arXiv subjects

Andrey Ustyuzhanin

Publications and source records attributed to Andrey Ustyuzhanin.

At least 19 recordsLinked to original sources

arXiv2026

Directing Open-Ended Evolution in Artificial Life via Multi-Scale Path Divergence

Open-ended evolution (OEE) in artificial life is typically driven by uninterpretable, black-box neural-network complexity metrics, leaving life-like systems disconnected from physical theories of complexity. We introduce MSPD (Multi-Scale Path Divergence, denoted $D_P$), a renormalization-group-inspired scalar that quantifies how the heterogeneity of a system's local transition laws is organized across temporal and spatial scales. MSPD is defined at the population level as a functional of the realised trajectory and is computed as a windowed finite-resolution estimator, with consistency between the two stated as a proposition. The metric is an explicit formula and plays a dual role: as a gradient-free fitness function and as a post-hoc analytical lens on any simulation that exposes local transition laws. On a Flow-Lenia substrate we establish three claims. (C1) Under fixed-context replay of the exact pathwise objective, MSPD-optimized parameters score higher than matched random parameters. (C2) Along optimized trajectories, states whose local transition laws are more heterogeneous yield larger future divergence than matched, less-heterogeneous states under exact-state stochastic continuations, so the metric tracks intrinsic dynamics rather than injected noise. (C5) Higher MSPD corresponds to stronger scale-dependent frustration --- larger differences between the dynamics expressed at different spatial extents --- linking MSPD to the frustration criterion of biological complexity in the sense of Vanchurin et al. All three transfer to Life-like cellular automata and Particle Life++, indicating that MSPD is not specific to a single substrate. A single explicit formula thus both \emph{directs} open-ended evolution and provides a principled bridge to the physics of complexity that black-box drivers do not.

cs.NE↗

arXiv2026

Generative design of inorganic materials

Materials discovery is fundamental to advance next-generation technologies as well as for sustainable and circular economy. Beyond computational screening, generative models are efficient at finding materials with desired properties, via multi-modal learning using multiscale data. This perspective examines the landscape of generative design for inorganic materials and discusses the integration of multi-modal learning with high-throughput experimental validation. We contextualize these challenges through the lens of a generative design framework as a unified approach to address the data-driven inverse design of functional materials. The central idea of the framework is constructed around a foundation AI model for inorganic materials interlinked deeply with various property databases and high-throughput experiments via a machine learning driven closed loop, which enables the framework to solve key challenges in functional materials. We argue that domain-specific implementations of such integrated workflows represent a promising pathway toward the unresolved challenge of data-driven inverse design for atom-engineered inorganic functional materials.

cond-mat.mtrl-sci↗

arXiv2026

Friction-Augmented Drifting Models for Resource-Efficient Domain Translation

Single-step generators promise high-fidelity synthesis at a fraction of the inference and training cost of ordinary differential equation (ODE)-based flow models, a central concern when compute is limited. Drifting Models (DMs) train a one-step generator by evolving samples under a kernel-based drift field, avoiding ODE integration entirely, but a two-particle surrogate of their iteration admits a \emph{locally repulsive} regime in which repulsion can dominate the attraction to the target. We introduce DMF (Drifting Model with Friction), which scales the drift field by a linearly-scheduled coefficient $1-\gamma(i)$. A closed-form analysis of the surrogate gives a per-step contraction threshold and a finite-horizon bound on the error trajectory, suggesting why friction can halt the iteration before it relaxes to a spurious force-balance fixed point. On FFHQ latent-space domain translation, DMF significantly improves on the frictionless DM it extends in both Fr\'echet Inception Distance (FID; paired $p=0.019$, Cohen's $d=1.71$) and CLIP-MMD (CMMD; $p=0.005$, $d=2.55$) with no additional forward passes or parameters, and on a 2D task it sharply improves DM's Fr\'echet (moment-matching) error while remaining on par under the 2-Wasserstein distance. DMF also achieves FID and CMMD comparable to the far more expensive Optimal Flow Matching (OFM) in our runs, at roughly $29\times$ lower training wall-clock on identical hardware. DMF thus delivers these gains with a single scheduled scalar.

cs.LG↗

arXiv2026

On the Codesign of Scientific Experiments and Industrial Systems

The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this coupling can be ignored -- when the problem can be successfully factored into simpler sub-tasks and the latter addressed serially -- there are situations in which that approach fails to converge to the absolute maximum of expected performance, as it results in a mis-alignment of the optimized hardware and software solutions. In this work we consider a few use cases of interest in fundamental science collected primarily from particle physics and related areas, and a pot-pourri of industrial and societal applications where the matter is similarly of relevance. We discuss the emergence of strong hardware-software coupling in some of those systems, as well as co-design procedures that may be deployed to identify the global maximum of their relevant utility functions. We observe how numerous opportunities exist to advance methods and tools for hardware-software co-design optimization, bridging fundamental science and industry through application- and challenge-driven projects, and shaping the future of scientific experiments and industrial systems.

physics.ins-det↗

arXiv2026

Symbolic regression for defect interactions in 2D materials

Machine learning models have become firmly established across all scientific fields. Extracting features from data and making inferences based on them with neural network models often yields high accuracy; however, this approach has several drawbacks. Symbolic regression is a powerful technique for discovering analytical equations that describe data, providing interpretable and generalizable models capable of predicting unseen data. Symbolic regression methods have gained new momentum with the advancement of neural network technologies and offer several advantages, the main one being the interpretability of results. In this work, we examined the application of the deep symbolic regression algorithm SEGVAE to determine the properties of two-dimensional materials with defects. Comparing the results with state-of-the-art graph neural network-based methods shows comparable or, in some cases, even identical outcomes. We also discuss the applicability of this class of methods in natural sciences.

cs.LG↗

arXiv2025

AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions

Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, predict, and design. In this roadmap we provide a forward-looking view of AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing. Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific workflows that connect simulations to experiments and generative systems grounded in synthesisability rather than purely idealised phases. Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation while maintaining reproducibility and physical interpretability. Taken together, these perspectives outline where AI-enabled science stands today, identify bottlenecks in data, methods and infrastructure, and chart concrete directions for building AI systems that are not only more powerful but also more transparent and capable of accelerating discovery in complex real-world environments.

physics.soc-ph↗

arXiv2025

Energy Underprediction from Symmetry in Machine-Learning Interatomic Potentials

Machine learning interatomic potentials (MLIAPs) have emerged as powerful tools for accelerating materials simulations with near-density functional theory (DFT) accuracy. However, despite significant advances, we identify a critical yet overlooked issue undermining their reliability: a systematic energy underprediction. This problem becomes starkly evident in large-scale thermodynamic stability assessments. By performing over 12 million calculations using nine MLIAPs for over 150,000 inorganic crystals in the Materials Project, we demonstrate that most frontier models consistently underpredict energy above hull (Ehull), a key metric for thermodynamic stability, total energy, and formation energy, despite the fact that over 90\% of test structures (DFT-relaxed) are in the training data. The mean absolute errors (MAE) for Ehull exceed ~30 meV/atom even by the best model, directly challenging claims of achieving ``DFT accuracy'' for property predictions central to materials discovery, especially related to (meta-)stability. Crucially, we trace this underprediction to insufficient handling of symmetry degrees of freedom (DOF), constituting both lattice symmetry and Wyckoff site symmetries for the space group. MLIAPs exhibit pronounced errors (MAE for Ehull $>$ ~40 meV/atom) in structures with high symmetry DOF, where subtle atomic displacements significantly impact energy landscapes. Further analysis also indicates that the MLIAPs show severe energy underprediction for a large proportion of near-hull materials. We argue for improvements on symmetry-aware models such as explicit DOF encoding or symmetry-regularized loss functions, and more robust MLIAPs for predicting crystal properties where the preservation and breaking of symmetry are pivotal.

cond-mat.mtrl-sci↗

arXiv2025

Wyckoff Transformer: Generation of Symmetric Crystals

Crystal symmetry plays a fundamental role in determining its physical, chemical, and electronic properties such as electrical and thermal conductivity, optical and polarization behavior, and mechanical strength. Almost all known crystalline materials have internal symmetry. However, this is often inadequately addressed by existing generative models, making the consistent generation of stable and symmetrically valid crystal structures a significant challenge. We introduce WyFormer, a generative model that directly tackles this by formally conditioning on space group symmetry. It achieves this by using Wyckoff positions as the basis for an elegant, compressed, and discrete structure representation. To model the distribution, we develop a permutation-invariant autoregressive model based on the Transformer encoder and an absence of positional encoding. Extensive experimentation demonstrates WyFormer's compelling combination of attributes: it achieves best-in-class symmetry-conditioned generation, incorporates a physics-motivated inductive bias, produces structures with competitive stability, predicts material properties with competitive accuracy even without atomic coordinates, and exhibits unparalleled inference speed.

cond-mat.mtrl-sci↗

arXiv2025

Predicting ionic conductivity in solids from the machine-learned potential energy landscape

Discovering new superionic materials is essential for advancing solid-state batteries, which offer improved energy density and safety compared to the traditional lithium-ion batteries with liquid electrolytes. Conventional computational methods for identifying such materials are resource-intensive and not easily scalable. Recently, universal interatomic potential models have been developed using equivariant graph neural networks. These models are trained on extensive datasets of first-principles force and energy calculations. One can achieve significant computational advantages by leveraging them as the foundation for traditional methods of assessing the ionic conductivity, such as molecular dynamics or nudged elastic band techniques. However, the generalization error from model inference on diverse atomic structures arising in such calculations can compromise the reliability of the results. In this work, we propose an approach for the quick and reliable screening of ionic conductors through the analysis of a universal interatomic potential. Our method incorporates a set of heuristic structure descriptors that effectively employ the rich knowledge of the underlying model while requiring minimal generalization capabilities. Using our descriptors, we rank lithium-containing materials in the Materials Project database according to their expected ionic conductivity. Eight out of the ten highest-ranked materials are confirmed to be superionic at room temperature in first-principles calculations. Notably, our method achieves a speed-up factor of approximately 50 compared to molecular dynamics driven by a machine-learning potential, and is at least 3,000 times faster compared to first-principles molecular dynamics.

cond-mat.mtrl-sci↗

arXiv2025

Time-local stochastic equation of motion for solid ionic electrolytes

Numerical studies of ionic motion through solid electrolytes commonly involve static nudged-elastic band (NEB) methods or costly \emph{ab initio} molecular dynamics (AIMD). Building on a time-local model of current carrier-electrolyte interaction and incorporating thermal motion, we introduce an approach that is intermediate between the two well-established methodologies by treating the electrolyte as an effective medium that interacts with the mobile particle. Through this coupling, the thermally vibrating electrolyte imparts energy to the charge carriers while also absorbing energy from them due to its own finite elasticity. Using a simple model system, we validate our approach through a series of numerical simulations. Our methodology reproduces both dissipative and diffusive behavior, and helps link microscopic system parameters to measurable macroscopic properties.

cond-mat.mtrl-sci↗

arXiv2025

Multi-property directed generative design of inorganic materials through Wyckoff-augmented transfer learning

Accelerated materials discovery is an urgent demand to drive advancements in fields such as energy conversion, storage, and catalysis. Property-directed generative design has emerged as a transformative approach for rapidly discovering new functional inorganic materials with multiple desired properties within vast and complex search spaces. However, this approach faces two primary challenges: data scarcity for functional properties and the multi-objective optimization required to balance competing tasks. Here, we present a multi-property-directed generative framework designed to overcome these limitations and enhance site symmetry-compliant crystal generation beyond P1 (translational) symmetry. By incorporating Wyckoff-position-based data augmentation and transfer learning, our framework effectively handles sparse and small functional datasets, enabling the generation of new stable materials simultaneously conditioned on targeted space group, band gap, and formation energy. Using this approach, we identified previously unknown thermodynamically and lattice-dynamically stable semiconductors in tetragonal, trigonal, and cubic systems, with bandgaps ranging from 0.13 to 2.20 eV, as validated by density functional theory (DFT) calculations. Additionally, we assessed their thermoelectric descriptors using DFT, indicating their potential suitability for thermoelectric applications. We believe our integrated framework represents a significant step forward in generative design of inorganic materials.

cond-mat.mtrl-sci↗

arXiv2025

Engineering Point Defects in MoS2 for Tailored Material Properties using Large Language Models

The tunability of physical properties in transition metal dichalcogenides (TMDCs) through point defect engineering offers significant potential for the development of next-generation optoelectronic and high-tech applications. Building upon prior work on machine learning-driven material design, this study focuses on the systematic introduction and manipulation of point defects in MoS2 to tailor their properties. Leveraging a comprehensive dataset generated via density functional theory (DFT) calculations, we explore the effects of various defect types and concentrations on the mate rial characteristics of TMDCs. Our methodology integrates the use of pre-trained large language models to generate defect configurations, enabling efficient predictions of defect-induced property modifications. This research differs from traditional methods of material generation and discovery by utilizing the latest advances in transformer model architecture, which have proven to be efficient and accurate discrete predictors. In contrast to high-throughput methods where configurations are generated randomly and then screened based on their physical properties, our approach not only enhances the understanding of defect-property relationships in TMDCs but also provides a robust framework for designing materials with bespoke properties. This facilitates the advancement of materials science and technology.

cond-mat.mtrl-sci↗

arXiv2024

EagleEye: Attention to Unveil Malicious Event Sequences from Provenance Graphs

Securing endpoints is challenging due to the evolving nature of threats and attacks. With endpoint logging systems becoming mature, provenance-graph representations enable the creation of sophisticated behavior rules. However, adapting to the pace of emerging attacks is not scalable with rules. This led to the development of ML models capable of learning from endpoint logs. However, there are still open challenges: i) malicious patterns of malware are spread across long sequences of events, and ii) ML classification results are not interpretable. To address these issues, we develop and present EagleEye, a novel system that i) uses rich features from provenance graphs for behavior event representation, including command-line embeddings, ii) extracts long sequences of events and learns event embeddings, and iii) trains a lightweight Transformer model to classify behavior sequences as malicious or not. We evaluate and compare EagleEye against state-of-the-art baselines on two datasets, namely a new real-world dataset from a corporate environment, and the public DARPA dataset. On the DARPA dataset, at a false-positive rate of 1%, EagleEye detects $\approx$89% of all malicious behavior, outperforming two state-of-the-art solutions by an absolute margin of 38.5%. Furthermore, we show that the Transformer's attention mechanism can be leveraged to highlight the most suspicious events in a long sequence, thereby providing interpretation of malware alerts.

cs.CR↗

arXiv2023

AI Competitions and Benchmarks: Competition platforms

The ecosystem of artificial intelligence competitions is a diverse and multifaceted landscape, encompassing a variety of platforms that each host numerous competitions annually, alongside a plethora of specialized websites dedicated to singular contests. These platforms adeptly manage the overarching administrative responsibilities inherent in orchestrating competitions, thus affording organizers the liberty to allocate greater attention to other facets of their contests. Notably, these platforms exhibit considerable diversity in their operational functionalities, economic models, and community dynamics. This chapter conducts an extensive review of the foremost services in this realm and elucidates several alternative methodologies that facilitate the independent hosting of such challenges. Keywords: competition platform, challenge hosting services, comparison.

cs.LG↗

arXiv2023

The DL Advocate: Playing the devil's advocate with hidden systematic uncertainties

We propose a new method based on machine learning to \emph{play the devil's advocate} and investigate the impact of unknown systematic effects in a quantitative way. This method proceeds by reversing the measurement process and using the physics results to interpret systematic effects under the Standard Model hypothesis. We explore this idea with two alternative approaches: the first one relies on a combination of gradient descent and optimisation techniques, its application and potentiality is illustrated with an example that studies the branching fraction measurement of a heavy-flavour decay. The second method employs reinforcement learning and it is applied to the determination of the $P_{5}^{'}$ angular observable in $B^0 \to K^{*0} μ^+μ^-$ decays. We find that for the former, the size of a hypothetical hidden systematic uncertainty strongly depends on the kinematic overlap between the signal and normalisation channel, while the latter is very robust against possible mismodellings of the efficiency.

hep-ex↗

arXiv2023

Symbolic expression generation via Variational Auto-Encoder

There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature. A widespread Deep Neural Networks do not provide interpretable solutions. Meanwhile, symbolic expressions give us a clear relation between observations and the target variable. However, at the moment, there is no dominant solution for the symbolic regression task, and we aim to reduce this gap with our algorithm. In this work, we propose a novel deep learning framework for symbolic expression generation via variational autoencoder (VAE). In a nutshell, we suggest using a VAE to generate mathematical expressions, and our training strategy forces generated formulas to fit a given dataset. Our framework allows encoding apriori knowledge of the formulas into fast-check predicates that speed up the optimization process. We compare our method to modern symbolic regression benchmarks and show that our method outperforms the competitors under noisy conditions. The recovery rate of SEGVAE is 65% on the Ngyuen dataset with a noise level of 10%, which is better than the previously reported SOTA by 20%. We demonstrate that this value depends on the dataset and can be even higher.

cs.LG↗

arXiv2022

Unveiling the complex structure-property correlation of defects in 2D materials based on high throughput datasets

Modification of physical properties of materials and design of materials with on-demand characteristics is at the heart of modern technology. Rare application relies on pure materials--most devices and technologies require careful design of materials properties through alloying, creating heterostructures of composites or controllable introduction of defects. At the same time, such designer materials are notoriously difficult for modelling. Thus, it is very tempting to apply machine learning methods for such systems. Unfortunately, there is only a handful of machine learning-friendly material databases available these days. We develop a platform for easy implementation of machine learning techniques to materials design and populate it with datasets on pristine and defected materials. Here we describe datasets of defects in represented 2D materials such as MoS2, WSe2, hBN, GaSe, InSe, and black phosphorous, calculated using DFT. Our study provides a data-driven physical understanding of complex behaviors of defect properties in 2D materials, holding promise for a guide to the development of efficient machine learning models. In addition, with the increasing enrollment of datasets, our database could provide a platform for designing of materials with predetermined properties.

cond-mat.mtrl-sci↗

arXiv2022

Code4ML: a Large-scale Dataset of annotated Machine Learning Code

Program code as a data source is gaining popularity in the data science community. Possible applications for models trained on such assets range from classification for data dimensionality reduction to automatic code generation. However, without annotation number of methods that could be applied is somewhat limited. To address the lack of annotated datasets, we present the Code4ML corpus. It contains code snippets, task summaries, competitions and dataset descriptions publicly available from Kaggle - the leading platform for hosting data science competitions. The corpus consists of ~2.5 million snippets of ML code collected from ~100 thousand Jupyter notebooks. A representative fraction of the snippets is annotated by human assessors through a user-friendly interface specially designed for that purpose. Code4ML dataset can potentially help address a number of software engineering or data science challenges through a data-driven approach. For example, it can be helpful for semantic code classification, code auto-completion, and code generation for an ML task specified in natural language.

cs.SE↗