Machine Learning Science and Technology最新文献_第2页

Improving materials property predictions for graph neural networks with minimal feature engineering 用最小特征工程改进图神经网络材料性能预测

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2023-08-11 DOI: 10.1088/2632-2153/acefab

Guoing Cong, Victor Fung

{"title":"Improving materials property predictions for graph neural networks with minimal feature engineering","authors":"Guoing Cong, Victor Fung","doi":"10.1088/2632-2153/acefab","DOIUrl":"https://doi.org/10.1088/2632-2153/acefab","url":null,"abstract":"Graph neural networks (GNNs) have been employed in materials research to predict physical and functional properties, and have achieved superior performance in several application domains over prior machine learning approaches. Recent studies incorporate features of increasing complexity such as Gaussian radial functions, plane wave functions, and angular terms to augment the neural network models, with the expectation that these features are critical for achieving a high performance. Here, we propose a GNN that adopts edge convolution where hidden edge features evolve during training and extensive attention mechanisms, and operates on simple graphs with atoms as nodes and distances between them as edges. As a result, the same model can be used for very different tasks as no other domain-specific features are used. With a model that uses no feature engineering, we achieve performance comparable with state-of-the-art models with elaborate features for formation energy and band gap prediction with standard benchmarks; we achieve even better performance when the dataset size increases. Although some domain-specific datasets still require hand-crafted features to achieve state-of-the-art results, our selected architecture choices greatly reduce the need for elaborate feature engineering and still maintain predictive power in comparison.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":" ","pages":""},"PeriodicalIF":6.8,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44410905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven Dynamics Reconstruction using RBF Network 基于RBF网络的数据驱动动态重构

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2023-07-31 DOI: 10.1088/2632-2153/acec31

Congcong Du, X. Wang, Zhangsen Wang, Dahui Wang

引用次数: 0

Standardizing chemical compounds with language models 用语言模型规范化合物

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2023-07-18 DOI: 10.1088/2632-2153/ace878

M. Cretu, A. Toniato, Amol Thakkar, Amin A. Debabeche, T. Laino, Alain C. Vaucher

{"title":"Standardizing chemical compounds with language models","authors":"M. Cretu, A. Toniato, Amol Thakkar, Amin A. Debabeche, T. Laino, Alain C. Vaucher","doi":"10.1088/2632-2153/ace878","DOIUrl":"https://doi.org/10.1088/2632-2153/ace878","url":null,"abstract":"With the growing amount of chemical data stored digitally, it has become crucial to represent chemical compounds accurately and consistently. Harmonized representations facilitate the extraction of insightful information from datasets, and are advantageous for machine learning applications. To achieve consistent representations throughout datasets, one relies on molecule standardization, which is typically accomplished using rule-based algorithms that modify descriptions of functional groups. Here, we present the first deep-learning model for molecular standardization. We enable custom standardization schemes based solely on data, which, as additional benefit, support standardization options that are difficult to encode into rules. Our model achieves over 98% accuracy in learning two popular rule-based standardization protocols. We then follow a transfer learning approach to standardize metal-organic compounds (for which there is currently no automated standardization practice), based on a human-curated dataset of 1512 compounds. This model predicts the expected standardized molecular format with a test accuracy of 80.7%. As standardization can be considered, more broadly, a transformation from undesired to desired representations of compounds, the same data-driven architecture can be applied to other tasks. For instance, we demonstrate the application to compound canonicalization and to the determination of major tautomers in solution, based on computed and experimental data.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":" ","pages":""},"PeriodicalIF":6.8,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45159553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The effects of topological features on convolutional neural networks—an explanatory analysis via Grad-CAM 拓扑特征对卷积神经网络的影响——基于Grad-CAM的解释性分析

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2023-07-12 DOI: 10.1088/2632-2153/ace6f3

Dongjin Lee, Seonghyeon Lee, Jae-Hun Jung

{"title":"The effects of topological features on convolutional neural networks—an explanatory analysis via Grad-CAM","authors":"Dongjin Lee, Seonghyeon Lee, Jae-Hun Jung","doi":"10.1088/2632-2153/ace6f3","DOIUrl":"https://doi.org/10.1088/2632-2153/ace6f3","url":null,"abstract":"Topological data analysis (TDA) characterizes the global structure of data based on topological invariants such as persistent homology, whereas convolutional neural networks (CNNs) are capable of characterizing local features in the global structure of the data. In contrast, a combined model of TDA and CNN, a family of multimodal networks, simultaneously takes the image and the corresponding topological features as the input to the network for classification, thereby significantly improving the performance of a single CNN. This innovative approach has been recently successful in various applications. However, there is a lack of explanation regarding how and why topological signatures, when combined with a CNN, improve discriminative power. In this paper, we use persistent homology to compute topological features and subsequently demonstrate both qualitatively and quantitatively the effects of topological signatures on a CNN model, for which the Grad-CAM analysis of multimodal networks and topological inverse image map are proposed and appropriately utilized. For experimental validation, we utilize two famous datasets: the transient versus bogus image dataset and the HAM10000 dataset. Using Grad-CAM analysis of multimodal networks, we demonstrate that topological features enforce the image network of a CNN to focus more on significant and meaningful regions across images rather than task-irrelevant artifacts such as background noise and texture.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":" ","pages":""},"PeriodicalIF":6.8,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45878620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive active Brownian particles searching for targets of unknown positions 寻找未知位置目标的自适应主动布朗粒子

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2023-07-12 DOI: 10.1088/2632-2153/ace6f4

Harpreet Kaur, T. Franosch, M. Caraglio

{"title":"Adaptive active Brownian particles searching for targets of unknown positions","authors":"Harpreet Kaur, T. Franosch, M. Caraglio","doi":"10.1088/2632-2153/ace6f4","DOIUrl":"https://doi.org/10.1088/2632-2153/ace6f4","url":null,"abstract":"Developing behavioral policies designed to efficiently solve target-search problems is a crucial issue both in nature and in the nanotechnology of the 21st century. Here, we characterize the target-search strategies of simple microswimmers in a homogeneous environment containing sparse targets of unknown positions. The microswimmers are capable of controlling their dynamics by switching between Brownian motion and an active Brownian particle and by selecting the time duration of each of the two phases. The specific conduct of a single microswimmer depends on an internal decision-making process determined by a simple neural network associated with the agent itself. Starting from a population of individuals with random behavior, we exploit the genetic algorithm NeuroEvolution of augmenting topologies to show how an evolutionary pressure based on the target-search performances of single individuals helps to find the optimal duration of the two different phases. Our findings reveal that the optimal policy strongly depends on the magnitude of the particle’s self-propulsion during the active phase and that a broad spectrum of network topology solutions exists, differing in the number of connections and hidden nodes.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":" ","pages":""},"PeriodicalIF":6.8,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42593976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Prediction of molecular field points using SE(3)-transformer model 利用SE（3）-变换器模型预测分子场点

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2023-07-11 DOI: 10.1088/2632-2153/ace67b

Florian B Hinz, Amr H. Mahmoud, M. Lill

{"title":"Prediction of molecular field points using SE(3)-transformer model","authors":"Florian B Hinz, Amr H. Mahmoud, M. Lill","doi":"10.1088/2632-2153/ace67b","DOIUrl":"https://doi.org/10.1088/2632-2153/ace67b","url":null,"abstract":"Due to their computational efficiency, 2D fingerprints are typically used in similarity-based high-content screening. The interaction of a ligand with its target protein, however, relies on its physicochemical interactions in 3D space. Thus, ligands with different 2D scaffolds can bind to the same protein if these ligands share similar interaction patterns. Molecular fields can represent those interaction profiles. For efficiency, the extrema of those molecular fields, named field points, are used to quantify the ligand similarity in 3D. The calculation of field points involves the evaluation of the interaction energy between the ligand and a small probe shifted on a fine grid representing the molecular surface. These calculations are computationally prohibitive for large datasets of ligands, making field point representations of molecules intractable for high-content screening. Here, we overcome this roadblock by one-shot prediction of field points using generative neural networks based on the molecular structure alone. Field points are predicted by training an SE(3)-Transformer, an equivariant, attention-based graph neural network architecture, on a large set of ligands with field point data. Resulting data demonstrates the feasibility of this approach to precisely generate negative, positive and hydrophobic field points within 0.5 Å of the ground truth for a diverse set of drug-like molecules.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":" ","pages":""},"PeriodicalIF":6.8,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46691335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep neural network approach for parameterized PDEs and Bayesian inverse problems 参数化偏微分方程和贝叶斯反问题的深度神经网络方法

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2023-07-11 DOI: 10.1088/2632-2153/ace67c

Harbir Antil, H. Elman, Akwum Onwunta, Deepanshu Verma

引用次数: 0

Semi-equivariant conditional normalizing flows, with applications to target-aware molecule generation 半等变条件归一化流，及其在目标感知分子生成中的应用

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2023-07-07 DOI: 10.1088/2632-2153/ace58c

Eyal Rozenberg, Daniel Freedman

{"title":"Semi-equivariant conditional normalizing flows, with applications to target-aware molecule generation","authors":"Eyal Rozenberg, Daniel Freedman","doi":"10.1088/2632-2153/ace58c","DOIUrl":"https://doi.org/10.1088/2632-2153/ace58c","url":null,"abstract":"Learning over the domain of 3D graphs has applications in a number of scientific and engineering disciplines, including molecular chemistry, high energy physics, and computer vision. We consider a specific problem in this domain, namely: given one such 3D graph, dubbed the base graph, our goal is to learn a conditional distribution over another such graph, dubbed the complement graph. Due to the three-dimensional nature of the graphs in question, there are certain natural invariances such a distribution should satisfy: it should be invariant to rigid body transformations that act jointly on the base graph and the complement graph, and it should also be invariant to permutations of the vertices of either graph. We propose a general method for learning the conditional probabilistic model, the central part of which is a continuous normalizing flow. We establish semi-equivariance conditions on the flow which guarantee the aforementioned invariance conditions on the conditional distribution. Additionally, we propose a graph neural network architecture which implements this flow, and which is designed to learn effectively despite the typical differences in size between the base graph and the complement graph. We demonstrate the utility of our technique in the molecular setting by training a conditional generative model which, given a receptor, can generate ligands which may successfully bind to that receptor. The resulting model, which has potential applications in drug design, displays high quality performance in the key ΔBinding metric.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":" ","pages":""},"PeriodicalIF":6.8,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41520736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust detection of marine life with label-free image feature learning and probability calibration 基于无标签图像特征学习和概率校准的海洋生物鲁棒检测

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2023-07-04 DOI: 10.1088/2632-2153/ace417

Tobias Schanz, Klas Ove Möller, S. Rühl, D. S. Greenberg

{"title":"Robust detection of marine life with label-free image feature learning and probability calibration","authors":"Tobias Schanz, Klas Ove Möller, S. Rühl, D. S. Greenberg","doi":"10.1088/2632-2153/ace417","DOIUrl":"https://doi.org/10.1088/2632-2153/ace417","url":null,"abstract":"Advances in in situ marine life imaging have significantly increased the size and quality of available datasets, but automatic image analysis has not kept pace. Machine learning has shown promise for image processing, but its effectiveness is limited by several open challenges: the requirement for large expert-labeled training datasets, disagreement among experts, under-representation of various species and unreliable or overconfident predictions. To overcome these obstacles for automated underwater imaging, we combine and test recent developments in deep classifier networks and self-supervised feature learning. We use unlabeled images for pretraining deep neural networks to extract task-relevant image features, allowing learning algorithms to cope with scarcity in expert labels, and carefully evaluate performance in subsequent label-based tasks. Performance on rare classes is improved by applying data rebalancing together with a Bayesian correction to avoid biasing inferred in situ class frequencies. A divergence-based loss allows training on multiple, conflicting labels for the same image, leading to better estimates of uncertainty which we quantify with a novel accuracy measure. Together, these techniques can reduce the required label counts ∼100-fold while maintaining the accuracy of standard supervised training, shorten training time, cope with expert disagreement and reduce overconfidence.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":" ","pages":""},"PeriodicalIF":6.8,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43371752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven plasma modelling: surrogate collisional radiative models of fluorocarbon plasmas from deep generative autoencoders 数据驱动的等离子体建模:来自深度生成自编码器的氟碳等离子体的替代碰撞辐射模型

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2023-06-26 DOI: 10.1088/2632-2153/aced7f

Gregory A. Daly, Jonathan E. Fieldsend, G. Hassall, G. Tabor

{"title":"Data-driven plasma modelling: surrogate collisional radiative models of fluorocarbon plasmas from deep generative autoencoders","authors":"Gregory A. Daly, Jonathan E. Fieldsend, G. Hassall, G. Tabor","doi":"10.1088/2632-2153/aced7f","DOIUrl":"https://doi.org/10.1088/2632-2153/aced7f","url":null,"abstract":"We have developed a deep generative model that can produce accurate optical emission spectra and colour images of an ICP plasma using only the applied coil power, electrode power, pressure and gas flows as inputs—essentially an empirical surrogate collisional radiative model. An autoencoder was trained on a dataset of 812 500 image/spectra pairs in argon, oxygen, Ar/O2, CF4/O2 and SF6/O2 plasmas in an industrial plasma etch tool, taken across the entire operating space of the tool. The autoencoder learns to encode the input data into a compressed latent representation and then decode it back to a reconstruction of the data. We learn to map the plasma tool’s inputs to the latent space and use the decoder to create a generative model. The model is very fast, taking just over 10 s to generate 10 000 measurements on a single GPU. This type of model can become a building block for a wide range of experiments and simulations. To aid this, we have released the underlying dataset of 812 500 image/spectra pairs used to train the model, the trained models and the model code for the community to accelerate the development and use of this exciting area of deep learning. Anyone can try the model, for free, on Google Colab.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":" ","pages":""},"PeriodicalIF":6.8,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42948754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0