Machine Learning: Science and Technology
@MLSTjournal
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A multidisciplinary, #openaccess journal devoted to the application and development of #machinelearning for the sciences. Published by @IOPPublishing.
Bristol, UK
Joined October 2019
We are now on Bluesky! Follow our account @iopp-mlresearch.bsky.social on Bluesky for more #MachineLearning and #AI research
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From Thursday, 5 June, X will no longer be our primary platform for sharing news. We would love to connect with you on: 🦋 Bluesky - https://t.co/TpdKfZMFJt LinkedIn - https://t.co/63ZbvafCID Facebook - https://t.co/KDmF5IZrHq Threads -
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Our paper is now out in @MLSTjournal, the BEST place for stat mech for ML! Response theory of first passage processes and the quasi-steady-state hypothesis helps optimize training protocols for neural nets👇@chemistrytau @BioSoftTAU @TelAvivUni
https://t.co/dRokyVxhGt
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My video interview with @QuantaMagazine about AI-designed physics experiments, AI as a Muse for new ideas in Science, and Artificial Scientists:
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We show how to estimate the impact of research ideas before they were even born -- just published in @MLSTjournal [paper] https://t.co/bGuHCLlrQi [GitHub]: https://t.co/G57FxG116R spearheaded by @GuXuemei Important for future artificial muses, to generate impactful new ideas.
github.com
Forecasting high-impact research topics via machine learning on evolving knowledge graphs - artificial-scientist-lab/Impact4Cast
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Our LLM4Mat-Bench paper is now published @MLSTjournal ✨ Test your favorite LLM on the benchmark to predict material properties. 📖Paper: https://t.co/L4146XlWmH 💻Code:
github.com
Code and data used to create and evaluate LLM4Mat-Bench - vertaix/LLM4Mat-Bench
#NewPaper Have you been wondering how your favorite LLM, e.g. Llama, Mistral, or Gemma performs on materials property prediction? We have just released LLM4Mat-Bench, an extensive benchmark for materials property prediction with LLMs! LLM4Mat-Bench has unique features: ☀️It
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Congratulations to all recipients of our Outstanding Reviewer Awards! 🎉Your commitment, timeliness, quality, & quantity of reports is celebrated across our journals. Let's celebrate all #PeerReview contributions that uphold #ResearchIntegrity Discover 👉 https://t.co/3mNc9fLqnG
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📜Our new paper on detecting quantum vortices with Convolutional Neural Networks has been published in @MLSTjournal! ✅The scheme offers efficient, system-wide vortex detection - even in noisy, experimental data. 🔗 Read here: https://t.co/8cUUQAJqo7
#MachineLearning
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Physics and AI: A powerful duo expanding our understanding of the Universe! Join our workshop at AI UK 2025, The Alan Turing Institute, to explore how physics can tackle AI's biggest challenges. In-person delegate booking opens 10am on March 12th: https://t.co/7jTGyNf7gs
#Turing
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Paper alert! "New gravitational wave discoveries enabled by machine learning", A. Koloniari, E. Koursoumpa, P. Nousi, P. Lampropoulos, N. Passalis, A. Tefas, N. Stergioulas, https://t.co/LuLWzjIt9k μέσω @MLSTjournal (Open Access)
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📣Join us on April 27, for the "AI-driven discoveries: Machine Learning for the Physical Sciences" workshop. This international event will bring together top researchers to discuss the role of #AI and machine learning in advancing physical sciences. https://t.co/dyOuYS8Ama
#AI
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✨Become an IOP Peer Review Excellence graduate this month! With more than 4,300 researchers already signed up to our free and rigorously assessed online course, come and take a look today - https://t.co/Qy5LogEDXp
#EarlyCareerResearcher #PhysicalScience #PeerReview
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New paper alert. "Stochastic black-box optimization using multifidelity score function estimator," published in @MLSTjournal (@IOPPublishing ) w\ @mandrakeMojito @SKoutsourelakis prof. Hans Bungartz. https://t.co/4X504ceoNe
#BlackBoxOptimization #SciML #UQ #ML
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👋 Are you an #EarlyCareerResearcher looking to gain #PeerReview experience, or an experienced supervisor eager to support the next generation? We've got you covered! Find out more 👇 https://t.co/8JQh7zDvJm
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Our new research article on "Characterizing out-of-distribution generalization of neural networks: application to the disordered SSH model" published in @MLSTjournal by our great student @KacperCybinski led by Prof. A. Dawid @MolecularRobot with @ICFOnians
https://t.co/7i94Rvkvp7
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I’m pretty excited about our new paper, which is a follow up to our last paper using AI to help solve a problem in theoretical particle physics. (With Lance, @f_charton, Matthias, Tianji, and @merz_garrett
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Link to paper: https://t.co/ZMurzz1wSd Previous AI/ML paper in @MLSTjournal : https://t.co/MVQt3iwX8S
arxiv.org
The planar three-gluon form factor for the chiral stress tensor operator in planar maximally supersymmetric Yang-Mills theory is an analog of the Higgs-to-three-gluon scattering amplitude in QCD....
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AI predicts that most of the world will warm much faster than previously predicted, according to a new study published today in @ERLjournal. ⏰ Find out more: https://t.co/V4Ul5WCFkq
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🦾Discover what our new journal 'Machine Learning: Engineering' contributes to the #MachineLearning and #AI landscape! Our Editor-in-Chief shares key insights about our exciting new journal which is now open for submissions! https://t.co/uX8TT6pl54
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Read the full article in @MLSTjournal at
Quantum multitasking? When a quantum computer processes data, it must translate it into understandable quantum data. A team led by @Tohoku_Univ has created an #algorithm capable of optimizing multiple targets at once in quantum compilation. @MLSTjournal #computing #technology
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