arXiv subjects

Aniruddh Vashisth

Publications and source records attributed to Aniruddh Vashisth.

6 recordsLinked to original sources

arXiv2025

Deep learning reveals key predictors of thermal conductivity in covalent organic frameworks

The thermal conductivity of covalent organic frameworks (COFs), an emerging class of nanoporous polymeric materials, is crucial for many applications, yet the link between their structure and thermal properties remains poorly understood. Analysis of a dataset containing over 2,400 COFs reveals that conventional features such as density, pore size, void fraction, and surface area do not reliably predict thermal conductivity. To address this, an attention-based machine learning model was trained, accurately predicting thermal conductivities even for structures outside the training set. The attention mechanism was then utilized to investigate the model's success. The analysis identified dangling molecular branches as a key predictor of thermal conductivity, leading us to define the dangling mass ratio (DMR), a descriptor that quantifies the fraction of atomic mass in dangling branches relative to the total COF mass. Feature importance assessments on regression models confirm the significance of DMR in predicting thermal conductivity. These findings indicate that COFs with dangling functional groups exhibit lower thermal transfer capabilities. Molecular dynamics simulations support this observation, revealing significant mismatches in the vibrational density of states due to the presence of dangling branches.

cs.CE↗

arXiv2025

Pyrite Bismuth Telluride Heterojunction for Hybrid Electromagnetic to Thermoelectric Energy Harvesting

The rapid proliferation of wireless networks and connected devices has led to pervasive electromagnetic (EM) energy dissipation into the environment, an underutilized resource for energy harvesting. Here, we demonstrate a pyrite (FeS$_2$)-bismuth telluride (Bi$_2$Te$_3$) heterojunction that enables hybrid electromagnetic-to-thermoelectric energy conversion. Fabricated via a simple cold-press compaction of powders, the heterojunction forms a Schottky interface at FeS$_2$, facilitating efficient RF absorption and localized heating. This heat is harvested by Bi$_2$Te$_3$ through thermoelectric conversion. Under 35~MHz RF irradiation at 1~W input power, the device achieved a local temperature rise of 46~$^\circ$C and a thermal gradient of 5.5~K across the Bi$_2$Te$_3$, resulting in a peak power density of approximately 13~mW/cm$^2$. Molecular dynamics (MD) simulations and density functional theory (DFT) calculations further elucidate the heat transport behavior and interfacial thermoelectric performance. This work introduces a new class of heterostructures for RF-responsive energy harvesting, offering a scalable route toward self-powered IoT and wireless sensing systems.

cond-mat.mtrl-sci↗

arXiv2025

Toward Sustainable Polymer Design: A Molecular Dynamics-Informed Machine Learning Approach for Vitrimers

Vitrimer is an emerging class of sustainable polymers with self-healing capabilities enabled by dynamic covalent adaptive networks. However, their limited molecular diversity constrains their property space and potential applications. Recent development in machine learning (ML) techniques accelerates polymer design by predicting properties and virtually screening candidates, yet the scarcity of available experimental vitrimer data poses challenges in training ML models. To address this, we leverage molecular dynamics (MD) data generated by our previous work to train and benchmark seven ML models covering six feature representations for glass transition temperature (Tg) prediction. By averaging predicted Tg from different models, the model ensemble approach outperforms individual models, allowing for accurate and efficient property prediction on unlabeled datasets. Two novel vitrimers are identified and synthesized, exhibiting experimentally validated higher Tg than existing bifunctional transesterification vitrimers, along with demonstrated healability. This work explores the possibility of using MD data to train ML models in the absence of sufficient experimental data, enabling the discovery of novel, synthesizable polymer chemistries with superior properties. The integrated MD-ML approach offers polymer chemists an efficient tool for designing polymers tailored to diverse applications.

cond-mat.mtrl-sci↗

arXiv2025

Localized Radiofrequency Heating for Enhanced Thermoelectric Energy Generation Using Natural Galena Ore

The efficiency of thermoelectric devices can be significantly enhanced by maintaining a stable temperature gradient, which can be achieved through localized heating. Radio waves serve as an ideal heat source for this purpose. In this study, we demonstrate the enhancement of thermoelectric performance in earth-abundant natural ore Galena (PbS) through localized radio frequency (RF) heating. RF heating experiments conducted at frequencies between \SI{35}{MHz} and \SI{45}{MHz} induced substantial localized heating in PbS, generating a temperature gradient of \SI{32}{K}. This resulted in a Seebeck voltage of \SI{-5.8}{mV/K}, approximately 13 times greater than the conventional Seebeck coefficient of PbS (\SI{440}{\micro V/K}). Additionally, a power factor of \SI{151}{mWm^{-1}K^{-2}} and an overall RF to thermoelectric conversion efficiency of 15\% were achieved. Molecular dynamics simulations corroborate the experimental findings, providing insights into the mechanism of thermal transport and RF-induced heating in PbS. These results highlight the potential of localized RF heating as an effective strategy for enhancing thermoelectric performance, with promising implications for ambient thermoelectric energy harvesting applications.

cond-mat.mtrl-sci↗

arXiv2024

AI-guided inverse design and discovery of recyclable vitrimeric polymers

Vitrimer is a new, exciting class of sustainable polymers with the ability to heal due to their dynamic covalent adaptive network that can go through associative rearrangement reactions. However, a limited choice of constituent molecules restricts their property space, prohibiting full realization of their potential applications. To overcome this challenge, we couple molecular dynamics (MD) simulations and a novel graph variational autoencoder (VAE) machine learning model for inverse design of vitrimer chemistries with desired glass transition temperature (Tg) and synthesize a novel vitrimer polymer. We build the first vitrimer dataset of one million chemistries and calculate Tg on 8,424 of them by high-throughput MD simulations calibrated by a Gaussian process model. The proposed novel VAE employs dual graph encoders and a latent dimension overlapping scheme which allows for individual representation of multi-component vitrimers. By constructing a continuous latent space containing necessary information of vitrimers, we demonstrate high accuracy and efficiency of our framework in discovering novel vitrimers with desirable Tg beyond the training regime. To validate the effectiveness of our framework in experiments, we generate novel vitrimer chemistries with a target Tg = 323 K. By incorporating chemical intuition, we synthesize a vitrimer with Tg of 311-317 K, and experimentally demonstrate healability and flowability. The proposed framework offers an exciting tool for polymer chemists to design and synthesize novel, sustainable vitrimer polymers for a facet of applications.

cond-mat.mtrl-sci↗

arXiv2023

Recyclable vitrimer-based printed circuit board for circular electronics

Electronics are integral to modern life; however, at their end-of-life these devices produce environmentally hazardous electronic waste (e-waste). Recycling the ubiquitous printed circuit boards (PCBs) that make up a substantial mass and volume fraction of e-waste is challenging due to their use of irreversibly cured thermoset epoxies. We present a PCB formulation using transesterification vitrimers (vPCBs), and an end-to-end fabrication process compatible with standard manufacturing ecosystems. We create functional prototypes of IoT devices transmitting 2.4 GHz radio signals on vPCBs with electrical and mechanical properties meeting industry standards. Fractures and holes in vPCBs can be repaired while retaining comparable performance over more than four repair cycles. We further demonstrate non-destructive decomposition of transesterification vitrimer composites with solid inclusions and metal attachments by polymer swelling with small molecule solvents. We hypothesize that unlike traditional solvolysis recycling, swelling does not degrade the materials. Through dynamic mechanical analysis we find negligible catalyst loss, minimal changes in storage modulus, and equivalent polymer backbone composition across multiple recycling cycles. We achieve 98% polymer recovery, 100% fiber recovery, and 91% solvent recovery which we reuse to create new vPCBs without degraded performance. Our cradle-to-cradle life-cycle assessment shows substantial environmental impact reduction over conventional PCBs in 11 categories.

physics.app-ph↗