Fast ionic conduction has a Raman fingerprint 🔬⚡
This study from AI for Science uses ML‑accelerated Raman calculations to reveal liquid‑like ion motion in solid electrolytes, enabling accurate, data‑efficient discovery of fast‑ion conductors for solid‑state batteries. 🔋
https://ow.ly/iXbv50YGURa
Want more clarity as you move through the publishing process? Our guide can support you.
Offering straightforward steps, and practical advice that helps you to avoid common errors and feel more confident in your submission.
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#ResearchTips
📣 Call for Posters
Share your research at Progress in Quantum Machine Learning (PQML) 2026. Posters welcomed across quantum ML, quantum computing, materials, physics, chemistry & algorithms.
🗓 Deadline: 30 April 2026
📍 Event 10 May 2026 | Beijing + Online
👉 Submit here: https://ow.ly/PYPw50YBMXj
Join us in celebrating exceptional reviewers! 🎉
Each year, our journal editorial teams select recipients for our Outstanding Reviewer Awards - recognising quality, quantity and timeliness of the report they completed. Explore the full list 🙌 https://ow.ly/pf1w50YwRTe
#ResearchIntegrity
The new issue of AI for Science is out now, featuring advances in ML‑accelerated Raman analysis, explainable chemical reasoning, and data‑driven design of metallic glasses.
🔓 All articles are gold open access and free to read 👉 https://ow.ly/mH6I50YyE6I
Join global experts at #PQML2026 — a one-day workshop on the future of quantum machine learning at Tsinghua University and online. Supported by ROPP, QST and MLST.
Be part of the next wave of quantum discovery.
Register now: https://ow.ly/9vuX50YwqgR
#QuantumMachineLearning #QuantumComputing
🆕🌟Introducing Machine Leaning: Science and Technology's first roadmap 👉 Roadmap on fast machine learning for science.
Unlock the roadmap: https://ow.ly/sVO950Ywji3
#machinelearning #ai #fastmachinelearning
🚀 Introducing TorchSim: a PyTorch‑native engine for fast, batched atomistic simulation with major MLIP speedups and differentiable MD. Ideal for high‑throughput materials research.
Find out more 👉 https://ow.ly/4xYm50Yvamx
If the publishing process feels unclear, our free guide can help you move forward with more confidence.
You’ll get practical steps, journal selection advice and simple tips to avoid common pitfalls.
Sign up to get your copy: https://ow.ly/prgH50Y7uxQ
#AcademicPublishing #ResearchTips
✨ Spring into March with a boost to your peer review confidence. Whether you're new to the process or ready to sharpen your #PeerReview expertise, our free online course has you covered. 📚 Come and take a look 👇https://ow.ly/OZMk50YlLiS #EarlyCareerResearcher
📢 Open for submissions: Focus Collection on Augmented Intelligence for #SmartManufacturing
This Focus Collection aims to shine a spotlight on innovations in augmented intelligence for the future of smart manufacturing.
Learn more 👉 https://ow.ly/CvG450Y9bQ3
#MachineLearning
💡Recent highlight in JPhys Complexity: This review compares different machine learning models for discovering equations from data Francisco Rodrigues
👉 Read the full review: https://ow.ly/TK5t50YhCs6
🔖This focus issue aims to showcase the role of #MachineLearning in deepening and expanding our physical understanding of chemical processes in systems of current scientific and technological interest.
Available to read and download now 👉https://ow.ly/Heb850YhyBW
📢 OPEN FOR SUBMISSIONS 👉 This focus collection invites submissions that explore the integration of AI and machine learning in #geophysical sciences.
Find out more: https://ow.ly/VoMz50Y9bpf
#MachineLearning
🌟 Help uphold the high standards of research we’re proud to publish! Become a peer reviewer and...✔️ advance your career ✔️ stay up to date with the latest developments in your field ✔️ enjoy rewards & recognition.
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🆕Open for submissions: Focus Collection on Foundation Models and LLMs for Life Sciences
This collection is looking for high-quality research at the intersection of multi-modal learning, foundation models, LLMs, & life sciences. Could this be your research?
Find out more: https://ow.ly/hrMu50Y9b3g
ML for #MaterialsEngineering 👇
The integration of ML into materials engineering is transforming how we design, model, and characterize materials. This collection aims to highlight advances in techniques developed for solving materials engineering problems.
Learn more: https://ow.ly/HE1q50Y9bzj
📈Dataset articles—helping you understand how to use and interpret data more efficiently.
These articles are set apart by their description on why a dataset was created and why it is important for advancing science.
Learn more: https://ow.ly/3LHz50Yelhb
#LoveDataWeek #MachineLearning
⛅Looking to publish your #AI for #ClimatePrediction research? Why not consider a Machine Learning: Earth focus collection.
'Focus collection on AI for Weather and Climate Prediction' is now open for submissions.
Find out more 👉 https://ow.ly/uBPv50Y9bkf
🔓Find a home for your #AI for healthcare research in an ML Health focus collection 👇
This collection seeks to explore how generative, foundation, and agentic AI systems can be safely and effectively developed, evaluated, and deployed in healthcare.
Find out more 👉 https://ow.ly/m3hu50Y9aUK
Want clearer steps for publishing your research?
Get your free guide that could help you to avoid errors, choose the right journal and move through each publishing stage with confidence!
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🚀 Supercharge your #PeerReview skills with our FREE online course! Explore essential modules, including 📝 peer review fundamentals, paper mills, authorship misconduct, ChatGPT and more... Enhance your expertise today 👉 https://ow.ly/SrJI50Y1HMr #EarlyCareerResearcher #PhysicalScience
🔬🤖 The second issue of AI for Science is out now. Highlights include atomistic simulation in PyTorch and universal ML interatomic potentials.
We're a gold open access journal exploring how AI is transforming scientific discovery. Free to read, free to publish 👉 https://ow.ly/cNm650Y1aWL
✨Kick off January by mastering peer review. Whether you're starting out or looking to refine your #PeerReview skills, explore our free online course - it's jam packed with comprehensive modules to help you! 📚 Check it out 👉 https://ow.ly/4kOm50XxqkE
#EarlyCareerResearcher
AI is driving the next leap in 2D materials fabrication.
From real-time optimisation and defect detection to autonomous robotic handling, AI is solving key bottlenecks in exfoliation and transfer, making wafer-scale production faster, cleaner, and more reliable 👉 https://ow.ly/7YLB50XLqBn
🆕Recently published: Global climate model bias correction using deep learning.
In this study a suite of data-driven deep learning models is developed for bias correction of climate model projections.
🔓Unlock the article: https://ow.ly/H0Ua50XtXfn
🔖Recently published in #MachineLearningHealth: Geometry-encoded deep learning (GeoDL) framework for real-time 3D dose verification for online adaptive radiotherapy.
Read it, share it, cite it here: https://ow.ly/vcNS50XtWlg
#MachineLearning
🔓Submit your manuscript before the end of 2025 to publish #openaccess at no cost to you!
Our Machine Learning Series is dedicated to the application and development of machine learning and AI across the sciences.
Learn more about the series: https://ow.ly/Usob50XtY1G
#MachineLearning #AI
🔖Recently published in #MachineLearningEarth: Deep Learning Meets Teleconnections: Improving S2S Predictions for European Winter Weather.
Read it and download today 👉 https://ow.ly/kppo50XtXbq
🌟Discover the latest research that has got the scientific community talking🌟
Be one of the first to unlock MLST's brand new research hub - where we will share the latest and most cited articles published in the journal, all in one place.
🔓Unlock the research: https://ow.ly/7yM550XtYnq
