arXiv subjects

José A. Márquez

Publications and source records attributed to José A. Márquez.

7 recordsLinked to original sources

arXiv2026

High-Throughput Photovoltaic Screening and Spacer-Dependent Shift Current in Two-Dimensional Hybrid Perovskites

High-throughput first-principles screening of structurally diverse two-dimensional hybrid organic-inorganic perovskites (2D HOIPs) provides a route to systematically explore their photovoltaic properties. Here, we develop a computational workflow with two distinct objectives: spectroscopic limited maximum efficiency (SLME) screening to identify materials with promising photovoltaic potential and nonlinear shift current calculations to characterize their bulk photovoltaic response. Applying this workflow to a curated dataset of experimentally reported 2D HOIPs, we identify more than 25 compounds with SLME values above 25\% and more than 25 compounds exhibiting shift current responses exceeding 10~$μ$A/V$^2$. We then focus on the Pb-I-based 2D HOIPs with $n=1$ to investigate the influence of spacer chemistry on the shift current response, revealing substantial spacer-dependent variations in both its magnitude and spectral peak position. Overall, these results highlight the potential of high-throughput first-principles calculations to accelerate the discovery of experimentally relevant 2D HOIPs with promising photovoltaic properties while revealing how organic spacer chemistry influences their nonlinear shift photocurrent response.

cond-mat.mtrl-sci↗

arXiv2026

ML-guided screening of chalcogenide perovskites as solar energy materials

Chalcogenide perovskites have emerged as promising absorber materials for next-generation photovoltaic devices, yet their experimental realization remains limited by competing phases, structural polymorphism, and synthetic challenges. Here, we present a fully data-driven and experimentally grounded screening and ranking framework to assess the stability and experimental feasibility of chalcogenide perovskites, integrating interpretable analytical descriptors, machine-learning models, and sustainability metrics. Using a curated experimental dataset of halide and chalcogenide compounds, we derive a new tolerance factor via the SISSO (sure independence screening and sparsifying operator) algorithm that more accurately distinguishes perovskite-forming compositions than established tolerance-factor-based screening criteria. This descriptor is combined with generative crystal structure prediction, composition-based bandgap estimation, and machine-learning-based feasibility assessment to systematically explore a wide chemical space of hypothetical chalcogenide perovskites. The resulting candidates are further evaluated using sustainability indicators, enabling multi-objective ranking tailored to both single-junction and tandem photovoltaic architectures. Beyond identifying several promising and previously unexplored chalcogenide perovskites, this work demonstrates a transferable screening strategy for chemically constrained materials spaces that balances optoelectronic performance, experimental viability, and long-term sustainability.

cond-mat.mtrl-sci↗

arXiv2026

An autonomous living database for perovskite photovoltaics

Scientific discovery is severely bottlenecked by the inability of manual curation to keep pace with exponential publication rates. This creates a widening knowledge gap. This is especially stark in photovoltaics, where the leading database for perovskite solar cells has been stagnant since 2021 despite massive ongoing research output. Here, we resolve this challenge by establishing an autonomous, self-updating living database (PERLA). Our pipeline integrates large language models with physics-aware validation to extract complex device data from the continuous literature stream, achieving human-level precision (>90%) and eliminating annotator variance. By employing this system on the previously inaccessible post-2021 literature, we uncover critical evolutionary trends hidden by data lag: the field has decisively shifted toward inverted architectures employing self-assembled monolayers and formamidinium-rich compositions, driving a clear trajectory of sustained voltage loss reduction. PERLA transforms static publications into dynamic knowledge resources that enable data-driven discovery to operate at the speed of publication.

cond-mat.mtrl-sci↗

arXiv2025

Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry

Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and (7) knowledge extraction and reasoning from scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research.

cs.LG↗

arXiv2024

From Text to Insight: Large Language Models for Materials Science Data Extraction

The vast majority of materials science knowledge exists in unstructured natural language, yet structured data is crucial for innovative and systematic materials design. Traditionally, the field has relied on manual curation and partial automation for data extraction for specific use cases. The advent of large language models (LLMs) represents a significant shift, potentially enabling efficient extraction of structured, actionable data from unstructured text by non-experts. While applying LLMs to materials science data extraction presents unique challenges, domain knowledge offers opportunities to guide and validate LLM outputs. This review provides a comprehensive overview of LLM-based structured data extraction in materials science, synthesizing current knowledge and outlining future directions. We address the lack of standardized guidelines and present frameworks for leveraging the synergy between LLMs and materials science expertise. This work serves as a foundational resource for researchers aiming to harness LLMs for data-driven materials research. The insights presented here could significantly enhance how researchers across disciplines access and utilize scientific information, potentially accelerating the development of novel materials for critical societal needs.

cond-mat.mtrl-sci↗

arXiv2020

Upper limit to the photovoltaic efficiency of imperfect crystals

The Shockley-Queisser (SQ) limit provides a convenient metric for predicting light-to-electricity conversion efficiency of a solar cell based on the band gap of the light-absorbing layer. In reality, few materials approach this radiative limit. We develop a formalism and a computational method to predict the maximum photovoltaic efficiency of imperfect crystals from first principles. Our scheme includes equilibrium populations of native defects, their carrier-capture coefficients, and the associated recombination rates. When applied to kesterite solar cells, we reveal an intrinsic limit of 20% for $\mathrm{Cu_2ZnSnSe_4}$, which falls far below the SQ limit of 32%. The effects of atomic substitution and extrinsic doping are studied, leading to pathways for enhanced efficiency of 31%. This approach can be applied to support targeted-materials selection for future solar-energy technologies.

physics.comp-ph↗

arXiv2018

The perovskite/transport layer interfaces dominate non-radiative recombination in efficient perovskite solar cells

Charge transport layers (CTLs) are key components of diffusion controlled perovskite solar cells, however, they can induce additional non-radiative recombination pathways which limit the open circuit voltage (V_OC) of the cell. In order to realize the full thermodynamic potential of the perovskite absorber, both the electron and hole transport layer (ETL/HTL) need to be as selective as possible. By measuring the quasi-Fermi level splitting (QFLS) of perovskite/CTL heterojunctions, we quantify the non-radiative interfacial recombination current for a wide range of commonly used CTLs, including various hole-transporting polymers, spiro-OMeTAD, metal oxides and fullerenes. We find that all studied CTLs limit the V_OC by inducing an additional non-radiative recombination current that is significantly larger than the loss in the neat perovskite and that the least-selective interface sets the upper limit for the V_OC of the device. The results also show that the V_OC equals the internal QFLS in the absorber layer of (pin, nip) cells with selective CTLs and power conversion efficiencies of up to 21.4%. However, in case of less selective CTLs, the V_OC is substantially lower than the QFLS which indicates additional losses at the contacts and/or interfaces. The findings are corroborated by rigorous device simulations which outline several important considerations to maximize the V_OC. This work shows that the real challenge to supress non-radiative recombination losses in perovskite cells on their way to the radiative limit lies in the suppression of carrier recombination at the perovskite/CTL interfaces.

physics.app-ph↗