arXiv subjects

Kedar Hippalgaonkar

Publications and source records attributed to Kedar Hippalgaonkar.

At least 19 recordsLinked to original sources

arXiv2026

Importance of Configurational Charge Ordering for Machine Learning Interatomic Potentials

Machine learning interatomic potentials (MLIPs) enable large-scale atomistic simulations but remain challenged in describing mixed-valence materials where charge ordering strongly influences thermodynamic stability. Here we investigate the role of configurational charge ordering in MLIP structural optimization of the battery cathode material NaFePO4. We show that conventional MLIPs fail to reproduce the correct stability of intermediate Na concentrations because structural optimization leads to incorrect Fe2+/Fe3+ charge assignments, resulting in incorrect energy ordering and convex-hull predictions. Analysis of the Fe2+/Fe3+ charge assignment during structural optimization reveals that MLIPs are unable to capture the configurational charge ordering, leading to energy errors that make it difficult to identify the experimentally observed phase as the true ground-state structure. To address this limitation, we introduce an approach that encodes charge-state information directly into the MLIP representation by distinguishing between Fe2+ and Fe3+ environments during training. Retraining CHGNet, MACE and MACELES with this representation enables accurate structural optimization, correct identification of charge ordering, and improved agreement with density functional theory convex hulls. Our results demonstrate that incorporating configurational charge ordering into the MLIP representation is essential for modeling charge-disordered materials. Beyond recovering the correct ground state, this encoding allows a specific charge ordering to be imposed and held fixed during optimization, a capability unconstrained DFT struggles to guarantee, and we show that it generalizes across four polyanion frameworks and four transition-metal ions (Fe, Mn, Co, Ni), establishing a broadly applicable strategy for modeling mixed-valence transition-metal systems.

cond-mat.mtrl-sci↗

arXiv2026

PUDA: An AI-Native Hardware Harness for Self-Driving Laboratories

Physical Unified Device Architecture (PUDA) is an AI-native hardware harness for self-driving laboratories (SDLs). Rather than building a human-centered graphical user interface (GUI) orchestration layer, PUDA creates a command-line runtime environment that lets agents observe, orient, decide, and act over experiments while hardware execution remains deterministic, atomic, and auditable. Headless by design, devices appear through discoverable command-line interfaces, JSON protocols are routed through a distributed messaging system, and command responses, data products, and reports are preserved as structured records. PUDA organizes protocols, runs, samples, measurements, and command logs into an AI-native data structure linked by run identifiers and timestamps, preserving provenance from submitted protocol through hardware response to resulting data products. PUDA separates scientific orchestration from physical operation and data telemetry: agents choose experiments, while PUDA executes validated commands and captures provenance-linked state, responses, and data. The contribution is not another optimizer, orchestrator, or recipe language. It is a practical execution and data environment for agentic SDLs; the broader physical AI implication is that PUDA provides an AI-native hardware harness for AI systems to interact with physical tools.

cond-mat.mtrl-sci↗

arXiv2026

Virp: neural network-accelerated prediction of physical properties in site-disordered materials

Among metallic alloys, ceramics, and even common compounds such as water ice, it is usual to find materials in which crystalline order is expressed as a probability. In such cases, one or more sites within a crystal can be occupied by multiple elements or vacancies, according to a set of probabilities. These crystal structures remain inaccessible to common first-principles materials simulation methodologies, which assumes perfect crystal order. Workaround strategies to this limitation include quasirandom structures and cluster expansion. These methods are system-specific and computationally expensive as they rely on large scale Monte Carlo simulations of enlarged unit cells. To address these limitations, we propose a pipeline combining a permutation-based virtual cell generation algorithm, sampling regime, and thermodynamic post-processing which greatly improves the feasibility of computation analyses for site-disordered materials. We demonstrate that the massive configurational space can be adequately sampled with 400 virtual cells, as long as the supercell definition is sufficiently large.

cond-mat.mtrl-sci↗

arXiv2026

Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates

The appearance of generative models has opened vast chemical spaces in the design of functional materials. Although machine learning interatomic potentials (MLIPs) have substantially accelerated phonon calculations, high-fidelity prediction of lattice thermal conductivity \k{appa}lat still requires accurate treatment of anharmonic interactions, which remains a key challenge for existing potentials across novel chemical spaces. To address this challenge, we present a comprehensive benchmark of 15 surrogate models for predicting \k{appa}lat using the Phonix database, which contains 6,966 entries with anharmonic phonon properties derived from first-principles calculations. Firstly, We categorize these surrogate models into three distinct groups: Physical-informed feature descriptors combined with ML models, end-to-end deep neural networks, and pre-trained MLIP-embeddings combined with ML models. By evaluating model performance across random, space-group disjoint (testing generalization to unseen crystal symmetries), and Out-Of-Distribution splits (OOD dataset that testing extrapolation to property regimes beyond the training range) based on \k{appa}lat, we probe both interpolation and exploration capabilities. Our results reveal that MLIP-embedded models excel in interpolation within well-sampled regions, deep neural network models especially ALiEGNN demonstrate superior robustness in OOD regimes critical for discovering novel low-\k{appa}lat. Additionally, we find a systematic degradation in performance when the structural representation is reduced. Although surrogate models exhibit lower accuracy than direct simulations using first-principles calculation, they reduce computational costs by orders of magnitude, enabling efficient high-throughput screening of thermoelectric materials with minimal loss in generative design workflows.

cond-mat.mtrl-sci↗

arXiv2026

Generative design of inorganic materials

Materials discovery is fundamental to advance next-generation technologies as well as for sustainable and circular economy. Beyond computational screening, generative models are efficient at finding materials with desired properties, via multi-modal learning using multiscale data. This perspective examines the landscape of generative design for inorganic materials and discusses the integration of multi-modal learning with high-throughput experimental validation. We contextualize these challenges through the lens of a generative design framework as a unified approach to address the data-driven inverse design of functional materials. The central idea of the framework is constructed around a foundation AI model for inorganic materials interlinked deeply with various property databases and high-throughput experiments via a machine learning driven closed loop, which enables the framework to solve key challenges in functional materials. We argue that domain-specific implementations of such integrated workflows represent a promising pathway toward the unresolved challenge of data-driven inverse design for atom-engineered inorganic functional materials.

cond-mat.mtrl-sci↗

arXiv2026

Navigating Order-(Dis)Order Family Trees via Group-Subgroup Transitions

As closed-loop materials discovery systems scale to produce millions of candidate compounds, the credibility of the novelty they reward becomes a critical concern. Novelty is commonly assessed against databases of ordered crystal structures, in which atomic sites are fully occupied. Yet, a predicted ordered structure may simply correspond to a particular ordering of a known disordered phase, whose sites are occupied by multiple species in the statistical average structure; we refer to such a structure as an ordered child of a disordered parent. Here, we introduce order-(dis)order family trees, a symmetry-based framework that organizes ordered and disordered structures through group-subgroup relations and enables novelty to be explicitly evaluated. We develop a high-throughput family matching procedure, to identify possible disordered parents and symmetry-related ordered relatives for a given ordered structure. As validation, we test our framework on synthesis-facing case studies (A-Lab), where it correctly recovers existing disordered parents for the targeted ordered structures. Extending this family-tree-based benchmark to experimental structure databases (ICSD), computational datasets (MP-20, Alex-MP-20, and GNoME), and crystal generative models further reveals that many ordered structures that appear novel as individual entries are, in fact, better understood as members of experimentally known order-(dis)order family trees. We also show that this is particularly evident in symmetry-agnostic all-atom generative models, which more frequently produce ordered structures derived from known disordered parents, whereas symmetry-constrained models are 2-4x less prone to this behavior. Our results establish order-(dis)order family trees as a key requirement for achieving genuine novelty in data-driven materials discovery.

cond-mat.mtrl-sci↗

arXiv2026

SWORD: Symmetry and Wyckoff-sequence of Ordered and Disordered crystals

Novelty in materials discovery requires candidates to be distinct, non-redundant, and thermodynamically plausible. While crystallographic databases continue to expand in both size and complexity, making efficient and reliable novelty assessment has become increasingly difficult. This becomes particularly acute when crystallographic disorder is involved, as partial occupancies greatly enlarge the structure-composition space and obscure the identification of genuinely distinct structures. Here, we introduce SWORD, a symmetry-aware, Wyckoff-based string representation compatible with both ordered and disordered crystals. SWORD provides (i) standardization of symmetry-equivalent structural descriptions into a consistent label, (ii) explicitly represents co-occupying species on partially occupied sites, and (iii) quantifies complex disorder through a degree of mixing descriptor that captures continuous variation in site stoichiometry. These features enable efficient structure grouping, duplicate identification, and finer refinement of disordered structures. Benchmarking against existing fingerprint and structure-matching methods shows that SWORD remains invariant under identity-preserving transformations while retaining interpretable sensitivity to structural perturbations. In addition, SWORD shows competitive performance in associating unrelaxed and intermediate configurations with their final relaxed states along relaxation trajectories. This feature could enable more reliable novelty assessment directly from partially relaxed or even unrelaxed generated structures. Finally, SWORD was used to showcase its capability of disorder-aware database-scale deduplication and curation for the Inorganic Crystal Structure Database (ICSD). The curated ICSD would serve as the basis for the materials informatics and data-driven materials design in the era of artificial intelligence.

cond-mat.mtrl-sci↗

arXiv2026

Closing the Loop in Epitaxy with Machine Learning: Joint Optimization of Growth and Geometry in On-Chip Lasers

Achieving device-to-device reproducibility is a critical bottleneck for scalable photonic integrated circuits, as subtle variations in bottom-up epitaxial growth and fabrication severely limit yield. We present a machine learning workflow for III-V multi-quantum well microring lasers that first optimizes growth and geometry parameters via multi-objective Bayesian optimization, then leverages variational autoencoders (VAEs) to attribute residual device-to-device variability to its underlying sources. By explicitly targeting threshold variance alongside absolute performance, we demonstrate 100% lasing yield across all designs. The optimized multi-quantum well microring laser fields achieved a median lasing threshold of $16~μ\mathrm{J}\,\mathrm{cm}^{-2}\,\mathrm{pulse}^{-1}$, a $73\%$ reduction in threshold variance relative to the previously reported best values, and a median emission wavelength of $1333~\mathrm{nm}$, in the telecommunications O-band. Furthermore, to diagnose residual performance dispersion under nominally identical conditions, VAEs were used to isolate the key components of device morphology that impact performance. This analysis successfully decoupled geometric from material disorder, quantitatively linking previously unmeasured morphological variations to population-level threshold fluctuations. This data-driven workflow bridges the gap between fundamental epitaxy and reliable manufacturing, establishing a generalizable blueprint for designing and yield-optimizing complex, non-linear optoelectronic devices.

physics.optics↗

arXiv2025

Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations

Understanding the anharmonic phonon properties of crystal compounds -- such as phonon lifetimes and thermal conductivities -- is essential for investigating and optimizing their thermal transport behaviors. These properties also impact optical, electronic, and magnetic characteristics through interactions between phonons and other quasiparticles and fields. In this study, we develop an automated first-principles workflow to calculate anharmonic phonon properties and build a comprehensive database encompassing more than 6,000 inorganic compounds. Utilizing this dataset, we train a graph neural network model to predict thermal conductivity values and spectra from structural parameters, demonstrating a scaling law in which prediction accuracy improves with increasing training data size. High-throughput screening with the model enables the identification of materials exhibiting extreme thermal conductivities -- both high and low. The resulting database offers valuable insights into the anharmonic behavior of phonons, thereby accelerating the design and development of advanced functional materials.

cond-mat.mtrl-sci↗

arXiv2025

AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions

Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, predict, and design. In this roadmap we provide a forward-looking view of AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing. Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific workflows that connect simulations to experiments and generative systems grounded in synthesisability rather than purely idealised phases. Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation while maintaining reproducibility and physical interpretability. Taken together, these perspectives outline where AI-enabled science stands today, identify bottlenecks in data, methods and infrastructure, and chart concrete directions for building AI systems that are not only more powerful but also more transparent and capable of accelerating discovery in complex real-world environments.

physics.soc-ph↗

arXiv2025

Energy Underprediction from Symmetry in Machine-Learning Interatomic Potentials

Machine learning interatomic potentials (MLIAPs) have emerged as powerful tools for accelerating materials simulations with near-density functional theory (DFT) accuracy. However, despite significant advances, we identify a critical yet overlooked issue undermining their reliability: a systematic energy underprediction. This problem becomes starkly evident in large-scale thermodynamic stability assessments. By performing over 12 million calculations using nine MLIAPs for over 150,000 inorganic crystals in the Materials Project, we demonstrate that most frontier models consistently underpredict energy above hull (Ehull), a key metric for thermodynamic stability, total energy, and formation energy, despite the fact that over 90\% of test structures (DFT-relaxed) are in the training data. The mean absolute errors (MAE) for Ehull exceed ~30 meV/atom even by the best model, directly challenging claims of achieving ``DFT accuracy'' for property predictions central to materials discovery, especially related to (meta-)stability. Crucially, we trace this underprediction to insufficient handling of symmetry degrees of freedom (DOF), constituting both lattice symmetry and Wyckoff site symmetries for the space group. MLIAPs exhibit pronounced errors (MAE for Ehull $>$ ~40 meV/atom) in structures with high symmetry DOF, where subtle atomic displacements significantly impact energy landscapes. Further analysis also indicates that the MLIAPs show severe energy underprediction for a large proportion of near-hull materials. We argue for improvements on symmetry-aware models such as explicit DOF encoding or symmetry-regularized loss functions, and more robust MLIAPs for predicting crystal properties where the preservation and breaking of symmetry are pivotal.

cond-mat.mtrl-sci↗

arXiv2025

Dis-GEN: Disordered crystal structure generation

A wide range of synthesized crystalline inorganic materials exhibit compositional disorder, where multiple atomic species partially occupy the same crystallographic site. As a result, the physical and chemical properties of such materials are dependent on how the atomic species are distributed among the corresponding symmetrical sites, making them exceptionally challenging to model using computational methods. For this reason, existing generative models cannot handle the complexities of disordered inorganic crystals. To address this gap, we introduce Dis-GEN, a generative model based on an empirical equivariant representation, derived from theoretical crystallography methodology. Dis-GEN is capable of generating symmetry-consistent structures that accommodate both compositional disorder and vacancies. The model is uniquely trained on experimental structures from the Inorganic Crystal Structure Database (ICSD) - the world's largest database of identified inorganic crystal structures. We demonstrate that Dis-GEN can effectively generate disordered inorganic materials while preserving crystallographic symmetry throughout the generation process. This approach provides a critical check point for the systematic exploration and discovery of disordered functional materials, expanding the scope of generative modeling in materials science.

cond-mat.mtrl-sci↗

arXiv2025

Data-Driven Design-Test-Make-Analyze Paradigm for Inorganic Crystals: Ultrafast Synthesis of Ternary Oxides

Data-driven methodologies hold the promise of revolutionizing inorganic materials discovery, but they often face challenges due to discrepancies between theoretical predictions and experimental validation. In this work, we present an end-to-end discovery framework that leverages synthesizability, oxidation state probability, and reaction pathway calculations to guide the exploration of transition metal oxide spaces. Two previously unsynthesized target compositions, ZnVO3 and YMoO3, passed preliminary computational evaluation and were considered for ultrafast synthesis. Comprehensive structural and compositional analysis confirmed the successful synthesis ZnVO3 in a partially disordered spinel structure, validated via Density Functional Theory (DFT). Exploration of YMoO3 led to YMoO3-x with elemental composition close to 1:1:3; the structure was subsequently identified to be Y4Mo4O11 through micro-electron diffraction (microED) analysis. Our framework effectively integrates multi-aspect physics-based filtration with in-depth characterization, demonstrating the feasibility of designing, testing, synthesizing, and analyzing (DTMA) novel material candidates, marking a significant advancement towards inorganic materials by design.

cond-mat.mtrl-sci↗

arXiv2025

Wyckoff Transformer: Generation of Symmetric Crystals

Crystal symmetry plays a fundamental role in determining its physical, chemical, and electronic properties such as electrical and thermal conductivity, optical and polarization behavior, and mechanical strength. Almost all known crystalline materials have internal symmetry. However, this is often inadequately addressed by existing generative models, making the consistent generation of stable and symmetrically valid crystal structures a significant challenge. We introduce WyFormer, a generative model that directly tackles this by formally conditioning on space group symmetry. It achieves this by using Wyckoff positions as the basis for an elegant, compressed, and discrete structure representation. To model the distribution, we develop a permutation-invariant autoregressive model based on the Transformer encoder and an absence of positional encoding. Extensive experimentation demonstrates WyFormer's compelling combination of attributes: it achieves best-in-class symmetry-conditioned generation, incorporates a physics-motivated inductive bias, produces structures with competitive stability, predicts material properties with competitive accuracy even without atomic coordinates, and exhibits unparalleled inference speed.

cond-mat.mtrl-sci↗

arXiv2025

Direct Joule-Heated Non-Equilibrium Synthesis Enables High Performing Thermoelectrics

High-throughput synthesis of bulk inorganic materials is crucial for accelerating functional materials discovery but is hindered by slow, energy-intensive solid-state methods. We introduce Direct Joule-Heated Synthesis (DJS), a rapid, single-step and scalable solid-state synthesis technique achieving a $10^5$-fold speedup and 20,000x energy efficiency improvement over conventional synthesis. DJS enables the synthesis of dense, bulk chalcogenides ($\mathrm{Bi_{0.5}Sb_{1.5}Te_3}$, $\mathrm{AgSbTe_2}$), achieving a zT of 2.3 at 573 K in optimally Cd/Se co-doped $\mathrm{AgSbTe_2}$, one of the highest for polycrystalline materials at this temperature. DJS enables optimal co-doping and rapid, non-equilibrium solidification, producing lamellar microstructures, interfacial regions, and cation-ordered nanodomains that scatter all-scale phonons, achieving ultralow lattice thermal conductivity (~0.2 $W m^{-1} K^{-1}$ at 573 K). DJS establishes a new benchmark for scalable and fast synthesis, accelerating functional material discovery.

cond-mat.mtrl-sci↗

arXiv2025

Multi-property directed generative design of inorganic materials through Wyckoff-augmented transfer learning

Accelerated materials discovery is an urgent demand to drive advancements in fields such as energy conversion, storage, and catalysis. Property-directed generative design has emerged as a transformative approach for rapidly discovering new functional inorganic materials with multiple desired properties within vast and complex search spaces. However, this approach faces two primary challenges: data scarcity for functional properties and the multi-objective optimization required to balance competing tasks. Here, we present a multi-property-directed generative framework designed to overcome these limitations and enhance site symmetry-compliant crystal generation beyond P1 (translational) symmetry. By incorporating Wyckoff-position-based data augmentation and transfer learning, our framework effectively handles sparse and small functional datasets, enabling the generation of new stable materials simultaneously conditioned on targeted space group, band gap, and formation energy. Using this approach, we identified previously unknown thermodynamically and lattice-dynamically stable semiconductors in tetragonal, trigonal, and cubic systems, with bandgaps ranging from 0.13 to 2.20 eV, as validated by density functional theory (DFT) calculations. Additionally, we assessed their thermoelectric descriptors using DFT, indicating their potential suitability for thermoelectric applications. We believe our integrated framework represents a significant step forward in generative design of inorganic materials.

cond-mat.mtrl-sci↗

arXiv2024

Accelerated Design of Microring Lasers with Multi-Objective Bayesian Optimization

On-chip coherent laser sources are crucial for the future of photonic integrated circuits, yet progress has been hindered by the complex interplay between material quality, device geometry, and performance metrics. We combine high-throughput characterization, statistical analysis, experimental design, and multi-objective Bayesian optimization to accelerate the design process for low-threshold, high-yield III-V microring lasers with room-temperature operation at communication wavelengths. We demonstrate a 1.6$\times$ reduction in threshold over expert-designed configurations, achieving a 100% lasing yield that emits within the O-band with a median threshold as low as 33$μ$J cm$^{-2}$ pulse$^{-1}$.

physics.optics↗

arXiv2024

Battling Botpoop using GenAI for Higher Education: A Study of a Retrieval Augmented Generation Chatbots Impact on Learning

Generative artificial intelligence (GenAI) and large language models (LLMs) have simultaneously opened new avenues for enhancing human learning and increased the prevalence of poor-quality information in student response - termed Botpoop. This study introduces Professor Leodar, a custom-built, Singlish-speaking Retrieval Augmented Generation (RAG) chatbot designed to enhance educational while reducing Botpoop. Deployed at Nanyang Technological University, Singapore, Professor Leodar offers a glimpse into the future of AI-assisted learning, offering personalized guidance, 24/7 availability, and contextually relevant information. Through a mixed-methods approach, we examine the impact of Professor Leodar on learning, engagement, and exam preparedness, with 97.1% of participants reporting positive experiences. These findings help define possible roles of AI in education and highlight the potential of custom GenAI chatbots. Our combination of chatbot development, in-class deployment and outcomes study offers a benchmark for GenAI educational tools and is a stepping stone for redefining the interplay between AI and human learning.

cs.HC↗