Materials Design (France)

Magnesium (Mg) metal batteries are widely regarded as a sustainable and high-capacity alternative to lithium-ion batteries. Despite their promise, their practical implementation is hindered by electrolyte instability at Mg metal anodes, which limits reversible Mg deposition and battery performance. In particular, widely-used Mg salts such as Mg(TFSI)2 and Mg(PF6)2 have been the subject of conflicting experimental and computational reports, leaving the origins of electrolyte decomposition poorly understood. In this work, we address this critical knowledge gap by systematically examining interfacial stability of three representative salts, namely, Mg(TFSI)2, Mg(PF6)2, and Mg(CB11H12)2 on three Mg anode surface models: metallic Mg (m-Mg), stoichiometric MgO (s-MgO), and non-stoichiometric MgO (ns-MgO), using ab initio molecular dynamics simulations. Our results demonstrate that oxide chemistry and surface heterogeneity critically dictate electrolyte stability. While m-Mg and s-MgO remain largely inert, ns-MgO surface triggers rapid anion decomposition, charge transfer, and irreversible passivation, explaining the experimental observations. Among the salts, Mg(CB11H12)2 shows exceptional stability across all surfaces, aligning with its reported cycling efficiency. Importantly, our study not only resolves the longstanding discrepancies between theory and experiment but also establishes a new computational framework that closely reflects experimental realities and provides critical atomistic insights into salt decomposition pathways.

Soft matter research has long been characterized by intrinsic complexity, arising from multiscale structure-property relationships, strong coupling between physical and chemical processes, and the need to reconcile theoretical abstraction with experimental observability. Computational tools have therefore become indispensable for navigating this complexity. From early algorithmic implementations developed for narrowly defined scientific problems, scientific software has evolved into sophisticated and often community-driven ecosystems. Despite these advances, persistent challenges remain in aligning rapidly evolving scientific demands with robust, generalizable, and sustainable software solutions. This Research Topic brings together contributions that reflect the central role of scientific software in contemporary soft matter research. Rather than treating software as a secondary technical component, the articles collected here emphasize its function as an enabling research infrastructure-one that shapes how data is generated, curated, analyzed, and ultimately transformed into knowledge. Across diverse applications and methodological perspectives, the contributions highlight how progress in soft matter research increasingly depends on the integration of sound software engineering practices with domain-specific scientific insight.A recurring theme across the Topic is the challenge of managing and reusing complex datasets generated across heterogeneous experimental and computational workflows. Exner et al. examine metadata stewardship in nanosafety research, identifying how project-centric data management practices have led to fragmentation and limited reuse. By advocating for flexible, machine-actionable metadata capture that can evolve alongside experimental workflows, their work reframes FAIR principles (Findable, Accessible, Interoperable, Reusable) as an integral part of day-to-day scientific practice rather than a post hoc compliance exercise. This perspective resonates strongly with the needs of soft matter research, where experiments, simulations, and models are tightly interwoven and rarely conform to rigid reporting templates.Building on this foundation, Maier et al. present the NanoCommons Knowledge Base as a practical realization of a community-oriented approach to data and tool integration. Their contribution illustrates how semantic interoperability and programmatic access can transform disparate datasets and modeling tools into a coherent knowledge ecosystem. By emphasizing data "visiting" rather than duplication and by supporting both human and machine access, the NanoCommons infrastructure demonstrates how scientific software can function as a shared resource that outlives individual projects and adapts to evolving research questions.In parallel with infrastructure-level developments, this Research Topic also highlights the importance of software that directly enables experimental investigation. Zakharov Looking toward the future, Cheimarios situates scientific software development within the broader transformation driven by artificial intelligence (AI). Focusing on soft matter physics as a demanding application domain, this review examines how machine-learned models, differentiable simulations, and automated pipelines are reshaping computational workflows. Importantly, the article emphasizes that these advances must be accompanied by lifecycle-oriented software practices, including reproducibility, provenance tracking, and governance frameworks. By integrating concepts from machine learning operations and FAIR principles for research software, this contribution provides a structured perspective on how AI-enabled scientific software can remain trustworthy, interpretable, and sustainable.Collectively, the articles in this Research Topic underscore a broader shift in soft matter research: scientific software is no longer merely a vehicle for implementing models, but a central medium through which scientific understanding is constructed, validated, and shared. Whether addressing metadata stewardship, knowledge integration, experimental control, or AI-driven modeling, the contributions converge on the need for software systems that are flexible yet robust, specialized yet interoperable.The challenges highlighted here-data fragmentation, reproducibility, scalability, and long-term sustainability-extend well beyond soft matter physics. As scientific inquiry becomes increasingly data-intensive and computationally adaptive, the approaches showcased in this Topic offer insights that are relevant across disciplines. By foregrounding scientific software as a core research output, this collection aims to stimulate further dialogue and innovation at the intersection of physics, materials science, and software engineering, ultimately supporting more transparent, reproducible, and impactful science.

Advances in QSAR are led by two core paradigms, 1) descriptor engineering, where complex fixedlength vectors of compounds are generated and conventional ML methods are applied to those representations and 2) raw chemical inputs (e.g., SMILES, 2D-graph) being provided to deep learning neural network models, which construct their own internal representations of molecules and learn iteratively over them.Here we present the Tsetlin Machine (TM) -which combines the accuracy and easy-use of existing rule-based QSAR ML methods (e.g., RF and XGBoost), the iterative learning aspect of NN algorithms and its intrinsic interpretability.The TM uses teams of finite-state automata which capture frequent patterns as propositional logic (clauses) via reinforcement learning.The benchmarking pipeline presented here demonstrates that TM-QSAR coupled with ECFP4 descriptors frequently performs better than existing rule-based QSAR methods for ROC-AUC, PRC-AUC and PPV, with a high capacity for inter-scaffold generalisation.However, due to the binary nature of TM-QSAR, performance it is currently limited when descretised continuous descriptors are used.TM-QSAR demonstrated particularly impressive classification scores for MOR (ROC-AUC = 0.87, PRC-AUC = 0.77) and CYPA4 (ROC-AUC = 0.92, PRC-AUC = 0.63), when compared to RF and XGBoost.Using TM in combination with substructural fingerprinting descriptors allows for an interpretability suite which can be extracted directly from clauses.Here we detail molecule property maps (TM-MPM) to view atom-wise TM-QSAR bioactivity contributions for single molecules and closed-form WAC scores (Weights Activations Clauses) for descriptor-wise contributions to regions of predicted chemical space.These methods show strong alignment of TM-QSAR interpretations to known ligandprotein interactions of the MOR target and gives non-linear, conditional interpretations for greater predicted bioactivity.Given this combination of accuracy, computational efficiency and interpretability, we provide a basis for TM-QSAR to be explored as a standard methodology in virtual screening toolkits.

Organic photovoltaics (OPVs) require joint optimization of materials parameters and active-layer microstructure to maximize device performance, including short-circuit current, 𝐽𝑠𝑐. We present a material property and microstructure-aware surrogate that predicts across diverse donor-acceptor systems and microstructures while providing calibrated uncertainties and interpretable design rules. We curate a 25k-sample, physics-informed dataset by sweeping electron and hole mobilities (𝜇𝑛, 𝜇𝑝) and exciton lifetime (𝜏𝑥) across multiple microstructure classes, and derive a compact feature set that couples materials parameters with graph-based microstructure descriptors (interfacial area, and connectivity/tortuosity) to predict 𝐽𝑠𝑐. A lightweight random-forest model achieves 𝑅2 ≥ 0.98 with ≤ 40% of the data used for training, and maintains accuracy under material-wise holdouts (i.e., material property holdouts). Partial-dependence analyses reveal regime transitions in the coupled design space: at low mobilities and short 𝐿𝑑, performance is dual-transport-limited; beyond a mobility threshold, 𝜏𝑥 (hence 𝐿𝑑) and interfacial proximity dominate 𝐽𝑠𝑐. The framework accelerates exploration of material-microstructure trade-offs, identifies whether to intervene via material (mobilities, and 𝜏𝑥) or processing (phase-separation length scale, connectivity, interfacial area), and is extensible to other organic optoelectronic systems. Code, features, and data are released to enable reproducibility and reuse.

This study utilized bioresource-derived monomers, eugenol methacrylate (EuMA) and capric methacrylate (CAMA), to create multifunctional biobased polymer particles exhibiting features such as aroma, antibacterial activity, and thermoregulation produced via precipitation polymerization. The monomers were made using the esterification of eugenol and capric acid with methacrylic anhydride, giving the vinyl groups, which were subsequently polymerized via free-radical polymerization. The biobased poly(EuMA-co-CAMA-co-methacrylic acid) (P(EuMA-co-CAMA-co-MAA)) particles were fabricated without surfactants or additional cross-linkers. They demonstrated a stable emulsion with a surface charge of −31.86 mV derived from the MAA segment. The particles were spherical with a smooth surface, exhibiting an average diameter of 1.80 μm and a polydispersity index of 0.273. The incorporation of EuMA demonstrated antimicrobial efficacy, significantly inhibiting S. epidermidis, with a minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of 2.5 mg/mL. Furthermore, the CAMA segment operated as a thermoregulator, exhibiting phase-change material (PCM) properties, with latent heats of melting (140 J/g) and crystallization (132 J/g), similar to those of pure capric acid. The results indicate that biobased P(EuMA-co-CAMA-co-MAA) particles are viable for fabric coatings, antimicrobial packaging, and other environmentally friendly textiles and consumer product applications.

The solid electrolyte interphase (SEI) plays a crucial role in the reversible metal-ion deposition at electrodes, impacting battery performance and lifespan. SEI formation results from the decomposition of salts and solvents at the electrode surface via redox reactions, with its growth governed by dynamic interactions between the electrode, electrolyte, and decomposed products. These interactions are difficult to capture experimentally. Here, using large-scale ab initio molecular dynamics simulations, we explored the formation and evolution of SEIs at the calcium anode under varying solvent, salt, and temperature conditions over 100 ps. Our simulations are inspired by several recent experiments, which show reversible calcium-ion deposition only with a few selected salts under specific experimental conditions. Our work not only deciphers these experiments but also provides detailed microscopic insights into (i) the solvent decomposition, (ii) the order of salt/solvent decomposition in the cell, (iii) the impact of electrode passivation on salt’s stability, and (iv) the role of nuclear dynamics and coordination geometry in anion decomposition, thereby influencing the reversible deposition of cations. These findings offer critical insights into the formation and evolution of SEIs at metal anodes and provide guidance for designing electrolytes to enhance the performance and longevity of metal anode batteries.

Electrochemical reduction of nitrate, which is a widespread pollutant, offers an alternate approach for the ammonia synthesis. Developing a stable catalyst that can selectively reduce nitrate to ammonia in a neutral medium is considered in this study. An active catalyst based on iron oxide supported on boron carbon nitride (γ-Fe2O3/BCN) is developed. The catalyst shows a Faradaic efficiency of 95% (at −0.59 V vs RHE) and yield rate of 7570 μg h–1 mgcat–1 (at −0.64 V vs RHE) of ammonia. The γ-Fe2O3/BCN catalyst also exhibits long-term stability in cyclic tests. The computational studies reveal that the charge transfer between γ-Fe2O3 and BCN in γ-Fe2O3/BCN has a critical role in enhancing the catalytic activity as compared to γ-Fe2O3 and BCN.

In this study, an effective computational methodology named Scoring-Assisted Generative Exploration (SAGE) was devised by integrating the GEGL framework and multiple QSAR models. De novo molecular design, which involves exploring chemical space for drug-like molecules with desired properties, has been significantly advanced by deep learning techniques. Generative deep learning has revolutionized the field of de novo molecular design by enabling direct learning from input data without relying on human-made rules. This study introduces an innovative computational method, Scoring-Assisted Generative Exploration (SAGE), designed to enhance chemical diversity and optimize properties through virtual synthesis simulation, generation of bridged bicyclic rings, and application of multiple scoring models for drug-likeness. SAGE was tested on six protein targets and successfully generated high-scoring molecules within reasonable steps by optimizing for target specificity, synthetic accessibility, solubility, and metabolic stability. Additionally, SAGE identified a top-ranked molecule as a dual inhibitor of acetylcholinesterase and monoamine oxidase B, demonstrating its capability to optimize multiple properties simultaneously. These findings underscore the potential of SAGE and de novo design strategies in advancing drug discovery and development. With the ability to rapidly explore vast chemical spaces and generate novel molecules with desired properties, deep learning-based approaches like SAGE have the potential to revolutionize the field of drug discovery and development.