Constructing artificial life and materials scientists with accelerated AI using Deep AndersoNN

TL;DR

Deep AndersoNN is a novel deep equilibrium model that accelerates AI training and inference, enabling rapid classification of materials and molecules with high accuracy, thus significantly reducing computational costs and environmental impact.

Contribution

The paper introduces Deep AndersoNN, a deep equilibrium neural network approach that achieves up to tenfold speed-up and high accuracy in materials and life sciences applications.

Findings

Achieves up to 10x speed-up in training and inference.

Classifies materials and molecules with up to 98% accuracy.

Reduces computational costs and carbon footprint significantly.

Abstract

Deep AndersoNN accelerates AI by exploiting the continuum limit as the number of explicit layers in a neural network approaches infinity and can be taken as a single implicit layer, known as a deep equilibrium model. Solving for deep equilibrium model parameters reduces to a nonlinear fixed point iteration problem, enabling the use of vector-to-vector iterative solvers and windowing techniques, such as Anderson extrapolation, for accelerating convergence to the fixed point deep equilibrium. Here we show that Deep AndersoNN achieves up to an order of magnitude of speed-up in training and inference. The method is demonstrated on density functional theory results for industrial applications by constructing artificial life and materials `scientists' capable of classifying drugs as strongly or weakly polar, metal-organic frameworks by pore size, and crystalline materials as metals, semiconductors, and insulators, using graph images of node-neighbor representations transformed from atom-bond networks. Results exhibit accuracy up to 98\% and showcase synergy between Deep AndersoNN and machine learning capabilities of modern computing architectures, such as GPUs, for accelerated computational life and materials science by quickly identifying structure-property relationships. This paves the way for saving up to 90\% of compute required for AI, reducing its carbon footprint by up to 60 gigatons per year by 2030, and scaling above memory limits of explicit neural networks in life and materials science, and beyond.