arXiv subjects

Pin-Wen Guan

Publications and source records attributed to Pin-Wen Guan.

6 recordsLinked to original sources

arXiv2025

Stability and Structure of Binary Metal Hydrides under Pressure, Electrochemical Potential and Combined Pressure-Electrochemistry

Metal hydrides can be tuned to have a diverse range of properties and find applications in hydrogen storage and superconductivity. Finding methods to control the synthesis of hydrides can open up new pathways to unlock novel hydride compounds with desired properties. We introduced the idea of utilizing electrochemistry as an additional tuning knob and in this work, we study the synthesis of binary metal hydrides using high pressure, electrochemistry and combined pressure-electrochemistry. Using density functional theory calculations, we predict the phase diagrams of selected transition metal hydrides under combined pressure and electrochemical conditions and demonstrate that the approach agrees well with experimental observations for most phases. We use the phase diagrams to determine trends in the stability of binary metal hydrides of scandium, yttrium and lanthanum as well as discuss the hydrogen-metal charge transfer at different pressures. Furthermore, we predict a diverse range of vanadium and chromium hydrides that could potentially be synthesized using pressure electrochemistry. These predictions highlight the value of exploring pressure-electrochemistry as a pathway to novel hydride synthesis.

cond-mat.mtrl-sci↗

arXiv2023

Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast-charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of DiffMix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.

physics.chem-ph↗

arXiv2021

Differentiable thermodynamic modeling

A new framework of thermodynamic modeling is proposed by introducing the concept of differentiable programming, where all the thermodynamic observables including both thermochemical quantities and phase equilibria can be differentiated with respect to the underlying model parameters, thus allowing the models learned by gradient-based optimization. It is shown that thermodynamic modeling and deep learning can be seamlessly integrated and unified within this framework. A preliminary successful application is demonstrated for the Cu-Rh system. It is expected that thermodynamic modeling in a deep learning style can increase prediction power of models, and provide more effective guidance for design, synthesis and optimization of multi-component materials with complex chemistry via learning various types of data.

cond-mat.mtrl-sci↗

arXiv2020

MeltNet: Predicting alloy melting temperature by machine learning

Thermodynamics is fundamental for understanding and synthesizing multi-component materials, while efficient and accurate prediction of it still remain urgent and challenging. As a demonstration of the "Divide and conquer" strategy decomposing a phase diagram into different learnable features, quantitative prediction of melting temperature of binary alloys is made by constructing the machine learning (ML) model "MeltNet" in the present work. The influences of model hyperparameters on the prediction accuracy is systematically studied, and the optimal hyperparameters are obtained by Bayesian optimization. A comprehensive error analysis is made on various aspects including training duration, chemistry and input features. It is found that except a few discrepancies mainly caused by less satisfactory treatment of metalloid/semimetal elements and large melting point difference with poor liquid mixing ability between constituent elements, MeltNet achieves overall success in prediction, especially capturing subtle composition-dependent features in the unseen chemical systems for the first time. The reliability, robustness and accuracy of MeltNet is further largely boosted by introducing the ensemble method with uncertainty quantification. Based on the state-of-the-art underlying techniques, MeltNet achieves a prediction mean average error (MAE) as low as about 120 K, at a minimal computational cost. We believe the present work has a general value for significant acceleration of predicting thermodynamics of complicated multi-component systems.

cond-mat.mtrl-sci↗

arXiv2020

$\mathcal{P}^2$: Combining pressure and electrochemistry to synthesize superhydrides

Recently, superhydrides have been computationally identified and subsequently synthesized with a variety of metals at very high pressures. In this work, we evaluate the possibility of synthesizing superhydrides by uniquely combining electrochemistry and applied pressure. We perform computational searches for palladium superhydrides using density functional theory and particle swarm optimization calculations over a broad range of pressures and electrode potentials. We incorporate exchange-correlation functional uncertainty using the Bayesian error estimation formalism to quantify the uncertainty associated with the identified stable phases. Based on a thermodynamic analysis, we construct pressure-potential phase diagrams and provide an alternate synthesis concept, $\mathcal{P}^2$ (pressure-potential), to accessing novel phases having high hydrogen content. Palladium-hydrogen is a widely-studied material system with the highest hydride phase being Pd$_3$H$_4$. Most strikingly for this system, at potentials above hydrogen evolution and $\sim$300 MPa pressure, we find the possibility to make palladium superhydrides (e.g., PdH$_{10}$). We demonstrate the generalizability of this approach for La-H, Y-H and Mg-H with 10-100 fold reduction in required pressure for stabilizing phases. In addition, the $\mathcal{P}^2$ strategy allows stabilizing new phases that cannot be done purely by either pressure or potential and is a general approach that is likely to work for synthesizing other superhydrides at modest pressures.

cond-mat.mtrl-sci↗

arXiv2020

Uncertainty Quantification of First Principles Computational Phase Diagram Predictions of Li-Si System Via Bayesian Sampling

In this work, an assessment of the CALPHAD method trained on only density functional theory (DFT) data is performed for the Li-Si binary system, as a case study. Using a parameter sampling approach based on the Bayesian Error Estimation Functional (BEEF-vdW) exchange-correlation potential. By using built-in ensemble of functionals from BEEF-vdW, the uncertainties of the Gibbs Free Energy fitting parameters are obtained and can be propagated to the resulting phase diagram. To find the best fitting form of the CALPHAD model, we implement a model selection step using the Bayesian information criterion (BIC). Applying the best selected CALPHAD model from the DFT calculation, to other sampled BEEF functionals, an ensemble of CALPHAD models is generated leading to an ensemble of phase diagram predictions. The resulting phase diagrams are then compiled into a single-phase diagram representing the most probable phase predicted as well as a quantitative metric of confidence for the prediction. This treatment of uncertainty resulting from DFT provides a rigorous way to ensure the correlated errors of DFT is accounted for in the estimation of uncertainty. From the phase diagram, we have determined intercalation voltages for lithiated silicon. In combination, we can generate a better understanding of the phase transitions and voltage profile to make a more analysis-informed prediction for experiments and the performance of Si-anodes within batteries.

cond-mat.mtrl-sci↗