Artificial Intelligence(AI)in materials science is driving significant advancements in the discovery of advanced materials for energy applications.The recent GNoME protocol identifies over 380,000 novel stable crystal...Artificial Intelligence(AI)in materials science is driving significant advancements in the discovery of advanced materials for energy applications.The recent GNoME protocol identifies over 380,000 novel stable crystals.From this,we identify over 38,500 materials with potential as energy materials forming the core of the Energy-GNoME database.Our unique combination of Machine Learning(ML)and Deep Learning(DL)tools mitigates cross-domain data bias using feature spaces,thus identifying potential candidates for thermoelectric materials,novel battery cathodes,and novel perovskites.First,classifiers with both structural and compositional features detect domains of applicability,where we expect enhanced reliability of regressors.Here,regressors are trained to predict key materials properties,like thermoelectric figure of merit(zT),band gap(E_(g)),and cathode voltage(△V_(c)).This method significantly narrows the pool of potential candidates,serving as an efficient guide for experimental and computational chemistry investigations and accelerating the discovery of materials suited for electricity generation,energy storage and conversion.展开更多
We focus on gas sorption within metal-organic frameworks(MOFs)for energy applications and identify the minimal set of crystallographic descriptors underpinning the most important properties of MOFs for CO_(2)and H_(2)...We focus on gas sorption within metal-organic frameworks(MOFs)for energy applications and identify the minimal set of crystallographic descriptors underpinning the most important properties of MOFs for CO_(2)and H_(2)O.A comprehensive comparison of several sequential learning algorithms for MOFs properties optimization is performed and the role played by those descriptors is clarified.In energy transformations,thermodynamic limits of important figures of merit crucially depend on equilibrium properties in a wide range of sorbate coverage values,which is often only partially accessible,hence possibly preventing the computation of desired objective functions.We propose a fast procedure for optimizing specific energy in a closed sorption energy storage system with only access to a single water Henry coefficient value and to the specific surface area.We are thus able to identify hypothetical candidate MOFs that are predicted to outperform state-of-the-art water-sorbent pairs for thermal energy storage applications.展开更多
文摘Artificial Intelligence(AI)in materials science is driving significant advancements in the discovery of advanced materials for energy applications.The recent GNoME protocol identifies over 380,000 novel stable crystals.From this,we identify over 38,500 materials with potential as energy materials forming the core of the Energy-GNoME database.Our unique combination of Machine Learning(ML)and Deep Learning(DL)tools mitigates cross-domain data bias using feature spaces,thus identifying potential candidates for thermoelectric materials,novel battery cathodes,and novel perovskites.First,classifiers with both structural and compositional features detect domains of applicability,where we expect enhanced reliability of regressors.Here,regressors are trained to predict key materials properties,like thermoelectric figure of merit(zT),band gap(E_(g)),and cathode voltage(△V_(c)).This method significantly narrows the pool of potential candidates,serving as an efficient guide for experimental and computational chemistry investigations and accelerating the discovery of materials suited for electricity generation,energy storage and conversion.
基金E.C.acknowledges financial support of the Italian National Project PRIN Heat transfer and Thermal Energy Storage Enhancement by Foams and Nanoparticles(2017F7KZWS)the research contract PTR 2019/21 ENEA(Sviluppo di modelli per la caratterizzazione delle proprietàdi scambio termico di PCM in presenza di additivi per il miglioramento dello scambio termico)funded by the Italian Ministry of Economic Development(MiSE).
文摘We focus on gas sorption within metal-organic frameworks(MOFs)for energy applications and identify the minimal set of crystallographic descriptors underpinning the most important properties of MOFs for CO_(2)and H_(2)O.A comprehensive comparison of several sequential learning algorithms for MOFs properties optimization is performed and the role played by those descriptors is clarified.In energy transformations,thermodynamic limits of important figures of merit crucially depend on equilibrium properties in a wide range of sorbate coverage values,which is often only partially accessible,hence possibly preventing the computation of desired objective functions.We propose a fast procedure for optimizing specific energy in a closed sorption energy storage system with only access to a single water Henry coefficient value and to the specific surface area.We are thus able to identify hypothetical candidate MOFs that are predicted to outperform state-of-the-art water-sorbent pairs for thermal energy storage applications.
