hydrogen will play a role 
 in the future composition and market share of the transportable fuels 
 economy due to its zero carbon emissions . there is growing interest in renewable hydrogen 
 production , by solar - based water hydrolysis , for example . hydrogen 
 can be readily used as a transportable and on - demand power source 
 that emits only pure water vapor . however , 
 a significant hurdle to adopting hydrogen technology is the storage 
 component , which is energy intensive and suffers from low volumetric 
 energy density  a factor of 3000 lower than that of gasoline . consequently , hydrogen is currently compressed 
 ( up to 700 bar ) or liquefied ( cooled to 20 k ) to achieve a higher 
 volumetric energy density . however , there are significant energy and 
 infrastructure ( capital ) requirements associated with these conventional 
 methods of storage , as well as safety concerns . chemisorption 
 or physisorption of hydrogen within solid state materials provides 
 an alternative to conventional mechanical - based approaches to densifying 
 hydrogen . exploiting the thermodynamic advantages of physical adsorption , 
 we consider porous material adsorbents to achieve a high density of 
 hydrogen that is deliverable within a moderate pressure range . chemisorbents , 
 on the other hand , require heat to release the hydrogen and can suffer 
 from slow release kinetics . advanced physisorbents 
 provide disruptive alternatives to hydrogen storage because of their 
 tunable interactions and nanoconfined environments that can attract , 
 capture , and release hydrogen with higher efficiency , convenience , 
 and safety . this is particularly the case for metal  organic 
 frameworks ( mofs ) , where several reviews have highlighted their steadily 
 improving storage performance . motivated by the human genome , the materials genome initiative 
 was set up by the white house to unify materials research , with the 
 common aim to  discover , develop , and deploy new materials 
 twice as fast  compared with current methods . in the same way that simple building blocks such as amino acids 
 can lead to proteins with diverse biological functions , combining 
 different chemical building blocks can form materials with diverse 
 functions . the nanoporous materials genome ( nmg ) represents a growing set of over 3 million predicted and synthesized materials contributed 
 by a variety of research groups . this initiative has tackled problems 
 including xenon / krypton separation , production 
 of fuels and chemical feedstocks , catalytic 
 conversion of carbon dioxide , room temperature 
 storage of hydrogen , small molecule adsorption 
 in open - site mofs , evolutionary design 
 of materials , and many mor , e including the recent search for methane storage materials . recently , simon et al . screened the complete 
 nmg database using a large - scale , multistep computational screening 
 process to discover the limits of methane storage . in the same spirit , this present work analyzes the nmg 
 database to identify the limits of hydrogen storage using porous materials . virtual ( or in silico ) screening has become a powerful tool to 
 discover promising candidate adsorbents in very large chemical or 
 structural parameter spaces . for example , the approach has been applied 
 to the selection of compounds that bind to proteins , mof analogues for water adsorption , mofs for fossil fuel purification , zeolitic materials for carbon capture , and porous adsorbents for the separation of linear , monobranched , 
 and dibranched isomers of alkynes for petrochemical separations . machine learning methods play a critical role 
 in the large - scale search for novel materials , where multiple structural 
 descriptors are available , and complex , nonlinear relationships are 
 observed between the target property and the descriptors in high - dimensional 
 feature spaces . property 
 relationships approach to identify mofs for methane storage , which 
 revealed the importance of pore sizes and void fraction . utilized machine learning to 
 optimize the dual adsorption of hydrogen and carbon dioxide in zeolites , while simon et al . accelerated the discovery 
 of porous materials for xenon / krypton separations based on random 
 forests . other examples include the prediction 
 of drug efficacy , biocompatibility of 
 polymers , solubility in different solvents , mutagenicity of chemicals , carbon capture 
 materials , and bacterial attachment to 
 polymers . this study is limited 
 to well - defined crystalline nanoporous materials that can be considered 
 a subset of the materials genome initiative . there are amorphous structures 
 capable of achieving high hydrogen capacity , such as activated carbons , 
 that are also competitive on cost and availability . for example , porous aromatic frameworks are amorphous despite 
 their short - range order and have exceptional hydrogen capacity . nonetheless , the crystalline set used in this study has helped demonstrate 
 the key characteristics that can be tuned to optimize hydrogen storage . in this work , we adopt a combination of thermodynamic models coupled 
 with a neural network machine learning algorithm and a concise literature 
 review to address the questions : what are the limits of hydrogen storage 
 using porous materials and are there additional promising candidate 
 materials to enhance the viability of a global hydrogen fuel economy ? while this manuscript was under review , two papers by gomez - gualdron 
 et al . and bobbitt et al . were published on the same topic showing 
 the feasibility of molecular simulation for evaluating mofs as cryo - adsorbents 
 for hydrogen storage . our work differs from these studies 
 by extending the approach to screen a much larger library of 850  000 
 materials and by using a combination of molecular simulation and machine 
 learning . we also provide additional insights from calculations at 
 room temperature and consider the energy cost of storage scenarios 
 from an engineering perspective to compare methods on an even playing 
 field . essential 
 considerations for hydrogen to compete with conventional fossil - based 
 fuels in the transportation sector are the volumetric and gravimetric 
 storage density , refuelling infrastructure costs , recurring storage 
 costs ( e.g. , to compress the hydrogen ) , fuel tanks costs , durability , 
 and the time to store / release . the hydrogen 
 storage challenge for vehicular transport , set by the u.s . doe ) , focused a wide range of research disciplines 
 toward the goal of densifying hydrogen for use as a transportation 
 fuel . the volumetric energy density , 
 which primarily determines driving range and cost of storage , has proven the more difficult of the requirements . 
 the u.s . doe quantified these requirements and set the progressive 
 benchmarks that led to ultimate targets of 2.4 kwh / l for volumetric 
 energy density at a net cost of $ 8 per kwh . industry has less 
 stringent guidelines , considering temperatures below 40 c 
 to produce liquid hydrogen and also above the pressure range considered 
 safe , into the 700 bar region . with industry setting 
 new benchmarks , the net deliverable energy is defined as the electrical energy from desorbed h2 minus 
 the energy required to cool and compress h2 ; see supporting information ( si ) for complete details . liquefaction , compression , and cryo - compression are three storage 
 methods of pure hydrogen that do not require other materials such 
 as chemicals , chemi - sorbents , or physical adsorbents . liquefaction 
 of hydrogen requires cooling to 20 k at a cost of about 0.24 kwh / l 
 resulting in a theoretical net energy of 2.1 kwh / l , while the 
 actual net energy is around 1.7 kwh / l depending on the method 
 of liquefaction . pressurizing hydrogen at room 
 temperature to 700 bar costs about 0.12 kwh / l with a total net 
 energy of 1.2 kwh / l , assuming isothermal compression . pressurization 
 of hydrogen to 100 bar and cooling to cryogenic temperatures of 77 
 k , also known as cryo - compression , only costs about 0.03 details of calculations 
 can be found in table si-1 using data from 
 the national institute of standards and technology ( nist ) . in summary , liquefied hydrogen has the highest 
 net volumetric energy followed by 700 bar compression at room temperature 
 and finally moderate pressurization at cryogenic temperatures . note 
 that the opposite trend is found when considering the molar ( or gravimetric ) 
 efficiency of storage , and these calculations do not consider the 
 volume or mass of equipment such as the tank , compressors , insulation , 
 valves , etc . in reality , the most appropriate type of storage 
 method is application - dependent . for example , storage for aviation 
 applications will consider both gravimetric and volumetric energy 
 densities as important but the cost not as critical , whereas a remote 
 community will consider cost much more important than gravimetric 
 or volumetric restrictions . location and method of hydrogen production 
 and the distribution network available for each application must also 
 be considered . for example , hydrogen pipelines deliver compressed 
 hydrogen over long distances without much loss , and liquefied hydrogen 
 tankers can deliver where pipelines are not available but suffer loss 
 from boil - off issues . this study aims to optimize volumetric storage 
 at 100 bar and cryogenic temperatures , which is an important intermediate 
 solution and is currently the most challenging regime . the 
 nmg consists of over 3 million structures including hypothetical mofs , computationally - ready experimental mofs ( core - mofs ) 
 taken from the cambridge structural database ( csd ) , hypothetical zeolites chosen from a set of energetically 
 feasible structures from the predicted crystallography open database 
 ( pcod ) , ideal silica zeolites from the 
 international zeolite association ( iza ) , hypothetical covalent organic frameworks ( cofs ) , hypothetical zeolitic imidazolate frameworks ( zifs ) that 
 are identical to hypothetical zeolites but with the si  o bond 
 replaced with zn  imidazolate , and 
 hypothetical porous polymer networks ( ppns ) ; see figure 1a . for 
 a more detailed review of these materials , ( a ) pie chart depicting 
 classes of materials in the nmg , consisting of over 850  000 
 structures . ( b ) structural parameter space represented as the average 
 property normalized by the maximum average . for example , core - mofs 
 have the highest average adsorption energy . note that some data sets 
 have some very wide distributions ; see table si-3 for the statistics and figure si-7 for 
 the complete graphical matrix of the structural parameters . core - mofs and iza zeolites are 
 the only experimentally derived materials considered in this study . developed the core - mof database by downloading all mof 
 structures from the csd and automatically preparing the structures 
 for molecular simulations , which involved removing solvent molecules 
 and the resetting of partially occupied and disordered atoms . the iza zeolites contain about 200 experimentally 
 observed structures , of which 30 have been synthesized in pure - silica 
 form . most of the structures were constructed 
 using simple , available , and known building blocks that are capable 
 of forming mofs . there are some experimentally confirmed mofs in the 
 hypothetical database , but no thorough analysis has been performed 
 between the sets of structures . it is likely that there will be a 
 problem synthesizing large pore mofs where the rigidity of the framework 
 is tested against thermodynamic forces and also for interpenetrated 
 mofs where sterically restricted growth is difficult to control . however , it is possible to use the method developed 
 by raccuglia et al . to predict the success of reactions between inorganic 
 and organic building blocks using machine learning trained on data 
 from failed experiments . the size 
 of chemical and parameter space explored by this data set of materials 
 is large . for example , some of the ppns have achieved surface areas 
 of 10  000 m / g and pore diameters of 88  , 
 well above the experimental average of 900 m / g and 5.4 
  according to the core - mof set . table si-3 lists the minimum , mean , median , and maximum values of each set 
 of materials . some metal centers can form much stronger interactions 
 with h2 such as the europium - based mof ( refcode : cufmog ) with one 
 of the highest adsorption energies of 11 kj / mol . here there are a few interesting 
 observations to be made regarding the coverage of chemical space by 
 different classes of materials . first , the experimental core - mofs 
 have average characteristics similar to those of the zeolites , with 
 small pore diameters , low density , low void fraction , and low surface 
 areas compared to the hypothetical mofs . this agrees with the general 
 understanding that mofs with large pores are difficult to synthesize 
 because of the increased lattice energy with specific volume . nonetheless , a key difference between experimental 
 mofs and zeolites is that , in mofs , high adsorption energies can be 
 coupled with larger pores and higher void fractions because of the 
 unique configurations of inorganic and organic components ( see figure si-11 ) . for example , bimetal indium and 
 manganese mof ( csd refcode : wavrao ) has a potential adsorption energy 
 of 4 kj / mol coupled with a large pore diameter of 15  and void 
 fraction of 0.3 . the data set of cofs 
 and ppns exhibits similar properties , understandable considering their 
 purely organic nature and long molecular building blocks . finally , 
 the properties of the hypothetical zifs are similar to those of the 
 hypothetical mofs , again understandable considering that zifs are 
 a subset of mofs . it is important to note that this is an early 
 snapshot of the growing hypothetical and experimental porous materials 
 genome database . hypothetical mofs , for 
 example , have only utilized 150 building blocks combined with a limited 
 number of topologies , while the csd shows there are a vast number 
 of alternative organic and inorganic components with a greater variety 
 of topologies . in silico screening is an approach that uses high - throughput , step - by - step 
 predictive calculations of the properties of large numbers of materials 
 to find promising candidates for a specific application . predictive calculations range from empirical 
 models , such as those implemented by goldsmith et al . based on surface 
 area and pore volume , to more fundamental 
 approaches such as the langmuir adsorption model and monte carlo simulations 
 on the grand canonical ensemble ( gcmc ) . each approach has its own 
 range of applicability depending on the temperature , pressure , and 
 regime of pore size and interaction strength . simulations of adsorption using classic molecular models have proven 
 accurate on a wide range of materials . for example , predicting natural gas storage using a combination 
 of simulation and analytical tools was validated using individual 
 structures and found reliable for screening over 650  000 candidates . in this study , we predicted room temperature 
 storage using the langmuir adsorption model with input from simulations . hydrogen uptake ( n ) is related to the langmuir equilibrium 
 constant ( k ) , pressure ( p ) , and 
 saturation capacity ( m ) as follows , the langmuir 
 constant is equal to the simulated henry coefficient ( kh ) divided by the saturation capacity . saturation capacity was estimated as the product 
 of pore volume ( vp ) and hydrogen density 
 (  ) . the density of liquid hydrogen at 70.8 g / l is a reasonable 
 value for  ; however , using an 
 empirical relationship between pore volume and hydrogen density improved r for predicting simulated uptake using the 
 langmuir equation from 0.88 to 0.98 ( see figure si-1 ) . the crystal density is 
 used throughout this study , ignoring any packing effects within the 
 tank , an ideal scenario with the intention to determine the limits 
 of hydrogen storage . for conversion from ideal to actual uptake at cryogenic temperatures , the analytical langmuir model failed 
 to adequately predict adsorption capacity because of the poor correlation 
 between hydrogen density and pore volume at high pressures . therefore , 
 we used gpu - based gcmc simulations to predict hydrogen uptake , using 
 the code developed by kim and smit that enabled significant speedup 
 ( over 40 times ) compared to the conventional non - gpu code . hydrogen was 
 treated as a single lennard - jones sphere with the widely used buch 
 potential , which shows excellent agreement 
 with experimental hydrogen isotherms . in this it is the most common force field for framework 
 atoms in the literature , with reasonably good accuracy for mofs according 
 to a review by basdogan and keskin . for 
 zeolites this force field has also proven adequate under cryogenic 
 conditions , with a slight improvement using modified uff parameters ; 
 see deeg et al . when exploring a materials 
 genome with over 850  000 structures , this can still be too 
 computationally demanding . the genome is also continually growing , 
 and therefore a smarter screening strategy than the conventional brute - force 
 approach is required . neural networks that progressively learn from 
 data were incorporated into the workflow to reduce the computational 
 time and to more rapidly converge on the top candidates . neural 
 network models for gas adsorption were trained on data sets generated 
 by gcmc to predict hydrogen adsorption capacity on the basis of structural 
 descriptors in figure 1b . the models were then used to target promising candidates in an 
 evolutionary manner . this process of 
 simulation followed by neural network training and targeting continued 
 until the predicted working capacity converged upon a maximum where 
 no new candidates were identified . bayesian regularized feed - forward 
 neural networks were used to build the initial structure they have been shown to generate robust , predictive 
 models of a wide variety of materials properties . the networks employed 
 input , hidden and output layers . the number of nodes in the input 
 layer was the same as the number of descriptors , the number of hidden 
 layer nodes was generally 2 or 3 , and one output node was used . the target property in this case was the volumetric working capacity 
 which was later converted to net energy by assuming that hydrogen 
 produces 0.066 kwh / mol of electrical energy ( calculated from 
 the gibbs free energy function ) at a cost of 0.03 kwh / l ; see table si-1 . the chosen descriptors were those 
 that could be rapidly calculated , such as adsorption energy ( ensemble 
 average at 77 k and infinite dilution ) , density , void fraction ( geometrically 
 calculated using zeo++ ) , surface area ( gravimetric and 
 volumetric ) , and pore size ( maximum ) . for more complex systems , such 
 as biological interactions , more descriptors are required , but for 
 gas adsorption a handful of descriptors has proven to be adequate . for room temperature storage , the maximum 
 net deliverable energy using adsorbents at pressures between 100 and 
 1 bar is about 0.4 kwh / l , as shown in figure 2 for the complete nmg ( 850  000 ) . this is well below the net energy delivered by high compression 
 ( 700 bar ) systems at 1.2 kwh / l . deliverable energy is maximized 
 at void fractions of 0.1 and pore sizes of 6  ; see figure si-5 for complete structure  property 
 dependence . the optimal storage pressure can be calculated from the 
 langmuir equilibrium constant and the delivery pressure . in this manner , the pressure required to maximize the net deliverable energy was calculated , 
 accounting for the cost of pressurization . optimization of storage 
 pressure for the available adsorbents raises the net deliverable energy 
 close to the doe target ; however , the pressures required are greater 
 than 1000 bar ( see figure 2 and figure si-4 ) . therefore , it 
 is likely more economical to operate without an adsorbent at room 
 temperature . room temperature simulations on the complete nmg ( 850  000 
 materials ) . net deliverable energy predicted at room temperature and 
 cycling between 100 and 1 bar using the langmuir model with simulated 
 henry coefficient and empirical relation for saturation capacity . the langmuir model coupled with 
 a temperature - dependent k suggested that storage 
 pressures as low as 10 bar can be optimal for some candidates ; see figure si-4 . unfortunately , the langmuir model 
 was not accurate enough to predict the majority of isotherms at cryogenic 
 temperatures . on the basis of this range , isotherms between 100 and 
 1 bar were simulated using gcmc , a more accurate method under these 
 conditions . the simulations were run in stages , where results from 
 each stage were fed into a neural network to generate models that 
 identified the next set of materials to simulate . the first 
 stage of gcmc simulations was run on the complete set of known iza 
 zeolites and a diverse set of hypothetical zeolites selected on the 
 basis of molecular similarity , shown 
 as red circles in figure 3 . a neural network model was then constructed , and a new set 
 of materials with improved properties were identified ; see figure si-6 for the complete set of predictions . the limited parameter space ( domain of applicability ) of zeolites 
 meant that the neural network model suggested structures with the 
 largest amount of void fraction , consisting of ppns and cofs . net deliverable 
 energy as a function of void fraction for the predictive and experimental 
 data at 77 k cycling between 100 and 1 bar . predictions include the 
 gcmc - simulated sample sets and the final neural network model for 
 the complete genome ( 850  000 materials ) . experimental 
 data from the literature is shown as black squares with top candidates 
 including nott-400 , mof-210 , zif-8 , and pcn-68 . the top 1000 structures suggested by the neural 
 model in the first stage were then simulated using gcmc , and the results 
 are shown as blue circles in figure 3 . as expected , the difference between the stage 1 neural 
 network predictions and the gcmc simulations were large because of 
 the limited information used to train each neural network model . however , 
 the new gcmc results were used to further retrain the neural model , 
 and more complex relationships between the structural descriptors 
 of the materials and performance were subsequently observed . for example , 
 an optimal range was identified for each parameter , including a pore 
 diameter of around 6  and surface area of 4000 m g. the range of materials found within these 
 optimal ranges included a combination of hypothetical mofs and core - mofs . a third stage of gcmc simulations was run for the next top 1000 
 structures suggested by the neural model which was once again retrained 
 on the new simulated data , shown as green circles in figure 3 . the new results identified 
 an optimal range of void fraction around 0.5 , highlighting a trade - off 
 between free space for adsorbed h2 molecules and a framework 
 to construct binding sites with a high affinity for hydrogen . a final 
 neural network model was developed with this additional simulation 
 data that revealed a convergence in the list of top candidates ; i.e. , 
 no new candidates were suggested . this can be seen in figure 3 where the final neural model 
 predictions are shown as pale gray circles . to ensure that the 
 neural network had a sufficiently large domain of applicability in 
 the available parameter space , a diverse test set of candidates ( based 
 on molecular similarity ) was tested . no new high performing candidates were discovered , confirming that 
 the neural network has captured enough of the parameter space to arrive 
 at a good approximation of the global maximum . furthermore , the neural 
 network model predicted the performance of the diverse test set with 
 good accuracy ( r = 0.88 and root mean 
 squared error of 3.64 ) , showing that it could accurately predict the 
 properties of materials not used in the training set ( see figure si-8 and table si-2 ) . the top candidates 
 were predicted to deliver a net energy of around 1.3 kwh / l , 
 well above the bare tank option at 1.02 kwh / l for the same operating 
 conditions . catenated hypothetical mofs were the most common among 
 the high performing candidates , along with core - mofs that will be 
 discussed below . compared to current industrial practice , this maximum 
 net deliverable energy by adsorbents is higher than the 700 bar compression 
 technology ( 1.2 kwh / l ) but lower than the liquefaction option 
 ( 2.1 kwh / l ) . the advantage of cryo - adsorption is clearly in 
 applications where high pressures ( 700 bar ) and low temperatures ( 20 
 k ) are not appropriate due to safety , source of hydrogen , available 
 floor space , engineering factors , cost , or other restrictions . for 
 example , adsorbents can reduce the high pressures in confined spaces , 
 which are considered unsafe or at least undesirable although carbon 
 fiber composites are raising the reliability of storage tanks . another 
 example to consider is for locations with access to liquid nitrogen 
 but not the equipment required to produce liquid hydrogen . in this 
 example hydrolyzers produce hydrogen at high temperatures , and therefore another 
 opportunity for adsorbents could be to adsorb this hydrogen along 
 heat exchangers . to better understand this predicted peak in 
 net energy at an optimal void fraction of 0.5 , the langmuir model 
 was fitted to the final neural network predictions ( solid dark gray 
 line in figure 3 ) . 
 by simply assuming that the saturation capacity and adsorption energy 
 are linear functions of void fraction these generalized 
 semiempirical relationships were observed previously , and the trends 
 are confirmed in this work . saturation capacity is an increasing function of void fraction while 
 adsorption energy ( represented as positive values , where a large positive 
 value is a strong attractive adsorption energy ) is a decreasing function 
 of void fraction . this model intuitively captures the natural trade - off 
 between saturation capacity and adsorption energy , which are proportional 
 to and inversely related to void fraction , respectively . although 
 void fraction plays a critical role in maximizing deliverable capacity , 
 other parameters also contribute to an optimum range as shown in figure si-6 . an optimal adsorption energy of 
 around 2 kj / mol can be found ( see figure si-10 ) along with optimum framework density at 2 cm / g , pore diameter at 10  , gravimetric surface area at 5000 
 m / g , and volumetric surface area at 3000 cm / m . it is important to note that there are significant 
 correlations between all parameters , as shown in figure si-7 . therefore , it may be possible to simplify future 
 screening using a single screening parameter such as void fraction . 
 however , other parameters could also be used , such as fractional volume 
 for adsorption defined by thornton et al . or binding fraction by bobbitt et al . it is important to note that the optimum value for each parameter 
 depends on the adsorption / desorption conditions and the method by 
 which the parameters are calculated . in this study , void fraction 
 is calculated geometrically using voronoi maps through the zeo++ package while other studies have used widom insertions of a helium probe 
 which can give very different values . gomez - gualdron et al . also based their studies on different adsorption / desorption 
 conditions along with molar volumes from standard temperature and 
 pressure ( stp ) instead of standard ambient temperature and pressure 
 ( satp ) which is likely to explain any discrepancy between the reported 
 maximum deliverable capacities . the predicted limit is based 
 on the materials currently available within the genome . with a growing 
 number of structures , it is important to understand the theoretical 
 limits of hydrogen storage . in this case a purely fictitious material 
 is considered with infinite pore volume , i.e. , 100% void space , and 
 with a tunable adsorption energy to achieve the optimal langmuir equilibrium 
 constant described previously . a maximum saturation capacity of 130 g / l is assumed from a simple packing of hydrogen as  hard - spheres  . 
 in this ideal scenario , the theoretical maximum energy deliverable 
 between 100 and 1 bar is 3.5 kwh / l , about 2.5 times above the 
 observed limit . it is difficult to predict how much closer adsorbents 
 will get to this theoretical limit given the natural trade - off between 
 adsorption energy and void fraction . experimental data were 
 collected from a range of reviews including sculley et al . , suh et al . , yang 
 et al . , hu et al . , lai et al . , and murray et al . a selection is plotted in figure 3 as black squares . although there is significant 
 scatter across void fraction and deliverable capacity , a maximum is 
 also observed close to that of the predictions at around 1.3 kwh / l . the reason for discrepancies between simulation 
 and experiment is often difficult to identify because of the multiple 
 and interdependent variables involved in the synthesis and measurements , 
 as well as the assumptions behind the simulations . adsorption in zif-8 
 has proven difficult to predict due to observed  gate - opening adsorption in mof-210 has also 
 proven difficult to predict because of its large unit cell containing 
 5562 atoms . nonetheless , figure 4 shows the comparison of top 
 candidates with experimental data along with the bare tank scenario . total 
 hydrogen uptake for top candidates with the highest working capacity 
 including mof-210 , zif-8 , and hypothetical mof-5059389 , along with the 
 bare tank scenario . gcmc simulations ( lines ) and experimental data 
 ( squares ) at 77 k. reasonably good agreement 
 is found between gcmc and experimental data for mof-210 and zif-8 . although the general isotherm trend for zif-8 is captured , the working 
 capacity is underpredicted because of the slight over and under predictions 
 at 1 and 100 bar , respectively , possibly due to the absence of molecular 
 flexibility in the simulation . pcn-68 depicts a similar isotherm to 
 that of mof-210 , where a large uptake is observed at higher pressures . a key feature for the top performing candidates 
 is a combination of minimal uptake at delivery pressures ( 1 bar ) and 
 maximum uptake at storage pressures ( 100 bar ) . this feature is demonstrated 
 in figure 5 for hypothetical 
 mof-5059389 with the highest working capacity of 40 h2 however , in reality 
 liquefaction costs 24% more than the theoretical value , which means 
 mof - based cryo - adsorption is a promising alternative . net deliverable 
 energy with and without mof for the available storage conditions associated 
 with liquefaction , cryo - compression , and compression . the 30% enhancement 
 is observed for the mof - filled tank at cryo - compression conditions 
 which corresponds to about 30% enhancement in volumetric capacity 
 to 40 the top 
 candidates including two hypothetical mofs , two core - mofs with no 
 known experimental hydrogen uptake , and two core - mofs where experimental 
 hydrogen uptake is available are illustrated in figure 6 . the top hypothetical mof candidates contain 
 long and thin ligands such as alkynes that maximize surface area and 
 porosity . hypmof-5003600 is an interpenetrated zinc - based cubic framework 
 while hypmof-5059389 is functionalized with hydroxyl groups , and both 
 strategies are typically adopted to maximize adsorption energy . core - mof 
 candidates include cadmium - based mof ( csd refcode : kecral10 ) linked 
 with cyanide that forms an open framework with hexagonal and square - shaped 
 channels . an additional core - mof ( csd 
 refcode : gunfaw01 ) was found with mixed - metal vertices ( chromium and 
 manganese ) linked with bipyridine and phenylpyridine that was originally 
 designed to exhibit specific magnetic properties . a contour plot of the predicted volumetric uptake for candidate 
 gunfaw01 across all temperatures and pressures can be found in figure si-9 . the top candidates share common 
 characteristics such as a void fraction close to 0.5 and a major pore 
 diameter of around 10  , along with high surface areas above 
 3000 cm / cm and 5000 m / g . the exception 
 is mof-210 with a wide distribution of pores from 10 up to 28  . top candidates for hydrogen storage at 77 k. 
 structures include two hypothetical mofs that have never been synthesized , 
 two mofs from the csd that were synthesized but never tested for hydrogen 
 storage , and two mofs that have been synthesized 
 and measured for hydrogen storage . the color 
 code for atoms : zn ( lavender ) , cd ( yellow ) , c ( gray ) , o(red ) , n ( blue ) , 
 h ( white ) , cr ( violet ) , mn ( dark - blue ) , and cu ( orange ) . an important conclusion of this 
 study is that most of the top candidates for hydrogen storage have 
 already been synthesized and tested . this result gives credit to the 
 research community , which has strategically identified structures 
 that maximize hydrogen storage . although many candidates are predicted 
 to perform at a similar level , there are no candidates predicted to 
 outperform the current candidates available . these calculations 
 are based on physical adsorption by rigid structures , while there 
 is much development in switchable structures under an external stimuli 
 such as light , pressure , temperature , humidity , sound , magnetic fields , 
 and others . these dynamic structures show great promise for the low energy release 
 of stored hydrogen , especially where uptake is enhanced at higher 
 pressures ( storage conditions ) and minimized at lower pressures ( delivery 
 conditions ) ; hence , the working capacity is maximized . chemisorbents 
 are also not considered in this study because of the inability to 
 cycle with pressure without the loss of hydrogen , as indicated by 
 doe studies on metal hydrides . however , 
 it is worth considering that there is no clear distinction between 
 physisorption and chemisorption . this has been demonstrated by mcdonald 
 et al . for co2 capture where a slight change in pressure 
 can induce chemisorbed species to desorb within short time frames . in addition , coln et al . calculated the adsorption energy with magnesium 
 alkoxide to be around 30 kj / mol which is intermediate between that 
 found in physisorbents ( 10 kj / mol ) and that found in chemisorbents 
 ( 60 kj / mol ) . mechanical , 
 chemical , hydrolytic , and thermal stability that can significantly 
 effect adsorption performance throughout the product lifecycle are 
 also important properties to consider . reviews on the stability 
 of physisorbents can be used as a guide in selecting stable structures 
 from the genome . however , robust computational models of stability 
 that can be applied to large - scale screening studies are desirable . industry will be driven by the economic advantages of using 
 sorbents . according to a recent techno - economic analysis of sorbents , 
 the baseline cost of mof production is between 35 and 72 usd / kg . carbon fiber - based hydrogen storage tanks cost 
 around 600 usd / kgh2 according to a doe report . considering the baseline cost of mof along with 
 the predicted storage enhancement of 30% , the total tank cost would 
 range from 560 to 704 usd / kgh2 . therefore , as a rough estimate , 
 the cost of mof production must be kept below 45 usd / kg to be economical 
 with current technologies . the nanoporous 
 materials genome , consisting of over 850  000 crystalline structures , 
 was computationally screened using a combination of molecular simulation 
 and machine learning techniques to explore the limits of physisorbed 
 hydrogen storage . analysis of the genome revealed that core - mofs have 
 a wider range of characteristics than zeolites including higher adsorption 
 energies coupled with greater void space , hypothetical mofs offer 
 the best combination of adsorption energy and volumetric surface area , 
 and ppns and cofs have the largest void fractions . neural networks 
 were found to accelerate the identification of improved materials 
 by learning from gcmc - simulated hydrogen isotherms in a kind of adaptive 
 evolution process . with a focus on the net energy derived from 
 working capacity between 100 and 1 bar , optimal candidates were discovered 
 that consisted of a collection of hypothetical mofs and core - mofs 
 found in experimental databases . mof-210 , pcn-68 , nott-400 , and zif-8 
 were four of the best materials identified , and experimental validation 
 for their high hydrogen storage performance was found in the literature . other top candidates included mofs that have been synthesized in the 
 literature but not yet measured for hydrogen capacity , such as the 
 cadmium - based framework and the mixed - metal chromium  manganese 
 based framework . finally , hypothetical mof candidates with a combination 
 of large void fraction and high adsorption energy were also predicted 
 to perform at a high level . optimal characteristics were determined , 
 including a void fraction of 0.5 , a pore diameter of 10  , and 
 a surface area of 5000 m / g , which offer quantitative guidelines 
 for the future design of nanoporous materials for hydrogen storage . finally , the maximum net deliverable energy was found to be around 
 1.3 kwh / l , which is well above the 1.02 kwh / l for the 
 bare tank scenario at identical operating conditions , 1 to 100 bar 
 at 77 k. in addition , this technology termed  cryo - adsorption  
 has significant engineering advantages over the current liquefaction 
 ( 20 k ) and mega - compression ( 700 bar ) storage technologies .
the 
 materials genome is in action : the molecular codes for millions of 
 materials have been sequenced , predictive models have been developed , 
 and now the challenge of hydrogen storage is targeted . 
 renewably generated 
 hydrogen is an attractive transportation fuel with zero carbon emissions , 
 but its storage remains a significant challenge . 
 nanoporous adsorbents 
 have shown promising physical adsorption of hydrogen approaching targeted 
 capacities , but the scope of studies has remained limited . here the 
 nanoporous materials genome , containing over 850  000 materials , 
 is analyzed with a variety of computational tools to explore the limits 
 of hydrogen storage . 
 optimal features that maximize net capacity at 
 room temperature include pore sizes of around 6  and void fractions 
 of 0.1 , while at cryogenic temperatures pore sizes of 10  and 
 void fractions of 0.5 are optimal . 
 our top candidates are found to 
 be commercially attractive as  cryo - adsorbents  , with 
 promising storage capacities at 77 k and 100 bar with 30% enhancement 
 to 40 
 g / l , a promising alternative to liquefaction at 20 k and compression 
 at 700 bar .