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Atomistic ML ecosystem roadmap published after CECAM workshop

By PulseAugur Editorial · [1 sources] ·

A new paper outlines a strategic roadmap for developing a robust ecosystem for atomistic machine learning (ML). The paper, stemming from discussions at CECAM in Lausanne, addresses the integration of ML into atomistic simulations, highlighting challenges in choosing between data-centric and physics-based approaches, and adapting software to modern hardware. It proposes long-term goals and concrete actions to foster a sustainable and impactful atomistic ML community. AI

IMPACT Aims to foster a more coordinated and impactful atomistic ML community by addressing integration challenges and proposing concrete actions.

RANK_REASON The item is an academic paper detailing research findings and future directions. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Atomistic ML ecosystem roadmap published after CECAM workshop

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The item is an academic paper detailing research findings and future directions. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · J\"org Behler, Michele Ceriotti, Cecilia Clementi, G\'abor Cs\'anyi, Alin-Marin Elena, Aditi Krishnapriyan, Joseph W. Abbott, Fabio Affinito, Albert P. Bart\'ok, Ilyes Batatia, Filippo Bigi, Florian N. Br\"unig, Yannick Calvino Alonso, Giuseppe Carleo, A… ·

    A strategic roadmap for an atomistic machine-learning ecosystem

    arXiv:2609.39090v1 Announce Type: cross Abstract: Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespread and impactful. This success is due largely to t…

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