Quantum Chemistry and Deep Learning

The application of deep learning and neural networks has had a significant impact on various industries. There are numerous opportunities to make a notable difference in areas such as autonomous vehicles, production, e-commerce, healthcare, robotics, and more. This is due to neural networks being able to identify intricate patterns in data that would have otherwise gone unnoticed. The resulting discoveries and inventions have been instrumental in the advancement of these fields.

Over the last few years, the application of deep learning to visual data processing has yielded great results. Convolutional neural networks (CNNs) are now commonly employed to attain superior performance on image-based datasets. Moreover, the use of deep learning algorithms to analyse CT scans and X-rays has enabled physicians to detect and diagnose illnesses such as cancer with greater accuracy.

The incorporation of deep learning into scientific research has enabled companies such as DeepMind to create cutting-edge algorithms and systems that have helped to propel research and discoveries in various fields. For example, DeepMind’s AlphaFold2 algorithm has been instrumental in advancing the understanding of protein structure and form.

In this post, we’ll look at how deep learning may be used in the field of science, specifically in the study of quantum chemistry.

What exactly is quantum chemistry?

Quantum chemistry, also referred to as molecular quantum mechanics, is a subfield of physical chemistry that applies principles of quantum mechanics to the study of chemical systems. In particular, it focuses on the use of quantum-mechanical computations to derive electronic contributions and effects, typically using approximations that are suitable for practical computations while still providing sufficient information about important contributions to wave functions and visible features.

The theory of density functionals (DFT)

Density-Functional Theory (DFT) is a computational quantum mechanical modelling technique used in the physical, chemical, and mechanical sciences to study atomic, molecular, and condensed-phase materials. This technique enables researchers to gain a better understanding of the electrical or nuclear structure of these materials. Functionals, or functions of functions, are used in DFT to accurately analyse systems with numerous electrons. In particular, electron density functionals focus on the location of electrons in the system.

Modelling the mapping of the energy density functional in density functional theory (DFT) is being conducted with the aid of deep learning. DFT is an essential tool in the simplification of quantum chemistry computations, however, it is challenged by the need to accurately map the electronic probability densities to energy. To overcome this obstacle, deep learning models are trained to act as a functional, translating the 3D electron density into the energy of chemical interactions.

For the same purpose, DeepMind has created the DM21 neural network model. Allow me to go into this model for you.

DeepMind’s DM21 Quantum Chemistry AI Model

By leveraging the principles of quantum physics, scientists are able to use quantum chemistry to predict the chemical traits of molecules. Through the advent of Density Functional Theory (DFT), researchers have been able to expedite these calculations; however, they are only able to do so when they possess a functional or mapping electronic probability density to energy. Despite the lack of an exact functional, scientists have been successfully utilising approximation functionals in a variety of disciplines, such as solid-state physics and nuclear spectroscopy, for many years.

Despite the fact that systems with fractional electron properties, such as fractional charge (FC) or fractional spin, are well understood, most existing approximations and approaches have been shown to have difficulty in accurately predicting the relevant outcomes of such systems, leading to what has been termed “pathological mistakes” (FS). In view of the fact that manually constructed functionals are particularly prone to mistakes, and it can be difficult to develop functionals that can satisfactorily deal with all edge cases, the DeepMind team opted to use a deep learning approach to address this issue.

DeepMind 21 (DM21) is a neural network model developed by DeepMind, a Google subsidiary, and used in quantum mechanical modelling to convert electron density to the energy of chemical interactions. This model is freely available for distribution and can be plugged into the PySCF simulator for further use.

In order to approximate the energy density functional part of Density Functional Theory (DFT), DM21 employs a Neural Network (NN) model. This model is built on a Multilayer Perceptron (MLP) structure and takes a grid of electron densities as input. Previous functional approximations have had flaws, including the inability to cope with systems featuring fractional electron character. DM21 has been successful in addressing and rectifying these issues.

Forming Competent Models

The DM21 model was trained using the multi-layer perceptron (MLP) neural network architecture and the supervised learning technique. The training dataset consisted of 1161 samples, with the output values/labels being high-accuracy reaction energies and the inputs being a spatial grid of Kohn-Sham (KS) orbital characteristics, or electron concentrations.

The model was trained using regression loss, and an additional gradient regularizer term was included in order to enable the model to be used in self-consistent field (SCF) computations.

Assessment and Standards

The Bond-Breaking Benchmark (BBB), GMTKN55, and QM9 were employed to evaluate the accuracy of the DM21 model. These datasets provided insight into chemical tasks that were not included in the model’s training data. The results of this assessment showed that the DM21 model outperformed four of the most current implementations, thus establishing a new state-of-the-art performance across these benchmarks.

In comparison to other hybrid functionals, a study conducted by the authors found that DM21 demonstrates a performance that is comparable to that of the far more expensive double-hybrid functionals.

In comparison to traditional methods of constructing functionals by hand, the DM21 model has been found to offer the most precise estimations of the chemical interaction energies of molecules, making it the most effective functional currently available.

This serves as an illustration of the impressive capacity of deep learning models to detect and assimilate patterns in data that have not previously been encountered. Not only will this approach supply models with accurate forecasts of energy-states, but it will also take into consideration the exceptional conditions that can lead to anomalous errors arising from fractional electrons.

Innovative advances in quantum chemistry made possible by DL

Artificial Intelligence (AI) has become an increasingly popular tool for exploring the fundamentals of physics and chemistry. Over the past few years, the application of deep learning techniques has enabled researchers to make groundbreaking progress in the field of quantum chemistry. Consequently, AI is becoming an increasingly dynamic field of study as more and more scientists are turning to it to gain further insight into the physical world.

In 2019, Stanford researchers leveraged a convolutional neural network to achieve successful results for a wide variety of organic compounds. In 2020, Caltech utilised machine learning to tackle the Navier-Stokes equation while in 2021, DeepMind’s AlphaFold2 AI was used to predict the structure of proteins. These advances demonstrate the potential of artificial intelligence and its capability to solve complex problems.

After examining the research we have completed and the progress we have made thus far, it is reasonable to state that deep learning and machine learning will become increasingly prevalent in the future, providing us with the potential to discover further groundbreaking scientific discoveries.

Join the Top 1% of Remote Developers and Designers

Works connects the top 1% of remote developers and designers with the leading brands and startups around the world. We focus on sophisticated, challenging tier-one projects which require highly skilled talent and problem solvers.
seasoned project manager reviewing remote software engineer's progress on software development project, hired from Works blog.join_marketplace.your_wayexperienced remote UI / UX designer working remotely at home while working on UI / UX & product design projects on Works blog.join_marketplace.freelance_jobs