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Researcher profile

Ayana Ghosh

Ayana Ghosh contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
cond-mat.mtrl-sciArtificial Intelligencecond-mat.dis-nncond-mat.mes-hallMachine Learningphysics.data-an

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Published work

6 published item(s)

preprint2026arXiv

Born-Qualified: An Autonomous Framework for Deploying Advanced Energy and Electronic Materials

Autonomous science is transforming how we discover materials and chemical systems for advanced energy technologies. However, many initially promising systems never reach deployment. This "valley of death" stems from optimization that prioritizes laboratory metrics over industrial viability. We propose a new strategy: "born-qualified" autonomous development, which embeds manufacturability, cost, and durability constraints from the outset. This approach is enabled by four pillars, including the development of multi-objective metrics, causal models, a modular infrastructure, and embedding manufacturing in the discovery loop. Realizing this vision will require sustained, community-wide commitment, but the potential return on that investment is commensurate with the scale of the challenge.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Intervention Strategies for Polarization Switching in Hybrid Improper Ferroelectrics

The potential of hybrid improper ferroelectrics (HIFs) in electronic and spintronic devices hinges on their ability to switch polarization. Although the coupling between octahedral rotation and tilt is well established, the factors that govern switching barriers remain elusive. In this study, we explore this area to demonstrate the critical role of causal reasoning in uncovering the mechanisms to control the ferroelectric switching barrier in HIFs. By combining causal discovery, causal interventions, and first-principles simulations, we identify tolerance factor, A-site cation radii mismatch, epitaxial strain, and octahedral rotation/tilt as key parameters and quantify how their interplay directly influences switching barrier. Three key insights emerge from our work: (a) the analysis identifies the structural descriptors controlling polarization reversal across a broad family of A-site-layered double perovskites and superlattices, (b) it uncovers non-trivial, material-specific rotation-tilt mechanisms, including a counterintuitive cooperative pathway where both rotation and tilt change while lowering the barrier, an effect mostly inaccessible to conventional Landau or first-principles-based approaches and (c) it maps these material-specific mechanisms to experimentally realizable parameters, showing that epitaxial strain from orthorhombic substrates (e.g., NdScO$_3$, NdGaO$_3$) selectively tunes octahedral distortions to achieve barrier reduction across varied compositions. These results establish actionable, materials-by-design principles linking composition, structure, and strain to polarization switching, while highlighting the potential of causal reasoning to guide intelligent, mechanism-driven strategies for engineering complex functional oxides.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Probing electron beam induced transformations on a single defect level via automated scanning transmission electron microscopy

The robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on the ensemble learning and iterative training (ELIT) of deep convolutional neural networks, is implemented on an operational microscope, enabling the exploration of the dynamics of specific atomic configurations under electron beam irradiation via an automated experiment in STEM. Combined with beam control, this approach allows studying beam effects on selected atomic groups and chemical bonds in a fully automated mode. Here, we demonstrate atomically precise engineering of single vacancy lines in transition metal dichalcogenides and the creation and identification of topological defects graphene. The ELIT-based approach opens the pathway toward the direct on-the-fly analysis of the STEM data and engendering real-time feedback schemes for probing electron beam chemistry, atomic manipulation, and atom by atom assembly.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy

Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest. However, applications of deep learning in experimental domains are often limited by the out-of-distribution drift between the experiments, where the network trained for one set of imaging conditions becomes sub-optimal for different ones. This limitation is particularly stringent in the quest to have an automated experiment setting, where retraining or transfer learning becomes impractical due to the need for human intervention and associated latencies. Here we explore the reproducibility of deep learning for feature extraction in atom-resolved electron microscopy and introduce workflows based on ensemble learning and iterative training to greatly improve feature detection. This approach both allows incorporating uncertainty quantification into the deep learning analysis and also enables rapid automated experimental workflows where retraining of the network to compensate for out-of-distribution drift due to subtle change in imaging conditions is substituted for a human operator or programmatic selection of networks from the ensemble. This methodology can be further applied to machine learning workflows in other imaging areas including optical and chemical imaging.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Exploring Electron Beam Induced Atomic Assembly via Reinforcement Learning in a Molecular Dynamics Environment

Atom-by-atom assembly of functional materials and devices is perceived as one of the ultimate targets of nanoscience and nanotechnology. While traditionally implemented via scanning probe microscopy techniques, recently it has been shown that the beam of a scanning transmission electron microscope can be used for targeted manipulation of individual atoms. However, the process is highly dynamic in nature and proceeds via a large number of weakly-understood individual steps. Hence, harnessing an electron beam towards atomic assembly requires automated methods to control the parameters and positioning of the beam in such a way as to fabricate atomic-scale structures reliably. Here, we create a molecular dynamics environment wherein individual atom velocities can be modified, effectively simulating a beam-induced interaction, and apply reinforcement learning to model construction of specific atomic units consisting of Si dopant atoms on a graphene lattice. We find that it is possible to engineer the reward function of the agent in such a way as to encourage formation of local clusters of dopants, whilst at the same time minimizing the amplitude of momentum changes. Inspection of the learned policies indicates that of fundamental importance is the component of velocity perpendicular to the material plane, and further, that the high stochasticity of the environment leads to conservative policies. This study shows the potential for reinforcement learning agents trained in simulated environments for potential use as atomic scale fabricators, and further, that the dynamics learned by agents encode specific elements of important physics that can be learned.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Understanding Magnetic Properties of Actinide-Based Compounds from Machine Learning

Actinide and lanthanide-based materials display exotic properties that originate from the presence of itinerant or localized f-electrons and include unconventional superconductivity and magnetism, hidden order; and heavy fermion behavior. Due to the strongly correlated nature of the 5f electrons, magnetic properties of these compounds depend sensitively on applied magnetic field and pressure, as well as on chemical doping. However, precise connection between the structure and magnetism in actinide-based materials is currently unclear. In this investigation, we established such structure-property links by assembling and mining two datasets that aggregate, respectively, the results of high-throughput DFT simulations and experimental measurements for the families of uranium and neptunium based binary compounds. Various regression algorithms were utilized to identify correlations among accessible attributes (features or descriptors) of the material systems and predict their cation magnetic moments and general forms of magnetic ordering. Descriptors representing compound structural parameters and cation f-subshell occupation numbers were identified as most important for accurate predictions. The best machine learning model developed employs the Random Forest Regression algorithm and can predict magnetic moment sizes and ordering forms in actinide-based systems with 10-20% of root mean square error.

0 citations0 reviewsNo code linkedOpen access