Open Challenges in Developing Generalizable Large Scale Machine Learning Models for Catalyst Discovery

Abstract

The development of machine learned potentials for catalyst discovery has predominantly been focused on very specific chemistries and material compositions. While effective in interpolating between available materials, these approaches struggle to generalize across chemical space. The recent curation of large-scale catalyst datasets has offered the opportunity to build a universal machine learning potential, spanning chemical and composition space. If accomplished, said potential could accelerate the catalyst discovery process across a variety of applications (CO 2 reduction, NH 3 production, etc.) without additional specialized training efforts that are currently required. The release of the Open Catalyst 2020 Dataset (OC20) 1 has begun just that, pushing the heterogeneous catalysis and machine learning communities towards building more accurate and robust models. In this perspective, we discuss some of the challenges and findings of recent developments on OC20. We examine the performance of current models across different materials and adsorbates to identify notably underperforming subsets. We then discuss some of the modeling efforts surrounding energy-conservation, approaches to finding and evaluating the local minima, and augmentation of off-equilibrium data. To complement the community's ongoing developments, we end with an outlook to some of the important challenges that have yet to be thoroughly explored for large-scale catalyst discovery.

Towards predictive design of electrolyte solutions by accelerating ab initio simulation with neural networks

“…The underlying theoretical details of these methods are beyond the scope of this review and we refer the reader to several excellent reviews of these methods that have recently been published for more detail. [45][46][47][48][49][50][51] At a high level these methods work by dening a mapping between atomic coordinates to energies and forces (occasionally virial tensors also). This mapping contains a large number of parameters (weights and biases) that can be systematically adjusted to minimise the error on a set of training data, combined with an algorithm to systematically optimise the parameters (Backpropagation).…”

AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials

“…When evaluating performance, we define success as finding an adsorption energy within an acceptable tolerance (0.1 eV in this work [2,33,42]) or lower of the DFT adsorption energy in OC20-Dense. Note that the ground truth adsorption energies in OC20-Dense are an upper bound, since it is possible that a lower adsorption energy may exist.…”

“…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,[27][28][29][30][31][32]. 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 [33]. This raises the question of whether a hybrid approach that uses both DFT and ML potentials can achieve high accuracy while main-taining efficiency.…”

Towards predictive design of electrolyte solutions by accelerating ab initio simulation with neural networks.

“…The underlying theoretical details of these methods are beyond the scope of this review and we refer the reader to several excellent reviews of these methods that have recently been published for more detail. [45][46][47][48][49][50][51] At a high level these methods work by defining a mapping between atomic coordinates to energies and forces (occasionally virial tensors also). This mapping contains a large number of parameters (weights and biases) that can be systematically adjusted to minimise the error on a set of training data, combined with an algorithm to systematically optimise the parameters (Backpropagation).…”