A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints

• URL: http://arxiv.org/abs/2403.13627v2
• Date: Thu, 29 May 2025 10:24:54 GMT
• Title: A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints
• Authors: Akihiro Fujii, Anh Khoa Augustin Lu, Koji Shimizu, Satoshi Watanabe,
• Abstract summary: Material design aims to discover novel compounds with desired properties.<n> Conventional element-substitution approaches readily incorporate various domain knowledge but remain confined to a narrow search space.<n>Deep generative models efficiently explore vast compositional landscapes, yet they struggle to flexibly integrate domain knowledge.<n>We propose a gradient-based material design framework that combines these strengths, offering both efficiency and adaptability.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Materials design aims to discover novel compounds with desired properties. However, prevailing strategies face critical trade-offs. Conventional element-substitution approaches readily and adaptively incorporate various domain knowledge but remain confined to a narrow search space. In contrast, deep generative models efficiently explore vast compositional landscapes, yet they struggle to flexibly integrate domain knowledge. To address these trade-offs, we propose a gradient-based material design framework that combines these strengths, offering both efficiency and adaptability. In our method, chemical compositions are optimised to achieve target properties by using property prediction models and their gradients. In order to seamlessly enforce diverse constraints, including those reflecting domain insights such as oxidation states, discretised compositional ratios, types of elements, and their abundance, we apply masks and employ a special loss function, namely the integer loss. Furthermore, we initialise the optimisation using promising candidates from existing dataset, effectively guiding the search away from unfavourable regions and thus helping to avoid poor solutions. Our approach demonstrates a more efficient exploration of superconductor candidates, uncovering candidate materials with higher critical temperature than conventional element-substitution and generative models. Importantly, it could propose new compositions beyond those found in existing databases, including new hydride superconductors absent from the training dataset but which share compositional similarities with materials found in literature. This synergy of domain knowledge and machine-learning-based scalability provides a robust foundation for rapid, adaptive, and comprehensive materials design for superconductors and beyond.

Related papers

• Data Driven Insights into Composition Property Relationships in FCC High Entropy Alloys [28.495739557732175]
  Structural High Entropy Alloys (HEAs) are crucial in advancing technology across various sectors.<n>The scarcity of integrated chemistry, process, structure, and property data presents significant challenges for predictive property modeling.<n>This work presents several sensitivity analyses, highlighting key elemental contributions to mechanical behavior.
  arXiv  Detail & Related papers  (2025-08-06T19:41:15Z)
• Offline Model-Based Optimization: Comprehensive Review [61.91350077539443]
  offline optimization is a fundamental challenge in science and engineering, where the goal is to optimize black-box functions using only offline datasets. Recent advances in model-based optimization have harnessed the generalization capabilities of deep neural networks to develop offline-specific surrogate and generative models. Despite its growing impact in accelerating scientific discovery, the field lacks a comprehensive review.
  arXiv  Detail & Related papers  (2025-03-21T16:35:02Z)
• MaRI: Material Retrieval Integration across Domains [13.686066106732696]
  Existing methods rely on datasets that capture shape-invariant and lighting-varied representations of materials.<n>We introduce MaRI, a framework designed to bridge the feature space gap between synthetic and real-world materials.
  arXiv  Detail & Related papers  (2025-03-11T07:23:11Z)
• Inverse Materials Design by Large Language Model-Assisted Generative Framework [35.04390544440238]
  AlloyGAN is a framework that integrates Large Language Model (LLM)-assisted text mining with Conditional Generative Adversarial Networks (CGANs)<n>For metallic glasses, the framework predicts thermodynamic properties with discrepancies of less than 8% from experiments.<n>By bridging generative AI with domain knowledge, AlloyGAN offers a scalable approach to accelerate the discovery of materials with tailored properties.
  arXiv  Detail & Related papers  (2025-02-25T11:52:59Z)
• Accelerated Gradient-based Design Optimization Via Differentiable Physics-Informed Neural Operator: A Composites Autoclave Processing Case Study [0.0]
  We introduce a novel Physics-Informed DeepONet (PIDON) architecture to effectively model the nonlinear behavior of complex engineering systems. We demonstrate the effectiveness of this framework in the optimization of aerospace-grade composites curing processes achieving a 3x speedup. The proposed model has the potential to be used as a scalable and efficient optimization tool for broader applications in advanced engineering and digital twin systems.
  arXiv  Detail & Related papers  (2025-02-17T07:11:46Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation. deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency. This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Adaptive Constraint Integration for Simultaneously Optimizing Crystal Structures with Multiple Targeted Properties [7.559885439354167]
  Simultaneous Multi-property optimization using Adaptive Crystal Synthesizer (SMOACS) SMOACS enables the integration of adaptive constraints into the optimization process without necessitating model retraining. We have demonstrated the band gap optimization while meeting a challenging constraint, that is, maintaining electrical neutrality in large atomic configurations up to 135 atom sites.
  arXiv  Detail & Related papers  (2024-10-11T06:35:48Z)
• Data-efficient and Interpretable Inverse Materials Design using a Disentangled Variational Autoencoder [2.563209727695243]
  Inverse materials design has proven successful in accelerating novel material discovery. Many inverse materials design methods use unsupervised learning where a latent space is learned to offer a compact description of materials representations. Here, we present a semi-supervised learning approach based on a disentangled variational autoencoder to learn a probabilistic relationship between features, latent variables and target properties.
  arXiv  Detail & Related papers  (2024-09-10T02:21:13Z)
• TALE: Training-free Cross-domain Image Composition via Adaptive Latent Manipulation and Energy-guided Optimization [59.412236435627094]
  TALE is a training-free framework harnessing the generative capabilities of text-to-image diffusion models. We equip TALE with two mechanisms dubbed Adaptive Latent Manipulation and Energy-guided Latent Optimization. Our experiments demonstrate that TALE surpasses prior baselines and attains state-of-the-art performance in image-guided composition.
  arXiv  Detail & Related papers  (2024-08-07T08:52:21Z)
• Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient [52.2669490431145]
  PropEn is inspired by'matching', which enables implicit guidance without training a discriminator. We show that training with a matched dataset approximates the gradient of the property of interest while remaining within the data distribution.
  arXiv  Detail & Related papers  (2024-05-28T11:30:19Z)
• Diffusion Model for Data-Driven Black-Box Optimization [54.25693582870226]
  We focus on diffusion models, a powerful generative AI technology, and investigate their potential for black-box optimization. We study two practical types of labels: 1) noisy measurements of a real-valued reward function and 2) human preference based on pairwise comparisons. Our proposed method reformulates the design optimization problem into a conditional sampling problem, which allows us to leverage the power of diffusion models.
  arXiv  Detail & Related papers  (2024-03-20T00:41:12Z)
• Functional Graphical Models: Structure Enables Offline Data-Driven Optimization [111.28605744661638]
  We show how structure can enable sample-efficient data-driven optimization. We also present a data-driven optimization algorithm that infers the FGM structure itself.
  arXiv  Detail & Related papers  (2024-01-08T22:33:14Z)
• VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis [73.50359502037232]
  VoxNeRF is a novel approach to enhance the quality and efficiency of neural indoor reconstruction and novel view synthesis.<n>We propose an efficient voxel-guided sampling technique that allocates computational resources to selectively the most relevant segments of rays.<n>Our approach is validated with extensive experiments on ScanNet and ScanNet++.
  arXiv  Detail & Related papers  (2023-11-09T11:32:49Z)
• Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation [17.164961143132473]
  We introduce a learning framework that demonstrates the efficacy of aligning the sampling trajectory of diffusion models with the optimization trajectory derived from traditional physics-based methods. Our method allows for generating feasible and high-performance designs in as few as two steps without the need for expensive preprocessing, external surrogate models, or additional labeled data. Our results demonstrate that TA outperforms state-of-the-art deep generative models on in-distribution configurations and halves the inference computational cost.
  arXiv  Detail & Related papers  (2023-05-29T09:16:07Z)
• Diffusing the Optimal Topology: A Generative Optimization Approach [6.375982344506753]
  Topology optimization seeks to find the best design that satisfies a set of constraints while maximizing system performance. Traditional iterative optimization methods like SIMP can be computationally expensive and get stuck in local minima. We propose a Generative Optimization method that integrates classic optimization like SIMP as a refining mechanism for the topology generated by a deep generative model.
  arXiv  Detail & Related papers  (2023-03-17T03:47:10Z)
• CAFE: Learning to Condense Dataset by Aligning Features [72.99394941348757]
  We propose a novel scheme to Condense dataset by Aligning FEatures (CAFE) At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art.
  arXiv  Detail & Related papers  (2022-03-03T05:58:49Z)
• Conservative Objective Models for Effective Offline Model-Based Optimization [78.19085445065845]
  Computational design problems arise in a number of settings, from synthetic biology to computer architectures. We propose a method that learns a model of the objective function that lower bounds the actual value of the ground-truth objective on out-of-distribution inputs. COMs are simple to implement and outperform a number of existing methods on a wide range of MBO problems.
  arXiv  Detail & Related papers  (2021-07-14T17:55:28Z)
• Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
  Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations. We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
  arXiv  Detail & Related papers  (2021-06-14T20:48:48Z)
• Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation [101.22379613810881]
  We consider data-driven optimization problems where one must maximize a function given only queries at a fixed set of points. This problem setting emerges in many domains where function evaluation is a complex and expensive process. We propose a tractable approximation that allows us to scale our method to high-capacity neural network models.
  arXiv  Detail & Related papers  (2021-02-16T06:04:27Z)
• Good practices for Bayesian Optimization of high dimensional structured spaces [15.488642552157131]
  We study the effect of different search space design choices for performing Bayesian Optimization in high dimensional structured datasets. We evaluate new methods to automatically define the optimization bounds in the latent space. We provide recommendations for the practitioners.
  arXiv  Detail & Related papers  (2020-12-31T07:00:39Z)
• An AI-Assisted Design Method for Topology Optimization Without Pre-Optimized Training Data [68.8204255655161]
  An AI-assisted design method based on topology optimization is presented, which is able to obtain optimized designs in a direct way. Designs are provided by an artificial neural network, the predictor, on the basis of boundary conditions and degree of filling as input data.
  arXiv  Detail & Related papers  (2020-12-11T14:33:27Z)
• Evolving Search Space for Neural Architecture Search [70.71153433676024]
  We present a Neural Search-space Evolution (NSE) scheme that amplifies the results from the previous effort by maintaining an optimized search space subset. We achieve 77.3% top-1 retrain accuracy on ImageNet with 333M FLOPs, which yielded a state-of-the-art performance. When the latency constraint is adopted, our result also performs better than the previous best-performing mobile models with a 77.9% Top-1 retrain accuracy.
  arXiv  Detail & Related papers  (2020-11-22T01:11:19Z)
• Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining [1.5293427903448025]
  We introduce an improved method for efficient black-box optimization, which performs the optimization in the low-dimensional, continuous latent manifold learned by a deep generative model. We achieve this by periodically retraining the generative model on the data points queried along the optimization trajectory, as well as weighting those data points according to their objective function value. This weighted retraining can be easily implemented on top of existing methods, and is empirically shown to significantly improve their efficiency and performance on synthetic and real-world optimization problems.
  arXiv  Detail & Related papers  (2020-06-16T14:34:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.