Property (philosophy) ≈ Property (philosophy)
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Neural Message Passing for Quantum Chemistry Open
Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already…
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Annual Review of Sociology Open
Outside of Indigenous studies, sociologists tend to treat land in the United States as governed exclusively by an entrenched private-property regime: Land is a commodity and an object for individual control. This review presents land in th…
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Recent advances and applications of machine learning in solid-state materials science Open
One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and app…
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Machine learning in materials informatics: recent applications and prospects Open
Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials scien…
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Analyzing Learned Molecular Representations for Property Prediction Open
Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerp…
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Causability and explainability of artificial intelligence in medicine Open
Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the problem of explainability is as old as AI itself and classic AI represented comprehensible retraceable approaches. However, their weakness w…
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Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals Open
Graph networks are a new machine learning (ML) paradigm that supports both relational reasoning and combinatorial generalization. Here, we develop universal MatErials Graph Network (MEGNet) models for accurate property prediction in both m…
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Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science Open
Our ability to collect “big data” has greatly surpassed our capability to analyze it, underscoring the emergence of the fourth paradigm of science, which is data-driven discovery. The need for data informatics is also emphasized by the Mat…
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Big Data�s Disparate Impact Open
Advocates of algorithmic techniques like data mining argue that these techniques eliminate human biases from the decision-making process. But an algorithm is only as good as the data it works with. Data is frequently imperfect in ways that…
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Implementation strategies in phonopy and phono3py Open
Scientific simulation codes are public property sustained by the community. Modern technology allows anyone to join scientific software projects, from anywhere, remotely via the internet. The phonopy and phono3py codes are widely used open…
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HotStuff Open
We present HotStuff, a leader-based Byzantine fault-tolerant replication protocol for the partially synchronous model. Once network communication becomes synchronous, HotStuff enables a correct leader to drive the protocol to consensus at …
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The Unreasonable Effectiveness of Deep Features as a Perceptual Metric Open
While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR an…
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Auxetic mechanical metamaterials Open
We review the topology–property relationship and the spread of Young's modulus–Poisson's ratio duos in three main classes of auxetic metamaterials.
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A strategy to apply machine learning to small datasets in materials science Open
There is growing interest in applying machine learning techniques in the research of materials science. However, although it is recognized that materials datasets are typically smaller and sometimes more diverse compared to other fields, t…
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Recommended Methods to Study Resistive Switching Devices Open
Resistive switching (RS) is an interesting property shown by some materials systems that, especially during the last decade, has gained a lot of interest for the fabrication of electronic devices, with electronic nonvolatile memories being…
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Embedding Watermarks into Deep Neural Networks Open
Deep neural networks have recently achieved significant progress. Sharing\ntrained models of these deep neural networks is very important in the rapid\nprogress of researching or developing deep neural network systems. At the same\ntime, i…
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Biophysical properties of the clinical-stage antibody landscape Open
Significance In addition to binding to a desired target molecule, all antibody drugs must also meet a set of criteria regarding the feasibility of their manufacture, stability in storage, and absence of off-target stickiness. This suite of…
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RaptorX-Property: a web server for protein structure property prediction Open
RaptorX Property (http://raptorx2.uchicago.edu/StructurePropertyPred/predict/) is a web server predicting structure property of a protein sequence without using any templates. It outperforms other servers, especially for proteins without c…
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Atomistic Line Graph Neural Network for improved materials property predictions Open
Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models. While most existing GNN models for atomi…
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Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models Open
Graph neural networks (GNN) has been considered as an attractive modelling method for molecular property prediction, and numerous studies have shown that GNN could yield more promising results than traditional descriptor-based methods. In …
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An empirical analysis of land property lawsuits and rainfalls Open
This article using the database of Taiwanese land property lawsuits studies the economic effects of rainfalls on land property lawsuits during the period of Japanese colonial rule (1920-1941). The results obtained from basic ordinary least…
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Delving into Transferable Adversarial Examples and Black-box Attacks Open
An intriguing property of deep neural networks is the existence of adversarial examples, which can transfer among different architectures. These transferable adversarial examples may severely hinder deep neural network-based applications. …
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Blockchains and the economic institutions of capitalism Open
Blockchains are a new digital technology that combines peer-to-peer network computing and cryptography to create an immutable decentralised public ledger. Where the ledger records money, a blockchain is a cryptocurrency, such as Bitcoin; b…
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A compact review of molecular property prediction with graph neural networks Open
As graph neural networks are becoming more and more powerful and useful in the field of drug discovery, many pharmaceutical companies are getting interested in utilizing these methods for their own in-house frameworks. This is especially c…
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Powders for powder bed fusion: a review Open
The quality of powder used in powder bed-based additive manufacturing plays a key role concerning process performance and end part properties. Even though this is a generally accepted fact, there is still a lack of a comprehensive understa…
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Surrogate data for hypothesis testing of physical systems Open
The availability of time series of the evolution of the properties of physical systems is increasing, stimulating the development of many novel methods for the extraction of information about their behaviour over time, including whether or…
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Chemprop: A Machine Learning Package for Chemical Property Prediction Open
Deep learning has become a powerful and frequently employed tool for the prediction of molecular properties, thus creating a need for open-source and versatile software solutions that can be operated by nonexperts. Among the current approa…
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A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics Open
Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials s…
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Prompt-to-Prompt Image Editing with Cross Attention Control Open
Recent large-scale text-driven synthesis models have attracted much attention thanks to their remarkable capabilities of generating highly diverse images that follow given text prompts. Such text-based synthesis methods are particularly ap…
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ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction Open
GNNs and chemical fingerprints are the predominant approaches to representing molecules for property prediction. However, in NLP, transformers have become the de-facto standard for representation learning thanks to their strong downstream …