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What are the Benefits of Machine-Learning Force Fields?

A long-standing challenge in atomic-scale MD simulations is that reliable simulation models are either too expensive (ab initio) to reach the simulation time- and length scales that would allow one to capture the complexity of realistic systems, or limited accuracy for relevant systems (conventional FF). The ML-FF approach offers a solution to this challenge and enables, for example, simulation of dynamical atomic-scale processes; these require high accuracy but take place on longer time scales, e.g. diffusion, crystallization, or deposition, such that the user can simulate such processes not only on model systems with significantly reduced complexity but on the more realistic complex systems without loss of accuracy. Moreover, ML-FFs also enable the user to rapidly generate and optimize a variety of different realistic input structures that can be used in, for example, ab initio calculations. This reduces the number of computationally expensive calculations significantly and allows for a larger throughput of different structural parameters, among other benefits.

How Do Machine-Learning Force Fields Fit in the ²ÝÁñÉçÇø Product Portfolio?

The QuantumATK atomistic modeling platform from ²ÝÁñÉçÇø is one of the most modern and versatile materials simulation codes on the market. It can be used to compute electronic, thermal, mechanical, optical, magnetic, ferroelectric, thermoelectric, and many other properties of complex molecules, amorphous and crystalline materials, interfaces, and even atomistic devices.

QuantumATK implements the moment-tensor-potential (MTP) class of ML-FFs, which is a modern, state-of-the-art algorithm that provides an excellent balance between accuracy and efficiency. One major advantage of QuantumATK is that several materials simulation frameworks are available in one unified platform, including DFT, tight-binding methods, and force fields. Advanced ML-FF training workflows, which require both DFT and FF simulations, are therefore created and executed within a single software platform.

QuantumATK also ships with custom workflows explicitly designed to minimize the number of DFT calculations needed to generate a reliable ML-FF, and automates most parts of the training workflow. The graphic below illustrates the main steps in the ML-FF training workflow for the MTP in QuantumATK:

Going Beyond Machine-Learning Force Fields

As the semiconductor industry continues advancing towards more advanced processing nodes, atomic-scale details of device interfaces and nanoscale thermal transport properties become increasingly important to device and processing design. Several ²ÝÁñÉçÇø TCAD tools are used to address these challenges, and the ML-FF methodology is key to connecting atomistic simulations to many of these tools, for example by providing atomic-scale insight into the detailed composition of high-K metal gate stacks for advanced logic or for computing thermal properties of novel materials and interfaces.