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References from Machine learning for advanced energy materials. Local targets link to admitted publications; unresolved targets remain external evidence.
10.1016/j.egyai.2020.100002 · 2020 · Admitted local publication
Quantifying the impacts of climate change and extreme climate events on energy systems
10.1038/s41560-020-0558-0 · 2020 · External reference
The role of cation vacancies in electrode materials for enhanced electrochemical energy storage: synthesis, advanced characterization, and fundamentals
10.34133/2020/1932952 · 2020 · External reference
A review on the complementarity of renewable energy sources: concept, metrics, application and future research directions
10.1016/j.solener.2019.11.087 · 2020 · External reference
Materials for sustainable energy
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Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry
10.1016/j.jechem.2020.05.044 · 2021 · External reference
Machine-learning assisted screening of energetic materials
10.1021/acs.jpca.0c02647 · 2020 · External reference
Density functional theory in materials science
10.1002/wcms.1125 · 2013 · External reference
Data-driven materials science: status, challenges, and perspectives
2019 · External reference
Machine learning: Accelerating materials development for energy storage and conversion
10.1002/inf2.12094 · 2020 · External reference
Big Data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design
10.1016/j.eng.2019.02.011 · 2019 · External reference
Machine learning for materials scientists: an introductory guide toward best practices
10.1021/acs.chemmater.0c01907 · 2020 · External reference
Evolving the materials genome: how machine learning is fueling the next generation of materials discovery
10.1146/annurev-matsci-082019-105100 · 2020 · External reference
Machine learning in materials modeling - fundamentals and the opportunities in 2D materials
2020 · External reference
Big-Data science in porous materials: materials genomics and machine learning
10.1021/acs.chemrev.0c00004 · 2020 · External reference
Designing exceptional gas-separation polymer membranes using machine learning
10.1126/sciadv.aaz4301 · 2020 · External reference
Pores for thought: generative adversarial networks for stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries
10.1038/s41524-020-0340-7 · 2020 · External reference
Machine learning-driven new material discovery
10.1039/d0na00388c · 2020 · External reference
Machine learning in materials science
10.1002/inf2.12028 · 2019 · External reference
A universal machine learning algorithm for large-scale screening of materials
10.1021/jacs.9b11084 · 2020 · External reference
Machine learning in chemical reaction space
10.1038/s41467-020-19267-x · 2020 · External reference
Machine-learning assisted screening of energetic materials
10.1021/acs.jpca.0c02647 · 2020 · External reference
Predicting material properties by integrating high-throughput experiments, high-throughput ab-initio calculations, and machine learning
10.1080/14686996.2019.1707111 · 2020 · External reference
Machine learning approaches for the prediction of materials properties
10.1063/5.0018384 · 2020 · External reference
Machine learning-guided synthesis of advanced inorganic materials
10.1016/j.mattod.2020.06.010 · 2020 · External reference
Property-oriented material design based on a data-driven machine learning technique
10.1021/acs.jpclett.0c00665 · 2020 · External reference
The role of machine learning in the understanding and design of materials
10.1021/jacs.0c09105 · 2020 · External reference
Artificial intelligence and machine learning for targeted energy storage solutions
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Machine learning and artificial neural network accelerated computational discoveries in materials science
10.1002/wcms.1450 · 2020 · External reference
Simulation and design of energy materials accelerated by machine learning
10.1002/wcms.1421 · 2020 · External reference
Autonomous materials synthesis by machine learning and robotics
10.1063/5.0020370 · 2020 · External reference
Five high-impact research areas in machine learning for materials science
10.1021/acs.chemmater.9b04078 · 2019 · External reference
Machine learning for molecular and materials science
10.1038/s41586-018-0337-2 · 2018 · External reference
Data mining-aided materials discovery and optimization
2017 · External reference
Machine learning for materials scientists: an introductory guide toward best practices
10.1021/acs.chemmater.0c01907 · 2020 · External reference
Materials discovery and design using machine learning
2017 · External reference
Machine learning for renewable energy materials
10.1039/c9ta02356a · 2019 · External reference
AI applications through the whole life cycle of material discovery
10.1016/j.matt.2020.06.011 · 2020 · External reference
A critical review of machine learning of energy materials
10.1002/aenm.201903242 · 2020 · External reference
Accelerating materials development via automation, machine learning, and high-performance computing
10.1016/j.joule.2018.05.009 · 2018 · External reference
Unresolved reference
2020 · External reference
Global Energy Transformation
2019 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
BATTERY 2030+. Inventing the sustainable batteries of the future
2020 · External reference
China's next renewable energy revolution: goals and mechanisms in the 13th five year plan for energy
10.1002/ese3.161 · 2017 · External reference
Paris agreement on climate change
10.1201/9781351116589-2 · 2019 · External reference
Solar and wind power generation systems with pumped hydro storage: review and future perspectives
10.1016/j.renene.2019.11.157 · 2020 · External reference
Hybrid power plant for energy storage and peak shaving by liquefied oxygen and natural gas
10.1016/j.apenergy.2018.06.042 · 2018 · External reference
Improving the accuracy of electricity carbon footprint: estimation of hydroelectric reservoir greenhouse gas emissions
10.1016/j.rser.2020.110433 · 2021 · External reference
A review of multihazard risk assessment: progress, potential, and challenges in the application to nuclear power plants
2020 · External reference
10.1093/oxfclm/kgab004
10.1093/oxfclm/kgab004 · External reference
Modeling and simulation of flow batteries
2020 · External reference
Recent advances in fuel cells based propulsion systems for unmanned aerial vehicles
10.1016/j.apenergy.2019.02.079 · 2019 · External reference
Batteries and fuel cells for emerging electric vehicle markets
10.1038/s41560-018-0108-1 · 2018 · External reference
Closed-loop optimization of fast-charging protocols for batteries with machine learning
10.1038/s41586-020-1994-5 · 2020 · External reference
Predicting the state of charge and health of batteries using data-driven machine learning
10.1038/s42256-020-0156-7 · 2020 · External reference
Machine learning assisted materials design and discovery for rechargeable batteries
10.1016/j.ensm.2020.06.033 · 2020 · External reference
Recent advances in alkali-doped polybenzimidazole membranes for fuel cell applications
10.1016/j.rser.2018.03.024 · 2018 · External reference
A cost-effective and chemically stable electrode binder for alkaline-acid direct ethylene glycol fuel cells
10.1016/j.apenergy.2019.114060 · 2020 · External reference
Unresolved reference
External reference
Molecular tuning of CO2-to-ethylene conversion
10.1038/s41586-019-1782-2 · 2020 · External reference
Radical transformation pathway towards sustainable electricity via evolutionary steps
10.1038/s41467-019-08855-1 · 2019 · External reference
Use machine learning to find energy materials
10.1038/d41586-017-07820-6 · 2017 · External reference
Development status and prospects of artificial intelligence in the field of energy conversion materials
10.3389/fenrg.2020.00167 · 2020 · External reference
The machine learning revolution in materials?
10.1557/mrs.2019.153 · 2019 · External reference
Points of significance: machine learning: supervised methods
10.1038/nmeth.4551 · 2018 · External reference
10.1016/j.egyai.2020.100014 · 2020 · Admitted local publication
A critical review of machine learning of energy materials
10.1002/aenm.201903242 · 2020 · External reference
The material code
10.1038/533022a · 2016 · External reference
New frontiers for the materials genome initiative
10.1038/s41524-019-0173-4 · 2019 · External reference
The AFLOW fleet for materials discovery
2017 · External reference
The open quantum materials database (OQMD): Assessing the accuracy of DFT formation energies
10.1038/npjcompumats.2015.10 · 2015 · External reference
Materials cloud, a platform for open computational science
10.1038/s41597-020-00637-5 · 2020 · External reference
Research progress and development trends of materials genome technology
10.1155/2020/5903457 · 2020 · External reference
Machine-learning-assisted materials discovery using failed experiments
10.1038/nature17439 · 2016 · External reference
Unresolved reference
External reference
Combining machine learning potential and structure prediction for accelerated materials design and discovery
10.1021/acs.jpclett.0c02357 · 2020 · External reference
Feature selection in machine learning: a new perspective
10.1016/j.neucom.2017.11.077 · 2018 · External reference
A survey of feature selection and feature extraction techniques in machine learning
2014 · External reference
Recent advances and applications of machine learning in solid-state materials science
10.1038/s41524-019-0221-0 · 2019 · External reference
A comparative study of feature selection methods for stress hotspot classification in materials
10.1007/s40192-018-0109-8 · 2018 · External reference
Correlation-based and causal feature selection analysis for ensemble classifiers
2010 · External reference
Comparison of variable selection methods for clinical predictive modeling
10.1016/j.ijmedinf.2018.05.006 · 2018 · External reference
A hybrid feature selection method to improve performance of a group of classification algorithms
2013 · External reference
Deep Learning in neural networks: an overview
10.1016/j.neunet.2014.09.003 · 2015 · External reference
Artificial intelligence to power the future of materials science and engineering
10.1002/aisy.202070042 · 2020 · External reference
Materials cartography: Representing and mining materials space using structural and electronic fingerprints
10.1021/cm503507h · 2015 · External reference
The materials genome initiative and artificial intelligence
10.1557/mrs.2018.122 · 2018 · External reference
Artificial intelligence is aiding the search for energy materials
10.1557/mrs.2019.51 · 2019 · External reference
Universal fragment descriptors for predicting properties of inorganic crystals
10.1038/ncomms15679 · 2017 · External reference
Using artificial intelligence to accelerate materials development
10.1557/mrs.2019.113 · 2019 · External reference
10.1007/978-3-319-24211-8
10.1007/978-3-319-24211-8 · External reference
ML4Chem: a machine learning package for chemistry and materials science
2020 · External reference
Artificial intelligence for materials discovery
10.1557/mrs.2019.158 · 2019 · External reference
Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery
10.1039/c8me00012c · 2018 · External reference
Prediction of higher heating value of biomass materials based on proximate analysis using gradient boosted regression trees method
2021 · External reference
Liquid electrolyte informatics using an exhaustive search with linear regression
10.1039/c7cp08280k · 2018 · External reference
Optimal subsampling for large sample logistic regression
10.1080/01621459.2017.1292914 · 2018 · External reference
A method of predicting visual detectability of low-velocity impact damage in composite structures based on logistic regression model
10.1016/j.cja.2020.10.006 · 2021 · External reference
Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials
10.1039/c6ee02697d · 2017 · External reference
Quantification of uncertainties in thermoelectric properties of materials from a first-principles prediction method: an approach based on Gaussian process regression
2019 · External reference
Active learning for accelerated design of layered materials
2018 · External reference
Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels
10.1038/s41598-020-74394-1 · 2020 · External reference
A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions
10.1016/j.jmp.2018.03.001 · 2018 · External reference
Ab initio calculations of the redox potentials of additives for lithium-ion batteries and their prediction through machine learning
10.1021/acsomega.8b00576 · 2018 · External reference
Neural network potentials in materials modeling
2020 · External reference
Inverse design in porous materials using artificial neural networks
2019 · External reference
Neural networks in materials science
2008 · External reference
The use of artificial neural networks in materials science based research
10.1016/j.matdes.2007.02.009 · 2007 · External reference
Convolutional, long short-term memory, fully connected deep neural networks
2015 · External reference
Informatics-aided density functional theory study on the li ion transport of tavorite-type LiMTO4F (M3+-T5+, M2+-T6+)
10.1021/ci500752n · 2015 · External reference
On pairwise naive bayes classifiers
2007 · External reference
Data-driven materials exploration for li-ion conductive ceramics by exhaustive and informatics-aided computations
10.1002/tcr.201800129 · 2019 · External reference
Using support vector machine for materials design
10.1007/s40436-013-0025-2 · 2013 · External reference
Materials space of solid-state electrolytes: unraveling chemical composition-structure-ionic conductivity relationships in garnet-type metal oxides using cheminformatics virtual screening approaches
10.1039/c7cp00518k · 2017 · External reference
Materials prediction via classification learning
10.1038/srep13285 · 2015 · External reference
Predicting materials properties and behavior using classification and regression trees
10.1016/j.msea.2006.06.100 · 2006 · External reference
Machine-learning-assisted high-throughput computational screening of high performance metal-organic frameworks
10.1039/d0me00005a · 2020 · External reference
Points of significance: principal component analysis
10.1038/nmeth.4346 · 2017 · External reference
Development of recycling strategy for large stacked systems: experimental and machine learning approach to form reuse battery packs for secondary applications
10.1016/j.jclepro.2020.124152 · 2020 · External reference
Q-learning algorithms: a comprehensive classification and applications
10.1109/access.2019.2941229 · 2019 · External reference
Screening stable and metastable ABO3 perovskites using machine learning and the materials project
10.1016/j.commatsci.2020.109614 · 2020 · External reference
Fast material search of lithium ion conducting oxides using a recommender system
10.1039/d0ta02556a · 2020 · External reference
Primer about machine learning in catalysis – a tutorial with code
10.1002/cctc.202000234 · 2020 · External reference
Machine learning algorithm validation: from essentials to advanced applications and implications for regulatory certification and deployment
10.1016/j.nic.2020.08.004 · 2020 · External reference
Identifying domains of applicability of machine learning models for materials science
10.1038/s41467-020-17112-9 · 2020 · External reference
Artificial intelligence in materials modeling and design
2020 · External reference
Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics
10.1557/mrc.2019.95 · 2019 · External reference
Machine learning and statistical analysis for materials science: stability and transferability of fingerprint descriptors and chemical insights
10.1021/acs.chemmater.6b04229 · 2017 · External reference
Data-driven studies of li-ion-battery materials
10.3390/cryst9010054 · 2019 · External reference
Advances in three-dimensional graphene-based materials: configurations, preparation and application in secondary metal (Li, Na, K, Mg, Al)-ion batteries
10.1039/c8ee03014f · 2019 · External reference
Insights from machine learning of carbon electrodes for electric double layer capacitors
10.1016/j.carbon.2019.08.090 · 2020 · External reference
Machine learning for continuous innovation in battery technologies
10.1038/s41578-020-0216-y · 2020 · External reference
Modelling and understanding battery materials with machine-learning-driven atomistic simulations
10.1088/2515-7655/abb011 · 2020 · External reference
A review of advanced and practical lithium battery materials
10.1039/c0jm04225k · 2011 · External reference
Mass load prediction for lithium-ion battery electrode clean production: a machine learning approach
2020 · External reference
Rechargeable alkali-ion battery materials: theory and computation
10.1021/acs.chemrev.9b00601 · 2020 · External reference
Machine learning approach in exploring the electrolyte additives effect on cycling performance of LiNi0.5Mn1.5O4 cathode and graphite anode-based lithium-ion cell
2020 · External reference
MultiDK: a multiple descriptor multiple kernel approach for molecular discovery and its application to organic flow battery electrolytes
10.1021/acs.jcim.6b00332 · 2017 · External reference
Machine learning prediction of coordination energies for alkali group elements in battery electrolyte solvents
10.1039/c9cp03679b · 2019 · External reference
Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning
2019 · External reference
Lithium ion conduction in cathode coating materials from on-the-fly machine learning
10.1021/acs.chemmater.9b04663 · 2020 · External reference
Data-driven first-principles methods for the study and design of alkali superionic conductors
10.1021/acs.chemmater.6b02648 · 2017 · External reference
Unsupervised discovery of solid-state lithium ion conductors
2019 · External reference
Toward designing highly conductive polymer electrolytes by machine learning assisted coarse-grained molecular dynamics
10.1021/acs.chemmater.9b04830 · 2020 · External reference
An accurate machine-learning calculator for optimization of Li-ion battery cathodes
10.1063/5.0015872 · 2020 · External reference
Application of machine learning methods for the prediction of crystal system of cathode materials in lithium-ion batteries
10.1016/j.commatsci.2016.02.021 · 2016 · External reference
Quantitative structure-property relationship study of cathode volume changes in lithium ion batteries using ab-initio and partial least squares analysis
2017 · External reference
Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries
10.1039/c8ra07112h · 2018 · External reference
Machine learning approaches for designing mesoscale structure of li-ion battery electrodes
10.3390/batteries5030054 · 2019 · External reference
Machine learning the voltage of electrode materials in metal-ion batteries
10.1021/acsami.9b04933 · 2019 · External reference
Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes
2020 · External reference
Toward data-driven applications in lithium-ion battery cell manufacturing
10.1002/ente.201900136 · 2020 · External reference
Assessment of critical materials and cell design factors for high performance lithium-sulfur batteries using machine learning
10.1016/j.cej.2020.124117 · 2020 · External reference
Li-ion battery electrode health diagnostics using machine learning
2020 · External reference
Machine learning for halide perovskite materials
10.1016/j.nanoen.2020.105380 · 2020 · External reference
Machine learning analysis on stability of perovskite solar cells
10.1016/j.solmat.2019.110284 · 2020 · External reference
Solar materials find their band gap
10.1016/j.joule.2020.05.001 · 2020 · External reference
Predicting inorganic photovoltaic materials with efficiencies >26% via structure-relevant machine learning and density functional calculations
10.1016/j.xcrp.2020.100236 · 2020 · External reference
Critical review of machine learning applications in perovskite solar research
10.1016/j.nanoen.2020.105546 · 2021 · External reference
Machine learning (ML)-assisted design and fabrication for solar cells
10.1002/eem2.12049 · 2019 · External reference
Machine learning for perovskites’ reap-rest-recovery cycle
10.1016/j.joule.2018.11.010 · 2019 · External reference
Machine-learning structural and electronic properties of metal halide perovskites using a hierarchical convolutional neural network
10.1038/s41524-020-0307-8 · 2020 · External reference
Accelerated discovery of efficient solar cell materials using quantum and machine-learning methods
10.1021/acs.chemmater.9b02166 · 2019 · External reference
Thermodynamic stability trend of cubic perovskites
10.1021/jacs.7b09379 · 2017 · External reference
Finding new perovskite halides via machine learning
10.3389/fmats.2016.00019 · 2016 · External reference
Identifying Pb-free perovskites for solar cells by machine learning
10.1038/s41524-019-0177-0 · 2019 · External reference
Discovery of novel two-dimensional photovoltaic materials accelerated by machine learning
10.1021/acs.jpclett.0c00721 · 2020 · External reference
Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning
10.1038/s41467-018-05761-w · 2018 · External reference
Predicting the thermodynamic stability of solids combining density functional theory and machine learning
10.1021/acs.chemmater.7b00156 · 2017 · External reference
Searching for hidden perovskite materials for photovoltaic systems by combining data science and first principle calculations
10.1021/acsphotonics.7b01479 · 2018 · External reference
Multi-fidelity machine learning models for accurate bandgap predictions of solids
10.1016/j.commatsci.2016.12.004 · 2017 · External reference
Accelerated discovery of potential ferroelectric perovskite: via active learning
10.1039/d0tc00985g · 2020 · External reference
High-efficiency organic solar cells with low non-radiative recombination loss and low energetic disorder
10.1038/s41566-019-0573-5 · 2020 · External reference
Machine learning for high performance organic solar cells: current scenario and future prospects
10.1039/d0ee02838j · 2021 · External reference
Machine learning property prediction for organic photovoltaic devices
10.1038/s41524-020-00429-w · 2020 · External reference
The use of deep learning to fast evaluate organic photovoltaic materials
2019 · External reference
Transfer learning using ensemble neural networks for organic solar cell screening
2019 · External reference
Computer-aided screening of conjugated polymers for organic solar cell: classification by random forest
10.1021/acs.jpclett.8b00635 · 2018 · External reference
Combining electronic and structural features in machine learning models to predict organic solar cells properties
10.1039/c8mh01135d · 2019 · External reference
Designing promising molecules for organic solar cells: via machine learning assisted virtual screening
10.1039/c9ta04097h · 2019 · External reference
Machine learning in materials discovery: confirmed predictions and their underlying approaches
10.1146/annurev-matsci-090319-010954 · 2020 · External reference
Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials
10.1126/sciadv.aay4275 · 2019 · External reference
Machine learning for accelerating the discovery of high-performance donor/acceptor pairs in non-fullerene organic solar cells
10.1038/s41524-020-00388-2 · 2020 · External reference
Database of two-dimensional hybrid perovskite materials: open-access collection of crystal structures, band gaps, and atomic partial charges predicted by machine learning
10.1021/acs.chemmater.0c02290 · 2020 · External reference
A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation
10.1073/pnas.1916392117 · 2020 · External reference
Catalytic materials and chemistry development using a synergistic combination of machine learning and ab initio methods
10.1016/j.commatsci.2019.109474 · 2020 · External reference
Recent advances in knowledge discovery for heterogeneous catalysis using machine learning
2020 · External reference
Predicting the activity and selectivity of bimetallic metal catalysts for ethanol reforming using machine learning
10.1021/acscatal.0c02089 · 2020 · External reference
Machine learning for heterogeneous catalyst design and discovery
10.1002/aic.16198 · 2018 · External reference
10.1021/acsomega.9b03673 · 2020 · Admitted local publication
An adaptive machine learning strategy for accelerating discovery of perovskite electrocatalysts
10.1021/acscatal.9b05248 · 2020 · External reference
Sharp increase in catalytic selectivity in acetylene semihydrogenation on pd achieved by a machine learning simulation-guided experiment
10.1021/acscatal.0c02158 · 2020 · External reference
Feasibility of activation energy prediction of gas-phase reactions by machine learning
10.1002/chem.201800345 · 2018 · External reference
Toward effective utilization of methane: machine learning prediction of adsorption energies on metal alloys
10.1021/acs.jpcc.7b12670 · 2018 · External reference
Machine-learning-augmented chemisorption model for CO2 electroreduction catalyst screening
10.1021/acs.jpclett.5b01660 · 2015 · External reference
Identifying active sites for CO2 reduction on dealloyed gold surfaces by combining machine learning with multiscale simulations
10.1021/jacs.9b04956 · 2019 · External reference
Machine learning meets volcano plots: computational discovery of cross-coupling catalysts
10.1039/c8sc01949e · 2018 · External reference
High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery
10.1039/d0cp00972e · 2020 · External reference
Oxygen reduction activities of strained platinum core-shell electrocatalysts predicted by machine learning
10.1021/acs.jpclett.0c00214 · 2020 · External reference
Predicted optimal bifunctional electrocatalysts for the hydrogen evolution reaction and the oxygen evolution reaction using chalcogenide heterostructures based on machine learning analysis of in silico quantum mechanics based high throughput screening
10.1021/acs.jpclett.9b03875 · 2020 · External reference
High-throughput screening of hydrogen evolution reaction catalysts in MXene materials
10.1021/acs.jpcc.0c02265 · 2020 · External reference
Accelerating atomic catalyst discovery by theoretical calculations-machine learning strategy
10.1002/aenm.201903949 · 2020 · External reference
A machine learning scheme for the catalytic activity of alloys with intrinsic descriptors
10.1039/d0ta06203k · 2020 · External reference
Accelerated discovery of CO2 electrocatalysts using active machine learning
10.1038/s41586-020-2242-8 · 2020 · External reference
Two-dimensional layered SnO2 nanosheets for ambient ammonia synthesis
10.1021/acsaem.0c00858 · 2020 · External reference
Machine learning-based high throughput screening for nitrogen fixation on boron-doped single atom catalysts
10.1039/c9ta12608b · 2020 · External reference
A machine learning model on simple features for CO2 reduction electrocatalysts
10.1021/acs.jpcc.0c05964 · 2020 · External reference
Machine-learning-accelerated screening of hydrogen evolution catalysts in MBenes materials
10.1016/j.apsusc.2020.146522 · 2020 · External reference
Machine learning-aided identification of single atom alloy catalysts
10.1021/acs.jpcc.0c01492 · 2020 · External reference
Accurate prediction of binding energies for two-dimensional catalytic materials using machine learning
10.1002/cctc.202000536 · 2020 · External reference
A machine learning framework for the analysis and prediction of catalytic activity from experimental data
10.1016/j.apcatb.2019.118257 · 2020 · External reference
Machine learning for catalysis informatics: recent applications and prospects
10.1021/acscatal.9b04186 · 2020 · External reference
Large-scale screening and machine learning to predict the computation-ready, experimental metal-organic frameworks for co2 capture from air
2020 · External reference
Role of pore chemistry and topology in the CO2 capture capabilities of MOFs: from molecular simulation to machine learning
10.1021/acs.chemmater.8b02257 · 2018 · External reference
Metallic metal-organic frameworks predicted by the combination of machine learning methods and ab initio calculations
10.1021/acs.jpclett.8b01707 · 2018 · External reference
Rapid and accurate machine learning recognition of high performing metal organic frameworks for CO2 capture
10.1021/jz501331m · 2014 · External reference
Machine learning exploration of the critical factors for CO2 adsorption capacity on porous carbon materials at different pressures
10.1016/j.jclepro.2020.122915 · 2020 · External reference
Applications of machine learning in metal-organic frameworks
10.1016/j.ccr.2020.213487 · 2020 · External reference
Distilling a materials synthesis ontology
10.1016/j.matt.2019.05.011 · 2019 · External reference
Machine learning force fields: construction, validation, and outlook
10.1021/acs.jpcc.6b10908 · 2017 · External reference
10.1038/s41467-020-19597-w
10.1038/s41467-020-19597-w · External reference
A mobile robotic chemist
10.1038/s41586-020-2442-2 · 2020 · External reference
