arXiv subjects

Nitya Nand Gosvami

Publications and source records attributed to Nitya Nand Gosvami.

11 recordsLinked to original sources

arXiv2026

UniFFBench: Evaluating Universal Machine Learning Force Fields Against Experimental Measurements

Universal machine learning force fields (UMLFFs) promise to revolutionize materials science by enabling rapid atomistic simulations across the periodic table. However, their evaluation has been limited to computational benchmarks that may not reflect real-world performance. We introduce UniFFBench, a comprehensive evaluation framework featuring the MinX dataset -- a diverse collection of 1,500+ mineral systems spanning 85 elements, extreme thermodynamic conditions (0--5000 K, 0--1000 GPa), and structural complexity, including partial occupancy and disorder. This diversity, combined with experimental reference values for validation, enables assessment of UMLFF generalization across chemical space and conditions substantially beyond typical training scenarios. Our systematic evaluation of six state-of-the-art UMLFFs reveals a substantial ``reality gap'': models achieving impressive performance on computational benchmarks often fail when confronted with experimental complexity. Even the best-performing models exhibit higher density prediction error than the threshold required for practical applications. We observe disconnects between simulation stability and mechanical property accuracy, with prediction errors correlating with training data representation rather than the modeling method.

cond-mat.mtrl-sci↗

arXiv2026

Natural Language Embeddings of Synthesis and Testing conditions Enhance Glass Dissolution Prediction

Long-term chemical durability of glass, crucial for immobilizing nuclear waste, is governed by glass properties such as composition, surface geometry, as well as external factors like thermodynamic conditions and surrounding medium. Despite decades of research, there are no models that account for these intrinsic and extrinsic factors to predict the dissolution rates of glass compositions. To address this challenge, we evaluate the role of natural language embeddings capturing the synthesis and testing conditions in enhancing the predictability of glass dissolution. Evaluating the approach on hand-curated ~700 datapoints extracted from the literature, we reveal that the machine learning (ML) model including natural language embeddings (NLP-ML) outperforms classical ML model in predicting glass dissolution rate. Furthermore, we developed a generalizable ML model by transforming the compositional features to structural descriptors of glass alongside NLP-derived features, enabling extrapolation capability to glass compositions with completely new elements absent in the training data. Evaluating this model on a completely new dataset of glass compositions 34 chemical components in contrast to the training dataset that had only 28 components, we demonstrate that the model indeed exhibits generalizability to glass compositions that are out-of-distribution. Altogether, this integrated approach offers a pathway towards high-fidelity glass dissolution prediction and accelerate the discovery of novel glass compositions with tailored durability for sustainable nuclear waste management.

cond-mat.mtrl-sci↗

arXiv2026

Velocity Weakening in Anisotropic Friction on a Tilted Titania Nanorod Forest

In this study, we demonstrate velocity-dependent directional friction on a surface structured with tilted (~57°) titania nanorods using standard and colloidal probe force microscopy. Friction is measured at four different sliding speeds in two configurations, along and opposite to the tilt and perpendicular to the tilt direction, exhibiting anisotropic friction. Furthermore, friction decreases logarithmically with increasing sliding speed, which is attributed to the viscoelastic bending of the nanorods caused by stress-induced defect migration. The velocity weakening is more pronounced in the direction perpendicular to the tilt than along and opposite to it. The experimental findings are corroborated by creep measurements, which are well-reproduced by the Standard Linear Solid (SLS) model of viscoelasticity. Our results may be applied to the development of direction- and velocity-dependent sensors for microscale sliding motion as a robust alternative to structured interfaces based on polymeric materials.

cond-mat.soft↗

arXiv2025

Autonomous Microscopy Experiments through Large Language Model Agents

Large language models (LLMs) are revolutionizing self driving laboratories (SDLs) for materials research, promising unprecedented acceleration of scientific discovery. However, current SDL implementations rely on rigid protocols that fail to capture the adaptability and intuition of expert scientists in dynamic experimental settings. We introduce Artificially Intelligent Lab Assistant (AILA), a framework automating atomic force microscopy through LLM driven agents. Further, we develop AFMBench a comprehensive evaluation suite challenging AI agents across the complete scientific workflow from experimental design to results analysis. We find that state of the art models struggle with basic tasks and coordination scenarios. Notably, Claude 3.5 sonnet performs unexpectedly poorly despite excelling in materials domain question answering (QA) benchmarks, revealing that domain specific QA proficiency does not necessarily translate to effective agentic capabilities. Additionally, we observe that LLMs can deviate from instructions, raising safety alignment concerns for SDL applications. Our ablations reveal that multi agent frameworks outperform single-agent architectures. We also observe significant prompt fragility, where slight modifications in prompt structure cause substantial performance variations in capable models like GPT 4o. Finally, we evaluate AILA's effectiveness in increasingly advanced experiments AFM calibration, feature detection, mechanical property measurement, graphene layer counting, and indenter detection. Our findings underscore the necessity for rigorous benchmarking protocols and prompt engineering strategies before deploying AI laboratory assistants in scientific research environments.

cs.CY↗

arXiv2025

Reactive Glass Metal Interaction under Ambient Conditions Enables Surface Modification of Gold Nanoislands

Stabilizing gold nanoparticles with tunable surface composition via reactive metal support interactions under ambient conditions remains a significant challenge. We discovered that a reactive glass metal interaction (RGMI) under ambient conditions, driven by the intrinsic catalytic activity of gold nanoislands (GNIs) and the unique properties of sodium aluminophosphosilicate glass, including its chemical composition, molar volume, and high Na ion mobility, enables the formation of robustly anchored GNIs with altered surface compositions. Comprehensive characterization reveals that the adsorption of Na and P at the GNI surfaces induces lattice distortions in the Au(111) planes. Additionally, a smooth GNI glass interface significantly influences the hot carrier dynamics of the GNIs. Altogether, RGMI presents a versatile strategy for engineering stable, multi element nanostructures with potential applications in heterogeneous catalysis, sensing, and optoelectronics.

cond-mat.mtrl-sci↗

arXiv2023

Glass Hardness: Predicting Composition and Load Effects via Symbolic Reasoning-Informed Machine Learning

Glass hardness varies in a non-linear fashion with the chemical composition and applied load, a phenomenon known as the indentation size effect (ISE), which is challenging to predict quantitatively. Here, using a curated dataset of over approx. 3000 inorganic glasses from the literature comprising the composition, indentation load, and hardness, we develop machine learning (ML) models to predict the composition and load dependence of Vickers hardness. Interestingly, when tested on new glass compositions unseen during the training, the standard data-driven ML model failed to capture the ISE. To address this gap, we combined an empirical expression (Bernhardt law) to describe the ISE with ML to develop a framework that incorporates the symbolic law representing the domain reasoning in ML, namely Symbolic Reasoning-Informed ML Procedure (SRIMP). We show that the resulting SRIMP outperforms the data-driven ML model in predicting the ISE. Finally, we interpret the SRIMP model to understand the contribution of the glass network formers and modifiers toward composition and load-dependent (ISE) and load-independent hardness. The deconvolution of the hardness into load-dependent and load-independent terms paves the way toward a holistic understanding of composition and ISE in glasses, enabling the accelerated discovery of new glass compositions with targeted hardness.

cond-mat.mtrl-sci↗

arXiv2021

Looking Through Glass: Knowledge Discovery from Materials Science Literature using Natural Language Processing

Most of the knowledge in materials science literature is in the form of unstructured data such as text and images. Here, we present a framework employing natural language processing, which automates text and image comprehension and precision knowledge extraction from inorganic glasses' literature. The abstracts are automatically categorized using latent Dirichlet allocation (LDA), providing a way to classify and search semantically linked publications. Similarly, a comprehensive summary of images and plots are presented using the 'Caption Cluster Plot' (CCP), which provides direct access to the images buried in the papers. Finally, we combine the LDA and CCP with the chemical elements occurring in the manuscript to present an 'Elemental map', a topical and image-wise distribution of chemical elements in the literature. Overall, the framework presented here can be a generic and powerful tool to extract and disseminate material-specific information on composition-structure-processing-property dataspaces, allowing insights into fundamental problems relevant to the materials science community and accelerated materials discovery.

cs.DL↗

arXiv2019

Cooling Rate Effects on the Structure of 45S5 Bioglass: Computational and Experimental Evidence of Si--P Avoidance

Due to its ability to bond with living tissues upon dissolution, 45S5 bioglass and related compositions are promising materials for the replacement, regeneration, and repair of hard tissues in the human body. However, the details of their atomic structure remain unclear. This is partially due to the non-equilibrium nature of glasses, as their non-crystalline structure is highly dependent on their thermal history, namely, the cooling rate used during quenching. Herein, using molecular dynamics (MD) simulations and magic angle spinning nuclear magnetic resonance (MAS-NMR) spectroscopy experiments, we investigate the structure of the nominal 45S5 bioglass composition prepared using cooling rates ranging over several orders of magnitude. We show that the simulations results are in very good agreement with experimental data, provided that they are extrapolated toward lower cooling rates achieved in experiments. These results highlight that previously reported inconsistencies between simulations and experiments stem from the difference in cooling rate, thereby addressing one of the longstanding questions on the structure of bioglass. Based on these results, we demonstrate the existence of a Si--P avoidance behavior, which may be key in controlling the bioactivity of 45S5 bioglass.

cond-mat.mtrl-sci↗

arXiv2019

Predicting Young's Modulus of Glasses with Sparse Datasets using Machine Learning

Machine learning (ML) methods are becoming popular tools for the prediction and design of novel materials. In particular, neural network (NN) is a promising ML method, which can be used to identify hidden trends in the data. However, these methods rely on large datasets and often exhibit overfitting when used with sparse dataset. Further, assessing the uncertainty in predictions for a new dataset or an extrapolation of the present dataset is challenging. Herein, using Gaussian process regression (GPR), we predict Young's modulus for silicate glasses having sparse dataset. We show that GPR significantly outperforms NN for sparse dataset, while ensuring no overfitting. Further, thanks to the nonparametric nature, GPR provides quantitative bounds for the reliability of predictions while extrapolating. Overall, GPR presents an advanced ML methodology for accelerating the development of novel functional materials such as glasses.

cond-mat.mtrl-sci↗

arXiv2013

Quantized Friction across Ionic Liquid Thin Films

Ionic liquids, salts in the liquid state under ambient conditions, are of great interest as precision lubricants. Ionic liquids form layered structures at surfaces, yet it is not clear how this nano-structure relates to their lubrication properties. We measured the friction force between atomically smooth solid surfaces across ionic liquid films of controlled thickness in terms of the number of ion layers. Multiple friction-load regimes emerge, each corresponding to a different number of ion layers in the film. In contrast to molecular liquids, the friction coefficients differ for each layer due to their varying composition.

cond-mat.mtrl-sci↗

arXiv2012

Enhanced quality factors and force sensitivity by attaching magnetic beads to cantilevers for atomic force microscopy in liquid

Dynamic-mode atomic force microscopy (AFM) in liquid remains complicated due to the strong viscous damping of the cantilever resonance. Here we show that a high-quality resonance (Q>20) can be achieved in aqueous solution by attaching a microgram-bead at the end of the nanogram-cantilever. The resulting increase in cantilever mass causes the resonance frequency to drop significantly. However, the force sensitivity --- as expressed via the minimum detectable force gradient --- is hardly affected, because of the enhanced quality factor. Via the enhancement of the quality factor, the attached bead also reduces the relative importance of noise in the deflection detector. It can thus yield an improved signal-to-noise ratio when this detector noise is significant. We describe and analyze these effects for a set-up which includes magnetic actuation of the cantilevers and which can be easily implemented in any AFM system that is compatible with an inverted optical microscope.

physics.ins-det↗