About

Amartya Banerjee is an Assistant Professor in the Department of Materials Science and Engineering at UCLA Samueli School of Engineering. His research interests include the development and application of mathematical and computational tools for the characterization and discovery of novel materials and structures, with a particular focus on first principles (quantum mechanical) methods. He works on extending the scope and capabilities of these methods for applications in mechanics, such as modeling defects, and in energy storage and conversion technologies. His research also encompasses the analysis and development of multiscale methods, the use of symmetry principles in various scientific and engineering problems—including materials discovery, molecular self-assembly, wave propagation, and the design of computational solvers—as well as numerical analysis and scientific computation.

Research topics

• Materials science
• Physics
• Optics
• Quantum mechanics
• Nanotechnology
• Chemistry
• Molecular physics

Selected publications

• Dirac Fermions and Flat Bands in Phosphorus Carbide Nanotubes: Structural and Quantum Phase Transitions in a Quasi-One-Dimensional Material

  The Journal of Physical Chemistry Letters · 2026-04-07

  preprintOpen accessSenior authorCorresponding

  Chemically realistic quasi-one-dimensional (1D) materials in which Dirac Fermions and highly degenerate flat bands coexist intrinsically at the Fermi level are exceedingly rare, while representing a highly desirable platform for correlated and topological quantum phenomena. Here, using specialized symmetry-adapted first-principles calculations we predict a new class of nanomaterials─phosphorus carbide nanotubes (P2C3NTs)─obtained by rolling monolayer P2C3, a two-dimensional material shown in a previous letter to host “double Kagome bands”. Both armchair and zigzag P2C3NTs are stable at room temperature and feature the rare coexistence of Dirac crossings and multiple flat bands at the Fermi level inherited from the underlying honeycomb–Kagome lattice, with the flat bands resilient to elastic deformations. Under large strain, the structure transforms from honeycomb–Kagome to “brick-wall”, accompanied by multiple coupled structural and quantum phase transitions. We also uncover localized edge states, spin splitting from vacancies and dopants, and strain-tunable magnetism. Together, these results establish P2C3NTs as a chemically specific and mechanically tunable 1D material platform with potential applications in quantum hardware and spintronics.

  PublisherOA PDFDOI

• Development of a Sustainable <i>In Situ</i> Gel System for Ocular Delivery of <i>p</i> -Coumaric Acid for Corneal Wound Healing

  Molecular Pharmaceutics · 2026-03-16

  article

  Corneal injuries are a leading cause of vision impairment, yet current therapies provide limited benefit due to poor ocular bioavailability and rapid drug clearance. To address this challenge, we developed a pH-sensitive in situ gel of p-coumaric acid (pCA) for sustained ocular delivery and enhanced wound healing. In vitro studies on SIRC cells identified 80 μg/mL pCA as a safe and effective concentration for supporting viability and migration. The optimized gel, formulated with Carbopol 940 and HPMC K100 M using central composite design, exhibited rapid gelation at ocular pH, pseudoplastic rheology, suitable zeta potential, and high entrapment efficiency (85.95%). FESEM confirmed pH-triggered sol–gel transition, while in vitro release demonstrated sustained delivery for 12 h (92.62% cumulative release). Safety was verified through RBC lysis, CAM, goat corneal histology, and rabbit eye irritation tests, all showing no adverse effects. In vivo evaluation in a rat corneal alkali burn model confirmed accelerated wound healing. This system offers a safe, biocompatible, and effective ocular therapy for corneal wounds.

  PublisherDOI

• Multiscale analysis of large twist ferroelectricity and swirling dislocations in bilayer hexagonal boron nitride

  Nanoscale · 2026-01-01

  articleOpen access

  7 stacking. Interface dislocations in these large heterodeformed bilayer configurations exhibit markedly smaller Burgers vectors compared to interface dislocations in small-twist and small-strain bilayer hBN. The BFIM model reproduces experimental results and provides a powerful, computationally efficient framework for predicting ferroelectricity in large-unit-cell heterostructures where atomistic simulations are prohibitively expensive.

  PublisherDOI

• A Dual-Layer Wideband Metasurface Absorber for 5 Ghz Wi-Fi Applications

  2025-12-14

  article

  This paper presents a compact, dual-layer meta surface absorber specifically optimized for the 5.5 GHz Wi-Fi band. The absorber employs a symmetrical fan-blade-shaped circular resonator patterned on an FR4 substrate, designed to support multiple closely spaced resonant modes. This geometry ensures polarization insensitivity and angular stability due to its four-fold rotational symmetry. The proposed structure achieves greater than 95 % absorption over a 1 GHz bandwidth centered at 5.5 GHz, effectively covering the entire <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5.0-6.0 \text{GHz}$</tex> range. Simulation results confirm stable performance under both TE and TM polarizations, with absorption exceeding 90 % up to 60° incidence. Compared to existing designs, the proposed absorber offers a rare combination of wide bandwidth, high angular tolerance, and structural simplicity-making it a promising candidate for electromagnetic interference suppression and device-level integration in next-generation wireless systems.

  PublisherDOI

• Modulating appetite via GHS-R1a inhibition with fluoxetine HCl: from computational prediction to preclinical validation in food-restricted rats

  Nutrire · 2025-11-21

  article
  PublisherDOI

• Valid Property-Enhanced Contrastive Learning for Targeted Optimization &amp; Resampling for Novel Drug Design

  ArXiv.org · 2025-08-31

  preprintOpen access1st authorCorresponding

  Efficiently steering generative models toward pharmacologically relevant regions of chemical space remains a major obstacle in molecular drug discovery under low-data regimes. We present VECTOR+: Valid-property-Enhanced Contrastive Learning for Targeted Optimization and Resampling, a framework that couples property-guided representation learning with controllable molecule generation. VECTOR+ applies to both regression and classification tasks and enables interpretable, data-efficient exploration of functional chemical space. We evaluate on two datasets: a curated PD-L1 inhibitor set (296 compounds with experimental $IC_{50}$ values) and a receptor kinase inhibitor set (2,056 molecules by binding mode). Despite limited training data, VECTOR+ generates novel, synthetically tractable candidates. Against PD-L1 (PDB 5J89), 100 of 8,374 generated molecules surpass a docking threshold of $-15.0$ kcal/mol, with the best scoring $-17.6$ kcal/mol compared to the top reference inhibitor ($-15.4$ kcal/mol). The best-performing molecules retain the conserved biphenyl pharmacophore while introducing novel motifs. Molecular dynamics (250 ns) confirm binding stability (ligand RMSD &lt; $2.5$ angstroms). VECTOR+ generalizes to kinase inhibitors, producing compounds with stronger docking scores than established drugs such as brigatinib and sorafenib. Benchmarking against JT-VAE and MolGPT across docking, novelty, uniqueness, and Tanimoto similarity highlights the superior performance of our method. These results position our work as a robust, extensible approach for property-conditioned molecular design in low-data settings, bridging contrastive learning and generative modeling for reproducible, AI-accelerated discovery.

  PublisherOA PDFDOI

• Adaptive Multimodal Protein Plug-And-Play With Diffusion-Based Priors

  2025-12-14

  article1st authorCorresponding

  In an inverse problem, the goal is to recover an unknown parameter (e.g., an image) that has typically undergone some lossy or noisy transformation during measurement. Recently, deep generative models, particularly diffusion models, have emerged as powerful priors for protein structure generation. However, integrating noisy experimental data from multiple sources to guide these models remains a significant challenge. Existing methods often require precise knowledge of experimental noise levels and manually tuned weights for each data modality. In this work, we introduce Adam-PnP, a Plug-andPlay framework that guides a pre-trained protein diffusion model using gradients from multiple, heterogeneous experimental sources. Our framework features an adaptive noise estimation scheme and a dynamic modality weighting mechanism integrated into the diffusion process, which reduce the need for manual hyperparameter tuning. Experiments on complex reconstruction tasks demonstrate significantly improved accuracy using Adam-PnP.

  PublisherDOI

• Electronic structure prediction of medium and high entropy alloys across composition space

  npj Computational Materials · 2025-11-21 · 1 citations

  articleOpen accessSenior author

  Abstract We propose machine learning (ML) models to predict the electron density — the fundamental unknown of a material’s ground state — across the composition space of concentrated alloys. From this, other physical properties can be inferred, enabling accelerated exploration. A significant challenge is that the number of descriptors and sampled compositions required for accurate prediction grows rapidly with species. To address this, we employ Bayesian Active Learning (AL), which minimizes training data requirements by leveraging uncertainty quantification capabilities of Bayesian Neural Networks. Compared to the strategic tessellation of the composition space, Bayesian-AL reduces the number of training data points by a factor of 2.5 for ternary (SiGeSn) and 1.7 for quaternary (CrFeCoNi) systems. We also introduce easy-to-optimize, body-attached-frame descriptors, which respect physical symmetries while keeping descriptor-vector size nearly constant as alloy complexity increases. Our ML models demonstrate high accuracy and generalizability in predicting both electron density and energy across composition space.

  PublisherDOI

• Strain-Tunable Topological Phase Transitions in Line- and Split-Graph Flat-Band Lattices

  ArXiv.org · 2025-01-20

  preprintOpen accessSenior author

  In recent years, materials with topological flat bands have attracted significant attention due to their association with extraordinary transport properties and strongly correlated electrons. Yet, generic principles linking lattice architecture, strain, and band topology remain scarce. Here, using a unified graph-theoretic framework we generate entire families of two-dimensional lattices and, using analytical tight-binding calculations, demonstrate that a single mechanical knob -- uniform in-plane strain -- drives universal transitions between trivial insulating, Dirac semimetal, and quantum spin-Hall phases across all lattices. The framework yields several flat band lattices that were hitherto absent or largely unexplored in the literature -- for example, the checkerboard split-graph and triangular-Kagome lattices -- whose strain-driven topological phase diagrams we establish here for the first time. The design rules implied by our studies provide a blueprint for engineering topological states in a wide variety of 2D materials, photonic crystals, and circuit lattices, and are anticipated to accelerate the discovery of strain-programmable quantum matter.

  PublisherOA PDFDOI

• Bloch Oscillations and Landau-Zener Transitions in Flat-Band Lattices with Quadratic and Linear Band Touchings

  ArXiv.org · 2025-10-03

  preprintOpen accessSenior author

  Bloch oscillations (BOs) describe the coherent oscillatory motion of electrons in a periodic lattice under a constant external electric field. Deviations from pure harmonic wave packet motion or irregular Bloch oscillations can occur due to Zener tunneling (Landau-Zener Transitions or LZTs), with oscillation frequencies closely tied to interband coupling strengths. Motivated by the interplay between flat-band physics and interband coupling in generating irregular BOs, here we investigate these oscillations in Lieb and Kagome lattices using two complementary approaches: coherent transport simulations and scattering matrix analysis. In the presence of unavoidable band touchings, half-fundamental and fundamental BO frequencies are observed in Lieb and Kagome lattices, respectively -- a behavior directly linked to their distinct band structures. When avoided band touchings are introduced, distinct BO frequency responses to coupling parameters in each lattice are observed. Scattering matrix analysis reveals strong coupling and potential LZTs between dispersive bands and the flat band in Kagome lattices, with the quadratic band touching enhancing interband interactions and resulting in BO dynamics that is distinct from systems with linear crossings. In contrast, the Lieb lattice -- a three level system -- shows independent coupling between the flat band and two dispersive bands, without direct LZTs occurring between the two dispersive bands themselves. Finally, to obtain a unifying perspective on these results, we examine BOs during a strain-induced transition from Kagome to Lieb lattices, and link the evolution of irregular BO frequencies to changes in band connectivity and interband coupling.

  PublisherOA PDFDOI

Frequent coauthors

• Shivang Agarwal

  14 shared

• Michael Horn

  Northwestern University

  13 shared

• Clarice D. Aiello

  University of California, Los Angeles

  10 shared

• Lin Lin

  Lawrence Berkeley National Laboratory

  10 shared

• Parikshit Das

  Defence Research Laboratory

  8 shared

• Ajay Kakati

  8 shared

• Santanu Das

  Indian Institute of Engineering Science and Technology, Shibpur

  8 shared

• Sayan Chatterjee

  8 shared

Awards & honors

• Numerous Travel Awards (from USACM, SES, MRS, IUTAM, etc.)
• US Junior Oberwolfach Fellowship, 2013
• Supported by the U.S. National Science Foundation
• John A. & Jane Dunning Copper Fellowship, 2008
• Best B.Tech Project, 2007