arXiv subjects

Ajinkya C. Hire

Publications and source records attributed to Ajinkya C. Hire.

7 recordsLinked to original sources

arXiv2025

Developing a Complete AI-Accelerated Workflow for Superconductor Discovery

The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting the materials space that can be explored. Here, we introduce a Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET), a machine-learning model trained to predict the Eliashberg spectral function and superconducting critical temperature with a mean-absolute-error of 0.87 K relative to DFT-based Allen-Dynes calculations. Intriguingly, BEE-NET achieves a true-negative-rate of 99.4\%, enabling highly efficient screening for the rare property of superconductivity. Integrated into a multi-stage, AI-accelerated discovery pipeline that incorporates elemental-substitution strategies and machine-learned interatomic potentials, our workflow reduced over 1.3 million candidate structures to 741 dynamically and thermodynamically stable compounds with DFT-confirmed $T_{\mathrm{c}} > 5$ K. We report the successful synthesis and experimental confirmation of superconductivity in two of these previously unreported compounds. This study establishes a data-driven framework that integrates machine learning, quantum calculations, and experiments to systematically accelerate superconductor discovery.

cond-mat.supr-con↗

arXiv2024

When More Data Hurts: Optimizing Data Coverage While Mitigating Diversity Induced Underfitting in an Ultra-Fast Machine-Learned Potential

Machine-learned interatomic potentials (MLIPs) are becoming an essential tool in materials modeling. However, optimizing the generation of training data used to parameterize the MLIPs remains a significant challenge. This is because MLIPs can fail when encountering local enviroments too different from those present in the training data. The difficulty of determining \textit{a priori} the environments that will be encountered during molecular dynamics (MD) simulation necessitates diverse, high-quality training data. This study investigates how training data diversity affects the performance of MLIPs using the Ultra-Fast Force Field (UF$^3$) to model amorphous silicon nitride. We employ expert and autonomously generated data to create the training data and fit four force-field variants to subsets of the data. Our findings reveal a critical balance in training data diversity: insufficient diversity hinders generalization, while excessive diversity can exceed the MLIP's learning capacity, reducing simulation accuracy. Specifically, we found that the UF$^3$ variant trained on a subset of the training data, in which nitrogen-rich structures were removed, offered vastly better prediction and simulation accuracy than any other variant. By comparing these UF$^3$ variants, we highlight the nuanced requirements for creating accurate MLIPs, emphasizing the importance of application-specific training data to achieve optimal performance in modeling complex material behaviors.

cond-mat.mtrl-sci↗

arXiv2024

Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function

Integrating deep learning with the search for new electron-phonon superconductors represents a burgeoning field of research, where the primary challenge lies in the computational intensity of calculating the electron-phonon spectral function, $α^2F(ω)$, the essential ingredient of Midgal-Eliashberg theory of superconductivity. To overcome this challenge, we adopt a two-step approach. First, we compute $α^2F(ω)$ for 818 dynamically stable materials. We then train a deep-learning model to predict $α^2F(ω)$, using an unconventional training strategy to temper the model's overfitting, enhancing predictions. Specifically, we train a Bootstrapped Ensemble of Tempered Equivariant graph neural NETworks (BETE-NET), obtaining an MAE of 0.21, 45 K, and 43 K for the Eliashberg moments derived from $α^2F(ω)$: $λ$, $ω_{\log}$, and $ω_{2}$, respectively, yielding an MAE of 2.5 K for the critical temperature, $T_c$. Further, we incorporate domain knowledge of the site-projected phonon density of states to impose inductive bias into the model's node attributes and enhance predictions. This methodological innovation decreases the MAE to 0.18, 29 K, and 28 K, respectively, yielding an MAE of 2.1 K for $T_c$. We illustrate the practical application of our model in high-throughput screening for high-$T_c$ materials. The model demonstrates an average precision nearly five times higher than random screening, highlighting the potential of ML in accelerating superconductor discovery. BETE-NET accelerates the search for high-$T_c$ superconductors while setting a precedent for applying ML in materials discovery, particularly when data is limited.

cond-mat.supr-con↗

arXiv2023

ZrNb(CO) RF superconducting thin film with high critical temperature in the theoretical limit

Superconducting radio-frequency (SRF) resonators are critical components for particle accelerator applications, such as free-electron lasers, and for emerging technologies in quantum computing. Developing advanced materials and their deposition processes to produce RF superconductors that yield nanoohms surface resistances is a key metric for the wider adoption of SRF technology. Here we report ZrNb(CO) RF superconducting films with high critical temperatures (Tc) achieved for the first time under ambient pressure. The attainment of a Tc near the theoretical limit for this material without applied pressure is promising for its use in practical applications. A range of Tc, likely arising from Zr doping variation, may allow a tunable superconducting coherence length that lowers the sensitivity to material defects when an ultra-low surface resistance is required. Our ZrNb(CO) films are synthesized using a low-temperature (100 - 200 C) electrochemical recipe combined with thermal annealing. The phase transformation as a function of annealing temperature and time is optimized by the evaporated Zr-Nb diffusion couples. Through phase control, we avoid hexagonal Zr phases that are equilibrium-stable but degrade Tc. X-ray and electron diffraction combined with photoelectron spectroscopy reveal a system containing cubic ZrNb mixed with rocksalt NbC and low-dielectric-loss ZrO2. We demonstrate proof-of-concept RF performance of ZrNb(CO) on an SRF sample test system. BCS resistance trends lower than reference Nb, while quench fields occur at approximately 35 mT. Our results demonstrate the potential of ZrNb(CO) thin films for particle accelerator and other SRF applications.

cond-mat.mtrl-sci↗

arXiv2022

Theory of Nb-Zr Alloy Superconductivity and First Experimental Demonstration for Superconducting Radio-Frequency Cavity Applications

Niobium-zirconium (Nb-Zr) alloy is an old superconductor that is a promising new candidate for superconducting radio-frequency (SRF) cavity applications. Using density-functional and Eliashberg theories, we show that addition of Zr to a Nb surface in small concentrations increases the critical temperature $T_c$ and improves other superconducting properties. Furthermore, we calculate $T_c$ for Nb-Zr alloys across a broad range of Zr concentrations, showing good agreement with the literature for disordered alloys as well as the potential for significantly higher $T_c$ in ordered alloys near 75%Nb/25%Zr composition. We provide experimental verification on Nb-Zr alloy samples and SRF sample test cavities prepared with either physical vapor or our novel electrochemical deposition recipes. These samples have the highest measured $T_c$ of any Nb-Zr superconductor to date and indicate a reduction in BCS resistance compared to the conventional Nb reference sample; they represent the first steps along a new pathway to greatly enhanced SRF performance. Finally, we use Ginzburg-Landau theory to show that the addition of Zr to a Nb surface increases the superheating field $B_{sh}$, a key figure of merit for SRF which determines the maximum accelerating gradient at which cavities can operate.

cond-mat.supr-con↗

arXiv2022

Data-Augmentation for Graph Neural Network Learning of the Relaxed Energies of Unrelaxed Structures

Computational materials discovery has continually grown in utility over the past decade due to advances in computing power and crystal structure prediction algorithms (CSPA). However, the computational cost of the \textit{ab initio} calculations required by CSPA limits its utility to small unit cells, reducing the compositional and structural space the algorithms can explore. Past studies have bypassed many unneeded \textit{ab initio} calculations by utilizing machine learning methods to predict formation energy and determine the stability of a material. Specifically, graph neural networks display high fidelity in predicting formation energy. Traditionally graph neural networks are trained on large data sets of relaxed structures. Unfortunately, the geometries of unrelaxed candidate structures produced by CSPA often deviate from the relaxed state, which leads to poor predictions hindering the model's ability to filter energetically unfavorable prior to \textit{ab initio} evaluation. This work shows that the prediction error on relaxed structures reduces as training progresses, while the prediction error on unrelaxed structures increases, suggesting an inverse correlation between relaxed and unrelaxed structure prediction accuracy. To remedy this behavior, we propose a simple, physically motivated, computationally cheap perturbation technique that augments training data to improve predictions on unrelaxed structures dramatically. On our test set consisting of 623 Nb-Sr-H hydride structures, we found that training a crystal graph convolutional neural networks, utilizing our augmentation method, reduced the MAE of formation energy prediction by 66\% compared to training with only relaxed structures. We then show how this error reduction can accelerates CSPA by improving the model's ability to filter out energetically unfavorable structures accurately.

physics.comp-ph↗

arXiv2021

A15 Nb$_3$Si -- A "high" Tc superconductor synthesized at a pressure of one megabar and metastable at ambient conditions

A15 Nb$_3$Si is, until now, the only high temperature superconductor produced at high pressure (~110 GPa) that has been successfully brought back to room pressure conditions in a metastable condition. Based on the current great interest in trying to create metastable-at-room-pressure high temperature superconductors produced at high pressure, we have restudied explosively compressed A15 Nb$_3$Si and its production from tetragonal Nb$_3$Si. First, diamond anvil cell pressure measurements up to 88 GPa were performed on explosively compressed A15 Nb$_3$Si material to trace Tc as a function of pressure. Tc is suppressed to ~ 5.2 K at 88 GPa. Then, using these Tc (P) data for A15 Nb$_3$Si, pressures up to 92 GPa were applied at room temperature (which increased to 120 GPa at 5 K) on tetragonal Nb$_3$Si. Measurements of the resistivity gave no indication of any A15 structure production, i.e., no indications of the superconductivity characteristic of A15 Nb$_3$Si. This is in contrast to the explosive compression (up to P~110 GPa) of tetragonal Nb$_3$Si, which produced 50-70% A15 material, Tc = 18 K at ambient pressure, in a 1981 Los Alamos National Laboratory experiment. Our theoretical calculations show that A15 Nb$_3$Si has an enthalpy vs the tetragonal structure that is 0.07 eV/atom smaller at 100 GPa, implying that the accompanying high temperature (1000 deg C) caused by explosive compression is necessary to successfully drive the reaction kinetics of the tetragonal -> A15 Nb$_3$Si structural transformation. Annealing experiments on the A15 explosively compressed material reaching time scales of 39 years are consistent with this viewpoint.

cond-mat.supr-con↗