Machine-Learned Force Fields for Realistic Physics

The QuantumATK simulation engines enable atomic-scale modeling using multiple simulation methods in one platform [1]: state-of-the-art density functional theory (DFT) with plane wave or LCAO basis sets, semi-empirical methods, conventional FFs (built-in database of 300 potentials) and ML FFs. All simulation engines share a common infrastructure for material property, molecular dynamics (MD), nudged elastic band (NEB), geometry optimization, and other simulations.

• ML FFs are 1000 to 10,000x faster than DFT, thus enabling dynamical modeling of realistic novel and complex systems containing even 100,000+ atoms, instead of small model 100-atom systems.
• ML FFs provide near-ab initio accuracy for multi-element materials, heterogeneous systems like interfaces, and systems far from equilibrium, including amorphous materials, phase transitions, or chemical reactions.
• ML FFs are often easier to develop than conventional FFs using the automated workflows available in QuantumATK. Accurate conventional FFs for such complex systems would require much more extensive and complicated development processes.

Built-in Library of Ready-to-Use Machine-Learned Force Fields

Value of Machine-Learned FFs

1. Why is it not possible to run all needed simulations using DFT instead?
   ML FFs are 1000 to 10 000x faster than DFT and can often be almost as accurate. Dynamical simulations with ML FFs reduce simulation time from days to minutes, from years to hours, thus enabling dynamical modeling of realistic novel and complex systems containing 1,000 - 100 000 atoms and more.
2. Why is it not possible to run all needed simulations using efficient conventional force field libraries instead?
   - It is possible to use conventional FFs for selected materials only, and the possibility to combine them to describe, for example, interfaces between different materials is very limited and inaccurate.
   - Conventional FFs are often not very accurate far from equilibrium, which is needed to describe amorphous materials, phase transitions, or chemical reactions.
3. Do I need to use machine learning? What about fitting a new conventional force field?
   Conventional FFs work well for relatively simple materials, but fitting accurate conventional force fields for complex materials has been shown to be very difficult, in fact near impossible for multi-element materials or heterogeneous systems like interfaces (in particular between metals and semiconductors)
4. What is the largest number of atoms that can be simulated with ML FFs?
   QuantumATK team has simulated 100,000 atom systems with ns long MD simulations. Larger systems can be also simulated with ML-FFs, depending on available computational resources. 

Machine-Learned FFs in QuantumATK

1. How did QuantumATK implement Moment Tensor Potential (MTP) ML FFs?
   QuantumATK team implemented MTPs in-house, i.e., not the original MTP package from literature [2,3]. QuantumATK team expects similar efficiency for QuantumATK and original implementations, however, no explicit efficiency comparisons were done, as they also depend on the MD simulation engine.
2. How long does it take to generate ML FFs with QuantumATK?
   In case of having 2-4 cluster nodes (32-72 cores available), obtain a good quality ML FF for:
   - Crystal structures (1-3 elements) in 1-2 days, depending on the complexity of the material
   - Interfaces and amorphous materials, including Active Learning MD, in 1-2 weeks, depending on the complexity
   - More complex systems (e.g, more than 3 elements, various interfaces or special applications like ALD), in several weeks
   In all cases, the most time-consuming part is generating training configurations and calculating training data with DFT. Fitting to the training data can be done in a few hours or max a day. Generated training configurations could be re-used when developing ML FFs for similar materials (e.g., different stoichiometry, common elements).
3. What software can be used to run MD simulations with pre-trained or generated ML FFs?
   Only QuantumATK software can be used to run MD simulations with these ML FFs. QuantumATK offers high-performance massively-parallel state-of-the-art MD simulation methods.
4. How are ML FFs licensed in QuantumATK?
   - Base license: Run calculations with ML FFs you fitted yourself
   - Elite license: Fit your own ML FF or access the built-in ML FF library
5. What are QuantumATK advantages for Machine-Learned FFs over competing solutions?
   - QuantumATK provides a user-friendly, automatic and efficient generation of curated training data instead of manual flows
   - Essential for good quality ML FFs and to minimize the training time and data, i.e., 10-100x reduction in the amount of DFT reference data
   - The unique, automated QuantumATK protocols are tailored for specific applications, such as molecular surface deposition or interfaces
   - ML FF models (NNP, GAP, etc.) in competing solutions are less accurate or slower than the MTP model used in QuantumATK [2,3]
   - QuantumATK has demonstrated highly accurate and efficient ML FFs for complex materials, such as amorphous alloys, interfaces with multiple materials involved, and molecular surface deposition, not just simple materials
6. Why generation of training configurations does not include ab initio MD simulations?
   Generation of training configurations with ab initio MD is a very computationally expensive process. From QuantumATK team's experience,  this often results in a lot more DFT calculations than needed, because subsequent snapshots from MD simulations are typically very correlated and similar. QuantumATK primarily validated this by using the training protocol which uses random displacements and strain for crystal structures. It provides good description of crystal properties, such as lattice constants, phonons, and elastic constants up to moderate temperatures without adding snapshots from ab initio MD simulations. For amorphous or other more complex structures, however, it can sometimes be beneficial to include ab initio MD training data, too.

References

[1] S. Smidstrup, T. Markussen, P. Vancraeyveld, J. Wellendorf, J. Schneider, T. Gunst, B. Vershichel, D. Stradi, P. A. Khomyakov, U. G. Vej-Hansen, M.-E. Lee, S. T. Chill, F. Rasmussen, G. Penazzi, F. Corsetti, A. Ojanpera, K. Jensen, M. L. N. Palsgaard, U. Martinez, A. Blom, M. Brandbyge, and K. Stokbro, "QuantumATK: An integrated platform of electronic and atomic-scale modelling tools", J. Phys.: Condens. Matter 32, 015901 (2020). arXiv: 1905.02794v2.

[2] A. V. Shapeev, "Moment tensor potentials: a class of systematically improvable interatomic potentials", Multi-scale Model. & Simul. 14, 1153 (2016).

[3] Y. Zuo, C. Chen, X. Li, Z. Deng, Y. Chen, J. Behler, G. Csányi, A. V. Shapeev, A. P. Thompson, M. A. Wood, and S. Ping Ong, "Performance and cost assessment of machine learning interatomic potentials",  J.  Phys. Chem. A 124, 731 (2020).

[4] ML FF features: https://www.synopsys.com/silicon/quantumatk/resources/feature-list.html#MLforcefield

[5] Materials in the pre-trained ready-to-use ML FF library: https://docs.quantumatk.com/manual/ForceField.html#pretrained-moment-tensor-potential-mtp-parameter-sets

[6] Tutorial on automatic ML FF training tools and GUI templates: https://docs.quantumatk.com/tutorials/mtp_hfo2/mtp_hfo2.html 

Useful Resources