ArticleACS energy letters2022
Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization.
Article in ACS energy letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Optimizing thermoelectric energy harvesting using deep reinforcement learning for dynamic energy management and system efficiency.Scientific reports · 2025Article
- Advancing Thermal Management Technology for Power Semiconductors through Materials and Interface Engineering.Accounts of materials research · 2025Article
- Machine Learning for Thermal Transport and Phonon High-order Anharmonicity in High Thermal Conductivity Materials: A Case Study in Boron Arsenide.Physical review materials · 2025Article
- Characterization and machine learning analysis of hybrid alumina-copper oxide nanoparticles in therminol 55 for medium temperature heat transfer fluid.Scientific reports · 2025Article
- Nonclassical Heat Transfer and Recent Progress.ASME journal of heat and mass transfer · 2025Article
- Convergence of nanotechnology and artificial intelligence in the fight against liver cancer: a comprehensive review.Discover oncology · 2025Review
- Nanoscale hyperthermia mesostructures for sustainable antimicrobial design.Cell reports. Physical science · 2024Article
- Article
- Non-Perturbative Determination of Isotope-induced Anomalous Vibrational Physics.Physical review. B · 2023Article
- Recent Advances on Composition-Microstructure-Properties Relationships of Precipitation Hardening Stainless Steel.Materials (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Recent advances in machine learning (ML) have impacted research communities based on statistical perspectives and uncovered invisibles from conventional standpoints. Though the field is still in the early stage, this progress has driven the thermal science and engineering communities to apply such cutting-edge toolsets for analyzing complex data, unraveling abstruse patterns, and discovering non-intuitive principles. In this work, we present a holistic overview of the applications and future opportunities of ML methods on crucial topics in thermal energy research, from bottom-up materials discovery to top-down system design across atomistic levels to multi-scales. In particular, we focus on a spectrum of impressive ML endeavors investigating the state-of-the-art thermal transport modeling (density functional theory, molecular dynamics, and Boltzmann transport equation), different families of materials (semiconductors, polymers, alloys, and composites), assorted aspects of thermal properties (conductivity, emissivity, stability, and thermoelectricity), and engineering prediction and optimization (devices and systems). We discuss the promises and challenges of current ML approaches and provide perspectives for future directions and new algorithms that could make further impacts on thermal energy research.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.