arXiv subjects

Anupam Bhattacharya

Publications and source records attributed to Anupam Bhattacharya.

5 recordsLinked to original sources

arXiv2025

Structure-Informed Learning of Flat Band 2D Materials

Flat electronic bands enhance electron-electron interactions and give rise to correlated states such as unconventional superconductivity or fractional topological phases. However, most current efforts towards flat-band materials discovery rely on density functional theory (DFT) calculations and manual band structures inspection, restraining their applicability to vast unexplored material spaces. While data-driven methods offer a scalable alternative, most existing models either depend on band structure inputs or focus on scalar properties like bandgap, which fail to capture flat-band characteristics. Here, we report a structure-informed framework for the discovery of previously unrecognized flat-band two-dimensional (2D) materials, which combines a data-driven flatness score capturing both band dispersion and density-of-states characteristics with multi-modal learning from atomic structure inputs. The framework successfully identified multiple flat-band candidates, with DFT validation of kagome-based systems confirming both band flatness and topological character. Our results show that the flatness score provides a physically meaningful signal for identifying flat bands from atomic geometry. The framework uncovers multiple new candidates with topologically nontrivial flat bands from unlabeled data, with consistent model performance across structurally diverse materials. By eliminating the need for precomputed electronic structures, our method enables large-scale screening of flat-band materials and expands the search space for discovering strongly correlated quantum materials.

cond-mat.mtrl-sci↗

arXiv2025

Quantifying hydrogen bonding using electrically tunable nanoconfined water

Hydrogen bonding plays a crucial role in biology and technology, yet it remains poorly understood and quantified despite its fundamental importance. Traditional models, which describe hydrogen bonds as electrostatic interactions between electropositive hydrogen and electronegative acceptors, fail to quantitatively capture bond strength, directionality, or cooperativity, and cannot predict the properties of complex hydrogen-bonded materials. Here, we introduce a novel approach that conceptualizes the effect of hydrogen bonds as elastic dipoles in an electric field, which captures a wide range of hydrogen bonding phenomena in various water systems. Using gypsum, a hydrogen bond heterostructure with two-dimensional structural crystalline water, we calibrate the hydrogen bond strength through an externally applied electric field. We show that our approach quantifies the strength of hydrogen bonds directly from spectroscopic measurements and reproduces a wide range of key properties of confined water reported in the literature. Using only the stretching vibration frequency of confined water, we can predict hydrogen bond strength, local electric field, O-H bond length, and dipole moment. Our work also introduces hydrogen bond heterostructures - a new class of electrically and chemically tunable materials that offer stronger, more directional bonding compared to van der Waals heterostructures, with potential applications in areas such as catalysis, separation, and energy storage.

cond-mat.mes-hall↗

arXiv2024

Elf autoencoder: unsupervised exploration of flat-band materials using electronic band structure fingerprints

Two-dimensional materials with flat electronic bands are promising for realizing exotic quantum phenomena such as unconventional superconductivity and nontrivial topology, but exploring their vast chemical space remains challenging. Here, we introduce an unsupervised convolutional autoencoder agent (elf) that operates on electronic band structure images and is capable of mapping band features and extracting the latent space representation as a fingerprint, enabling autonomous clustering of materials with common electronic properties beyond traditional chemical paradigms. Unsupervised visualisation of the latent space then helps to uncover hidden chemical trends and identify promising candidates based on similarities to well-studied exemplars. Our framework paves the way for the accelerated discovery of novel flat-band materials with desirable electronic characteristics. It complements high-throughput ab initio methods by rapidly screening candidates and guides further investigations into the mechanisms governing the emergence of flat-band physics. We believe the elf autoencoder will be a valuable tool for the autonomous discovery of previously unexplored flat-band materials, aiding in the unbiased identification of compounds with desirable electronic properties in vast 2D chemical space.

cond-mat.mes-hall↗

arXiv2022

Machine learning approach to genome of two-dimensional materials with flat electronic bands

Many-body physics of electron-electron correlations plays a central role in condensed mater physics, it governs a wide range of phenomena, stretching from superconductivity to magnetism, and is behind numerous technological applications. To explore this rich interaction-driven physics, two-dimensional (2D) materials with flat electronic bands provide a natural playground thanks to their highly localised electrons. Currently, thousands of 2D materials with computed electronic bands are available in open science databases, awaiting such exploration. Here we used a new machine learning algorithm combining both supervised and unsupervised machine intelligence to automate the otherwise daunting task of materials search and classification, to build a genome of 2D materials hosting flat electronic bands. To this end, a feedforward artificial neural network was employed to identify 2D flat band materials, which were then classified by a bilayer unsupervised learning algorithm. Such a hybrid approach of exploring materials databases allowed us to reveal completely new material classes outside the known flat band paradigms, offering new systems for in-depth study on their electronic interactions.

cond-mat.mes-hall↗

arXiv2021

Strain driven emergence of topological non-triviality in YPdBi thin films

Half-Heusler compounds exhibit a remarkable variety of emergent properties such as heavy-fermion behaviour, unconventional superconductivity and magnetism. Several of these compounds have been predicted to host topologically non-trivial electronic structures. Remarkably, recent theoretical studies have indicated the possibility to induce non-trivial topological surface states in an otherwise trivial half-Heusler system by strain engineering. Here, using magneto-transport measurements and first principles DFT-based simulations, we demonstrate topological surface states on strained [110] oriented thin films of YPdBi grown on (100) MgO. These topological surface states arise in an otherwise trivial semi-metal purely driven by strain. Furthermore, we observe the onset of superconductivity in these strained films highlighting the possibility of engineering a topological superconducting state. Our results demonstrate the critical role played by strain in engineering novel topological states in thin film systems for developing next-generation spintronic devices.

cond-mat.mtrl-sci↗