Abstract
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3–10+ years.
| Original language | English |
|---|---|
| Article number | 101214 |
| Journal | Current Opinion in Solid State and Materials Science |
| Volume | 35 |
| Early online date | 26 Feb 2025 |
| DOIs | |
| Publication status | Published - Mar 2025 |
Bibliographical note
Publisher Copyright:© 2025 Elsevier Ltd
Funding
Funding for the "Machine Learning Potentials - Status and Future (MLIP-SAFE) " workshop and development of this paper was provided by the National Science Foundation through an AI Institute Planning Grant, Award Number 2020243. KC thanks the National Institute of Standards and Technology for funding, computational, and data management resources. This work was performed with funding from the CHIPS Metrology Program, part of CHIPS for America, National Institute of Standards and Technology, U.S. Department of Commerce. Certain commercial equipment, instruments,software, or materials are identified in this paper in order to specify the experimental procedure adequately. Such identifications are not intended to imply recommendation or endorsement by NIST, nor it is inte-ded to imply that the materials or equipment identified are necessarily the best available for the purpose. SM acknowledges support from NSF Grant OAC-2311632 and the Simons Center for Computational Physical Chemistry (Simons Foundation grant 839534, MT) .r software, or materials are identified in this paper in order to specify the experimental procedure adequately. Such identifications are not inten-ded to imply recommendation or endorsement by NIST, nor it is inten-ded to imply that the materials or equipment identified are necessarily the best available for the purpose. SM acknowledges support from NSF Grant OAC-2311632 and the Simons Center for Computational Physical Chemistry (Simons Founda-tion grant 839534, MT) .
| Funders | Funder number |
|---|---|
| NSF | OAC-2311632 |
| Simons Center for Computational Physical Chemistry (Simons Founda-tion) | 839534 |