The use of supervised machine learning to develop fast and accurate interatomic potential models is transforming molecular and materials research by greatly accelerating atomic-scale simulations with little loss of accuracy. Three years ago, Jörg Behler published a perspective in this journal providing an overview of some of the leading methods in this field. In this perspective, we provide an updated discussion of recent developments, emerging trends, and promising areas for future research in this field. We include in this discussion an overview of three emerging approaches to developing machine-learned interatomic potential models that have not been extensively discussed in existing reviews: moment tensor potentials, message-passing networks, and symbolic regression.
This paper reports the implementation of renewable, biodegradable, and abundant cellulose nanofibrils (CNFs) in triboelectric nanogenerator (TENG) development. Flexible and transparent CNF thin films are triboelectric positive material with nanoscale surface roughness. They are paired with FEP (fluorinated ethylene propylene) to assemble TENG devices, which exhibit comparable performance to the reported TENG devices built on synthetic polymers. CNF-based TENG is further integrated within a fiberboard made from recycled cardboard fibers using a chemical-free cold pressing method. The fiberboard produces up to ~30 V and ~90 μA electric outputs when subjected to a normal human step. This development shows great promises in creating large-scale and environmentally sustainable triboelectric board for flooring, packaging and supporting infrastructures from CNF and other natural wood-extracted materials.
The length and time scales of atomistic simulations are limited by the computational cost of the methods used to predict material properties. In recent years there has been great progress in the use of machine learning algorithms to develop fast and accurate interatomic potential models, but it remains a challenge to develop models that generalize well and are fast enough to be used at extreme time and length scales. To address this challenge, we have developed a machine learning algorithm based on symbolic regression in the form of genetic programming that is capable of discovering accurate, computationally efficient manybody potential models. The key to our approach is to explore a hypothesis space of models based on fundamental physical principles and select models within this hypothesis space based on their accuracy, speed, and simplicity. The focus on simplicity reduces the risk of overfitting the training data and increases the chances of discovering a model that generalizes well. Our algorithm was validated by rediscovering an exact Lennard-Jones potential and a Sutton Chen embedded atom method potential from training data generated using these models. By using training data generated from density functional theory calculations, we found potential models for elemental copper that are simple, as fast as embedded atom models, and capable of accurately predicting properties outside of their training set. Our approach requires relatively small sets of training data, making it possible to generate training data using highly accurate methods at a reasonable computational cost. We present our approach, the forms of the discovered models, and assessments of their transferability, accuracy and speed.
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