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Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely-trusted many-body method for solving the Schr\"{o}dinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability with system complexity and near-perfect utilization of computational power makes FN-DMC ideally positioned to leverage new advances in computing to address increasingly complex scientific problems. Even though the method is widely used as a computational gold standard, reproducibility across the numerous FN-DMC code implementations has yet to be demonstrated. This difficulty stems from the diverse array of DMC algorithms and trial wave functions, compounded by the method's inherent stochastic nature. This study represents a community-wide effort to address the titular question, affirming that: Yes, FN-DMC is reproducible (when handled with care). Using the water-methane dimer as the canonical test case, we compare results from eleven different FN-DMC codes and show that the approximations to treat the non-locality of pseudopotentials are the primary source of the discrepancies between them. In particular, we demonstrate that, for the same choice of determinantal component in the trial wave function, reliable and reproducible predictions can be achieved by employing the T-move (TM), the determinant locality approximation (DLA), or the determinant T-move (DTM) schemes, while the older locality approximation (LA) leads to considerable variability in results. This work lays the foundation to establish accurate and reproducible FN-DMC estimates for all future studies across applications in materials science, physics, chemistry, and biology.

The accurate treatment of non-covalent interactions is necessary to model a wide range of applications, from molecular crystals to surface catalysts to aqueous solutions and many more. Quantum diffusion Monte Carlo (DMC) and coupled cluster theory with single, double and perturbative triple excitations [CCSD(T)] are considered two widely-trusted methods for treating non-covalent interactions. However, while they have been well-validated for small molecules, recent work has indicated that these two methods can disagree by more than $7.5\,$kcal/mol for larger systems. The origin of this discrepancy remains unknown. Moreover, the lack of systematic comparisons, particularly for medium-sized complexes, has made it difficult to identify which systems may be prone to such disagreements and the potential scale of these differences. In this work, we leverage the latest developments in DMC to compute interaction energies for the entire S66 dataset, containing 66 medium-sized complexes with a balanced representation of dispersion and electrostatic interactions. Comparison to previous CCSD(T) references reveals systematic trends, with DMC predicting stronger binding than CCSD(T) for electrostatic-dominated systems, while the binding becomes weaker for dispersion-dominated systems. We show that the relative strength of this discrepancy is correlated to the ratio of electrostatic and dispersion interactions, as obtained from energy decomposition analysis methods. Finally, we pinpoint systems in the S66 dataset where these discrepancies are particularly prominent, offering cost-effective benchmarks to guide future developments in DMC, CCSD(T) as well as the wider electronic structure theory community.

Brick kilns are a major source of air pollution in Pakistan, with many operating without regulation. A key challenge in Pakistan and across the Indo-Gangetic Plain is the limited air quality monitoring and lack of transparent data on pollution sources. To address this, we present a two-fold AI approach that combines low-resolution Sentinel-2 and high-resolution imagery to map brick kiln locations. Our process begins with a low-resolution analysis, followed by a post-processing step to reduce false positives, minimizing the need for extensive high-resolution imagery. This analysis initially identified 20,000 potential brick kilns, with high-resolution validation confirming around 11,000 kilns. The dataset also distinguishes between Fixed Chimney and Zigzag kilns, enabling more accurate pollution estimates for each type. Our approach demonstrates how combining satellite imagery with AI can effectively detect specific polluting sources. This dataset provides regulators with insights into brick kiln pollution, supporting interventions for unregistered kilns and actions during high pollution episodes., Comment: Submitted to Nature Scientific Data - Under Review 25 pages in total, 6 images, 4 tables and 1 supplementary document

This paper investigates the factuality of large language models (LLMs) as knowledge bases in the legal domain, in a realistic usage scenario: we allow for acceptable variations in the answer, and let the model abstain from answering when uncertain. First, we design a dataset of diverse factual questions about case law and legislation. We then use the dataset to evaluate several LLMs under different evaluation methods, including exact, alias, and fuzzy matching. Our results show that the performance improves significantly under the alias and fuzzy matching methods. Further, we explore the impact of abstaining and in-context examples, finding that both strategies enhance precision. Finally, we demonstrate that additional pre-training on legal documents, as seen with SaulLM, further improves factual precision from 63% to 81%., Comment: CIKM 2024, short paper

Obtaining annotated table structure data for complex tables is a challenging task due to the inherent diversity and complexity of real-world document layouts. The scarcity of publicly available datasets with comprehensive annotations for intricate table structures hinders the development and evaluation of models designed for such scenarios. This research paper introduces a novel approach for generating annotated images for table structure by leveraging conditioned mask images of rows and columns through the application of latent diffusion models. The proposed method aims to enhance the quality of synthetic data used for training object detection models. Specifically, the study employs a conditioning mechanism to guide the generation of complex document table images, ensuring a realistic representation of table layouts. To evaluate the effectiveness of the generated data, we employ the popular YOLOv5 object detection model for training. The generated table images serve as valuable training samples, enriching the dataset with diverse table structures. The model is subsequently tested on the challenging pubtables-1m testset, a benchmark for table structure recognition in complex document layouts. Experimental results demonstrate that the introduced approach significantly improves the quality of synthetic data for training, leading to YOLOv5 models with enhanced performance. The mean Average Precision (mAP) values obtained on the pubtables-1m testset showcase results closely aligned with state-of-the-art methods. Furthermore, low FID results obtained on the synthetic data further validate the efficacy of the proposed methodology in generating annotated images for table structure., Comment: Accepted in ICDAR 2024

CO$_2$ capture using carbon-based materials, particularly graphene and graphene-like materials, is a promising strategy to deal with CO$_2$ emissions. However, significant gaps remain in our understanding of the molecular-level interaction between CO$_2$ molecules and graphene, particularly, in terms of chemical bonding and electron transfer. In this work, we employ random structure search and density functional theory to understand the adsorption of CO$_2$ molecules on Ca, Sr, Na, K, and Ti decorated graphene surfaces. Compared to the pristine material, we observe enhanced CO$_2$ adsorption on the decorated graphene surfaces. Particularly on group 2 metals and titanium decorated graphene, CO$_2$ can be strongly chemisorbed as a bent CO$_2$ anion or as an oxalate, depending on the number of CO$_2$ molecules. Electronic structure analysis reveals the adsorption mechanism to involve an ionic charge transfer from the metal adatom to the adsorbed CO$_2$. Overall, this study suggests that reducing CO$_2$ to oxalate on group 2 metals and titanium metal-decorated graphene surfaces is a potential strategy for CO$_2$ storage., Comment: Main manuscript and Supplementary information provided

In the present manuscript, we focus on a novel tri-nonlocal Kirchhoff problem, which involves the $p(x)$-fractional Laplacian equations of variable order. The problem is stated as follows: \begin{eqnarray*} \left\{ \begin{array}{ll} M\Big(\sigma_{p(x,y)}(u)\Big)(-\Delta)^{s(\cdot)}_{p(\cdot)}u(x) =\lambda |u|^{q(x)-2}u\left(\int_\O\frac{1}{q(x)} |u|^{q(x)}dx \right)^{k_1}+\beta|u|^{r(x)-2}u\left(\int_\O\frac{1}{r(x)} |u|^{r(x)}dx \right)^{k_2} \quad \mbox{in }\Omega, \\ u=0 \quad \mbox{on }\partial\Omega, \end{array} \right. \end{eqnarray*} where the nonlocal term is defined as $$ \sigma_{p(x,y)}(u)=\int_{\Omega\times \Omega}\frac{1}{p(x,y)}\frac{|u(x)-u(y)|^{p(x,y)}}{|x-y|^{N+s(x,y)p(x,y)}} \,dx\,dy. $$ Here, $\Omega\subset\mathbb{R}^{N}$ represents a bounded smooth domain with at least $N\geq2$. The function $M(s)$ is given by $M(s) = a - bs^\gamma$, where $a\geq 0$, $b>0$, and $\gamma>0$. The parameters $k_1$, $k_2$, $\lambda$ and $\beta$ are real parameters, while the variables $p(x)$, $s(\cdot)$, $q(x)$, and $r(x)$ are continuous and can change with respect to $x$. To tackle this problem, we employ some new methods and variational approaches along with two specific methods, namely the Fountain theorem and the symmetric Mountain Pass theorem. By utilizing these techniques, we establish the existence and multiplicity of solutions for this problem separately in two distinct cases: when $a>0$ and when $a=0$. To the best of our knowledge, these results are the first contributions to research on the variable-order $p(x)$-fractional Laplacian operator., Comment: 21 pages

Molecular hydrogen is at the core of hydrogen energy applications and has the potential to significantly reduce the use of carbon dioxide emitting energy processes. However, hydrogen gas storage is a major bottleneck for its large-scale use as current storage methods are energy intensive. Among different storage methods, physisorbing molecular hydrogen at ambient pressure and temperatures is a promising alternative - particularly thanks to tuneable lightweight nanomaterials and high throughput screening methods. Nonetheless, understanding hydrogen adsorption in well-defined nanomaterials remains experimentally challenging and reference information is scarce despite the proliferation of works predicting hydrogen adsorption. In this work, we focus on Li, Na, Ca, and K, decorated graphene sheets as substrates for hydrogen adsorption and compute the most accurate adsorption energies available to date using quantum diffusion Monte Carlo (DMC). Building on our previous insights at the density functional theory (DFT) level, we find that a weak covalent chemisorption of molecular hydrogen, known as Kubas binding, is feasible on Ca decorated graphene according to DMC, in agreement with DFT. This finding is in contrast to previous DMC predictions of the 4H$_2$/Ca$^+$ gas cluster where chemisorption is not favoured. However, we find that the adsorption energy of hydrogen on metal decorated graphene according to a widely-used DFT method is not fully consistent with DMC and the discrepancies are not systematic. The reference adsorption energies reported herein can be used to find better work-horse methods for application in large-scale modelling of hydrogen adsorption. Furthermore, the implications of this work affect strategies for finding suitable hydrogen storage materials and high-throughput methods.

This paper deals with a class of nonlocal variable $s(.)$-order fractional $p(.)$-Kirchhoff type equations: \begin{eqnarray*} \left\{ \begin{array}{ll} \mathcal{K}\left(\int_{\mathbb{R}^{2N}}\frac{1}{p(x,y)}\frac{|\varphi(x)-\varphi(y)|^{p(x,y)}}{|x-y|^{N+s(x,y){p(x,y)}}} \,dx\,dy\right)(-\Delta)^{s(\cdot)}_{p(\cdot)}\varphi(x) =f(x,\varphi) \quad \mbox{in }\Omega, \\ \varphi=0 \quad \mbox{on }\mathbb{R}^N\backslash\Omega. \end{array} \right. \end{eqnarray*} Under some suitable conditions on the functions $p,s, \mathcal{K}$ and $f$, the existence and multiplicity of nontrivial solutions for the above problem are obtained. Our results cover the degenerate case in the $p(\cdot)$ fractional setting.