Abstract
Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications. A common task for many computational methods is the need to accurately compute the adsorption energy for an adsorbate and a catalyst surface of interest. Traditionally, the identification of lowenergy adsorbatesurface configurations relies on heuristic methods and researcher intuition. As the desire to perform highthroughput screening increases, it becomes challenging to use heuristics and intuition alone. In this paper, we demonstrate machine learning potentials can be leveraged to identify lowenergy adsorbatesurface configurations more accurately and efficiently. Our algorithm provides a spectrum of tradeoffs between accuracy and efficiency, with one balanced option finding the lowest energy configuration 87.36% of the time, while achieving a ~2000× speedup in computation. To standardize benchmarking, we introduce the Open Catalyst Dense dataset containing nearly 1000 diverse surfaces and ~100,000 unique configurations.
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Introduction
The design of novel heterogeneous catalysts plays an essential role in the synthesis of everyday fuels and chemicals. To accommodate the growing demand for energy while combating climate change, efficient, lowcost catalysts are critical to the utilization of renewable energy^{1,2,3,4}. Given the enormity of the material design space, efficient screening methods are highly sought after^{4,5,6,7}. Computational catalysis offers the potential to screen vast numbers of materials to complement more time and costintensive experimental studies.
A critical task for many firstprinciples approaches to heterogeneous catalyst discovery is the calculation of adsorption energies. The adsorption energy is the energy associated with a molecule, or adsorbate, interacting with a catalyst surface. Adsorbates are often selected to capture the various steps, or intermediates, in a reaction pathway (e.g., *CHO in CO_{2} reduction). Adsorption energy is calculated by finding the adsorbatesurface configuration that minimizes the structure’s overall energy. Thus, the adsorption energy is the global minimum energy across all potential adsorbate placements and configurations. These adsorption energies are the starting point for the calculation of the free energy diagrams to determine the most favorable reaction pathways on a catalyst surface^{8}. It has been demonstrated that adsorption energies of reaction intermediates can be powerful descriptors that correlate with experimental outcomes such as activity or selectivity^{9,10,11,12,13}. This ability to predict trends in catalytic properties from first principles is the basis for efficient catalyst screening approaches^{1,14}.
Finding the adsorption energy presents a number of complexities. There are numerous potential binding sites for an adsorbate on a surface, and for each binding site there are multiple ways to orient the adsorbate (see bottom left in Fig. 1). When an adsorbate is placed on a catalyst’s surface, the adsorbate and surface atoms will interact with each other. To determine the adsorption energy for a specific adsorbatesurface configuration, the atom positions need to be relaxed until a local energy minimum is reached. Density Functional Theory (DFT)^{15,16,17} is the most common approach to performing this adsorbatesurface relaxation. DFT first computes a singlepoint calculation where the output is the system’s energy and the peratoms forces. A relaxation then performs a local optimization where peratom forces are iteratively calculated with DFT and used to update atom positions with an optimization algorithm (e.g., conjugate gradient^{18}) until a local energy minimum is found. To find the global minimum, a strategy for sampling adsorbatesurface configurations and/or a technique such as minima hopping^{19,20} for overcoming energy barriers during optimization is required.
Adsorption energy (ΔE_{ads}) is calculated as the energy of the adsorbatesurface (E_{sys}) minus the energy of the clean surface (i.e., slab) (E_{slab}) and the energy of the gas phase adsorbate or reference species (E_{gas}), as defined by Chanussot et al. and detailed in the Supporting Information (SI)^{2,4}.
Relaxed adsorbatesurface structures must respect certain desired properties in order for their adsorption energy to be both accurate and valid. One example of a constraint is the adsorbate should not be desorbed, i.e., float away, from the surface in the final relaxed structure (Fig. 1, bottom right). In addition, if the adsorbate has multiple atoms it should not dissociate or break apart into multiple adsorbates because it would no longer be the adsorption energy of the molecule of interest^{19,21}. Similarly, if the adsorbate induces significant changes in the surface compared to the clean surface, the E_{slab} reference would create a surface mismatch. It is important to note that if a relaxed structure breaks one of these constraints it does not necessarily mean the relaxation was inaccurate; these outcomes do arise but they lead to invalid or inaccurate adsorption energies as it has been defined.
Identifying the globally optimal adsorbatesurface configuration has historically relied on expert intuition or more recently heuristic approaches. Intuition and trial and error can be used for oneoff systems of interest but it does not scale to large numbers of systems. Commonly used heuristics are often based on surface symmetry^{22,23}. These methods have been used successfully in past descriptorbased studies^{9,10,24,25,26,27}. More recently, a graphbased method has been used to identify unique adsorbatesurface configurations^{28}. Nevertheless, as the complexity of the surfaces and adsorbates increase, the challenge of finding the lowest energy adsorbatesurface configuration grows substantially. This is especially challenging when the adsorbate is flexible, having multiple configurations of its own, such that there are many effective degrees of freedom in the system.
While DFT offers the ability to accurately estimate atomic forces and energies, it is computationally expensive, scaling O(N^{3}) with the number of electrons. Evaluating a single adsorbatesurface configuration with a full DFT relaxation can take ~24 h to compute^{2,29}. Since numerous configurations are typically explored to find the adsorption energy, all the DFT calculations involved can take days or even weeks. Hypothetically, if one were to bruteforce screen 100,000 materials from the Materials Project database^{30} for CO_{2} Reduction Reaction (CO_{2}RR) using 5 adsorbate descriptors, ~90 surfaces/material, and ~100 sites/surface, one would need ~4.5 billion CPUdays of compute, an intractable problem for even the world’s largest supercomputers. To significantly reduce the required computation, a promising approach is to accelerate the search of lowest energy adsorbatesurface configurations with machinelearned potentials.
Recently, machine learning (ML) potentials for estimating atomic forces and energies have shown significant progress on standard benchmarks while being orders of magnitude faster than DFT^{2,31,32,33,34,35,36}. While ML accuracies on the large and diverse Open Catalyst 2020 Dataset (OC20) dataset have improved to 0.3 eV for relaxed energy estimation, an accuracy of 0.1 eV is still desired for accurate screening^{37}. This raises the question of whether a hybrid approach that uses both DFT and ML potentials can achieve high accuracy while maintaining efficiency.
Assessing the performance of new methods for finding lowenergy adsorbatesurface configurations is challenging without standardized validation data. It is common for new methods to be tested on a relatively small number of systems, which makes generalization difficult to evaluate^{19,28,38,39,40}. While OC20 contains O(1M) “adsorption energies”, it did not sample multiple configurations per adsorbatesurface combination meaning the one configuration that was relaxed is unlikely to be the global minimum. This makes OC20 an inappropriate dataset for finding the minimum binding energy^{2}. To address this issue, we introduce the Open Catalyst 2020Dense Dataset (OC20Dense). OC20Dense includes two splits—a validation and test set. The validation set is used for development; and the test set for reporting performance. Each split consists of ~1000 unique adsorbatesurface combinations from the validation and test sets of the OC20 dataset. No data from OC20Dense is used for training. To explore the generalizability of our approach, we take ~250 combinations from each of the four OC20 subsplits—InDomain (ID), OutofDomain (OOD)Adsorbate, OODCatalyst, and OODBoth. For each combination, we perform a dense sampling of initial configurations and calculate relaxations using DFT to create a strong baseline for evaluating estimated adsorption energies.
We propose a hybrid approach to estimating adsorption energies that takes advantage of the strengths of both ML potentials and DFT. We sample a large number of potential adsorbate configurations using both heuristic and random strategies and perform relaxations using ML potentials. The best krelaxed energies can then be refined using singlepoint DFT calculations or with full DFT relaxations. Using this approach, the appropriate tradeoffs may be made between accuracy and efficiency.
Considerable research effort has been dedicated to determining the lowest energy adsorbatesurface configuration through the improvement of initial structure generation and global optimization strategies^{19,21,28,38,39,40,41}. Peterson^{19} adopted the minima hopping method and developed a global optimization approach that preserves adsorbate identity using constrained minima hopping. However, the method relies entirely on DFT to perform the search, still making it computationally expensive. More recently, Jung et al.^{21} proposed an active learning workflow where a Gaussian process is used to run constrained minima hopping simulations. Structures generated by their simulations are verified by DFT and iteratively added to the training set until model convergence is achieved. The trained model then runs parallel constrained minima hopping simulations, a subset is refined with DFT, and the final adsorption energy identified. We note that prior attempts to use machine learning models to accelerate this process have typically relied on bespoke models for each adsorbate/catalyst combination, which limits broader applicability^{42,43}. One possibility to greatly expand the versatility of these methods while continuing to reduce the human and computational cost is using generalizable machine learning potentials to accelerate the search for lowenergy adsorbatesurface configurations.
The contributions of this work are threefold:

We propose the AdsorbML algorithm to identify the adsorption energy under a spectrum of accuracyefficiency tradeoffs.

We develop the Open Catalyst 2020Dense Dataset (OC20Dense) to benchmark the task of adsorption energy search.

We benchmark literature Graph Neural Network (GNN) models on OC20Dense using the proposed AdsorbML algorithm; identifying several promising models wellsuited for practical screening applications.
Results
OC20Dense evaluation
To evaluate methods for computing adsorption energies, we present the Open Catalyst 2020Dense Dataset (OC20Dense) that closely approximates the ground truth adsorption energy by densely exploring numerous configurations for each unique adsorbatesurface system. Each OC20Dense split comprises ~1000 unique adsorbatesurface combinations spanning 74 adsorbates, 800+ inorganic bulk crystal structures, and a total of 80,000 + heuristically and randomly generated configurations. A summary of the two splits are provided in Table 1. The dataset required ~4 million CPUhrs of compute to complete. A more detailed discussion on OC20Dense can be found in “Methods”.
We report results on a wide range of GNNs previously benchmarked on OC20 to evaluate the performance of existing models on OC20Dense. These include SchNet^{31}, DimeNet++^{32,33}, PaiNN^{44}, GemNetOC^{34}, GemNetOCMD^{34}, GemNetOCMDLarge^{34}, SCNMDLarge^{35}, and eSCNMDLarge^{45} where MD corresponds to training on OC20 and its accompanying ab initio Molecular Dynamics (MD) dataset. Models were not trained as part of this work; trained models were taken directly from previously published work and can be found at https://github.com/OpenCatalystProject/ocp/blob/main/MODELS.md. Of the models, (e)SCNMDLarge and GemNetOCMDLarge are currently the top performers on both OC20 and Open Catalyst 2022 Dataset (OC22). Exploring the extent these trends hold for OC20Dense will be important to informing how well progress on OC20 translates to more important downstream tasks like the one presented here.
Ideally, the ground truth for OC20Dense would be the minimum relaxed energy over all possible configurations for each adsorbatesurface system. Since the number of possible configurations is combinatorial, the community has developed heuristic approaches to adsorbate placement on a catalyst surface^{22,23}. When evaluating only heuristic configurations, we refer to this as DFTHeuristicOnly (DFTHeur). To add to the configuration space, we also uniformly sample sites on the surface at random with the adsorbate placed on each of those sites with a random rotation along the z axis and slight wobble around the x and y axis. When evaluating against both heuristic and random configurations, we refer to this as DFTHeuristicOnly (DFTHeur). Although computationally more expensive, this benchmark provides a more thorough search of configurations and a more accurate estimate of the adsorption energies than using only heuristic configurations, a common baseline used by the community. More details on the two benchmarks can be found in “Methods”.
ML relaxations
We explore to what extent ML predictions can find the adsorption energy within a threshold of the DFT minimum energy, or lower. While a perfect ML surrogate to DFT will only be able to match DFT, small errors in the forces and optimizer differences have the potential to add noise to relaxations and result in configurations previously unexplored^{46}. For each model, relaxations are performed on an identical set of adsorbate configurations. Initial configurations are created based off heuristic strategies commonly used in the literature^{22,23} and randomly generated configurations on the surface. MLdriven relaxations are run on all initial configurations; systems not suitable for adsorption energy calculations due to physical constraints are removed, including dissociation, desorption, and surface mismatch. An indepth discussion on relaxation constraints can be found in “Methods”.
When evaluating performance, we define success as finding an adsorption energy within an acceptable tolerance (0.1 eV in this work^{2,37,46}) or lower of the DFT adsorption energy in OC20Dense. Note that the ground truth adsorption energies in OC20Dense are an upper bound, since it is possible that a lower adsorption energy may exist. When evaluating MLpredicted adsorption energies, the results must be verified using a singlepoint DFT calculation, since an evaluation metric without a lower bound could be easily gamed by predicting low energies (see SI). To reliably evaluate ML we consider an ML adsorption energy successful if its within 0.1 eV of the DFT adsorption energy or lower, and a corresponding DFT singlepoint evaluation of the predicted ML structure is within 0.1 eV of the predicted ML energy. This ensures that a ML prediction not only found a low adsorption energy but is accurate and not artificially inflated. Results are reported in Table 2, where top OC20 models including eSCNMDLarge and GemNetOCMDLarge achieve success rates of 56.52% and 48.03%, respectively. Energy MAE between ML and DFT adsorption energies are also reported in Table 2, correlating well with success rates and OC20 S2EF metrics.
While the current state of models have made incredible progress^{37}, higher success rates are needed for everyday practitioners. In a highthroughput setting where successful candidates go on to more expensive analyses or even experimental synthesis, a success rate of ~50% could result in a substantial waste of time and resources studying false positives. As model development will continue to help improve metrics, this work explores hybrid ML+DFT strategies to improve success rates at the cost of additional compute.
AdsorbML algorithm
We introduce the AdsorbML algorithm to use ML to accelerate the adsorbate placement process (Fig. 2). For each model, we explore two strategies that incorporate ML followed by DFT calculations to determine the adsorption energy. We note that this strategy is general and can be used with any initial configuration algorithm.
In both approaches, the first step is to generate ML relaxations. However, rather than taking the minimum across MLrelaxed energies, we rank the systems in order of lowest to highest energy. The best k systems with lowest energies are selected and (1) DFT singlepoint calculations are done on the corresponding structures (ML+SP) or (2) DFT relaxations are performed from MLrelaxed structures (ML+RX). The first strategy aims to get a more reliable energy measurement of the MLpredicted relaxed structure, while the second treats ML as a preoptimizer with DFT completing the relaxation. By taking the k lowest energy systems, we provide the model with k opportunities to arrive at acceptably accurate adsorption energy. As we increase k, more DFT compute is involved, but compared to a full DFT approach, we still anticipate significant savings. The adsorption energy for a particular system is obtained by taking the minimum of the best k DFT followup calculations.
In both strategies, ML energies are used solely to rank configurations, with the final energy prediction coming from a DFT calculation. While computationally, it would be ideal to fully rely on ML, the use of DFT both improves accuracy and provides a verification step to bring us more confidence in our adsorption energy predictions.
Experiments
Our goal is to find comparable or better adsorption energies to those found using DFT alone in OC20Dense. The metric we use to quantify this task is success rate, which is the percentage of OC20Dense systems where our ML+DFT adsorption energy is within 0.1 eV or lower than the DFT adsorption energy. A validation of the ML energy is not included in these experiments since all final adsorption energies will come from at least a single DFT call, ensuring all values are valid. Another metric we track is the speedup compared to the DFTHeur+Rand baseline. Speedup is evaluated as the ratio of DFT electronic steps used by DFTHeur+Rand to the proposed hybrid ML+DFT strategy. A more detailed discussion on the metrics can be found in “Methods”. Unless otherwise noted, all results are reported on the test set, with results on the validation set found in the SI. When evaluating the common baseline of DFTHeur that uses only DFT calculations, a success rate of 87.76% is achieved at a speedup of 1.81×.
ML+SP
The results of using singlepoint evaluations on MLrelaxed states are summarized in Fig. 3. eSCNMDLarge and GemNetOCMDLarge achieve a success rate of 86+% at k = 5 with eSCNMDLarge outperforming all models with a success rate of 88.27%, slightly better than the DFTHeur baseline. Other models including SchNet and DimeNet++ do significantly worse with success metrics as low as 3.13% and 7.99%, respectively; suggesting the predicted relaxed structures are highly unfavorable. The speedups are fairly comparable across all models, ranging between 1400× and 1500× for k = 5, orders of magnitude faster than the DFTHeur baseline. Specifically, eSCNMDLarge and GemNetOCMDLarge give rise to speedups of 1384× and 1388×, respectively. If speed is of most importance, speedups as high as 6817× are achievable with k = 1 while still maintaining success rates of 82% for eSCNMDLarge. At a more balanced tradeoff, k = 3, success rates of 87.36% and 84.43% are attainable for eSCNMDLarge and GemNetOCMDLarge while maintaining speedups of 2296× and 2299×, respectively. In Fig. 4, the minimum energy binding sites of several systems are compared as identified with ML+SP across different models.
ML+RX
While singlepoint evaluations offer a fast evaluation of ML structures, performance is heavily reliant on the accuracy of the predicted relaxed structure. This is particularly apparent when evaluating the max peratom force norm of MLrelaxed structures with DFT. SchNet and DimeNet++ have on average a max force, f_{max}, of 2.00 eV/Å and 1.21 eV/Å, respectively, further supporting the challenge these models face in obtaining valid relaxed structures. On the other hand, models like GemNetOCMDLarge and eSCNMDLarge have an average f_{max} of 0.21 eV/Å and 0.15 eV/Å, respectively. While these models are a lot closer to valid relaxed structures (i.e., f_{max} ≤ 0.05 eV/Å), these results suggest that there is still room for further optimization. Results on DFT relaxations from MLrelaxed states are plotted in Fig. 3. eSCNMDLarge and GemNetOCMDLarge outperform all models at all k values, with a 90.60% and 91.61% success rate at k = 5, respectively. Given the additional DFT costs associated with refining relaxations, speedups unsurprisingly decrease. At k = 5, we see speedups of 215× and 172× for eSCNMDLarge and GemNetOCMDLarge, respectively. Both SchNet and DimeNet++ see much smaller speedups at 42× and 55×, respectively. The much smaller speedups associated with SchNet and DimeNet++ suggest that a larger number of DFT steps is necessary to relax the previously unfavorable configurations generated by the models. Conversely, eSCNMDLarge’s much larger speedup can be attributed to the near relaxed states (average f_{max} ~0.15 eV/Å) it achieves in its predictions. With k = 1, speedups of 1064× are achievable while still maintaining a success rate of 84.13% for eSCNMDLarge. At a more balanced tradeoff, k = 3, success rates of 89.28% and 89.59% are attainable for eSCNMDLarge and GemNetOCMDLarge while maintaining speedups of 356× and 288×, respectively.
The results suggest a spectrum of accuracy and efficiency tradeoffs that one should consider when selecting a strategy. For our best models, ML+SP results are almost 8x faster than ML+RX with only a marginal performance decrease in success rates (3–4%), suggesting a worthwhile comprise. This difference is much more significant for worse models.
In Table 3, we measure the distribution of predictions that are much better, in parity, or much worse than the ground truth, where much better/worse corresponds to being lower/higher than 0.1 eV of the DFT adsorption energy. Across both strategies, we observe that the most accurate models do not necessarily find much better minima. For instance, at k = 5 ML+RX, eSCNMDLarge finds 9.10% of systems with much lower minima, compared to DimeNet++ finding 15.57%. Similarly, while eSCNMDLarge outperformed models in ML+SP, it observes less of an improvement with ML+RX; a consequence of the model arriving at a considerable local minima that a subsequent DFT relaxation has minimal benefit. This further suggests that some form of noise in models can aid in finding better minima. The full set of tabulated results for ML+SP and ML+RX experiments can be found in the SI for the OC20Dense test and validation sets.
Distribution splits
In addition, we evaluate success metrics across the different dataset subsplits. OC20Dense uniformly samples from the four OC20 splits—ID, OODAdsorbate, OODCatalyst, and OODBoth. Across our best models, we observe that performance remains consistent across the different distribution splits (Fig. 5). This suggests that for applications including adsorbates or surfaces that are not contained in OC20, AdsorbML still provides accurate and meaningful results. While we expect results to be consistent with OC20 where ID outperforms OOD, that is not necessarily the case here. eSCNMDLarge, ML+SP at k = 5, achieves 86.00% on ID while a 88.35% success rate on OODBoth, with similar trends on ML+RX. We attribute this discrepancy to the fairly small sample size per split (250). The full set of results can be found in the SI.
Configuration analysis
Alongside the main results, we explore the performance of using only heuristic or only random ML configurations on the OC20Dense validation set. Results are reported on SCNMDLarge, for the ML+SP strategy. At k = 5, when only random configurations are used, success drops from 87.77% to 82.94%. More drastically, when only considering heuristic configurations, success drops significantly to 62.18%. This suggests that random configurations can have a larger impact. Additional results can be found in the SI.
Discussion
We envision this work as an important but initial step towards reducing the computational cost of DFT for not just catalysis applications, but computational chemistry more broadly. AdsorbML provides a spectrum of accuracy and efficiency tradeoffs one can choose depending on the application and computational resources available. For example, if we are interested in screening the largest number of CO_{2} reduction reaction catalysts possible, given a fixed compute budget, we could choose ML+SP at k = 2 for a 85% success rate while screening ~3400× more materials than would have been possible with DFT alone. On the other hand, if depth of study is more important, ML+RX is a good alternative as the structures are fully optimized with DFT and the computational speedup comes from reducing the total number of relaxation steps required. In this scenario, the ML potential serves as an efficient preoptimization step. Even though ML models comprise a small portion of the overall compute (see SI for details), we expect these requirements to be reduced even further as more effort is placed on inference efficiency in the future.
One observation that merits additional studies is that ML models found much better minima between 5 and 15% of the time, depending on the efficiency tradeoffs (Table 3). If our ML models were perfect there would be no instances with lower adsorption energies; however, implicit noise in the form of inaccurate force predictions allows the ML models to traverse unexplored regions of the potential energy surface. Exploring to what extent implicit and explicit noise^{46,47} impact ML relaxations and downstream tasks such as success rate is an important area of future research.
Another natural extension to this work is focusing on alternative methods of global optimization and initial configuration generation. Here, we focused on accelerating bruteforce approaches to finding the global minimum by enumerating initial adsorbatesurface configurations. However, there are likely to be much more efficient approaches to global optimization such as minima hopping^{20}, constrained optimization^{19,21}, Bayesian optimization, or a directly learned approach. It is worth noting that while our enumeration spanned a much larger space than traditional heuristic methods, it was not exhaustive and allencompassing. We found that increasing the number of random configurations beyond what was sampled had diminishing returns, as the change in success rate from heuristic + 80% random DFT to heuristic + 100% random DFT was only 1.6% (see the SI for more details). If screening more ML configurations continues to be advantageous, thinking about how we handle duplicate structures could further help accuracy and efficiency. We explore this briefly in the SI, where removing systems with nearly the same ML energies resulted in marginal benefit.
While current models like GemNetOC and eSCNMDLarge demonstrate impressive success rates on OC20Dense, ML relaxations without any subsequent DFT are still not accurate enough for practical applications (Table 2). In order for future modeling work to address this challenge, there are a number of observations worth highlighting. First, there is a positive correlation between success rate on OC20Dense and both the S2EF and relaxationbased Initial Structure to Relaxed Energy (IS2RE) OC20 tasks. Thus, relaxationbased IS2RE and S2EF metrics can be used as proxies when training models on OC20. Another important note on model development is that OC20Dense’s validation set is a subset of the OC20 validation set; as a result, the OC20 validation data should not be used for training when evaluating on OC20Dense. Lastly, it is strongly encouraged that results reported on the OC20Dense validation set be evaluated using a DFT singlepoint calculation because the success rate metric can be manipulated by predicting only low energies. This could be done with as few as ~1000 singlepoint calculations. Alongside the release of the OC20Dense test set, we will explore releasing a public evaluation server to ensure consistent evaluation and accessibility for DFT evaluation, if there’s interest.
Tremendous progress in datasets and machine learning for chemistry has enabled models to reach the point where they can substantially enhance and augment DFT calculations. Our results demonstrate that current stateoftheart ML models not only accelerate DFT calculations for catalysis but enable more accurate estimates of properties that require global optimization such as adsorption energies. While the models used in this work are best suited for idealized adsorbatesurface catalysts, finetuning strategies can help enable applications to other chemistries including metalorganic frameworks and zeolites^{29}. Similarly, the models used in this work were trained on a consistent level of DFT theory (revised Perdew–Burke–Ernzerhof, no spin polarization), generalizing to other functionals and levels of theory could also be enabled with finetuning or other training strategies. Given the timeline of ML model development, these results would not have been possible even a couple of years ago. We anticipate this work will accelerate the largescale exploration of complex adsorbatesurface configurations for a broad range of chemistries and applications. Generalizing these results to more diverse materials and molecules without reliance on DFT is a significant community challenge moving forward.
Methods
Open Catalyst 2020Dense Dataset (OC20Dense)
The evaluation of adsorption energy estimations requires a ground truth dataset that thoroughly explores the set of potential adsorption configurations. While OC20 computed adsorption energies for O(1M) systems, the energies may not correspond to the minimum of that particular adsorbatesurface combination. More specifically, for a given catalyst surface, OC20 considers all possible adsorption sites but only places the desired adsorbate on a randomly selected site in one particular configuration. The tasks presented by OC20 enabled the development of more accurate machinelearned potentials for catalysis^{34,35,47,48,49}, but tasks like IS2RE, although correlate well, are not always sufficient when evaluating performance as models are penalized when finding a different, lower energy minima—a more desirable outcome. As a natural extension to OC20’s tasks, we introduce OC20Dense to investigate the performance of models to finding the adsorption energy.
OC20Dense is constructed to closely approximate the adsorption energy for a particular adsorbatesurface combination. To accomplish this, a dense sampling of initial adsorption configurations is necessary. OC20Dense consists of two splits—a validation and test set. For each split, ~1000 unique adsorbatesurface combinations from the respective OC20 validation/test set are sampled. A uniform sample is then taken from each of the subsplits (ID, OODAdsorbate, OODCatalyst, OODBoth) to explore the generalizability of models on this task. For each adsorbatesurface combination, two strategies were used to generate initial adsorbate configurations: heuristic and random. The heuristic strategy serves to represent the average catalysis researcher, where popular tools like CatKit^{23} and Pymatgen^{22} are used to make initial configurations. Given an adsorbate and surface, Pymatgen enumerates all symmetrically identical sites, an adsorbate is placed on the site, and a random rotation along the z axis followed by slight wobbles in the x and y axis is applied to the adsorbate. While heuristic strategies seek to capture best practices, they do limit the possible search space with no guarantee that the true minimum energy is selected. To address this, we also randomly enumerate M sites on the surface and then place the adsorbate on top of the selected site. In this work, M = 100 is used and a random rotation is applied to the adsorbate in a similar manner. In both strategies, we remove unreasonable configurations—adsorbates not placed on the slab and/or placed too deep into the surface. DFT relaxations were then run on all configurations with the results filtered to remove those that desorb, dissociate or create surface mismatches. The minimum energy across those remaining is considered the adsorption energy. While random is meant to be a more exhaustive enumeration, it is not perfect and could likely miss some adsorbate configurations. The OC20Dense validation set was created in a similar manner but contained notable differences, details are outlined in the SI.
The OC20Dense test set comprises 989 unique adsorbate+surface combinations spanning 74 adsorbates and 837 bulks. Following the dense sampling, a total of 56,282 heuristic and 49,432 random configurations were calculated with DFT. On average, there were 56 heuristic and 50 random configurations per system (note—while M = 100 random sites were generated, less sites were available upon filtering.) In total, ~4 million hours of compute were used to create the dataset. All DFT calculations were performed using Vienna Ab initio Simulation Package (VASP)^{50,51,52,53}. A discussion on DFT settings and details can be found in the SI.
Evaluation metrics
To sufficiently track progress, we propose two primary metrics—success rate and DFT speedup. Success rate is the proportion of systems in which a strategy returns energy that is within σ, or lower of the DFT adsorption energy. A margin of σ = 0.1 eV is selected as the community is often willing to tolerate a small amount of error for practical relevance^{2,37}. Tightening this threshold for improved accuracy is a foreseeable step once models+strategies saturate. While high success rates are achievable with increased DFT compute, we use DFT speedup as a means to evaluate efficiency. Speedup is measured as the ratio of DFT electronic, or selfconsistency (SC), steps used by DFTHeur+Rand and the proposed strategy. Electronic steps are used as we have seen them correlate better with DFT compute time than the number of ionic, or relaxation, steps. DFT calculations that failed or resulted in invalid structures were included in speedup evaluation as they still represent realized costs in screening. We chose not to include compute time in this metric as results are often hardwaredependent and can make comparing results unreliable. ML relaxation costs are excluded from this metric as hardware variance along with CPU+GPU timings make it nontrivial to normalize. While ML timings are typically negligible compared to the DFT calculations, a more detailed analysis of ML timings can be found in the SI. Metrics are reported against the rigorous ground truth—DFTHeur+Rand, and compared to a community heuristic practice—DFTHeur. Formally, metrics are defined in Equs. (2) and (3).
where i is an adsorbatesurface system, N the total number of unique systems, \({\mathbb{1}}(x)\) is the indicator function, \(\hat{\square }\) is the proposed strategy, N_{SCsteps} is the number of selfconsistency, or electronic steps, and \(\min (E)\) is the minimum energy across all configurations of that particular system. For both metrics, higher is better.
Relaxation constraints
It is possible that some of the adsorbatesurface configurations we consider may relax to a state that are necessary to discard in our analysis. For this work, we considered three such scenarios: (1) desorption, (2) dissociation, and (3) significant adsorbateinduced surface changes. Desorption, the adsorbate molecule not binding to the surface, is far less detrimental because desorbed systems are generally high energy. Still, it is useful to understand when none of the configurations considered have actually adsorbed to the surface. Dissociation, the breaking of an adsorbate molecule into different atoms or molecules, is problematic because the resulting adsorption energy is no longer consistent with what is of interest, i.e., the adsorption energy of a single molecule, not two or more smaller molecules. Including these systems can appear to correspond to lower adsorption energies, but due to the energy not representing the desired system it can result in false positives. Lastly, we also discard systems with significant adsorbateinduced surface changes because, just as with dissociation, we are no longer calculating the energy of interest. In calculating adsorption energy, a term is included for the energy of the clean, relaxed surface. An underlying assumption in this calculation is that the corresponding adsorbatesurface system’s resulting surface must be comparable to the corresponding clean surface, otherwise this referencing scheme fails and the resulting adsorption energy is inaccurate. For each of these instances, we developed detection methods as a function of neighborhood connectivity, distance information, and atomic covalent radii. Depending on the user’s application, one may decide to tighten the thresholds defined within. Details on each of the detection methods and further discussion can be found in the SI.
Data availability
The full open dataset is provided at https://github.com/OpenCatalystProject/AdsorbML.
Code availability
All accompanying code is provided at https://github.com/OpenCatalystProject/AdsorbML.
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A.P., L.Z. and Z.U. conceptualized the project and performed preliminary experiments. J.L., M.S. and B.M.W. substantially expanded the scope of the project, developed the final methodology, conducted all experiments, analyzed the results, and prepared the codebase and dataset for release under the guidance of Z.U. and L.Z. B.W. contributed to the methodology for detecting invalid configurations. A.D. contributed to the AdsorbML methodology and provided guidance on models and experiments. L.Z. and M.U. supervised the project. All authors contributed to the writing and editing of the paper.
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Lan, J., Palizhati, A., Shuaibi, M. et al. AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials. npj Comput Mater 9, 172 (2023). https://doi.org/10.1038/s41524023011215
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DOI: https://doi.org/10.1038/s41524023011215