arXiv subjects

Badri Narayanan

Publications and source records attributed to Badri Narayanan.

18 recordsLinked to original sources

arXiv2024

First-principles investigation of thermodynamics and electronic transitions in vacancy-ordered rare-earth perovskite nickelates

Controlled introduction of oxygen vacancies offers an effective route to induce metal-to-insulator transition in strongly correlated rare-earth nickelates ($R$NiO$_3$) at room temperature. However, the role played by the rare-earth cations on the structure, thermodynamic stability, and electronic properties of oxygen-deficient nickelates remains unclear. Here, we employ density functional theory calculations with Hubbard corrections (DFT + $U$) to investigate the whole family of $R$NiO$_{2.5}$ ($R$ = Pr-Er) compounds in two commonly observed oxygen-vacancy ordered configurations, namely brownmillerite, and square planar. We find that square planar polymorph is always more stable ($\sim$0.4 eV/u.f) than the brownmillerite for all rare-earth cations, owing to the exceedingly low volumetric strains (< 1\%). Formation energy of $R$NiO$_{2.5}$ gradually increases with decreasing size of $R$ owing to stronger Ni-O covalent interactions in pristine $R$NiO$_3$ with small $R^{3+}$ cations. This necessitates more oxygen-lean environments for synthesis of $R$NiO$_{2.5}$ with smaller $R^{3+}$ cations. Analysis of the density of states and band structures reveals that electronic structure of $R$NiO$_{2.5}$ is governed by two factors: (a) localization of electron on NiO$_6$ octahedra yielding a Mott insulating state with strong correlations as Ni $e_g$ is half filled, and (b) crystal field splitting in the NiO$_4$ tetrahedra/square planar polyhedra. Brownmillerite $R$NiO$_{2.5}$ is metallic, while square planar $R$NiO$_{2.5}$ is an insulator with a predicted gap of $\sim$ 0.2-0.3 eV, depending on the $R^{3+}$ cation. Crystal orbital Hamilton population (COHP) analysis indicates that the Ni-O bond belonging to square-planar NiO$_4$ polyhedra exhibit much greater covalent character than those in NiO$_6$ octahedra in square planar $R$NiO$_{2.5}$.

cond-mat.mtrl-sci↗

arXiv2022

Utilizing unsupervised learning to improve sward content prediction and herbage mass estimation

Sward species composition estimation is a tedious one. Herbage must be collected in the field, manually separated into components, dried and weighed to estimate species composition. Deep learning approaches using neural networks have been used in previous work to propose faster and more cost efficient alternatives to this process by estimating the biomass information from a picture of an area of pasture alone. Deep learning approaches have, however, struggled to generalize to distant geographical locations and necessitated further data collection to retrain and perform optimally in different climates. In this work, we enhance the deep learning solution by reducing the need for ground-truthed (GT) images when training the neural network. We demonstrate how unsupervised contrastive learning can be used in the sward composition prediction problem and compare with the state-of-the-art on the publicly available GrassClover dataset collected in Denmark as well as a more recent dataset from Ireland where we tackle herbage mass and height estimation.

cs.CV↗

arXiv2022

Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation

Herbage mass yield and composition estimation is an important tool for dairy farmers to ensure an adequate supply of high quality herbage for grazing and subsequently milk production. By accurately estimating herbage mass and composition, targeted nitrogen fertiliser application strategies can be deployed to improve localised regions in a herbage field, effectively reducing the negative impacts of over-fertilization on biodiversity and the environment. In this context, deep learning algorithms offer a tempting alternative to the usual means of sward composition estimation, which involves the destructive process of cutting a sample from the herbage field and sorting by hand all plant species in the herbage. The process is labour intensive and time consuming and so not utilised by farmers. Deep learning has been successfully applied in this context on images collected by high-resolution cameras on the ground. Moving the deep learning solution to drone imaging, however, has the potential to further improve the herbage mass yield and composition estimation task by extending the ground-level estimation to the large surfaces occupied by fields/paddocks. Drone images come at the cost of lower resolution views of the fields taken from a high altitude and requires further herbage ground-truth collection from the large surfaces covered by drone images. This paper proposes to transfer knowledge learned on ground-level images to raw drone images in an unsupervised manner. To do so, we use unpaired image style translation to enhance the resolution of drone images by a factor of eight and modify them to appear closer to their ground-level counterparts. We then ... ~\url{www.github.com/PaulAlbert31/Clover_SSL}.

cs.CV↗

arXiv2021

Semi-supervised dry herbage mass estimation using automatic data and synthetic images

Monitoring species-specific dry herbage biomass is an important aspect of pasture-based milk production systems. Being aware of the herbage biomass in the field enables farmers to manage surpluses and deficits in herbage supply, as well as using targeted nitrogen fertilization when necessary. Deep learning for computer vision is a powerful tool in this context as it can accurately estimate the dry biomass of a herbage parcel using images of the grass canopy taken using a portable device. However, the performance of deep learning comes at the cost of an extensive, and in this case destructive, data gathering process. Since accurate species-specific biomass estimation is labor intensive and destructive for the herbage parcel, we propose in this paper to study low supervision approaches to dry biomass estimation using computer vision. Our contributions include: a synthetic data generation algorithm to generate data for a herbage height aware semantic segmentation task, an automatic process to label data using semantic segmentation maps, and a robust regression network trained to predict dry biomass using approximate biomass labels and a small trusted dataset with gold standard labels. We design our approach on a herbage mass estimation dataset collected in Ireland and also report state-of-the-art results on the publicly released Grass-Clover biomass estimation dataset from Denmark. Our code is available at https://git.io/J0L2a

cs.CV↗

arXiv2021

Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset

The dairy industry uses clover and grass as fodder for cows. Accurate estimation of grass and clover biomass yield enables smart decisions in optimizing fertilization and seeding density, resulting in increased productivity and positive environmental impact. Grass and clover are usually planted together, since clover is a nitrogen-fixing plant that brings nutrients to the soil. Adjusting the right percentages of clover and grass in a field reduces the need for external fertilization. Existing approaches for estimating the grass-clover composition of a field are expensive and time consuming - random samples of the pasture are clipped and then the components are physically separated to weigh and calculate percentages of dry grass, clover and weeds in each sample. There is growing interest in developing novel deep learning based approaches to non-destructively extract pasture phenotype indicators and biomass yield predictions of different plant species from agricultural imagery collected from the field. Providing these indicators and predictions from images alone remains a significant challenge. Heavy occlusions in the dense mixture of grass, clover and weeds make it difficult to estimate each component accurately. Moreover, although supervised deep learning models perform well with large datasets, it is tedious to acquire large and diverse collections of field images with precise ground truth for different biomass yields. In this paper, we demonstrate that applying data augmentation and transfer learning is effective in predicting multi-target biomass percentages of different plant species, even with a small training dataset. The scheme proposed in this paper used a training set of only 261 images and provided predictions of biomass percentages of grass, clover, white clover, red clover, and weeds with mean absolute error of 6.77%, 6.92%, 6.21%, 6.89%, and 4.80% respectively.

cs.CV↗

arXiv2020

Active Learning A Neural Network Model For Gold Clusters \& Bulk From Sparse First Principles Training Data

Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size-dependent structural motifs and their dynamical evolution has been of longstanding interest. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size-dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well-trained NN potentials can provide high level of flexibility, transferability and accuracy on-par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities of training data to ensure that the model adequately samples the energy landscape both near and far-from-equilibrium. Here, we introduce an active learning (AL) scheme that trains a NN model on-the-fly with minimal amount of first-principles based training data. Our AL workflow is initiated with a sparse training dataset (1 to 5 data points) and is updated on-the-fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with ~500 total reference calculations. Our NN predictions are within 30 meV/atom and 40 meV/Åof the reference DFT calculations. Moreover, our AL-NN model also adequately captures the various size-dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments.

physics.comp-ph↗

arXiv2020

BLAST: Bridging Length/time scales via Atomistic Simulation Toolkit

The ever-increasing power of supercomputers coupled with highly scalable simulation codes have made molecular dynamics an indispensable tool in applications ranging from predictive modeling of materials to computational design and discovery of new materials for a broad range of applications. Multi-fidelity scale bridging between the various flavors of molecular dynamics i.e. ab-initio, classical and coarse-grained models has remained a long-standing challenge. Here, we introduce our framework BLAST (Bridging Length/time scales via Atomistic Simulation Toolkit) that leverages machine learning principles to address this challenge. BLAST is a multi-fidelity scale bridging framework that provide users with the capabilities to train and develop their own classical atomistic and coarse-grained interatomic potentials (force fields) for molecular simulations. BLAST is designed to address several long-standing problems in the molecular simulations community, such as unintended misuse of existing force fields due to knowledge gap between developers and users, bottlenecks in traditional force field development approaches, and other issues relating to the accuracy, efficiency, and transferability of force fields. Here, we discuss several important aspects in force field development and highlight features in BLAST that enable its functionalities and ease of use.

cs.CE↗

arXiv2019

A coarse-grained deep neural network model for liquid water

We introduce a coarse-grained deep neural network model (CG-DNN) for liquid water that utilizes 50 rotational and translational invariant coordinates, and is trained exclusively against energies of ~30,000 bulk water configurations. Our CG-DNN potential accurately predicts both the energies and molecular forces of water; within 0.9 meV/molecule and 54 meV/angstrom of a reference (coarse-grained bond-order potential) model. The CG-DNN water model also provides good prediction of several structural, thermodynamic, and temperature dependent properties of liquid water, with values close to that obtained from the reference model. More importantly, CG-DNN captures the well-known density anomaly of liquid water observed in experiments. Our work lays the groundwork for a scheme where existing empirical water models can be utilized to develop fully flexible neural network framework that can subsequently be trained against sparse data from high-fidelity albeit expensive beyond-DFT calculations.

physics.comp-ph↗

arXiv2019

Machine Learning Prediction of Accurate Atomization Energies of Organic Molecules from Low-Fidelity Quantum Chemical Calculations

Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the tradeoff between accuracy and computational cost. Here, we assess multiple ML approaches for predicting the atomization energy of organic molecules. Our resulting models learn the difference between low-fidelity, B3LYP, and high-accuracy, G4MP2, atomization energies, and predict the G4MP2 atomization energy to 0.005 eV (mean absolute error) for molecules with less than 9 heavy atoms and 0.012 eV for a small set of molecules with between 10 and 14 heavy atoms. Our two best models, which have different accuracy/speed tradeoffs, enable the efficient prediction of G4MP2-level energies for large molecules and are available through a simple web interface.

physics.comp-ph↗

arXiv2018

Comparing optimization strategies for force field parameterization

Classical molecular dynamics (MD) simulations enable modeling of materials and examination of microscopic details that are not accessible experimentally. The predictive capability of MD relies on the force field (FF) used to describe interatomic interactions. FF parameters are typically determined to reproduce selected material properties computed from density functional theory (DFT) and/or measured experimentally. A common practice in parameterizing FFs is to use least-squares local minimization algorithms. Genetic algorithms (GAs) have also been demonstrated as a viable global optimization approach, even for complex FFs. However, an understanding of the relative effectiveness and efficiency of different optimization techniques for the determination of FF parameters is still lacking. In this work, we evaluate various FF parameter optimization schemes, using as example a training data set calculated from DFT for different polymorphs of Ir$O_2$. The Morse functional form is chosen for the pairwise interactions and the optimization of the parameters against the training data is carried out using (1) multi-start local optimization algorithms: Simplex, Levenberg-Marquardt, and POUNDERS, (2) single-objective GA, and (3) multi-objective GA. Using random search as a baseline, we compare the algorithms in terms of reaching the lowest error, and number of function evaluations. We also compare the effectiveness of different approaches for FF parameterization using a test data set with known ground truth (i.e generated from a specific Morse FF). We find that the performance of optimization approaches differs when using the Test data vs. the DFT data. Overall, this study provides insight for selecting a suitable optimization method for FF parameterization, which in turn can enable more accurate prediction of material properties and chemical phenomena.

physics.comp-ph↗

arXiv2018

Pressure-Induced Phase Transformation in $β$-Eucryptite: an X-Ray Diffraction and Density Functional Theory Study

Certain alumino-silicates display exotic properties enabled by their framework structure made of corner-sharing tetrahedral rigid units. Using \textit{in situ} diamond-anvil cell x-ray diffraction (XRD), we study the pressure-induced transformation of $β$ eucryptite, a prototypical alumino-silicate. $β$ eucryptite undergoes a phase transformation at moderate pressures, but the atomic structure of the new phase has not yet been reported. Based on density functional theory stability studies and Rietveld analysis of XRD patterns, we find that the pressure-stabilized phase belongs to the Pna2$_1$ space group. Furthermore, we discover two other possible pressure-stabilized polymorphs, P1c1 and Pca2$_1$.

cond-mat.mtrl-sci↗

arXiv2017

Evolutionary optimization of a charge transfer ionic potential model for Ta/Ta-oxide hetero-interfaces

Tantalum, tantalum oxide and their hetero-interfaces are of tremendous technological interest in several applications spanning electronics, thermal management, catalysis and biochemistry. For example, local oxygen stoichiometry variation in TaOx memristors comprising of metallic (Ta) and insulating oxide (Ta2O5) have been shown to result in fast switching on the sub-nanosecond timescale over a billion cycles, relevant to neuromorphic computation. Despite its broad importance, an atomistic scale understanding of oxygen stoichiometry variation across Ta/TaOx hetero-interfaces, such as during early stages of oxidation and oxide growth, is not well understood. This is mainly due to the lack of a variable charge interatomic potential model for tantalum oxides that can accurately describe the ionic interactions in the metallic (Ta) and oxide (TaOx) environment as well as at their interfaces. To address this challenge, we introduce a charge transfer ionic potential (CTIP) model for Ta/Ta-oxide system by training against lattice parameters, cohesive energies, equations of state, and elastic properties of various experimentally observed Ta2O5 polymorphs. The best set of CTIP parameters are determined by employing a single-objective global optimization scheme driven by genetic algorithms followed by local Simplex optimization. Our newly developed CTIP potential accurately predicts structure, thermodynamics, energetic ordering of polymorphs, as well as elastic and surface properties of both Ta and Ta2O5, in excellent agreement with DFT calculations and experiments. We employ our newly parameterized CTIP potential to investigate the early stages of oxidation of Ta at different temperatures and atomic/molecular nature of the oxidizing species.

cond-mat.mtrl-sci↗

arXiv2017

Perovskite Quantum Organismoids

A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making1-3. This behavior, known as habituation, is universal among forms of life with a central nervous system, and interestingly observed even in single cellular organisms that do not possess a brain4-5. Here, we report the discovery of habituation based plasticity utilizing a perovskite quantum system by dynamical modulation of electron localization via reversible dopant incorporation. Microscopic mechanisms and pathways that enable this organismic collective charge-lattice interaction are elucidated by a combination of first-principles theory, synchrotron investigations, ab-initio dynamical simulations and in-situ environmental breathing studies. We implement a new learning algorithm inspired from the conductance relaxation behavior of perovskites that naturally incorporates habituation and demonstrate "learning to forget": a key feature of animal and human brains6. Most surprisingly, our results show that incorporating this elementary skill in learning dramatically boosts the capability of artificial cognitive systems.

cond-mat.mtrl-sci↗

arXiv2016

Ice grains grow by dissolution, ripening and grain boundary migration

Despite the exponential growth in computing resources and the availability of a myriad of different theoretical water models, an accurate, yet computationally efficient molecular level description of mesoscopic grain growth remains a grand challenge. The underlying phase transitions and dynamical processes in deeply supercooled systems are often rendered inaccessible due to limitations imposed by system sizes and timescales, which is further compounded by their sluggish kinetics. Here, we introduce a machine-learned, bond-order-based potential model (BOP) that more accurately describes the anomalous behavior, as well as structural and thermodynamical properties of both liquid water and ice, with at least two orders of magnitude cheaper computational cost than existing atomistic water models. In a significant departure from conventional force-field fitting, we use a multilevel evolutionary strategy that trains the BOP model against not just energetics but temperature dependent properties inferred from on-the-fly molecular dynamics simulations as well. We use the BOP model to probe the homogeneous ice nucleation and growth process by performing molecular dynamics on multi-million molecule systems for up to microsecond time scales. These massively parallel, long-time simulations naturally capture the competition between cubic, hexagonal, and stacking disordered ice phases during nucleation and early stages of growth leading to the formation of nanometer sized grains. Subsequently, we elucidate the hitherto elusive mechanism of grain coarsening of the crystallized mixed ice phases, which occur through grain dissolution and Ostwald ripening followed by grain boundary migration.

cond-mat.soft↗

arXiv2016

In-situ 3D Imaging of Catalysis Induced Strain in Gold Nanoparticles

Multi-electron transfer processes, such as hydrogen and oxygen evolution reactions, are crucially important in energy and biological science but require favorable catalysts to achieve fast kinetics. Nanostructuring catalysts can dramatically improve their properties, which can be difficult to understand due to strain and size dependent thermodynamics, the influence of defects, and substrate dependent activities. Here, we report 3D imaging of single gold nanoparticles during catalysis of ascorbic acid decomposition using Bragg coherent diffractive imaging (BCDI) as a route to eliminate ensemble effects while elucidating the strain-activity connection. Local strains were measured in single nanoparticles and modeled using reactive molecular dynamics (RMD) simulations and finite element analysis (FEA) simulations. RMD reveals a new chemical pathway for local strain generation in the gold lattice: chemisorption of hydroxyl ions. FEA reveals that the RMD results are transferable to the larger nanocrystal sizes studied in the experiment. Our study reveals the strain-activity connection and opens a powerful new avenue for joint theoretical and experimental studies of multi-electron transfer processes catalyzed by nanocrystals.

cond-mat.mtrl-sci↗

arXiv2016

Unraveling the Planar-Globular Transition in Gold Nanoclusters through Evolutionary Search

Au nanoclusters are of technological relevance for catalysis, photonics, sensors, and of fundamental scientific interest owing to planar to globular structural transformation at an anomalously high number of atoms i.e. in the range 12-14. The nature and causes of this transition remain a mystery. In order to unravel this conundrum, high throughput density functional theory (DFT) calculations, coupled with a global structural optimization scheme based on a modified genetic algorithm (GA) are conducted. More than 20,000 Au$_{12}$, Au$_{13}$, and Au$_{14}$ nanoclusters are evaluated. With any DFT functional, globular and planar structures coexist across the size range of interest. The planar-globular transition is gradual at room temperature rather than a sharp transition as previously believed. The effects of anionicity, $\textit{s-d}$ band hybridization and long range interactions on the dimensional transition are quantified by using the structures adjacent to minima. Anionicity marginally changes the relative stability of the clusters. The degree of $\textit{s-d}$ hybridization is varied via changing the Hubbard U value which corroborate that $\textit{s-d}$ hybridization alone does not stabilize planar structures. van der Waals interactions, on the other hand, stabilize globular structures. These results elucidate the balance between the different reasons of the dimensional transition in gold nanoclusters.

cond-mat.mtrl-sci↗

arXiv2015

Describing the diverse geometries of gold from nanoclusters to bulk-- a first-principles based hybrid bond order potential

Molecular dynamics simulations using empirical force fields (EFFs) are crucial for gaining fundamental insights into atomic structure and long timescale dynamics of Au nanoclusters with far-reaching applications in energy and devices. This approach is thwarted by the failure of currently available EFFs in describing the size-dependent dimensionality and diverse geometries exhibited by Au clusters (e.g., planar, hollow cages, pyramids). Owing to their ability to account for bond directionality, bond-order based EFFs, such as the Tersoff-type Bond Order Potential (BOP), are well suited for such a description. Nevertheless, the predictive power of existing BOP parameters is severely limited in the nm length scale owing to the predominance of bulk Au properties used to train them. Here, we mitigate this issue by introducing a new hybrid bond order potential (HyBOP), which account for (a) short-range interactions via Tersoff-type BOP terms and (b) long-range effects by a scaled LJ term whose contribution depends on the local atomic density. We optimized the independent parameters for our HyBOP using a global optimization scheme driven by genetic algorithms. Moreover, to ensure good transferability of these parameters across different length scales, we used an extensive training dataset encompasses structural and energetic properties of a thousand 13-atom Au clusters, surface energies, as well as bulk polymorphs, obtained from density functional theory (DFT) calculations. Our newly developed HyBOP has been found to accurately describe (a) global minimum energy configurations at different clusters sizes, (b) critical size of transition from planar to globular clusters, (c) evolution of structural motifs with cluster size, and (d) thermodynamics, structure, elastic properties of bulk polymorphs as well as surfaces, in excellent agreement with DFT calculations and spectroscopic experiments.

cond-mat.mtrl-sci↗

arXiv2011

Reactive force field for lithium-aluminum silicates with applications to eucryptite phases

We have parameterized a reactive force field (ReaxFF) for lithium aluminum silicates using density functional theory (DFT) calculations of structural properties of a number of bulk phase oxides, silicates, and aluminates, as well as of several representative clusters. The force field parameters optimized in this study were found to predict lattice parameters and heats of formation of selected condensed phases in excellent agreement with previous DFT calculations and with experiments. We have used the newly developed force-field to study the eucryptite phases in terms of their thermodynamic stability and their elastic properties. We have found that (a) these ReaxFF parameters predict the correct order of stability of the three crystalline polymorphs of eucryptite, α, β, and γ, and (b) that upon indentation, a new phase appears at applied pressures \geq 7 GPa. The high pressure phase obtained upon indentation is amorphous, as illustrated by the radial distribution functions calculated for different pairs of elements. In terms of elastic properties analysis, we have determined the elements of the stiffness tensor for α- and β- eucryptite at the level of ReaxFF, and discussed the elastic anisotropy of these two polymorphs. Polycrystalline average properties of these eucryptite phases are also reported to serve as ReaxFF predictions of their elastic moduli (in the case of α-eucryptite), or as tests against values known from experiments or DFT calculations (β- eucrypite). The ReaxFF potential reported here can also describe well single-species systems (e.g., Li-metal, Al-metal, and condensed phases of silicon), which makes it suitable for investigating struc- ture and properties of suboxides, atomic-scale mechanisms responsible for phase transformations, as well as oxidation-reduction reactions.

cond-mat.mtrl-sci↗