arXiv subjects

Steven B. Torrisi

Publications and source records attributed to Steven B. Torrisi.

16 recordsLinked to original sources

arXiv2026

Synthesizing like a chemist: an iterative, feedback-driven loop for materials discovery

Most computationally predicted materials are never synthesized because conventional synthesis optimization is slow, expertise-dependent, and iterative. Here we present a closed-loop framework that automates this expert workflow by placing human tacit knowledge in the loop through a large language model (LLM) that distills synthesis knowledge from the literature, high-throughput hyperspectral imaging for rapid film evaluation, and multi-objective Bayesian optimization guided by experimental feedback. In a paired optimization campaign, LLM-assisted initialization produced more Pareto-optimal samples and higher hypervolume than a Latin hypercube sampling baseline at matched trial counts, and this advantage persisted throughout iterative optimization. We demonstrate the framework by synthesizing the previously unreported perovskite-inspired compound Rb3BiI6 as thin films and validating the optimized films by optical bandgap analysis and X-ray diffraction. The framework transforms synthesis prediction from single-shot recommendation to iterative learning, providing a generalizable strategy to accelerate automated and fully autonomous experimental materials discovery.

cond-mat.mtrl-sci↗

arXiv2026

The potential and viability of V2G for California BEV drivers

Vehicle-to-Grid (V2G) adoption is hindered by uncertainties regarding its effects on battery lifetime and vehicle usability. These uncertainties are compounded by limited insight into real-world vehicle usage. Here, we leverage real-world Californian BEV usage data to design and evaluate a user-centric V2G strategy. We identified four clustered driver profiles for V2G assessment, ranging from "Daily Chargers" to "Public Chargers". We show that V2G participation is most feasible for "Daily Chargers," and that the effects on battery lifetime depend on calendar aging sensitivity. For batteries with low sensitivity, V2G participation increases capacity loss for all drivers. However, for batteries with high sensitivity, V2G participation can lead to negligible changes in capacity or even improved capacity retention, particularly for drivers who tend to keep their batteries at high states of charge. Our findings enable stakeholders to better assess the potential and viability of V2G adoption.

eess.SY↗

arXiv2026

Spectra-Scope : A toolkit for automated and interpretable characterization of material properties from spectral data

Spectroscopy is a central pillar of materials characterization, providing useful information on properties like structure, composition, or excited state dynamics of a system. However, many spectroscopic techniques present challenges in the development of interpretable, performant, and reliable supervised learning models due to the wide range of possible nonlinear correlations that can exist between the signal and the response variable (target) of interest. Here, we present Spectra-Scope, an open-source AutoML framework for automatic characterization of material properties from spectroscopy data using interpretable machine learning (ML) models. The software is implemented in Python and a no-code web application. It comprises tools for data preprocessing, nonlinear feature extraction, machine learning model training, and feature selection. Users can easily train different types of simple, interpretable ML models on a set of feature transformations quickly and with modest computational resources. In this work, we outline the methods of Spectra-Scope and its effectiveness across diverse datasets, with applications to materials and agricultural spectroscopy data. We show that Spectra-Scope can reproduce performance of comparable models in the literature, and highlight how our emphasis on interpretability can be used to rationalize the behavior of individual models and understand the physical processes behind spectral features.

cond-mat.mtrl-sci↗

arXiv2025

Short-Range Order and Li$_x$TM$_{4-x}$ Probability Maps for Disordered Rocksalt Cathodes

Short-range order (SRO) in the cation-disordered state is a controlling factor influencing the probability of finding Li$_{4}$ tetrahedron clusters in disordered rocksalt (DRX) cathode materials. However, the prevalent Li$_4$ probability below the random limit across reported DRX compositions has not been systematically investigated, active strategies to surpass the random limit of Li$_4$ probability are lacking, and the fundamental ordering behavior on the face-centered cubic (FCC) lattice remains insufficiently explored. This research quantitatively examines pair SRO parameters and Li$_x$TM$_{4-x}$ probabilities via exhaustive Monte Carlo mapping across a simplified subset of the parameter space. The results indicate that, in the disordered state, the Li$_4$ probability is governed by the nearest neighbor (NN) pair-wise SRO parameter, and that these quantities do not necessarily represent a simple attenuation of their corresponding low-temperature long-range order, particularly for the important cases of Layered and Spinel-like orderings. Strategies are proposed to mitigate or even reverse the lithium and transition metals mixing tendency of NN pair SRO to achieve Li$_4$ probabilities that exceed the random limit. This study advances the fundamental thermodynamic understanding of ordering behaviors, which can be generalized to any FCC system.

cond-mat.mtrl-sci↗

arXiv2025

Tailored ordering enables high-capacity cathode materials

Newly designed Li-ion battery cathode materials with high capacity and greater flexibility in chemical composition will be critical for the growing electric vehicles market. Cathode structures with cation disorder were once considered suboptimal, but recent demonstrations have highlighted their potential in Li$_{1+x}$M$_{1-x}$O$_{2}$ chemistries with a wide range of metal combinations M. By relaxing the strict requirements of maintaining ordered Li diffusion pathways, countless multi-metal compositions in LiMO$_2$ may become viable, aiding the quest for high-capacity cobalt-free cathodes. A challenge presented by this freedom in composition space is designing compositions which possess specific, tailored types of both long- and short-range orderings, which can ensure both phase stability and Li diffusion. However, the combinatorial complexity associated with local cation environments impedes the development of general design guidelines for favorable orderings. Here we propose ordering design frameworks from computational ordering descriptors, which in tandem with low-cost heuristics and elemental statistics can be used to simultaneously achieve compositions that possess favorable phase stability as well as configurations amenable to Li diffusion. Utilizing this computational framework, validated through multiple successful synthesis and characterization experiments, we not only demonstrate the design of LiCr$_{0.75}$Fe$_{0.25}$O$_2$, showcasing initial charge capacity of 234 mAhg$^{-1}$ and 320 mAhg$^{-1}$ in its 20% Li-excess variant Li$_{1.2}$Cr$_{0.6}$Fe$_{0.2}$O$_2$, but also present the elemental ordering statistics for 32 elements, informed by one of the most extensive first-principles studies of ordering tendencies known to us.

cond-mat.mtrl-sci↗

arXiv2025

Anomalous Reversal of Stability in Mo-containing Oxides: A Difficult Case Exhibiting Sensitivity to DFT+U and Distortion

Accurate predictions of the properties of transition metal oxides using density functional theory (DFT) calculations are essential for the computational design of energy materials. In this work, we investigate the anomalous reversal of the stability of structural distortions (where distorted structures go from being energetically favorable to sharply unfavorable relative to undistorted ones) induced by DFT+U on Mo d-orbitals in layered AMoO$_2$ (A = Li, Na, K) and rutile-like MoO$_2$. We highlight the significant impact of varying U$_{\text{eff}}$ values on the structural stability, convex hull, and thermodynamic stability predictions, noting that deviations can reach up to the order of 100 meV/atom across these energetic quantities. We find the transitions in stability are coincident with changes in the electron localization and magnetic behavior. The anomalous reversal persists across PBE, r$^2$SCAN functionals, and also with vdW-dispersion energy corrections (PBE+D3). In Mo-containing oxide systems, high U$_{\text{eff}}$ leads to inaccurate descriptions of physical quantities and structural relaxations under artificial symmetry constraints, as demonstrated by the phonon band structures, the Heyd-Scuseria-Ernzerhof (HSE06) hybrid functional results, and comparisons with experimental structural data. We conclude that high U$_{\text{eff}}$ values (around 4 eV and above, depending on the specific structures and compositions) might be unsuitable for energetic predictions in A-Mo-O chemical spaces. Our results suggest that the common practice of applying DFT+U to convex hull constructions, especially with high U$_{\text{eff}}$ values derived from fittings, should be carefully evaluated to ensure that ground states are correctly reproduced, with careful consideration of dynamic stability and possible energetically favorable distortions.

cond-mat.mtrl-sci↗

arXiv2024

Interpretable Multimodal Machine Learning Analysis of X-ray Absorption Near-Edge Spectra and Pair Distribution Functions

We used interpretable machine learning to combine information from multiple heterogeneous spectra: X-ray absorption near-edge spectra (XANES) and atomic pair distribution functions (PDFs) to extract local structural and chemical environments of transition metal cations in oxides. Random forest models were trained on simulated XANES, PDF, and both combined to extract oxidation state, coordination number, and mean nearest-neighbor bond length. XANES-only models generally outperformed PDF-only models, even for structural tasks, although using the metal's differential PDFs (dPDFs) instead of total PDFs narrowed this gap. When combined with PDFs, information from XANES often dominates the prediction. Our results demonstrate that XANES contain rich structural information and highlight the utility of species-specificity. This interpretable, multimodal approach is quick to implement with suitable databases and offers valuable insights into the relative strengths of different modalities, guiding researchers in experiment design and identifying when combining complementary techniques adds meaningful information to a scientific investigation.

cond-mat.mtrl-sci↗

arXiv2023

Complexity of Many-Body Interactions in Transition Metals via Machine-Learned Force Fields from the TM23 Data Set

This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predictions across the transition metals for two leading MLFF models: a kernel-based atomic cluster expansion method implemented using sparse Gaussian processes (FLARE), and an equivariant message-passing neural network (NequIP). Early transition metals present higher relative errors and are more difficult to learn relative to late platinum- and coinage-group elements, and this trend persists across model architectures. Trends in complexity of interatomic interactions for different metals are revealed via comparison of the performance of representations with different many-body order and angular resolution. Using arguments based on perturbation theory on the occupied and unoccupied d states near the Fermi level, we determine that the large, sharp d density of states both above and below the Fermi level in early transition metals leads to a more complex, harder-to-learn potential energy surface for these metals. Increasing the fictitious electronic temperature (smearing) modifies the angular sensitivity of forces and makes the early transition metal forces easier to learn. This work illustrates challenges in capturing intricate properties of metallic bonding with current leading MLFFs and provides a reference data set for transition metals, aimed at benchmarking the accuracy and improving the development of emerging machine-learned approximations.

cond-mat.mtrl-sci↗

arXiv2023

Oxygen Vacancy Formation Energy in Metal Oxides: High Throughput Computational Studies and Machine Learning Predictions

The oxygen vacancy formation energy ($ΔE_{vf}$) governs defect dynamics and is a useful metric to perform materials selection for a variety of applications. However, density functional theory (DFT) calculations of $ΔE_{vf}$ come at a greater computational cost than the typical bulk calculations available in materials databases due to the involvement of multiple vacancy-containing supercells. As a result, available repositories of direct calculations of $ΔE_{vf}$ remain relatively scarce, and the development of machine learning models capable of delivering accurate predictions is of interest. In the present, work we address both such points. We first report the results of new high-throughput DFT calculations of oxygen vacancy formation energies of the different unique oxygen sites in over 1000 different oxide materials, which together form the largest dataset of directly computed oxygen vacancy formation energies to date, to our knowledge. We then utilize the resulting dataset of $\sim$2500 $ΔE_{vf}$ values to train random forest models with different sets of features, examining both novel features introduced in this work and ones previously employed in the literature. We demonstrate the benefits of including features that contain information specific to the vacancy site and account for both cation identity and oxidation state, and achieve a mean absolute error upon prediction of $\sim$0.3 eV/O, which is comparable to the accuracy observed upon comparison of DFT computations of oxygen vacancy formation energy and experimental results. Finally, we demonstrate the predictive power of the developed models in the search for new compounds for solar-thermochemical water-splitting applications, finding over 250 new AA$^{\prime}$BB$^{\prime}$O$_6$ double perovskite candidates.

cond-mat.mtrl-sci↗

arXiv2022

High-Throughput $\textit{Ab Initio}$ Design of Atomic Interfaces using InterMatch

Forming a hetero-interface is a materials-design strategy that can access an astronomically large phase space. However, the immense phase space necessitates a high-throughput approach for optimal interface design. Here we introduce a high-throughput computational framework, InterMatch, for efficiently predicting charge transfer, strain, and superlattice structure of an interface by leveraging the databases of individual bulk materials. Specifically, the algorithm reads in the lattice vectors, density of states, and the stiffness tensors for each material in their isolated form from the Materials Project. From these bulk properties, InterMatch estimates the interfacial properties. We benchmark InterMatch predictions for the charge transfer against experimental measurements and supercell density-functional theory calculations. We then use InterMatch to predict promising interface candidates for doping transition metal dichalcogenide MoSe$_2$. Finally, we explain experimental observation of factor of 10 variation in the supercell periodicity within a few microns in graphene/$α$-RuCl$_3$ by exploring low energy superlattice structures as a function of twist angle using InterMatch. We anticipate our open-source InterMatch algorithm accelerating and guiding ever-growing interfacial design efforts. Moreover, the interface database resulting from the InterMatch searches presented in this paper can be readily accessed through https://contribs.materialsproject.org/projects/intermatch/ .

cond-mat.mtrl-sci↗

arXiv2020

Effects of Structural Distortions on the Electronic Structure of T-type Transition Metal Dichalcogenides

Single-layer transition metal dichalcogenides (TMDCs) can adopt two distinct structures corresponding to different coordination of the metal atoms. TMDCs adopting the T-type structure exhibit a rich and diverse set of phenomena, including charge density waves (CDW) in a $\sqrt{13}\times\sqrt{13}$ supercell pattern in TaS$_2$ and TaSe$_2$, and a possible excitonic insulating phase in TiSe$_2$. These properties make the T-TMDCs desirable components of layered heterostructure devices. In order to predict the emergent properties of combinations of different layered materials, one needs simple and accurate models for the constituent layers which can take into account potential effects of lattice mismatch, relaxation, strain, and structural distortion. Previous studies have developed ab initio tight-binding Hamiltonians for H-type TMDCs [arXiv:1709.07510]. Here we extend this work to include T-type TMDCs. We demonstrate the capabilities of our model using three example systems: a 1-dimensional sinusoidal ripple, the 2$\times$2 CDW in TiSe$_2$, and the $\sqrt{13}\times\sqrt{13}$ CDW in TaS$_2$. Using the technique of band unfolding we compare the electronic structure of the distorted crystals to the pristine band structure and find excellent agreement with direct DFT calculations, provided the magnitude of the distortions remains in the linear regime.

cond-mat.mes-hall↗

arXiv2020

Electronic structure calculations of twisted multi-layer graphene superlattices

Quantum confinement endows two-dimensional (2D) layered materials with exceptional physics and novel properties compared to their bulk counterparts. Although certain two- and few-layer configurations of graphene have been realized and studied, a systematic investigation of the properties of arbitrarily layered graphene assemblies is still lacking. We introduce theoretical concepts and methods for the processing of materials information, and as a case study, apply them to investigate the electronic structure of multi-layer graphene-based assemblies in a high-throughput fashion. We provide a critical discussion of patterns and trends in tight binding band structures and we identify specific layered assemblies using low-dispersion electronic bands as indicators of potentially interesting physics like strongly correlated behavior. A combination of data-driven models for visualization and prediction is used to intelligently explore the materials space. This work more generally aims to increase confidence in the combined use of physics-based and data-driven modeling for the systematic refinement of knowledge about 2D layered materials, with implications for the development of novel quantum devices.

cond-mat.mes-hall↗

arXiv2019

Two-Dimensional Forms of Robust CO$_2$ Reduction Photocatalysts

Novel photoelectrocatalysts that use sunlight to power the CO$_2$ reduction reaction will be crucial for carbon-neutral power and energy-efficient industrial processes. Scalable photoelectrocatalysts must satisfy a stringent set of criteria, such as stability under operating conditions, product selectivity, and efficient light absorption. Two-dimensional materials can offer high specific surface area, tunability, and potential for heterostructuring, providing a fresh landscape of candidate catalysts. From a set of promising bulk CO$_2$ reduction photoelectrocatalysts, we screen for candidate mono-layers of these materials, then study their catalytic feasibility and suitability. For stable monolayer candidates, we verify the presence of visible-light band gaps, check that band edges can support CO$_2$ reduction, determine exciton binding energies, and compute surface reactivity. We find for SiAs, ZnTe, and ZnSe monolayers, visible light absorption is possible, and reaction selectivity biases towards CO production. We thus identify SiAs, ZnTe, and ZnSe monolayers as targets for further investigation, expanding the chemical space for CO$_2$ photoreduction.

cond-mat.mtrl-sci↗

arXiv2019

On-the-Fly Active Learning of Interpretable Bayesian Force Fields for Atomistic Rare Events

Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model. This severely limits the practical application of these models in systems with dynamics governed by important rare events, such as chemical reactions and diffusion. We present an adaptive Bayesian inference method for automating the training of interpretable, low-dimensional, and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations. Within an active learning framework, the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model. The method is applied to a range of single- and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency, while requiring a minimal amount of ab initio training data. We provide a fully open-source implementation of our method, as well as a procedure to map trained models to computationally efficient tabulated force fields.

physics.comp-ph↗

arXiv2018

Relaxation and Domain Formation in Incommensurate 2D Heterostructures

We introduce configuration space as a natural representation for calculating the mechanical relaxation patterns of incommensurate two-dimensional (2D) bilayers, bypassing supercell approximations to encompass aperiodic relaxation patterns. The approach can be applied to a wide variety of 2D materials through the use of a continuum model in combination with a generalized stacking fault energy for interlayer interactions. We present computational results for small-angle twisted bilayer graphene and molybdenum disulfide (MoS$_2$), a representative material of the transition metal dichalcogenide (TMDC) family of 2D semiconductors. We calculate accurate relaxations for MoS$_2$ even at small twist-angle values, enabled by the fact that our approach does not rely on empirical atomistic potentials for interlayer coupling. The results demonstrate the efficiency of the configuration space method by computing relaxations with minimal computational cost for twist angles down to $0.05^\circ$, which is smaller than what can be explored by any available real space techniques. We also outline a general explanation of domain formation in 2D bilayers with nearly-aligned lattices, taking advantage of the relationship between real space and configuration space.

cond-mat.mes-hall↗

arXiv2016

Perpendicular laser cooling with a rotating wall potential in a Penning trap

We investigate the impact of a rotating wall potential on perpendicular laser cooling in a Penning ion trap. By including energy exchange with the rotating wall, we extend previous Doppler laser cooling theory and show that low perpendicular temperatures are more readily achieved with a rotating wall than without. Detailed numerical studies determine optimal operating parameters for producing low temperature, stable 2-dimensional crystals, important for quantum information processing experiments employing Penning traps.

quant-ph↗