arXiv subjects

Gopalakrishnan Sai Gautam

Publications and source records attributed to Gopalakrishnan Sai Gautam.

At least 19 recordsLinked to original sources

arXiv2026

Extending the Pnictide Chemical Space for Photovoltaics

Developing novel and efficient materials that are beyond silicon for photovoltaic (PV) applications is required to meet the upcoming energy needs of our societies. To identify novel candidate materials that can act as PVs, we use first principles calculations to perform a systematic screening of the pnictide chemical space (i.e., nitrides and phosphides). Specifically, we explore three different ternary and quaternary pnictide classes, namely, ABCX$_2$, BB'B"X$_2$, and A$_4$BX$_2$ (A = Li, Na, or K; B, B', B" = Ca, Sr, Mg, or Zn; C = Al, Ga, or In; X = N or P), leading to a set of 104 possible pnictide compositions. Based on our evaluations of ground state structures, 0 K thermodynamic stabilities, electronic structures, carrier effective masses, dynamic stabilities, and intrinsic point defect formation energies, we arrive at three promising candidates, namely, NaCaInN$_2$, NaSrInN$_2$, and K$_4$ZnP$_2$. Notably, all the identified candidates are thermodynamically (meta)stable, exhibit direct (or nearest direct) band gaps that are optimal for PV applications, and are resistant to forming several types of point defects. We hope that our first principles driven workflow and the identified candidates will advance the development of novel PV materials and reinvigorate interest in the exploration of pnictides.

cond-mat.mtrl-sci↗

arXiv2026

Phase stability and ionic transport in post-spinel CaV$_2$O$_4$ cathode

Calcium-ion batteries (CBs) represent an alternative to lithium-ion technology but their advancement is limited by the lack of high-performance intercalation cathodes. Identified via computational screening, post-spinel CaV$_2$O$_4$ has emerged as a promising candidate, though its practical application is hindered by limited electrochemical capacity. Hence, we investigate the thermodynamic and ionic transport characteristics of Ca$_x$V$_2$O$_4$ ($0 \leq x \leq 1$) in this work, by integrating the cluster expansion formalism with Monte Carlo simulations and density functional theory based calculations. We construct the temperature-composition phase diagram of Ca$_x$V$_2$O$_4$ revealing several stable phases ($α$ through $ζ$) that can appear during electrochemical operations at different voltages. Importantly, we observe the formation of the $\varepsilon$ phase at $x \sim 0.83$ across a 370-590~K temperature window via invariant reactions, which agrees with observations in the experimental voltage profiles. Further, migration barrier calculations confirm that Ca mobility is severely impeded within the $α$ ($x \sim 0$) and $γ$ ($x \sim 0.5$) phases. With the strong Ca-vacancy ordering contributing to the high barrier in $γ$ and the persistent two-phase region stretching across the $δ$ ($x \sim 0.67$) and the $γ$ phases, we expect the accessible electrochemical capacity in the CaV$_2$O$_4$ system to be kinetically limited to at most half the theoretical capacity at 298~K, in agreement with experiments. Strategies including cation doping and particle size reduction can be considered to flatten the potential energy landscape of $γ$ and improve Ca mobility. Our computational findings highlight the interplay between stability and transport and provide design strategies that can enable the practical use of CaV$_2$O$_4$ as a CB cathode.

cond-mat.mtrl-sci↗

arXiv2026

Probing Structure and Ionic Transport in Molten Lithium Carbonate

Li$_2$CO$_3$ (LC) is a cornerstone material for clean energy technologies, including high-temperature molten carbonate fuel cells, electrochemical carbon capture, and lithium-based batteries. However, capturing the complex, many-body interactions governing the structure and transport in LC in its molten state has remained a challenge, constrained by the computational cost of \textit{ab initio} methods and the accuracy limitations of classical force fields. To address this gap, we deploy equivariant graph-based machine learned interatomic potentials, specifically, the multi atomic cluster expansion (MACE) and neural equivariant interatomic potential (NequIP) architectures that are trained on melt-quench \textit{ab initio} molecular dynamics data. Our benchmarking demonstrates that MACE provides superior transferability and precision in predicting energies and forces compared to NequIP. Subsequently, we use the optimized MACE model to perform large-scale molecular dynamics simulations to probe the properties of molten LC. Besides describing the structural features, such as the dominant presence of C-O pair correlations under molten conditions, our MACE model reproduces experimentally-measured static structure factors and shear viscosity values. Further, our simulations indicate that Li transport in LC is fundamentally dominated by concerted motion, as evidenced by Haven's ratios being significantly below unity (0.20-0.40). Notably, we identify a temperature-driven transition from anisotropic (and highly concerted) Li transport, supported by persistent oxygen-centered Voronoi cages at 1000~K, to isotropic (and less concerted) diffusion at 1400~K. Thus, we provide fundamental insights into the structural and transport properties of molten LC and also demonstrate a robust and scalable framework for the accelerated design of molten salt electrolytes and ionic liquids.

cond-mat.mtrl-sci↗

arXiv2026

Geometry-based Discovery of Calcium Battery Cathodes Accelerated by Foundational Machine-Learned Models

Calcium batteries (CBs) are an attractive post-Li-ion technology, offering the appeal of Ca's natural abundance and high volumetric energy density. However, practical realization of CBs remains limited by the scarcity of positive electrode (cathode) materials that support reversible Ca$^{2+}$ (de)intercalation under electrochemical conditions. To address this challenge, we screen the materials project (MP) database for novel host structures that can intercalate Ca using geometry- and chemistry-based design principles. Specifically, we employ the Voronoi polyhedral volume as a descriptor of site compatibility for hosting Ca in potential frameworks. Further, we down-select candidate structures progressively through criteria including charge neutrality, absence of non-Ca mobile cations, thermodynamic (meta)stability, average voltage, and Ca migration barriers ($E_m$) using foundational machine-learning (ML) models. Subsequently, we validate the ensemble-ML-predicted $E_m$ in a subset of the final candidates using density functional theory based nudged elastic band calculations. Overall, from an initial pool of 52,945 MP structures, our workflow identifies 37 promising Ca cathode candidates, several of which exhibit favorable combinations of thermodynamic (meta)stability, voltage, and Ca-mobility, marking them as strong candidates for synthesis and electrochemical characterization. Particularly, we identify two Ca cathode candidates with markedly low Ca$^{2+}~E_m$ (CaSc$_2$V$_2$O$_8$ and CaVSO$_4$F$_3$), and four cathode candidates with thermodynamically stable charged states (Ca$_3$(CoO$_2$)$_4$, Ca$_3$Mn$_4$(TeO$_6$)$_2$, CaVF$_5$, and CaVSO$_4$F$_3$). Beyond identifying Ca-cathodes, our work establishes geometry-based descriptors and ML-based workflows as transferable methods for high-throughput screening, enabling the rapid discovery of novel materials for battery and other applications.

cond-mat.mtrl-sci↗

arXiv2026

Experimental and computational diffusion analysis in Ni-X binary and Ni-Al-X (X = Cr, Mo, Ta, W, Re) ternary systems

An extensive diffusion analysis is presented for binary Ni-X and ternary Ni-Al-X (X = Cr, Mo, Ta, W, Re) systems, which play a crucial role in microstructural evolution and phase stability in Ni-Al-based superalloys. Specifically, we highlight changes in the diffusion coefficients of X in the presence of Al and compare diffusional interactions across systems considered. First-principles calculations, combined with activation energies derived from temperature-dependent experiments, reveal consistent trends in Ni-X systems, with variations in activation energies largely attributed to differences in migration energies. In ternary systems, diffusion coefficients estimated from intersecting diffusion profiles show that the main interdiffusion coefficient of X is comparable to its binary counterpart, with similar activation energies. However, cross-diffusion coefficients are shown to significantly influence fluxes, either enhancing or reducing diffusion lengths depending on the relative directions of diffusing elements. For Ni-Al-Re, a single-profile method is employed to overcome uncertainties in estimating composition gradients at the near-end-member intersecting composition. The diffusion coefficients obtained correlate well with the nature of diffusion paths when represented on Gibbs triangles. To extend these findings, a physics-informed neural network (PINN) optimization method is applied to extract composition-dependent diffusion coefficients across the full composition range. The analysis demonstrates the necessity of incorporating experimentally estimated diffusion coefficients as equality constraints, without which optimization reliability is compromised. Overall, the results establish a robust framework for diffusion studies in Ni-Al-X systems, highlighting the critical role of cross-diffusion effects and constraint-enhanced numerical methods.

cond-mat.mtrl-sci↗

arXiv2026

Exploring Multi-Transition-Metal NASICON Frameworks as High-Performance Cathodes for Sodium-Ion Batteries

The search for sustainable, high-performance cathodes has driven a growing interest in sodium superionic conductor (NASICON)-type phosphates for sodium-ion batteries (SIBs). To identify promising NASICONs containing earth-abundant transition metals (TMs) and to systematically examine the role of multiple TMs in influencing the various properties of NASICON cathodes, we employ density functional theory calculations to investigate nine NASICON compositions containing Mn, Cr, and/or Fe, and spanning unary, binary, and ternary combinations. Our calculations reveal that unary systems, in terms of their Na intercalation phase behavior, exhibit well-defined stabilization at intermediate Na contents ($x$ in Na$_x$TM$_2$(PO$_4$)$_3$), while binary and ternary systems display more complex phase behavior, with some systems showing a shift of thermodynamic minima from $x$ = 3 to 2. Intercalation voltages highlight the dominant role of Fe$^{4+}$/Fe$^{3+}$ redox activity in elevating average voltages ($\sim$4.0 V), while Mn and Cr introduce intermediate-to-low voltage redox activity. Electronic structure data demonstrate non-systematic changes in the band gap, especially in systems containing multiple TMs. Na$^+$ mobility results identify mixed-TM frameworks as favorable, achieving Na$^+$ migration barriers in the 0.3-0.4 eV range. Importantly, we identify Na$_x$MnFe$_{0.5}$Cr$_{0.5}$(PO$_4$)$_3$ to be a promising ternary composition for subsequent experimental validation, offering an optimal intersection of phase stability, voltages, thermodynamic (meta)stability, and Na$^+$ migration barriers. Together, our study provides fundamental insights into the interplay between compositional complexity, thermodynamic stability, electronic structure, and ionic transport in NASICONs, and offers actionable design principles for utilising multi-TM NASICONs as high performance SIB cathodes.

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arXiv2025

Evaluation of Foundational Machine Learned Interatomic Potentials for Migration Barrier Predictions

Fast, and accurate prediction of ionic migration barriers ($E_m$) is crucial for designing next-generation battery materials that combine high energy density with facile ion transport. Given the computational costs associated with estimating $E_m$ using conventional density functional theory (DFT) based nudged elastic band (NEB) calculations, we benchmark the accuracy in $E_m$ and geometry predictions of five foundational machine learned interatomic potentials (MLIPs), which can potentially accelerate predictions of ionic transport. Specifically, we assess the accuracy of MACE-MP-0, Orb-v3, SevenNet, CHGNet, and M3GNet models, coupled with the NEB framework, against DFT-NEB-calculated $E_m$ across a diverse set of battery-relevant chemistries and structures. Notably, MACE-MP-0 and Orb-v3 exhibit the lowest mean absolute errors in $E_m$ predictions across the entire dataset and over data points that are not outliers, respectively. Importantly, Orb-v3 and SevenNet classify `good' versus `bad' ionic conductors with an accuracy of $>$82\%, based on a threshold $E_m$ of 500~meV, indicating their utility in high-throughput screening approaches. Notably, intermediate images generated by MACE-MP-0 and SevenNet provide better initial guesses relative to conventional interpolation techniques in $>$71\% of structures, offering a practical route to accelerate subsequent DFT-NEB relaxations. Finally, we observe that accurate $E_m$ predictions by MLIPs are not correlated with accurate (local) geometry predictions. Our work establishes the use-cases, accuracies, and limitations of foundational MLIPs in estimating $E_m$ and should serve as a base for accelerating the discovery of novel ionic conductors for batteries and beyond.

cond-mat.mtrl-sci↗

arXiv2025

Phase Behavior and Ion Transport in Lithium-Niobium-Tantalum Oxide Alloys

Lithium niobate-tantalate mixtures have garnered considerable interest for their ability to merge the desirable properties of both end members, enabling diverse high-value applications, such as high-performance faradaic capacitors, non-linear optics, and protective coatings in rechargeable batteries. While numerous studies on the application of $\mathrm{LiNb_xTa_{1-x}O_3}$ exist, the phase behavior and properties of $\mathrm{Li_3Nb_xTa_{1-x}O_4}$ remain largely unexplored. In this work, we employ a multiscale approach that encompasses first-principles phonon calculations, cluster expansion, and Monte Carlo simulations to derive the temperature-composition phase diagram for $\mathrm{Li_3Nb_xTa_{1-x}O_4}$. Our findings reveal the critical role of vibrational entropy in accurately predicting phase stability, which promotes the solubility of Nb in $\mathrm{Li_3TaO_4}$ while suppressing the miscibility of Ta in $\mathrm{Li_3NbO_4}$. Additionally, we demonstrate that Nb/Ta mixing offers a promising avenue for tailoring the Li-ion conductivities of $\mathrm{Li_3Nb_xTa_{1-x}O_4}$. On the technical side, we demonstrated the importance of including vibrational entropy effects explicitly in Monte Carlo simulations dealing with multicomponent systems, beyond simple binary mixtures. On the application side, this study provides fundamental insights into the phase behavior and Li-ion transport properties of $\mathrm{Li_3Nb_xTa_{1-x}O_4}$, paving the way for its potential applications in energy storage and other fields.

cond-mat.mtrl-sci↗

arXiv2025

First-principles investigation of Sr-Ce-M-O perovskites for solar thermochemical water splitting

Using density functional theory based calculations, we systematically examine the utility of Sr-M-O and Sr-Ce-M-O perovskites for solar thermochemical water splitting, a promising route for sustainable hydrogen production. Importantly, we identify Sr$_{0.5}$Ce$_{0.5}$MnO$_3$ and Sr$_{0.5}$Ce$_{0.5}$CrO$_3$ to be promising candidates, exhibiting optimal oxygen vacancy formation energy and 0 K thermodynamic stability.

cond-mat.mtrl-sci↗

arXiv2025

Experimental and Computational Demonstration of a Highly Stable, in-situ Pt Decorated Sputtered ZnO Hydrogen Sensor for sub-ppm Level Detection

In this work, we present a Pt decorated ZnO thin film-based gas sensor for hydrogen detection, fabricated using a sputtering technique and an in-situ Pt decoration approach. Specifically, we deposit a ZnO thin film on an interdigitated electrode substrate, with Pt nanoclusters added to the (002) polar plane by brief sputtering (1 to 6 s) to create an active sensing interface. Our sensor demonstrates optimal performance at an operating temperature of 498 K, with rapid response and recovery times (10 and 3 s), high selectivity, and long-term stability. We find the Pt decorated ZnO sensor, with a Pt deposition time of 2 s, to exhibit enhanced response (~52,987%) to 1% hydrogen concentration, indicating its suitability for industrial and environmental monitoring applications. Additionally, our device demonstrates reliable detection of low hydrogen concentrations (~100 ppb), with a response of ~38% and no response drift over one year of testing, underscoring the long-term stability of the sensor. To elucidate the role of Pt deposition and pristine ZnO in hydrogen sensing, we perform density functional theory calculations, analysing adsorption and reaction energetics involving H2, O2, O, OH, and H2O, and lattice oxygen atoms on the ZnO (002) surface with and without Pt decoration. Our computational data is in agreement with our experiments, identifying the oxygen-exposed (002) surface to be most active for hydrogen sensing in both pristine and Pt decorated ZnO. Further, our computations highlight the role of Pt in enhancing hydrogen sensitivity via i) activating an autoreduction pathway of adsorbed OH, ii) spontaneous dissociation of adsorbed molecular H2, and iii) keeping the lattice oxygen pathway of forming H2O active. Our systematic approach of designing sensors combining an experimental setup with theoretical insights, is key in developing and optimizing efficient hydrogen gas sensors.

cond-mat.mtrl-sci↗

arXiv2025

Leveraging transfer learning for accurate estimation of ionic migration barriers in solids

Ionic mobility determines the rate performance of several applications, such as batteries, fuel cells, and electrochemical sensors and is exponentially dependent on the migration barrier ($E_m$), a difficult to measure/calculate quantity. Previous approaches to identify materials with high ionic mobility have relied on imprecise descriptors given the lack of generalizable models to predict $E_m$. Here, we present a graph neural network based architecture that leverages principles of transfer learning to efficiently and accurately predict $E_m$ across a diverse set of materials. We use a model pre-trained simultaneously on seven distinct bulk properties (labeled MPT), modify the MPT model to classify different migration pathways in a structure, and fine-tune (FT) on a manually-curated literature-derived dataset of 619 $E_m$ data points calculated with density functional theory. Importantly, our best-performing FT model (labeled MODEL-3) demonstrates substantial improvements in prediction accuracy compared to classical machine learning methods, graph models trained from scratch, and a universal machine learned interatomic potential, with a R$^2$ score of 0.703 and a mean absolute error of 0.261 eV on the test set. Notably, MODEL-3 is able to distinguish different migration pathways within a structure and also demonstrates excellent ability to generalize across intercalant compositions and chemistries. As a classifier, MODEL-3 exhibits 80\% accuracy and 82.8\% precision in identifying materials that are `good' ionic conductors (i.e., structures with $E_m <$0.65~eV). Thus, our work demonstrates the effective use of FT strategies and architectural modifications necessary for making swift and accurate $E_m$ predictions, which will be useful for materials discovery in batteries and for predicting other data-scarce material properties.

cond-mat.mtrl-sci↗

arXiv2025

A literature-derived dataset of migration barriers for quantifying ionic transport in battery materials

The rate performance of any electrode or solid electrolyte material used in a battery is critically dependent on the migration barrier ($E_m$) governing the motion of the intercalant ion, which is a difficult-to-estimate quantity both experimentally and computationally. The foundation for constructing and validating accurate machine learning (ML) models that are capable of predicting $E_m$, and hence accelerating the discovery of novel electrodes and solid electrolytes, lies in the availability of high-quality dataset(s) containing $E_m$. Addressing this critical requirement, we present a comprehensive dataset comprising 619 distinct literature-reported $E_m$ values calculated using density functional theory based nudged elastic band computations, across 443 compositions and 27 structural groups consisting of various compounds that have been explored as electrodes or solid electrolytes in batteries. Our dataset includes compositions that correspond to fully charged and/or discharged states of electrode materials, with intermediate compositions incorporated in select instances. Crucially, for each compound, our dataset provides structural information, including the initial and final positions of the migrating ion, along with its corresponding $E_m$ in easy-to-use .xlsx and JSON formats. We envision our dataset to be a highly useful resource for the scientific community, facilitating the development of advanced ML models that can predict $E_m$ precisely and accelerate materials discovery.

cond-mat.mtrl-sci↗

arXiv2025

Effect of alloying additions on the lattice ordering of Ti$_2$AlNb intermetallic

Alloys based on the orthorhombic-Ti$_2$AlNb intermetallic phase (O-phase) are promising materials for high-temperature applications in jet engines, given that they can potentially replace Ni-based superalloys in some operating regions of the engines. However, the O-phase is prone to lattice disordering at high temperatures, primarily via anti-site defect formation across the Ti and Nb sites, which can reduce the material's creep resistance and high-temperature tensile properties, necessitating the need to identify strategies to mitigate the disorder. Here, we focus on identifying suitable alloying additions to suppress the disordering of the O-phase using density functional theory and nudged elastic band calculations. Specifically, we consider six different alloying additions, namely, V, Cr, Fe, Mo, Ta, and W, and examine their role in the thermodynamics of anti-site formation and the kinetics of atomic diffusion. Upon verifying the ground state structure and formation energy of Ti$_2$AlNb, we observe the proclivity of all alloying elements (except V) to occupy the Nb site in the O-phase structure. Subsequently, we find that none of the alloying additions can effectively suppress anti-site formation in Ti$_2$AlNb, highlighting the unfavourable thermodynamics. However, we find that Mo and W additions to Ti$_2$AlNb can kinetically suppress the disorder by reducing the diffusivities of Ti and Nb, by $\approx$4$\times$ and 8$\times$ compared to the pristine O-phase, respectively, at an operating temperature of 823 K. Thus, Mo and W additions represent a promising strategy to improve the creep resistance of Ti$_2$AlNb-based alloys.

cond-mat.mtrl-sci↗

arXiv2025

Real-space methods for ab initio modelling of surfaces and interfaces under external potential bias

Accurate ab initio modelling of surfaces and interfaces, especially under an applied external potential bias, is important for describing and characterizing various phenomena that occur in electronic, catalytic, and energy storage devices. Leveraging the ability of real-space density functional theory (DFT) codes to accommodate generic boundary conditions, we introduce two methods for applying an external potential bias that can be suitable for modelling surfaces and interfaces. In the first method, an external constant electric field is applied by modifying the DFT Hamiltonian via the introduction of an auxiliary linear potential while solving the electrostatic potential arising in DFT using a Poisson equation with zero-Neumann boundary conditions. The second method directly enforces the desired external potential bias by imposing constraints on the electrostatic potential, thereby naturally mimicking experimental conditions. We describe the underlying DFT governing equations for the two setups within the real-space formalism employing finite-element discretization. First, we validate the constant electric field setup within real-space finite-element DFT (DFT-FE) with an equivalent approach using plane-wave DFT (i.e., using periodic boundary conditions) on three representative benchmark systems, namely La-terminated Li$_7$La$_3$Zr$_2$O$_{12}$, GaAs (111), and Al FCC (111) slabs. Subsequently, we present a comprehensive evaluation of the two setups in terms of the average ground-state properties, such as surface and adsorption energies. The methods developed in our work provide an attractive alternative to plane-wave DFT approaches in applying external potential bias that usually suffer from the periodic boundary conditions restrictions and poor scalability on parallel computing architectures.

cond-mat.mtrl-sci↗

arXiv2025

Exploration of amorphous V$_2$O$_5$ as cathode for magnesium batteries

Development of energy storage technologies that can exhibit higher energy densities, better safety, and lower supply-chain constraints than the current state-of-the-art Li-ion batteries (LIBs) is crucial for our transition into sustainable energy use. In this context, Mg batteries (MBs) offer a promising pathway to design energy storage systems with superior volumetric energy densities than LIBs but require the development of positive electrodes (cathodes) exhibiting high energy and power densities. Notably, amorphous materials that lack long range order can exhibit `flatter' potential energy surfaces than crystalline frameworks, possibly resulting in faster Mg$^{2+}$ motion. Here, we use a combination of ab initio molecular dynamics (AIMD), and machine learned interatomic potential (MLIP) based calculations to explore amorphous V$_2$O$_5$ as a potential cathode for MBs. Using an AIMD-generated dataset, we train and validate moment tensor potentials that can accurately model amorphous (Mg)V$_2$O$_5$ Due to the amorphization of V$_2$O$_5$, we observe a 10-14% drop in the average Mg intercalation voltage $-$ but the voltage remains higher than sulfide Mg cathodes. Importantly, we find a $\sim$seven (five) orders of magnitude higher Mg$^{2+}$ diffusivity in amorphous MgV$_2$O$_5$ than its crystalline version (thiospinel-Mg$_x$Ti$_2$S$_4$), which is directly attributable to the amorphization of the structure. Also, we note the Mg$^{2+}$ motion in the amorphous structure is significantly cross-correlated at low temperatures, with the correlation decreasing with increase in temperature. Thus, our work highlights the potential of amorphous V$_2$O$_5$ as a cathode that can exhibit both high energy and power densities, resulting in the practical deployment of MBs.

cond-mat.mtrl-sci↗

arXiv2025

Ab-initio investigation of transition metal dichalcogenides for the hydrogenation of carbon dioxide to methanol

We computationally investigate the catalytic potential of MoSe$_2$, WS$_2$, and WSe$_2$ nanoribbons and nanosheets for the partial hydrogenation of CO$_2$ to methanol by comparing their electronic, adsorption, and defect properties to MoS$_2$, a known thermo-catalyst. We identify Se-deficient MoSe$_2$ (followed by WSe$_2$) nanosheets to be favorable for selective methanol formation.

cond-mat.mtrl-sci↗

arXiv2024

Investigating Ionic Diffusivity in Amorphous Solid Electrolytes using Machine Learned Interatomic Potentials

Investigating Li$^+$ transport within the amorphous lithium phosphorous oxynitride (LiPON) framework, especially across a Li||LiPON interface, has proven challenging due to its amorphous nature and varying stoichiometry, necessitating large supercells and long timescales for computational models. Notably, machine learned interatomic potentials (MLIPs) can combine the computational speed of classical force fields with the accuracy of density functional theory (DFT), making them the ideal tool for modelling such amorphous materials. Thus, in this work, we train and validate the neural equivariant Interatomic potential (NequIP) framework on a comprehensive DFT-based dataset consisting of 13,454 chemically relevant structures to describe LiPON. With an optimized training (validation) energy and force mean absolute errors of 5.5 (6.1) meV/atom and 13.6 (13.2) meV/Å, respectively, we employ the trained potential in model Li-transport in both bulk LiPON and across a Li||LiPON interface. Amorphous LiPON structures generated by the optimized potential do resemble those generated by ab initio molecular dynamics, with N being incorporated on non-bridging apical and bridging sites. Subsequent analysis of Li$^+$ diffusivity in the bulk LiPON structures indicates broad agreement with computational and experimental literature so far. Further, we investigate the anisotropy in Li$^+$ transport across the Li(110)||LiPON interface, where we observe Li-transport across the interface to be one order-of-magnitude slower than Li-motion within the bulk Li and LiPON phases. Nevertheless, we note that this anisotropy of Li-transport across the interface is minor and do not expect it to cause any significant impedance buildup. Finally, our work highlights the efficiency of MLIPs in enabling high-fidelity modelling of complex non-crystalline systems over large length and time scales.

cond-mat.mtrl-sci↗

arXiv2024

Optimal pre-train/fine-tune strategies for accurate material property predictions

Overcoming the challenge of limited data availability within materials science is crucial for the broad-based applicability of machine learning within materials science. One pathway to overcome this limited data availability is to use the framework of transfer learning (TL), where a pre-trained (PT) machine learning model (on a larger dataset) can be fine-tuned (FT) on a target (typically smaller) dataset. Our study systematically explores the effectiveness of various PT/FT strategies to learn and predict material properties with limited data. Specifically, we leverage graph neural networks (GNNs) to PT/FT on seven diverse curated materials datasets, encompassing sizes ranging from 941 to 132,752 datapoints. We consider datasets that cover a spectrum of material properties, ranging from band gaps (electronic) to formation energies (thermodynamic) and shear moduli (mechanical). We study the influence of PT and FT dataset sizes, strategies that can be employed for FT, and other hyperparameters on pair-wise TL among the datasets considered. We find our pair-wise PT-FT models to consistently outperform models trained from scratch on the target datasets. Importantly, we develop a GNN framework that is simultaneously PT on multiple properties (MPT), enabling the construction of generalized GNN models. Our MPT models outperform pair-wise PT-FT models on several datasets considered, and more significantly, on a 2D material band gap dataset that is completely out-of-distribution from the PT datasets. Finally, we expect our PT/FT and MPT frameworks to be generalizable to other GNNs and materials properties, which can accelerate materials design and discovery for various applications.

cond-mat.mtrl-sci↗