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Search · four archives
24 papers · ranked by Valyu relevance
David Opeoluwa Oyewola, Emmanuel Gbenga Dada
Machine Learning has found application in solving complex problems in different fields of human endeavors such as intelligent gaming, automated transportation, cyborg technology, environmental protection, enhanced health care, innovation in banking and home security, and smart homes. This research is motivated by the…
Jeyan Thiyagalingam, Mallikarjun Shankar, Geoffrey Fox, Tony Hey
The breakthrough in Deep Learning neural networks has transformed the use of AI and machine learning technologies for the analysis of very large experimental datasets. These datasets are typically generated by large-scale experimental facilities at national laboratories. In the context of science, scientific machine…
Ricardo Vinuesa, Jean Rabault, Hossein Azizpour, Sebastian Bauer + 8 more
'Bingni W. Brunton' 'Arne Elofsson' 'Elias Jarlebring' 'Hedvig Kjellström' 'Stefano Markidis' 'David Marlevi' 'Paola Cinnella' 'Steven L. Brunton'] Technological advancements have substantially increased computational power and data availability, enabling the application of powerful machine-learning (ML) techniques…
Titus Neupert, Mark H. Fischer, Eliška Greplová, Kenny Choo + 1 more
'M. Michael Denner'] In these lecture notes, we discuss supervised, unsupervised, and reinforcement learning. The notes start with an exposition of machine learning methods without neural networks, such as principle component analysis, t-SNE, clustering, as well as linear regression and linear classifiers. We continue…
Authors not listed
Artificial intelligence (AI) is reshaping scientific research by accelerating discovery and enabling the analysis of complex data that traditional methods struggle to handle. This review examines over 310,000 journal articles and patents from the CAS Content Collection (2015–2025), with a focus on, biomedical research…
Moe Kyaw Thu, Shotaro Beppu, Masaru Yarime, Sotaro Shibayama + 1 more
'Alessandro Muscio'] The progress of science increasingly relies on machine learning (ML) and machines work alongside humans in various domains of science. This study investigates the team structure of ML-related projects and analyzes the contribution of ML to scientific knowledge production under different team…
Matthias Rupp, O. Anatole von Lilienfeld, Kieron Burke
- AI Artificial Intelligence, see Sec. II A - B3LYP Becke, three-parameter, Lee-Yang-Parr, a hybrid DFT functional - CCSD(T) Coupled Cluster with Single, Double and perturbative Triple excitations, an electronic structure method - DFT Density Functional Theory, an electronic structure method - DFTB Density Functional…
Authors not listed
Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision- making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that…
Helen Meskhidze
The increasing precision of observations of the large-scale structure of the universe has created a problem for simulators: running the simulations necessary to interpret these observations has become impractical. Simulators have thus turned to machine learning (ML) algorithms instead. Though ML decreases computational…
Nour Makke, Sanjay Chawla
Machine learning is rapidly making its pathway across all of the natural sciences, including physical sciences. The rate at which ML is impacting non-scientific disciplines is incomparable to that in the physical sciences. This is partly due to the uninterpretable nature of deep neural networks. Symbolic machine…
Abigail R. Azari, Jeffrey W. Lockhart, Michael W. Liemohn, Xianzhe Jia
'Xianzhe Jia'] Recent improvements in data collection volume from planetary and space physics missions have allowed the application of novel data science techniques. The Cassini mission for example collected over 600 gigabytes of scientific data from 2004 to 2017. This represents a surge of data on the Saturn system.…
Alberto Termine, Emanuele Ratti, Alessandro Facchini
In recent years, the dissemination of machine learning (ML) methodologies in scientific research has prompted discussions on theory-ladenness. More specifically, the issue of theory-ladenness has re-emerged as questions about whether and how ML models (MLMs) and ML modelling strategies are impacted by the domain theory…
Jian Li, Jianing Wang, Eryong Xue
Cultivating scientific literacy is a goal widely shared by educators and students around the world. Many studies have sought to enhance students’ proficiency in scientific literacy through various approaches. However, there is a need to explore the attributes associated with advanced levels of scientific literacy…
Daphne Ezer, Kirstie Whitaker
Data science can be incorporated into every stage of a scientific study. Here we describe how data science can be used to generate hypotheses, to design experiments, to perform experiments, and to analyse data. We also present our vision for how data science techniques will be an integral part of the laboratory of the…
Tanja Krumpe, Christian Scharinger, Wolfgang Rosenstiel, Peter Gerjets + 1 more
In this paper, we demonstrate how machine learning (ML) can be used to beneficially complement the traditional analysis of behavioral and physiological data to provide new insights into the structure of mental states, in this case, executive functions (EFs) with a focus on inhibitory control. We used a modified Flanker…
Authors not listed
Accurate extrapolation in data-scarce scientific systems remains a central challenge for machine intelligence. In microbial bioprocessing, kinetic parameters change non-monotonically with reactor volume due to interacting hydrodynamic, oxygen-transfer, and mixing effects, rendering classical empirical scaling laws…
Kyle Swanson, Wesley Wu, Nash L. Bulaong, John E. Pak + 1 more
Science frequently benefits from teams of interdisciplinary researchers. However, most scientists don’t have access to experts from multiple fields. Fortunately, large language models (LLMs) have recently shown an impressive ability to aid researchers across diverse domains by answering scientific questions. Here, we…
Anubhav Jain
The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials…
Shreyansh Agrawal, Harsh B. Anadkat, Kiran K. Athimoolam, Harsh Bhardwaj + 8 more
Recent advances in artificial intelligence (AI) have prompted claims about autonomous “AI scientists,” yet systematic evaluations of these capabilities remain scarce. This exploratory study investigates whether current AI frameworks can execute scientific research tasks beyond isolated demonstrations. We tested eight…
Dennis Bersenev, Ayako Yachie-Kinoshita, Sucheendra K. Palaniappan
Publications focused on scientific discoveries derived from analyzing large biological datasets typically follow the cycle of hypothesis generation, experimentation, and data interpretation. The reproduction of findings from such papers is crucial for confirming the validity of the scientific, statistical, and…
A. Sina Booeshaghi, Laura Luebbert, Lior Pachter
We develop a machine-automated approach for extracting results from papers, which we assess via a comprehensive review of the entire eLife corpus. Our method facilitates a direct comparison of machine and peer review, and sheds light on key challenges that must be overcome in order to facilitate AI-assisted science. In…
Pierre Bongrand, Binh P. Nguyen, Fei Guo
During the last decade, artificial intelligence (AI) was applied to nearly all domains of human activity, including scientific research. It is thus warranted to ask whether AI thinking should be durably involved in biomedical research. This problem was addressed by examining three complementary questions (i) What are…
Authors not listed
Step-by-step thinking is essential in all domains of chemical sciences and engineering. While machine learning tools are broadly used, algorithms that automate reasoning are far less common. We elaborate on seven categories of human reasoning activities and connect each to applications in chemical science and…
Yuanqi Du, Chenru Duan, Andres Bran, Anna Sotnikova + 5 more
Large language models (LLMs) have demonstrated outstanding capabilities in general problem-solving and been shown to improve productivity in certain domains. Thanks to their flexibility, recent work has leveraged them for diverse scientific applications, ranging from predictive modeling, scientific Q&A, and even as…