Latest posts tagged with #MachineLearningScienceandTechnology on Bluesky
Lots of #MachineLearningScienceandTechnology published research by @labcosmo.bsky.social and @kulikgroup.bsky.social at the #AI4Science workshop #NeurIPS2025. Happy to see so much #compchem here! 🧪⚗️⚛️ #machinelearning @iopp-mlresearch.bsky.social (terrible pictures by yours truly 🥲)
Our fantastic #MachineLearningScienceandTechnology editor-in-chief @kylecranmer.bsky.social giving a shout out to our collaboration with the #ML4PS workshop at #NeurIPS2025 @iopp-mlresearch.bsky.social Thank you, Kyle! 🥳 #machinelearning #chemistry #physics
#MachineLearningScienceandTechnology paper being a spotlight at the #ML4PS workshop at #NeurIPS2025
‘Continual learning for particle accelerators’
iopscience.iop.org/article/10.1...
@iopp-mlresearch.bsky.social
On my way to #NeurIPS2025! Happy to chat about @iopp-mlresearch.bsky.social #MachineLearningScienceandTechnology, #MachineLearningHealth, #MachineLearningEarth, and #MachineLearningEngineering 🤖 Thursday through Sunday, or at our booth in the Exhibitor space (Booth T4). Come say hi! 😊 #EditorsLife
It always makes my day when #MachineLearningScienceandTechnology is featured in the #JournalClub (@iopp-mlresearch.bsky.social). Thanks for sharing, @grynova.bsky.social 👏🏻😊
In preparation for tomorrow, catch up on last year's results published in #MachineLearningScienceandTechnology (@iopp-mlresearch.bsky.social), bit.ly/4nuQu6h. Good luck to all the participants and congratulations to @benblaiszik.bsky.social for organizing this incredible event #machinelearning
It is a honor to serve this community. Thank you so much for publishing with us, @mathildepapillon.bsky.social 😊
#MachineLearningScienceandTechnology
I always enjoy when #compchem articles get published in #MachineLearningScienceandTechnology 😊 #CompChemSky
#MachineLearning (ML) for orbital-free density functional theory (OF-DFT) and OF-DFT for ML. Manzhos and coworkers tackle the kinetic energy functional in this new #MachineLearningScienceandTechnology article. #compchem #CompChemSky Read it here: bit.ly/45eGM0f
⚛️Great new #MLST article on ‘Evidential #DeepLearning for #UncertaintyQuantification and #AnomalyDetection in Jet Identification’
From University of Illinois Urbana-Champaign researchers @markneubauer.bsky.social
Paper ➡️http://bit.ly/3TMiu8A
#MachineLearningScienceandTechnology #machinelearning
⚛️New Review Article out now in #MachineLearningScienceandTechnology on Opportunities in #continual_learning for applications of #machine_learning in #ParticleAccelerators 🤖
Authors from Jefferson Lab; Uni of Houston; SLAC Nat Lab; Oak Ridge Nat Lab
Read the Review ➡️ bit.ly/4lzy8Ag
#Datasets and where to find them #MachineLearningScienceandTechnology (but also every journal in the #machinelearning series 😉)
🔎Great new work in #MachineLearningScienceandTechnology !
A multiscale #Bayesian approach that turns few X-rays into comprehensive chemical maps
Researchers from @heriotwattuni.bsky.social, EPFL & DQMP, Uni of Geneva
Read the full paper ➡️ bit.ly/4lKMwoX
#Multiscale #Electron #Microscopy #Denoising
Figure 1 from the article text. The caption reads: Schematic illustration of the three-phase validation and evaluation pipeline for crystal structure submissions in LAM Crystal Philately: pre-submission validation, random sampling quality check, and final evaluation with LAM-based energy calculations. The process includes: (1) an optional pre-submission phase, where original structures can be optimized using LAM to improve submission validity; (2) a sampling phase for quality checking the submissions; and (3) a final evaluation phase, where deduplication, scoring, and database ingestion take place.
🚨Challenges article alert!🚨
In this paper, Zhang, Wang and coworkers describe the #OpenLAMChallenges, competitions designed to benchmark #machinelearning methods and accelerate ML-driven discovery. Read the #MachineLearningScienceandTechnology article here: bit.ly/3TTlZtP 1/2
⚛️Great #MachineLearningScienceandTechnology paper on Multi-target property prediction and #optimization using #LatentSpaces of #generativemodel
From @samikaski.bsky.social & @aalto.fi & @orionpharma.bsky.social researchers
Paper ➡️ bit.ly/4ndky7c
#DrugDiscovery #ML #molecular #graphgeneration
@viti-vitines.bsky.social and coauthors develop a new #MachineLearning algorithm called "logic replicant" to tackle the problem of multiclass classification with small datasets. #MachineLearningScienceandTechnology
Read the article here: bit.ly/3ZK5KCF
🚨 Dataset article alert! 🚨
Wellawatte, @mlederbauer.bsky.social, @pschwllr.bsky.social and coauthors introduce a new open-source #dataset with >1,000 entries specifically designed for #LLMs applications in #chemistry. #MachineLearningScienceandTechnology #ChemSky 🧪
Article here: bit.ly/4kHdY6x
🚨 #Benchmark paper alert! 🚨
@andreniyongabo.bsky.social, Kangming Li, @adjiboussodieng.bsky.social and @jae3goals.bsky.social pave the way for #benchmarking #LLMs for materials property prediction with their LLM4Mat-Bench #MachineLearningScienceandTechnology
Read the paper here: bit.ly/43nxfnW
Great new paper in #MachineLearningScienceandTechnology 🚨Optimal #spectroscopic measurement design: #Bayesian framework for rational #data acquisition
From researchers at Osaka Uni and Institute of Statistical Mathematics, Japan
Paper ➡️ iopscience.iop.org/article/10.1...
This #MachineLearningScienceandTechnology article introduces the correlator Transformer #CoTra, which classifies different phases of matter and yields full #interpretability in terms of physical correlation functions. #ML #QuantumMatter Check it out here: iopscience.iop.org/article/10.1...
Figure 2 of the article, showing a 'Sketch of the physical problem: two mutually interacting particles, A and B, undergo excitation-transfer processes from A, initially prepared in its excited state, to B. The latter is also incoherently coupled to a sink. We consider this as a starting configuration for the building up of a network: one by one, we add particles between A and B and seek for the potential increase of the excitation-transfer efficiency. The inter-particle interactions depends on their relative positions.'
In this #MachineLearningScienceandTechnology article (with a sprinkle of @iopp-quantum.bsky.social), Sgroi and coworkers design a chain of particles that achieves efficient excitation-transfer performances using a #transferlearning approach. Read it here: iopscience.iop.org/article/10.1...
In this #MachineLearningScienceandTechnology paper, Aarts and coworkers investigated how higher-order moments and cumulants are learnt in #DiffusionModels by
deriving exact expressions for the moment- and cumulant-generating functionals.
Read the article here: iopscience.iop.org/article/10.1...
I couldn't think of a greater way to start the week: "LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction" (@adjiboussodieng.bsky.social and @jae3goals.bsky.social) is out in #MachineLearningScienceandTechnology 🤩 #EditorsLife
iopscience.iop.org/article/10.1...
Optimizing the network structure improves the effectiveness of generative modeling by a tensor tree - Great new work in #MachineLearningScienceandTechnology from researchers at University of Tokyo & Kyoto University
Paper: iopscience.iop.org/article/10.1...
#generativemodel #ML #optimization
Figure 6 from the article, with caption: Comparison between the generated images using models from trainings A1 and B1 on β-Tubulin-1 dataset. (a) Widefield or LR image, (b) ground truth or HR image, (c) generated image using the model from training A1, PSNR: 11±2, SSIM: 0.5±0.4, (d) generated image using the model from training B1, PSNR: 14±2, SSIM: 0.6±0.3. In (e), the overlay of the inference obtained with training B1 model (c, yellow) and the reference STORM image (b, green).
In this #MachineLearningScienceandTechnology article, Scapicchio and co-authors adapted the enhanced super-resolution generative adversarial network (ESRGAN) to #microscopy images. Read more about the results in their paper, iopscience.iop.org/article/10.1... #SuperResolutionMicroscopy #cells
#MachineLearningScienceandTechnology game 👾: Who can find @kylecranmer.bsky.social (easy) and @pavlodral.bsky.social (a little harder) in this picture? 🤩
In this #MachineLearningScienceandTechnology paper, Gao and coworkers introduce a novel #MachineLearning approach to address the issues of process monitoring in industrial and chemical process systems. #Faultdiagnostics #chemistry #ChemSky
Paper: iopscience.iop.org/article/10.1...
Have you ever wondered how to estimate uncertainties in #LHC data analyses? In this #MachineLearningScienceandTechnology article, Schöfbeck introduces a new framework aided by #MachineLearning. Check it out!
Paper: iopscience.iop.org/article/10.1...
In this #MachineLearningScienceandTechnology #benchmark, Rende and Viteritti explore the suitability of different attention mechanisms in #transformers to approximate the ground states of #quantum many-body #Hamiltonians.
Check out their paper here iopscience.iop.org/article/10.1...
Figure 8 of the Machine Learning: Science and Technology paper by Setty, Abdusalamov, and Motzoi shows the solution of the 2D Poisson equation. Head to the website for additional information.
In this great #MachineLearningScienceandTechnology paper, Setty, Abdusalamov, and Motzoi tackle #differentialequations via self-adaptive, physics-informed #quantumML, a promising step to their near-term evaluation on #quantumdevices.
Read the article here: iopscience.iop.org/article/10.1...
