arXiv subjects

Jose L. Mendoza-Cortes

Publications and source records attributed to Jose L. Mendoza-Cortes.

At least 19 recordsLinked to original sources

arXiv2026

Can Strain or Anion Interchange Make an Unstable Structure Stable? Energetics, Lattice Dynamics and Strain-Tunable Band Gaps of Lithium Chalcohalide Antiperovskites (Li$_{3}$$BA$) and their Anion Interchange Variants (Li$_{3}$$AB$)

Lithium chalcohalide antiperovskites are a promising, non-toxic alternative to lead halide perovskites, with potential as solid electrolytes for Li-ion batteries. we computationally investigate a relatively unexplored anion-interchange mechanism by which cubic Li$_{3}$$AB$ derivatives are obtained from the parent cubic antiperovskite Li$_{3}$$BA$ ($A$ = O, S, Se, Te, Po; $B$ = F, Cl, Br, I). The calculated relative energy landscape provides a useful guide for anion-site selectivity and its role in structural stability. The energetic stability results reveal that the smaller anion inside the octahedron stabilizes the structures. The lattice-dynamic calculations confirm that Li$_{3}$F$A$ ($A$ = Te, Po) and Li$_{3}$O$B$ ($B$ = Cl, Br, I), which are the most energetically stable compounds, are dynamically stable cubic phases without imaginary phonon modes. However, Li$_{3}$FS and Li$_{3}$FSe, while energetically stable, are dynamically unstable at equilibrium and become dynamically stable under triaxial compressive strain. In addition, we report the electronic structure and density of states (DOS) of all compounds, which show a substantial change in band gap upon anion interchange. The strain engineering of the lithium chalcohalide family illustrates how a few percent of the strain can tune the electronic band gap within the electrochemical stability window for solid battery applications. This study unveils essential characteristics of the anion site-interchange mechanism and provides a foundation for the understanding and design of lithium chalcohalide antiperovskites.

cond-mat.mtrl-sci↗

arXiv2026

CheMLFlow: An Open-Source Platform for Cheminformatics and Materials Informatics Applications

CheMLFlow is an open-source platform for building and executing end-to-end, high-throughput, and agentic workflows for scientific and technological applications. CheMLFlow targets a common bottleneck in scientific machine learning development, where researchers often need to assemble data acquisition, curation, representation, model training, validation, screening, interpretation, and reporting into a reproducible pipeline, even when their primary research contribution concerns only one stage. CheMLFlow provides modular workflow components, ready-to-run reference pipelines, standardized artifacts, and evaluation outputs that reduce orchestration overhead and support benchmarking across methods and datasets. The platform is designed to be extensible, reproducible, and automation friendly, with pluggable representations and models, deterministic splits, explicit run artifacts, batch execution, and report generation. As scientific software increasingly moves toward agent assisted experimentation, CheMLFlow's configuration driven workflows and structured outputs also provide a practical interface for coding agents to help users construct experiments, inspect results, and summarize findings under human supervision. This article describes the system architecture, core workflows, and benchmarks that reach literature performance for quantum mechanical, physicochemical and bioactivity property prediction, and use cases involving time series datasets demonstrating applications beyond molecular chemistry datasets.

cs.LG↗

arXiv2026

How Does Intercalation Reshape Layered Structures? A First-Principles Study of Sodium Insertion in Layered Potassium Birnessite

This study presents a first-principles study at the level of hybrid-level density functional theory of the sodium intercalation process in a layered potassium birnessite (a layered manganese dioxide, δ-MnO2). Understanding the intercalation processes of δ-MnO2 is a vital step in advancing its potential innovative applications. Through a formation energy formalism, we analyze the stability of the structure as sodium ions (Na+) are intercalated between layers. Simulated Raman spectra allow us to find relationships between the vibrational and structural properties of the material, i.e. we identify the most important vibrational modes and related them to the structural/geometrical change. The diffusion of Na+ and K+ ions in birnessite is studied by transition state theory, determining the energy barriers to ion displacement in the interlayer. The symmetry and planar density of the system are characterized by simulated X-ray diffraction and geometrical analysis of the optimized structures. Through binding energy analysis, we also find that the Na+ ions are more loosely bound to the lattice as they reach the saturation limit. Finally, the electronic properties are studied via spin-polarized densities of states. As intercalants are added, the electronic properties are profoundly modified, resulting from modification of Mn oxidation states, lattice distortions, and symmetry effects. Moreover, some of the intercalated structures behave as bipolar magnetic semiconductors with potential applications in spintronics devices. In other words, the band gaps and magnetic behavior of the system can be controlled by intercalation. This work provides an overarching analysis of intercalated birnessite and describes the essential properties of potassium birnessite and co-intercalation with Sodium as a next-generation energy, electronic, and spintronic material.

cond-mat.mtrl-sci↗

arXiv2026

Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic Potentials

The Hessian matrix (second derivatives) encodes far richer local curvature of the potential energy surface than energies and forces alone. However, training machine-learning interatomic potentials (MLIPs) with full Hessians is often impractical because explicitly forming and storing Hessian matrices scales quadratically in cost and memory. We introduce Projected Hessian Learning (PHL), a scalable second-order training framework that injects curvature information using only Hessian-vector products (HVPs). Rather than constructing the Hessian, PHL projects curvature along stochastic probe directions and uses an unbiased stochastic trace-based loss with favorable system-size scaling, enabling curvature-informed training without quadratic memory growth. We benchmark PHL on a chemically diverse dataset of reactants, products, transition states, intrinsic reaction coordinates, and normal-mode sampled geometries computed at omegaB97XD/6-31G(d). We compare energy-force training (E-F), two HVP-based schemes (E-F-HVP with one-hot or randomized probes), and full energy-force-Hessian training (E-F-H). With randomized probes per minibatch, both HVP schemes match full-Hessian training in energy, force, and Hessian accuracy while delivering >24x epoch speedups for the small molecular systems studied. In a fixed-probe regime with one HVP per molecule, randomized projections consistently outperform one-column probing, especially for far-from-equilibrium geometries. Overall, PHL replaces explicit Hessian supervision with force-complexity curvature training, retaining most second-order accuracy gains while scaling to larger, more complex molecular systems.

physics.chem-ph↗

arXiv2026

Ferrocene-functionalized covalent organic framework exceeding the ultimate hydrogen storage targets: a first-principles multiscale computational study

The development of efficient hydrogen storage materials is crucial for advancing the hydrogen economy and meeting the U.S. Department of Energy's targets of 6.5 wt% and 50 g H 2 /L for automotive applications. We present a computational study of ferrocene-functionalized covalent organic frameworks (COFs) for hydrogen storage. Following the M ulti-binding S ites U nited in C ovalent- O rganic F ramework (MSUCOF) approach, we introduce MSUCOF-4-FeCp, designed by incorporating ferrocene (FeCp 2 ) moieties into IRCOF-102. Notably, it achieves exceptional performance with gravimetric and volumetric uptakes of 18.0 wt% and 72.6 g H 2 /L at 298 K and 700 bar. The material exhibits optimal binding energies (15-20 kJ/mol) ensuring both high storage capacity and deliverable hydrogen under practical conditions. This work establishes ferrocene functionalization as a cost-effective alternative to precious metal incorporation in COFs.

cond-mat.mtrl-sci↗

arXiv2025

A first-principles theoretical study on two-dimensional MX and MX$_2$ metal halides: bandgap engineering, magnetism, and catalytic descriptors

Metal halides, particularly MX and MX$_2$ compounds (where M represents metal elements and X = F, Cl, Br, I), have attracted significant interest due to their diverse electronic and optoelectronic properties. However, a comprehensive understanding of their structural and electronic behavior, particularly the evolution of these properties from bulk to low-dimensional forms, remains limited. To address this gap, we performed first-principles calculations to develop a database of 60 MX and MX$_2$ metal halides, detailing their structural and electronic properties in both bulk and slab configurations. Calculations were performed using the advanced \texttt{HSE06-D3} hybrid functional for density functional theory (DFT), ensuring high precision in predicting material properties despite the associated computational cost. The results reveal that these materials are predominantly semiconductors, but their bandgaps range from 0 to 9 eV. A detailed analysis of the transition from bulk to slab structures highlights notable shifts in electronic properties, including bandgap modifications. Upon dimensional reduction, 9 materials exhibit an indirect-to-direct bandgap transition, enhancing their potential for energy conversion. Beyond structural dimensionality, the influence of chemical composition on bandgap variations was also examined. To further assess their practical applicability, the catalytic and magnetic properties of these metal halides were systematically evaluated. These findings not only illuminate previously underexplored MX and MX$_2$ metal halides but also identify promising candidates for electronic, optoelectronic, catalytic and spintronic applications. This database serves as a valuable resource for guiding future research and technology development in low-dimensional materials.

cond-mat.mtrl-sci↗

arXiv2025

Does Hessian Data Improve the Performance of Machine Learning Potentials?

Integrating machine learning into reactive chemistry, materials discovery, and drug design is revolutionizing the development of novel molecules and materials. Machine Learning Interatomic Potentials (MLIPs) accurately predict energies and forces at quantum chemistry levels, surpassing traditional methods. Incorporating force fitting into MLIP training significantly improves the representation of potential-energy surfaces (PES), enhancing model transferability and reliability. This study introduces and evaluates incorporating Hessian matrix training into MLIPs, capturing second-order curvature information of PES. Our analysis specifically examines MLIPs trained solely on stable molecular geometries, assessing their extrapolation capabilities to non-equilibrium configurations. We show that integrating Hessian information substantially improves MLIP performance in predicting energies, forces, and Hessians for non-equilibrium structures. Hessian-trained MLIPs notably enhance reaction pathway modeling, transition state identification, and vibrational spectra accuracy, benefiting molecular dynamics simulations and Nudged Elastic Band (NEB) calculations. By comparing models trained with various combinations of energy, force, and Hessian data on a small-molecule reactive dataset, we demonstrate Hessian inclusion leads to improved accuracy in reaction modeling and vibrational analyses while simultaneously reducing the total data needed for effective training. The primary trade-off is increased computational expense, as Hessian training demands more resources than conventional methods. Our results offer comprehensive insights into the strengths and limitations of Hessian integration in MLIP training, enabling practitioners in computational chemistry to make informed decisions aligned with their research goals and available computational resources.

physics.chem-ph↗

arXiv2025

How Can We Engineer Electronic Transitions Through Twisting and Stacking in TMDC Bilayers and Heterostructures? A First-Principles Approach

Layered two-dimensional (2D) materials exhibit unique properties, expanding opportunities in material design. We investigate MX$_2$ transition metal dichalcogenides (TMDCs) (M = Mo, W; X = S, Se, Te) in homo- and heterobilayers with different stacking and twist angles. Twisted bilayers introduce Moiré patterns, significantly altering electronic properties. Using first-principles Density Functional Theory (DFT) with range-separated hybrid functionals, we examine 30 MX$_2$ combinations, revealing how stacking and composition influence stability and band gap energy (E$_g$). Notably, the MoTe$_2$/WSe$_2$ heterostructure with a 60\textdegree~shift maintains a direct band gap, highlighting its potential for applications. Homobilayers under low-strain conditions exhibit diverse stacking-dependent electronic behaviors, where MoS$_2$, WS$_2$, and WSe$_2$ transition between direct and indirect band gaps at specific twist angles. MoS$_2$ can even switch between semiconductor and metallic states. Critical twist angles (17.9\textdegree, 42.1\textdegree, 77.9\textdegree, and 102.1\textdegree) in twisted WS$_2$ and WSe$_2$ bilayers yield symmetric Moiré patterns with tunable band gaps. Our findings emphasize that controlling heterostructures and twist angles is a powerful strategy for engineering electronic properties, offering a pathway for next-generation materials.

cond-mat.mtrl-sci↗

arXiv2024

Terahertz control of surface topology probed with subatomic resolution

Light-induced phase transitions offer a method to dynamically modulate topological states in bulk complex materials. Yet, next-generation devices demand nanoscale architectures with contact resistances near the quantum limit and precise control over local electronic properties. The layered material WTe$_2$ has gained attention as a likely Weyl semimetal, with topologically protected linear electronic band crossings hosting massless chiral fermions. Here, we demonstrate a topological phase transition facilitated by light-induced shear motion of a single atomic layer at the surface of bulk WTe$_2$, thereby opening the door to nanoscale device concepts. Ultrafast terahertz fields enhanced at the apex of an atomically sharp tip resonantly couple to the key interlayer shear mode of WTe$_2$ via a ferroelectric dipole at the interface, inducing a structural phase transition at the surface to a metastable state. Subatomically resolved differential imaging, combined with hybrid-level density functional theory, reveals a shift of 7 $\pm$ 3 picometres in the top atomic plane. Tunnelling spectroscopy links electronic changes across the phase transition with the electron and hole pockets in the band structure, suggesting a reversible, light-induced annihilation of the topologically-protected Fermi arc surface states in the top atomic layer.

cond-mat.mtrl-sci↗

arXiv2024

Nitrogen-containing Surface Ligands Lead to False Positives for Photofixation of N$_2$ on Metal Oxide Nanocrystals: An Experimental and Theoretical Study

Many ligands commonly used to prepare nanoparticle catalysts with precise nanoscale features contain nitrogen (e.g., oleylamine); here, we found that the use of nitrogen-containing ligands during the synthesis of metal oxide nanoparticle catalysts substantially impacted product analysis during photocatalytic studies. We confirmed these experimental results via hybrid Density Functional Theory computations of the materials' electronic properties to evaluate their viability as photocatalysts for nitrogen reduction. This nitrogen ligand contamination, and subsequent interference in photocatalytic studies, is avoidable through the careful design of synthetic pathways that exclude nitrogen-containing constituents. This result highlights the urgent need for careful evaluation of catalyst synthesis protocols, as contamination by nitrogen-containing ligands may go unnoticed since the presence of nitrogen is often not detected or probed.

physics.chem-ph↗

arXiv2024

Beyond Lithium-Ion Batteries: Are Effective Electrodes Possible for Alkaline and Other Alkali Elements? Exploring Ion Intercalation in Surface-Modified Few-Layer Graphene and Examining Layer Quantity and Stages

In the quest for better energy storage solutions, the role of designing effective electrodes is crucial. Previous research has shown that using materials like single-side fluorinated graphene can improve the stability of ion insertion in few-layer graphene (FLG), which is vital as we move beyond lithium-ion batteries. Alternatives such as sodium and potassium, which are more abundant on Earth, appear promising, but thorough studies on how these ions insert into electrodes in stages are still needed. Our research focuses on the initial three alkali (Li, Na, K) and alkaline (Be, Mg, Ca) earth metals. Using Density Functional Theory (DFT) with advanced calculations, we've investigated how these ions interact with modified graphene at various stages of insertion. This method provides more precise electrical data and has helped us understand the complex interactions involved. Specifically, we found a new site for ion insertion that is energetically favorable. We also explored how modifying the graphene surface affects ions of different sizes and charges and examined how the number of graphene layers influences these interactions. Our discoveries are crucial for developing new materials that could replace lithium-ion batteries and provide a foundation for adjusting electrical properties in battery design through ion staging and surface modifications.

cond-mat.mtrl-sci↗

arXiv2023

Transforming Materials Discovery for Artificial Photosynthesis: High-Throughput Screening of Earth-Abundant Semiconductors

We present a highly efficient workflow for designing semiconductor structures with specific physical properties, which can be utilized for a range of applications, including photocatalytic water splitting. Our algorithm generates candidate structures composed of earth-abundant elements that exhibit optimal light-trapping, high efficiency in \ce{H2} and/or \ce{O2} production, and resistance to reduction and oxidation in aqueous media. To achieve this, we use an ionic translation model trained on the Inorganic Crystal Structure Database (ICSD) to predict over thirty thousand undiscovered semiconductor compositions. These predictions are then screened for redox stability under Hydrogen Evolution Reaction (HER) or Oxygen Evolution Reaction (OER) conditions before generating thermodynamically stable crystal structures and calculating accurate band gap values for the compounds. Our approach results in the identification of dozens of promising semiconductor candidates with ideal properties for artificial photosynthesis, offering a significant advancement toward the conversion of sunlight into chemical fuels.

physics.app-ph↗

arXiv2023

MultiBinding Sites United in Covalent-Organic Frameworks (MSUCOF) for H$_2$ Storage and Delivery at Room Temperature

The storage of hydrogen gas (H$_2$) has presented a significant challenge that has hindered its use as a fuel source for transportation. To meet the Department of Energy's ambitious goals of achieving $50$ g L$^{-1}$ volumetric and $6.5$ wt \% gravimetric uptake targets, materials-based approaches are essential. Designing materials that can efficiently store hydrogen gas requires careful tuning of the interactions between the gaseous H$_2$ and the surface of the material. Metal-Organic Frameworks (MOFs) and Covalent-Organic Frameworks (COFs) have emerged as promising materials due to their exceptionally high surface areas and tunable structures that can improve gas-framework interactions. However, weak binding enthalpies have limited the success of many current candidates, which fail to achieve even $10$ g L$^{-1}$ volumetric uptake at ambient temperatures. To overcome this challenge, We utilized quantum mechanical (QM) based force fields (FF) to investigate the uptake and binding enthalpies of 3 linkers chelated with 7 different transition metals (TM), including both precious metals (Pd and Pt) and first row TM (Co, Cu, Fe, Ni, Mn), to design 24 different COFs in-silico. By applying QM-based FF with grand canonical Monte Carlo (GCMC) from 0-700 bar and 298 K, We demonstrated that Co-, Ni-, Mn-, Fe-, Pd-, and Pt-based MSUCOFs can already achieve the Department of Energy's hydrogen storage targets for 2025. Surprisingly, the COFs that incorporated the more affordable and abundant first-row TM often outperformed the precious metals. This promising development brings us one step closer to realizing a hydrogen-based energy economy.

physics.chem-ph↗

arXiv2023

Rare Isotope-Containing Diamond Color Centers for Fundamental Symmetry Tests

Detecting a non-zero electric dipole moment (EDM) in a particle would unambiguously signify physics beyond the Standard Model. A potential pathway towards this is the detection of a nuclear Schiff moment, the magnitude of which is enhanced by the presence of nuclear octupole deformation. However, due to the low production rate of isotopes featuring such "pear-shaped" nuclei, capturing, detecting, and manipulating them efficiently is a crucial prerequisite. Incorporating them into synthetic diamond optical crystals can produce defects with defined, molecule-like structures and isolated electronic states within the diamond band gap, increasing capture efficiency, enabling repeated probing of even a single atom, and producing narrow optical linewidths. In this study, we used density functional theory (DFT) to investigate the formation, structure, and electronic properties of crystal defects in diamond containing $^{229}$Pa, a rare isotope that is predicted to have an exceptionally strong nuclear octupole deformation. In addition, we identified and studied stable lanthanide-containing defects with similar electronic structures as non-radioactive proxies to aid in experimental methods. Our findings hold promise for the existence of such defects and can contribute to the development of a quantum information processing-inspired toolbox of techniques for studying rare isotopes.

quant-ph↗

arXiv2023

A poset version of Ramanujan results on Eulerian numbers and zeta values

We explore the operad of finite posets and its algebras. We use order polytopes to investigate the combinatorial properties of zeta values. By generalizing a family of zeta value identities, we demonstrate the applicability of this approach. In addition, we offer new proofs of some of Ramanujan's results on the properties of Eulerian numbers, interpreting his work as dealing with series inheriting the algebraic structure of disjoint unions of points. Finally, we establish a connection between our findings and the linear independence of zeta values.

math.CO↗

arXiv2023

Polychrony as Chinampas

In this paper, we study the flow of signals through linear paths with the nonlinear condition that a node emits a signal when it receives external stimuli or when two incoming signals from other nodes arrive coincidentally with a combined amplitude above a fixed threshold. Sets of such nodes form a polychrony group and can sometimes lead to cascades. In the context of this work, cascades are polychrony groups in which the number of nodes activated as a consequence of other nodes is greater than the number of externally activated nodes. The difference between these two numbers is the so-called profit. Given the initial conditions, we predict the conditions for a vertex to activate at a prescribed time and provide an algorithm to efficiently reconstruct a cascade. We develop a dictionary between polychrony groups and graph theory. We call the graph corresponding to a cascade a chinampa. This link leads to a topological classification of chinampas. We enumerate the chinampas of profits zero and one and the description of a family of chinampas isomorphic to a family of partially ordered sets, which implies that the enumeration problem of this family is equivalent to computing the Stanley-order polynomials of those partially ordered sets.

math.CO↗

arXiv2022

Obtaining transferable chemical insight from solving machine-learning classification problems: Thermodynamical properties prediction, atomic composition as good as Coulomb matrix

Machine learning (ML) can be used to construct surrogate models for the fast prediction of a property of interest. ML can thus be applied to chemical projects, where the usual experimentation or calculation techniques can take hours or days for just one sample. In this manner, the most promising candidate samples could be extracted from an extensive database and subjected to further in-depth analysis. Despite their broad applicability, it can be challenging to apply ML methods to a given chemical problem since a multitude of design decisions must be made, such as the molecular descriptor to use or the optimizer to train the model. Here we present a methodology for the meaningful exploration of a given molecular problem through classification experiments. This conceptually simple methodology results in transferable insight on the selected problem and can be used as a platform from which prediction difficulty is estimated, molecular representations are tested and refined, and more precise or ambitious projects can be undertaken. Physicochemical insight can also be obtained. This methodology is illustrated through the use of multiple molecular descriptors for the prediction of enthalpy, Gibbs' free energy, zero-point vibrational energy, and constant-volume calorific capacity of the molecules from the public database QM9 [Ramakrishnan2014] with 133,885 organic molecules. A noteworthy result is that for the classification problem we propose, the low-resolution descriptor `atomic composition' [Tchagang2019] can reach a classification rate almost on par with the high-resolution `sorted Coulomb matrix' [Rupp2012,Montavon2012,Hansen2013] ($>90\%$), provided that an appropriate optimizer is used during training.

physics.chem-ph↗

arXiv2022

Relativistic Quantum Calculations to Understand the Contribution of f-Orbitals and Chemical Bonding of Actinides with Organic Ligands

The nuclear waste problem is one of the main interests of the rare earth and actinide elements chemistry. Studies of Actinide-containing compounds are at the frontier of the applications of current theoretical methods due to the need to consider relativistic effects and approximations to the Dirac equation in them. Here, we employ four-component relativistic quantum calculations and scalar approximations to understand the contribution of f-type atomic orbitals in the chemical bonding of actinides (Ac) to organic ligands. We studied the relativistic quantum structure of an isostructural family made of Plutonium (Pu), Americium (Am), Californium (Cf), and Berkelium (Bk) atoms with the redox-active model ligand; DOPO (2,4,6,8-tetra-tert-butyl-1-oxo-1H-phenoxazin-9-olate). Crystallographic structures were available to validate our calculations for all mentioned elements except for Cf. In short, state-of-the-art relativistic calculations were performed at different levels of theory to investigate the relativistic effects and electron correlations on geometrical structures and bonding energies of $Ac$-DOPO$_3$ complexes ($Ac$=Pu, Am, Cf, Bk) : 1) the scalar relativistic zeroth order regular approximation (ZORA) within the hybrid density functional theory (DFT) and 2) the four-component Dirac equation with the Dirac-Hartree-Fock (4c-DHF) and Lévy-Leblond (LL) Hamiltonians. We show that scalar DFT-ZORA could be used as an efficient theoretical approximation to first approximate the geometry and electronic properties of actinides which are difficult to synthesize or characterize; but knowing that the higher levels of theory, like the 4c-DHF, gives closer results to experiments than the scalar DFT-ZORA. We also performed spin-free calculations of geometric parameters for the Americium and Berkelium compounds.

physics.chem-ph↗