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10 merged results for "Quantum Machine Learning"

Partial results: at least one source did not answer. Available results are shown rather than treating an upstream outage as zero matches.

Source status
  1. A Comparative Study of Multiwalled WS(2) Nanotubes Using Universal Machine Learning Interatomic Potentials and an Atomistic Force Field.

    Bandura AV, Evarestov RA, Kuzmin A, Lukyanov SI · 2026 · Chemphyschem : a European journal of chemical physics and physical chemistry

    limited evidence Transparent signal score 43/100 · policy 1.0.0

    Found in pubmed · DOI 10.1002/cphc.70575

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    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
  2. Machine-learned interatomic potentials for battery materials: from fundamental methodology to emerging applications in electrodes, electrolytes, and interfaces.

    Makino K, Kato T, Terashima S, Matsuoka Y · 2026 · Physical chemistry chemical physics : PCCP

    limited evidence Transparent signal score 43/100 · policy 1.0.0

    Found in pubmed · DOI 10.1039/d6cp01595f

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    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
  3. moTSart: accelerating automated transition state search with generative models in a low-data regime.

    Galustian L, Karwounopoulos J, Demuth T, De Landsheere J · 2026 · Digital discovery

    limited evidence Transparent signal score 45/100 · policy 1.0.0

    Found in pubmed · DOI 10.1039/d6dd00259e

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    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
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    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
  4. Active delta-learning for surface hopping: efficient fitting of potential energy surfaces.

    Martyka M, Jankowska J, Dral PO · 2026 · Faraday discussions

    limited evidence Transparent signal score 43/100 · policy 1.0.0

    Found in pubmed · DOI 10.1039/d6fd00066e

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    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
  5. Deep learning of tautomer stability from crystallographic proton positions.

    Pan X, Han C, Han F, Zhang Y · 2026 · Chemical science

    limited evidence Transparent signal score 45/100 · policy 1.0.0

    Found in pubmed · DOI 10.1039/d6sc03714c

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    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
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    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
  6. Transforming Nanomaterials Development with Artificial Intelligence Techniques.

    Al-Raeei M · 2026 · Nanotechnology, science and applications

    limited evidence Transparent signal score 45/100 · policy 1.0.0

    Found in pubmed · DOI 10.2147/nsa.s641862

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    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
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    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
  7. The Future of AI in Breast Cancer: Quantum Computing and Next-Gen Algorithms for Precision Oncology.

    Sah R, Jain T, Bisht PR, Saini A · 2026 · Current drug research reviews

    limited evidence Transparent signal score 43/100 · policy 1.0.0

    Found in pubmed · DOI 10.2174/0125899775478789260914150116

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    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
  8. Suppressing Charge Recombination in DSSCs with ZrO(2) Compact Layers: Experimental Validation and Mathematical Modeling.

    Yavuz Hİ, Ozenbas M · 2026 · Nanomaterials (Basel, Switzerland)

    limited evidence Transparent signal score 43/100 · policy 1.0.0

    Found in pubmed · DOI 10.3390/nano16181192

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    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
  9. The Emerging Role of Artificial Intelligence in Cardiovascular Care: From Imaging to Predictive Analytics.

    Hatab T, Fares M · 2026 · Heart views : the official journal of the Gulf Heart Association

    limited evidence Transparent signal score 45/100 · policy 1.0.0

    Found in pubmed · DOI 10.4103/heartviews.heartviews_41_26

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    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
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    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
  10. High-throughput physics-based enzyme engineering.

    Wang X, Qiu X, Wu Y, Niu T · 2026 · bioRxiv : the preprint server for biology

    limited evidence Transparent signal score 45/100 · policy 1.0.0

    Found in pubmed · DOI 10.64898/2026.09.15.751901

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    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer