partitioning. The latter can be used to connect short-range potentials to well-established density-dependent long-range dispersion correction methods. For two systems, specifically gold nanoclusters on diamond (110) surfaces and organic $π$-conjugated molecules on silver (111) surfaces, we train models on sparse structure relaxation data from density functional theory and show the ability of the models to deliver highly efficient structure optimizations and semi-quantitative energy predictions of adsorption structures.

cond-mat.mtrl-sci↗