arXiv subjects

Guillaume Lambard

Publications and source records attributed to Guillaume Lambard.

8 recordsLinked to original sources

arXiv2026

A 3-semi-perfect 1-factorization of the six-dimensional hypercube

For a 1-factorization $F=\{M_1,\ldots,M_d\}$ of the hypercube $Q_d$, let $G[F]$ have vertex set $F$, with $M_iM_j$ an edge exactly when $M_i\cup M_j$ is a Hamilton cycle. Behague proved that $Q_{k+\ell}$ has a 1-factorization $F$ with $G[F]\cong K_{k,\ell}$ for all positive $k,\ell$ except possibly $k=\ell=3$. We give an explicit 1-factorization of $Q_6$ for which $G[F]\cong K_{3,3}$, resolving the exceptional case. The construction is supplied as a finite certificate. Its correctness can be checked directly from the tables in the paper or by either of two independent, short, standard-library verifiers supplied with the certificate.

math.CO↗

arXiv2026

Finetuning-Free Diffusion Model with Adaptive Constraint Guidance for Inorganic Crystal Structure Generation

Generative diffusion models have emerged as powerful tools for the discovery of inorganic crystal structures, yet steering their sampling process toward user-defined physical and chemical objectives remains challenging. We present a computational framework that integrates adaptive constraint guidance into a pre-trained crystal diffusion model, enabling the generation of candidate structures that satisfy targeted structural and chemical requirements without model retraining. The approach incorporates differentiable constraint functions directly during sampling, providing an interpretable mechanism for expert-driven exploration of the crystal structure space. To assess the reliability of generated candidates, we introduce a multi-stage validation workflow combining descriptor-based analysis, duplicate removal, comparison with reference crystal databases, graph neural network energy prediction, and thermodynamic stability evaluation through convex-hull analysis. The framework is applied to several classes of inorganic compounds and to constraints involving atomic volume, local coordination environments, and near-neighbor structural motifs. Results demonstrate that adaptive guidance effectively redirects the sampling distribution toward structures exhibiting the desired characteristics while preserving chemical plausibility. Subsequent validation reveals which generated candidates remain viable after energetic and thermodynamic screening. The proposed methodology provides a practical and transparent strategy for incorporating expert knowledge into crystal generative models and establishes a general computational framework for constrained materials discovery.

cond-mat.mtrl-sci↗

arXiv2026

Beyond Structure: Revolutionising Materials Discovery via AI-Driven Synthesis Protocol-Property Relationships

The current structure-centric paradigm in artificial intelligence (AI)-driven materials discovery, despite delivering thousands of candidate structures, is stalling at a critical barrier: the synthesizability gap. We argue that closing this gap demands a pivot to a synthesis-first paradigm in which executable synthesis protocols, not just atomic configurations, are treated as primary design variables. We outline a roadmap built on three pillars: (i) representing synthesis procedures as machine-readable protocols, (ii) deploying generative and inverse-design models to propose actionable reaction pathways and recipes, and (iii) integrating closed-loop optimisation to refine protocols against experimental realities and sustainability constraints. Framed in terms of the causal backbone P->X->y from protocol P to structure X and properties y, this perspective sets out methodological building blocks, standards needs and self-driving laboratory (SDL) integration strategies to accelerate reproducible, data-first materials discovery.

cond-mat.mtrl-sci↗

arXiv2025

An Analytical Exploration of the Erdös-Moser Equation $ \sum_{i=1}^{m-1} i^k = m^k $ Using Approximation Methods

The Erdös-Moser equation $ \sum_{i=1}^{m - 1} i^k = m^k $ is a longstanding challenge in number theory, with the only known integer solution being $ (k,m) = (1,3) $. Here, we investigate whether other solutions might exist by using the Euler-MacLaurin formula to approximate the discrete sum $ S(m-1,k) $ with a continuous function $ S_{\mathbb{R}}(m-1,k) $. We then analyze the resulting approximate polynomial $ P_{\mathbb{R}}(m) = S_{\mathbb{R}}(m-1,k) - m^k $ under the rational root theorem to look for integer roots. Our approximation confirms that for $ k=1 $, the only solution is $ m=3 $, and for $ k \geq 2 $ it suggests there are no further positive integer solutions. However, because Diophantine problems demand exactness, any omission of correction terms in the Euler-MacLaurin formula could mask genuine solutions. Thus, while our method offers valuable insights into the behavior of the Erdös-Moser equation and illustrates the analytical challenges involved, it does not constitute a definitive proof. We discuss the implications of these findings and emphasize that fully rigorous approaches, potentially incorporating prime-power constraints, are needed to conclusively resolve the conjecture.

math.NT↗

arXiv2025

Exploring the Expertise of Large Language Models in Materials Science and Metallurgical Engineering

The integration of artificial intelligence into various domains is rapidly increasing, with Large Language Models (LLMs) becoming more prevalent in numerous applications. This work is included in an overall project which aims to train an LLM specifically in the field of materials science. To assess the impact of this specialized training, it is essential to establish the baseline performance of existing LLMs in materials science. In this study, we evaluated 15 different LLMs using the MaScQA question answering (Q&A) benchmark. This benchmark comprises questions from the Graduate Aptitude Test in Engineering (GATE), tailored to test models' capabilities in answering questions related to materials science and metallurgical engineering. Our results indicate that closed-source LLMs, such as Claude-3.5-Sonnet and GPT-4, perform the best with an overall accuracy of ~84%, while the open-source models, Llama3-70b and Phi3-14b, top at ~56% and ~43%, respectively. These findings provide a baseline for the raw capabilities of LLMs on Q&A tasks applied to materials science, and emphasize the substantial improvement that could be brought to open-source models via prompt engineering and fine-tuning strategies. We anticipate that this work could push the adoption of LLMs as valuable assistants in materials science, demonstrating their utility in this specialized domain and related sub-domains.

physics.comp-ph↗

arXiv2024

Mining experimental data from Materials Science literature with Large Language Models: an evaluation study

This study is dedicated to assessing the capabilities of large language models (LLMs) such as GPT-3.5-Turbo, GPT-4, and GPT-4-Turbo in extracting structured information from scientific documents in materials science. To this end, we primarily focus on two critical tasks of information extraction: (i) a named entity recognition (NER) of studied materials and physical properties and (ii) a relation extraction (RE) between these entities. Due to the evident lack of datasets within Materials Informatics (MI), we evaluated using SuperMat, based on superconductor research, and MeasEval, a generic measurement evaluation corpus. The performance of LLMs in executing these tasks is benchmarked against traditional models based on the BERT architecture and rule-based approaches (baseline). We introduce a novel methodology for the comparative analysis of intricate material expressions, emphasising the standardisation of chemical formulas to tackle the complexities inherent in materials science information assessment. For NER, LLMs fail to outperform the baseline with zero-shot prompting and exhibit only limited improvement with few-shot prompting. However, a GPT-3.5-Turbo fine-tuned with the appropriate strategy for RE outperforms all models, including the baseline. Without any fine-tuning, GPT-4 and GPT-4-Turbo display remarkable reasoning and relationship extraction capabilities after being provided with merely a couple of examples, surpassing the baseline. Overall, the results suggest that although LLMs demonstrate relevant reasoning skills in connecting concepts, specialised models are currently a better choice for tasks requiring extracting complex domain-specific entities like materials. These insights provide initial guidance applicable to other materials science sub-domains in future work.

cs.CL↗

arXiv2019

SMILES-X: autonomous molecular compounds characterization for small datasets without descriptors

There is more and more evidence that machine learning can be successfully applied in materials science and related fields. However, datasets in these fields are often quite small ($\ll1000$ samples). It makes the most advanced machine learning techniques remain neglected, as they are considered to be applicable to big data only. Moreover, materials informatics methods often rely on human-engineered descriptors, that should be carefully chosen, or even created, to fit the physicochemical property that one intends to predict. In this article, we propose a new method that tackles both the issue of small datasets and the difficulty of task-specific descriptors development. The SMILES-X is an autonomous pipeline for molecular compounds characterisation based on a \{Embed-Encode-Attend-Predict\} neural architecture with a data-specific Bayesian hyper-parameters optimisation. The only input to the architecture -- the SMILES strings -- are de-canonicalised in order to efficiently augment the data. One of the key features of the architecture is the attention mechanism, which enables the interpretation of output predictions without extra computational cost. The SMILES-X shows new state-of-the-art results in the inference of aqueous solubility ($\overline{RMSE}_{test} \simeq 0.57 \pm 0.07$ mols/L), hydration free energy ($\overline{RMSE}_{test} \simeq 0.81 \pm 0.22$ kcal/mol, which is $\sim 24.5\%$ better than molecular dynamics simulations), and octanol/water distribution coefficient ($\overline{RMSE}_{test} \simeq 0.59 \pm 0.02$ for LogD at pH 7.4) of molecular compounds. The SMILES-X is intended to become an important asset in the toolkit of materials scientists and chemists. The source code for the SMILES-X is available at \href{https://github.com/GLambard/SMILES-X}{github.com/GLambard/SMILES-X}.

physics.comp-ph↗

arXiv2012

Indirect dark matter search with the ANTARES neutrino telescope

Using the data recorded by the ANTARES neutrino telescope during 2007 and 2008, a search for high energy neutrinos coming from the direction of the Sun has been performed. The neutrino selection criteria have been chosen so as to maximize the rejection of the atmospheric background with respect to possible signals produced by the self-annihilation of weakly interactive massive particles accumulated in the centre of the Sun. After data unblinding, the number of neutrinos observed was found to be compatible with background expectations. The results obtained were compared to the fluxes predicted by the Constrained Minimal Supersymmetric Standard Model, and 90% upper limits for this model were obtained. Our limits are competitive with those obtained by other neutrino telescopes such as IceCube and SuperKamiokande, which give ANTARES limits for the spin-dependent WIMP-proton cross-section that are more stringent than those obtained by direct search experiments.

hep-ex↗