Research graph
References from Machine learning for fast development of advanced energy materials. Local targets link to admitted publications; unresolved targets remain external evidence.
Heterogeneous multilayer generalized operational perceptron
10.1109/tnnls.2019.2914082 · 2019 · External reference
Superhuman AI for multiplayer poker
10.1126/science.aay2400 · 2019 · External reference
Towards artificial general intelligence with hybrid Tianjic chip architecture
10.1038/s41586-019-1424-8 · 2019 · External reference
Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model
10.1080/13658816.2016.1244608 · 2017 · External reference
Improved protein structure prediction using potentials from deep learning
10.1038/s41586-019-1923-7 · 2020 · External reference
Deep reinforcement learning for de novo drug design
10.1126/sciadv.aap7885 · 2018 · External reference
Information fusion in visual question answering: a survey
10.1016/j.inffus.2019.03.005 · 2019 · External reference
Inkjet-printed unclonable quantum dot fluorescent anti-counterfeiting labels with artificial intelligence authentication
2019 · External reference
Deep learning
10.1038/nature14539 · 2015 · External reference
The role of big data in smart city
2016 · External reference
Marketing in computer-mediated environments: Research synthesis and new directions
10.1509/jm.12.0020 · 2014 · External reference
Advances in computational methods to predict the biological activity of compounds
10.1517/17460441.2010.492827 · 2010 · External reference
Analysis of workability behavior of Al–SiC P/M composites using backpropagation neural network model and statistical technique
10.1016/j.commatsci.2009.06.013 · 2009 · External reference
Unresolved reference
2004 · External reference
Unresolved reference
2013 · External reference
10.1145/130385.130401
10.1145/130385.130401 · External reference
Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China
10.1016/j.rser.2018.10.018 · 2019 · External reference
Machine learning-based optimal design of a phase change material integrated renewable system with on-site PV, radiative cooling and hybrid ventilations—study of modelling and application in five climatic regions
2020 · External reference
Machine-learning-accelerated perovskite crystallization
10.1016/j.matt.2020.02.012 · 2020 · External reference
How machine learning can help select capping layers to suppress perovskite degradation
10.1038/s41467-020-17945-4 · 2020 · External reference
Predicting the formability of hybrid organic–inorganic perovskites via an interpretable machine learning strategy
10.1021/acs.jpclett.1c01939 · 2021 · External reference
Machine learning roadmap for perovskite photovoltaics
10.1021/acs.jpclett.1c01961 · 2021 · External reference
Machine learning for understanding compatibility of organic–inorganic hybrid perovskites with post-treatment amines
10.1021/acsenergylett.8b02451 · 2019 · External reference
Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning
10.1038/s41467-018-05761-w · 2018 · External reference
Machine learning assisted materials design and discovery for rechargeable batteries
10.1016/j.ensm.2020.06.033 · 2020 · External reference
Performance assessment of tariff-based air source heat pump load shifting in a UK detached dwelling featuring phase change-enhanced buffering
10.1016/j.applthermaleng.2013.12.019 · 2014 · External reference
A review on cooling performance enhancement for phase change materials integrated systems—flexible design and smart control with machine learning applications
10.1016/j.buildenv.2020.106786 · 2020 · External reference
Machine learning for high performance organic solar cells: current scenario and future prospects
10.1039/d0ee02838j · 2021 · External reference
Materials discovery and design using machine learning
2017 · External reference
Definitions, methods, and applications in interpretable machine learning
10.1073/pnas.1900654116 · 2019 · External reference
Supervised machine learning: a review of classification techniques
2007 · External reference
Unresolved reference
External reference
Machine learning with big data: challenges and approaches
10.1109/access.2017.2696365 · 2017 · External reference
Learning hierarchical features for scene labeling
10.1109/tpami.2012.231 · 2012 · External reference
Multiple imputation by chained equations: what is it and how does it work?
10.1002/mpr.329 · 2011 · External reference
Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes
10.1016/s0895-4356(96)00002-9 · 1996 · External reference
Decision trees for uplift modeling with single and multiple treatments
10.1007/s10115-011-0434-0 · 2012 · External reference
Unresolved reference
2022 · External reference
10.1109/icra.2018.8460874
10.1109/icra.2018.8460874 · External reference
Unresolved reference
External reference
Large scale distributed deep networks
2012 · External reference
Recent advances and applications of machine learning in solid-state materials science
10.1038/s41524-019-0221-0 · 2019 · External reference
10.1145/2046684.2046689
10.1145/2046684.2046689 · External reference
Multidimensional scaling
10.1002/wcs.1203 · 2013 · External reference
Unsupervised machine learning for networking: Techniques, applications and research challenges
10.1109/access.2019.2916648 · 2019 · External reference
10.1109/ist.2016.7738244
10.1109/ist.2016.7738244 · External reference
Unresolved reference
External reference
Unresolved reference
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Taming technical bias in machine learning pipelines
2020 · External reference
Unresolved reference
External reference
Learning to perceive and act by trial and error
10.1023/a:1022619109594 · 1991 · External reference
Unresolved reference
1996 · External reference
Unresolved reference
2018 · External reference
Unresolved reference
1996 · External reference
Unresolved reference
2004 · External reference
Unresolved reference
2015 · External reference
Unresolved reference
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An efficient destination prediction approach based on future trajectory prediction and transition matrix optimization
10.1109/tkde.2018.2883938 · 2018 · External reference
Unresolved reference
External reference
Unresolved reference
2007 · External reference
A framework for learning predictive structures from multiple tasks and unlabeled data
2005 · External reference
Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor
10.1080/00207543.2020.1733125 · 2020 · External reference
Machine learning interpretability: A survey on methods and metrics
10.3390/electronics8080832 · 2019 · External reference
Reinforcement learning
1999 · External reference
Unresolved reference
2008 · External reference
On hyperparameter optimization of machine learning algorithms: Theory and practice
10.1016/j.neucom.2020.07.061 · 2020 · External reference
Descriptors for machine learning of materials
2018 · External reference
Machine learning model towards evaluating data gathering methods in manufacturing and mechanical engineering
2023 · External reference
Artificial intelligence is aiding the search for energy materials
10.1557/mrs.2019.51 · 2019 · External reference
Using artificial intelligence to accelerate materials development
10.1557/mrs.2019.113 · 2019 · External reference
Unresolved reference
2016 · External reference
Unresolved reference
External reference
Artificial intelligence for materials discovery
10.1557/mrs.2019.158 · 2019 · External reference
Dynamic nonlinear partial least squares modeling using gaussian process regression
10.1021/acs.iecr.9b00701 · 2019 · External reference
Unresolved reference
External reference
Machine learning: accelerating materials development for energy storage and conversion
10.1002/inf2.12094 · 2020 · External reference
Linear regression analysis: part 14 of a series on evaluation of scientific publications
2010 · External reference
A simulation study of alternatives to ordinary least squares
10.1080/01621459.1977.10479910 · 1977 · External reference
Data-driven materials science: status, challenges, and perspectives.
2019 · External reference
Logistic regression in data analysis: an overview
10.1504/ijdats.2011.041335 · 2011 · External reference
Analysis of passenger's choice between shuttle bus and illegal taxi
10.1016/j.sbspro.2013.08.220 · 2013 · External reference
Forecasting stock performance in indian market using multinomial logistic regression
2012 · External reference
Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials
10.1039/c6ee02697d · 2017 · External reference
Comparison of logistic regression and linear regression in modeling percentage data
10.1128/aem.67.5.2129-2135.2001 · 2001 · External reference
Logistic regression
10.1038/nmeth.3904 · 2016 · External reference
Neural network potentials in materials modeling
2020 · External reference
Performance of neural networks in materials science
10.1179/174328408x311053 · 2009 · External reference
The use of artificial neural networks in materials science based research
10.1016/j.matdes.2007.02.009 · 2007 · External reference
Evolving unsupervised deep neural networks for learning meaningful representations
10.1109/tevc.2018.2808689 · 2018 · External reference
Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection
10.1016/j.conbuildmat.2017.09.110 · 2017 · External reference
10.1109/iccke.2015.7365866
10.1109/iccke.2015.7365866 · External reference
A review of principal component analysis algorithm for dimensionality reduction
2021 · External reference
10.1109/infrkm.2010.5466902
10.1109/infrkm.2010.5466902 · External reference
The prediction of spark-ignition engine performance and emissions based on the SVR algorithm
2022 · External reference
Random forests for land cover classification
10.1016/j.patrec.2005.08.011 · 2006 · External reference
An introduction to decision tree modeling
10.1002/cem.873 · 2004 · External reference
10.1609/aaai.v30i1.10295
10.1609/aaai.v30i1.10295 · External reference
Machine learning for molecular and materials science
10.1038/s41586-018-0337-2 · 2018 · External reference
Human Population and the Global Environment: Population growth, rising per capita material consumption, and disruptive technologies have made civilization a global ecological force
1974 · External reference
Renewable energy development in rural areas of Iran
10.1016/j.rser.2016.07.042 · 2016 · External reference
Solar power generation by PV (photovoltaic) technology: a review
10.1016/j.energy.2013.02.057 · 2013 · External reference
Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing
10.1016/j.joule.2022.03.003 · 2022 · External reference
A new generation of AI: a review and perspective on machine learning technologies applied to smart energy and electric power systems
10.1002/er.4333 · 2019 · External reference
Insights from machine learning techniques for predicting the efficiency of fullerene derivatives‐based ternary organic solar cells at ternary blend design
10.1002/aenm.201900891 · 2019 · External reference
Back‐contact solar cells: a review.
10.1002/pip.657 · 2006 · External reference
Semiconductor quantum dots and quantum dot arrays and applications of multiple exciton generation to third-generation photovoltaic solar cells
10.1021/cr900289f · 2010 · External reference
Development of organic semiconductor photodetectors: from mechanism to applications
10.1002/adom.201800522 · 2019 · External reference
Machine-learning-assisted discovery of perovskite materials with high dielectric breakdown strength
10.1039/d2ma00839d · 2022 · External reference
Polymer photocatalysts for solar-to-chemical energy conversion
10.1038/s41578-020-00254-z · 2021 · External reference
Machine-learning modeling for ultra-stable high-efficiency perovskite solar cells
10.1002/aenm.202201463 · 2022 · External reference
Accelerated search for perovskite materials with higher Curie temperature based on the machine learning methods
10.1016/j.commatsci.2018.04.031 · 2018 · External reference
Data-driven design of high-performance MASnxPb1-xI3 perovskite materials by machine learning and experimental realization
10.1038/s41377-022-00924-3 · 2022 · External reference
Machine learning stability and band gap of lead-free halide double perovskite materials for perovskite solar cells
10.1016/j.solener.2021.09.030 · 2021 · External reference
Machine learning aided design of perovskite oxide materials for photocatalytic water splitting
10.1016/j.jechem.2021.01.035 · 2021 · External reference
Rationalizing perovskite data for machine learning and materials design
10.1021/acs.jpclett.8b03232 · 2018 · External reference
Microstructure maps of complex perovskite materials from extensive Monte Carlo sampling using machine learning enabled energy model
10.1021/acs.jpclett.1c00410 · 2021 · External reference
Photoelectrochemical properties, machine learning, and symbolic regression for molecularly engineered halide perovskite materials in water
10.1021/acsami.2c00568 · 2022 · External reference
Predicting the crystal structure and lattice parameters of the perovskite materials via different machine learning models based on basic atom properties
10.3390/cryst12111570 · 2022 · External reference
Improved physics-based structural descriptors of perovskite materials enable higher accuracy of machine learning
10.1016/j.commatsci.2021.110714 · 2021 · External reference
Investigation of opto-electronic properties and stability of mixed-cation mixed-halide perovskite materials with machine-learning implementation
2021 · External reference
The coming electric vehicle transformation
10.1126/science.aax0704 · 2019 · External reference
Transportation emissions scenarios for New York City under different carbon intensities of electricity and electric vehicle adoption rates
10.1038/s41560-020-00740-2 · 2021 · External reference
Accurate predictions of lithium-ion battery life
10.1038/d41586-019-01138-1 · 2019 · External reference
A review on the key issues of the lithium ion battery degradation among the whole life cycle
10.1016/j.etran.2019.100005 · 2019 · External reference
Aging mechanisms under different state-of-charge ranges and the multi-indicators system of state-of-health for lithium-ion battery with Li (NiMnCo) O2 cathode
10.1016/j.jpowsour.2018.07.018 · 2018 · External reference
Quantifiability of inherent cell-to-cell variations of commercial lithium-ion batteries
10.1016/j.etran.2021.100129 · 2021 · External reference
A method of cell-to-cell variation evaluation for battery packs in electric vehicles with charging cloud data
10.1016/j.etran.2020.100077 · 2020 · External reference
A review of lithium-ion battery for electric vehicle applications and beyond
10.1016/j.egypro.2019.01.783 · 2019 · External reference
Machine learning accelerates the discovery of light-absorbing materials for double perovskite solar cells
10.1021/acs.jpcc.1c07262 · 2021 · External reference
Interfaces in garnet‐based all‐solid‐state lithium batteries.
10.1002/aenm.202001318 · 2020 · External reference
Advances and prospects of sulfide all‐solid‐state lithium batteries via one‐to‐one comparison with conventional liquid lithium ion batteries
10.1002/adma.201900376 · 2019 · External reference
Lithium solid-state batteries: State-of-the-art and challenges for materials, interfaces and processing
10.1016/j.jpowsour.2021.229919 · 2021 · External reference
Combinatorial and high‐throughput materials science.
10.1002/anie.200603675 · 2007 · External reference
Commentary: the materials project: a materials genome approach to accelerating materials innovation
10.1063/1.4812323 · 2013 · External reference
A perspective on organic electrode materials and technologies for next generation batteries
10.1016/j.jpowsour.2020.228814 · 2021 · External reference
Promises and challenges of next-generation “beyond Li-ion” batteries for electric vehicles and grid decarbonization
10.1021/acs.chemrev.0c00767 · 2020 · External reference
Machine learning: an advanced platform for materials development and state prediction in lithium‐ion batteries.
10.1002/adma.202101474 · 2022 · External reference
A naive Bayes model for robust remaining useful life prediction of lithium-ion battery
10.1016/j.apenergy.2013.12.020 · 2014 · External reference
10.1109/icphm.2015.7245069
10.1109/icphm.2015.7245069 · External reference
Developing multivariate linear regression models to predict the electrochemical performance of lithium ion batteries based on material property parameters
10.1149/2.0421707jes · 2017 · External reference
Remaining useful life estimation of lithium-ion cells based on k-nearest neighbor regression with differential evolution optimization
10.1016/j.jclepro.2019.119409 · 2020 · External reference
State‐of‐health estimation for the lithium‐ion battery based on gradient boosting decision tree with autonomous selection of excellent features.
10.1002/er.7292 · 2022 · External reference
Application of artificial neural networks in design of lithium-ion batteries
10.1016/j.jpowsour.2018.05.040 · 2018 · External reference
10.1109/icphm49022.2020.9187060
10.1109/icphm49022.2020.9187060 · External reference
A convolutional neural network model for battery capacity fade curve prediction using early life data
10.1016/j.jpowsour.2022.231736 · 2022 · External reference
Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning
2022 · External reference
Study on heat-transfer mechanism of wallboards containing active phase change material and parameter optimization with ventilation
10.1016/j.applthermaleng.2018.04.083 · 2018 · External reference
Thermal performance and optimized thickness of active shape-stabilized PCM boards for side-wall cooling and under-floor heating system
10.1177/1420326x16671983 · 2016 · External reference
Simulation study of a pipe-encapsulated PCM wall system with self-activated heat removal by nocturnal sky radiation
10.1016/j.renene.2019.07.060 · 2020 · External reference
Performance investigation of the direct absorption solar collector based on phase change slurry
10.1016/j.applthermaleng.2019.114244 · 2019 · External reference
Multivariable optimisation of a new PCMs integrated hybrid renewable system with active cooling and hybrid ventilations
2019 · External reference
Artificial neural network based multivariable optimization of a hybrid system integrated with phase change materials, active cooling and hybrid ventilations
10.1016/j.enconman.2019.111859 · 2019 · External reference
Analysis and design of air ventilated building integrated photovoltaic (BIPV) system incorporating phase change materials
10.1016/j.enconman.2019.05.073 · 2019 · External reference
Support vector machines used to estimate the battery state of charge
10.1109/tpel.2013.2243918 · 2013 · External reference
Transient cooling of electronics using phase change material (PCM)-based heat sinks
10.1016/j.applthermaleng.2007.06.010 · 2008 · External reference
Investigation on the cooling and temperature uniformity of power battery pack based on gradient phase change materials embedded thin heat sinks
10.1016/j.applthermaleng.2020.115304 · 2020 · External reference
Review on thermal energy storage with phase change materials and applications
10.1016/j.rser.2007.10.005 · 2009 · External reference
A machine learning methodology for the diagnosis of phase change material-based thermal management systems
10.1016/j.applthermaleng.2022.119864 · 2023 · External reference
Implementation of ANN and GA on building with PCM at various setpoints, PCM types, and installation locations to boost energy saving and CO2 saving
10.1016/j.enganabound.2022.08.006 · 2022 · External reference
Exergy-based optimisation of a phase change materials integrated hybrid renewable system for active cooling applications using supervised machine learning method
10.1016/j.solener.2019.11.067 · 2020 · External reference
Thermophysical properties prediction of carbon-based nano-enhanced phase change material's using various machine learning methods
10.1016/j.jtice.2022.104662 · 2023 · External reference
Exploring efficacy of machine learning (artificial neural networks) for enhancing reliability of thermal energy storage platforms utilizing phase change materials
10.1016/j.ijheatmasstransfer.2022.122628 · 2022 · External reference
Machine learning prediction and multiobjective optimization for cooling enhancement of a plate battery using the chaotic water-microencapsulated PCM fluid flows
10.1016/j.jtice.2023.104680 · 2023 · External reference
10.1109/adconip55568.2022.9894191
10.1109/adconip55568.2022.9894191 · External reference
Machine learning-based prediction and augmentation of dish solar distiller performance using an innovative convex stepped absorber and phase change material with nanoadditives
10.1016/j.psep.2022.03.052 · 2022 · External reference
Experimental analysis of CPV/T solar dryer with nano-enhanced PCM and prediction of drying parameters using ANN and SVM algorithms
10.1016/j.solener.2021.02.028 · 2021 · External reference
Application of phase change material and artificial neural networks for smoothing of heat flux fluctuations
10.3390/en14123531 · 2021 · External reference
Linear regression model in estimating solar radiation in perlis
10.1016/j.egypro.2012.05.156 · 2012 · External reference
Forecasting the probability of solar power output using logistic regression algorithm
2020 · External reference
10.1051/e3sconf/202129701029
10.1051/e3sconf/202129701029 · External reference
Solar radiation prediction using recurrent neural network and artificial neural network: a case study with comparisons
10.1016/j.renene.2020.04.042 · 2020 · External reference
Battery state-of-charge estimator using the SVM technique
10.1016/j.apm.2013.01.024 · 2013 · External reference
10.1088/1742-6596/2325/1/012004
10.1088/1742-6596/2325/1/012004 · External reference
Robust random forest based non-fullerene organic solar cells efficiency prediction
10.1016/j.orgel.2019.105465 · 2020 · External reference
Crystal structure classification in ABO3 perovskites via machine learning
10.1016/j.commatsci.2020.110191 · 2021 · External reference
Artificial neural network for predicting annual output energy of building-integrated photovoltaics based on the 2-terminal perovskite/silicon tandem cells under realistic conditions
10.1016/j.egyr.2022.08.233 · 2022 · External reference
Gaussian kernel based SVR model for short-term photovoltaic MPP power prediction
10.32604/csse.2022.020367 · 2022 · External reference
Robust Gaussian process modeling using EM algorithm
10.1016/j.jprocont.2016.04.003 · 2016 · External reference
Neural networks in materials science and engineering: a review of salient issues
2015 · External reference
Fast material search of lithium ion conducting oxides using a recommender system
10.1039/d0ta02556a · 2020 · External reference
Using support vector machine for materials design
10.1007/s40436-013-0025-2 · 2013 · External reference
Liquid electrolyte informatics using an exhaustive search with linear regression
10.1039/c7cp08280k · 2018 · External reference
10.1088/1742-6596/699/1/012001
10.1088/1742-6596/699/1/012001 · External reference
10.1088/1757-899x/261/1/012018
10.1088/1757-899x/261/1/012018 · External reference
10.1088/1757-899x/1047/1/012082
10.1088/1757-899x/1047/1/012082 · External reference
