Breaking the Nanosecond Barrier

The Quest for Ultrafast Molecular Simulations with Ab Initio Accuracy

For decades, scientists peering into the molecular world have been like astronomers with blurry telescopes—until now.

Imagine trying to understand a complex dance by watching just a few seconds of it. This has been the fundamental challenge for scientists studying molecular dynamics. Molecular dynamics (MD) simulations serve as a "computational microscope," allowing researchers to observe the intricate movements of atoms and molecules over time. However, achieving both high accuracy and sufficient simulation speed has remained an elusive goal—until recent breakthroughs in strong scaling began to shatter these barriers. By dramatically accelerating simulations while preserving ab initio (first-principles) accuracy, scientists are now opening new windows into processes like protein folding, chemical reactions, and material transformations that were previously impossible to observe in detail.

The Quantum Accuracy Challenge

At its core, molecular dynamics is a computational technique that studies how atomic coordinates evolve under given conditions by numerically simulating the motion of molecular systems based on the principles of classical mechanics. Each atom experiences forces from its neighbors, causing molecules to twist, fold, and interact in complex ways that determine everything from how proteins function to how materials strengthen.

There are three primary approaches to MD simulations, each with distinct trade-offs:

Classical MD (CMD)

Uses pre-defined analytical expressions to describe interatomic forces, enabling relatively fast simulations but with limited accuracy—errors can be as high as 10.0 kcal/mol, making it unreliable for studying chemical reactions where bonds form or break.⁴

Ab Initio MD (AIMD)

Calculates forces by solving the fundamental equations of quantum mechanics, providing high accuracy but at enormous computational cost. The time complexity scales cubically with system size (O(N³)), restricting AIMD to small systems of just thousands of atoms over picoseconds.⁴ ⁸

Machine Learning MD (MLMD)

Employs machine learning models trained on quantum mechanical data to approximate interatomic forces, offering near ab initio accuracy at a fraction of the computational cost, with time complexity reduced to linear scaling (O(N)).² ⁴

The term "ab initio accuracy" refers to calculations with quantum mechanical precision, typically requiring energy prediction errors below 1.0 kcal/mol (43.4 meV/atom), as defined by John A. Pople.⁴ Achieving this gold standard while simulating biologically or physically relevant timescales represents the grand challenge in the field.

MD Approaches Comparison

Comparison of accuracy vs. computational cost for different MD approaches. MLMD offers the best balance for large-scale simulations.

The Breakthrough: Scaling MD to New Speeds

A landmark achievement in strong scaling recently emerged from research optimizing the DeePMD-kit software on the Fugaku supercomputer. The goal was audacious: push the boundaries of how quickly MD simulations with ab initio accuracy can run by improving their strong scaling—the ability to run faster by using more computing cores for the same problem size.

Fugaku Supercomputer

World's fastest supercomputer used for breakthrough simulations

31.7x Improvement

Performance enhancement over previous state-of-the-art

149 ns/day

Unprecedented simulation speed with ab initio accuracy

The Fugaku supercomputer, where the breakthrough strong scaling achievements were realized, enabling unprecedented molecular simulation speeds.

Methodology: A Three-Pronged Optimization Approach

The research team addressed bottlenecks at multiple levels through innovative strategies:⁸

Node-Based Parallelization Scheme

Traditional approaches assigned atoms to individual processor cores, creating massive communication overhead. The new method organized computation by node, perfectly matching Fugaku's network-on-chip ring bus and Tofu Interconnect D network. This reduced overall communication overhead by 81% in strong scaling scenarios.

Computational Kernel Optimization

The mathematically intensive matrix operations were accelerated using single-value embedding generalized matrix multiplication (sve-gemm) and mixed-precision calculations. By removing the TensorFlow framework dependency and simplifying redundant kernels, the team achieved a 14.11-fold improvement in calculation efficiency.

Intra-Node Load Balancing

A spatial decomposition strategy ensured atoms were distributed more evenly across processor cores within each node, reducing atomic dispersion between MPI ranks by 79.7% and improving overall performance by up to 18.5%.

Results and Analysis: Shattering Performance Barriers

The optimization efforts produced dramatic improvements. For a copper system of approximately 500,000 atoms, the enhanced DeePMD-kit achieved 149 nanoseconds per day on 12,000 Fugaku nodes (576,000 CPU cores), representing a 31.7-fold improvement over the previous state-of-the-art.⁸

Table 1: Performance Comparison of Neural Network MD Packages
Work	Year	System	#Atoms	Hardware	Performance (ns/day)
Simple-NN	2019	SiO₂	14K	Unknown	<1
SNAP ML-IAP	2021	C	1B	204.6K CPU cores + 27.3K GPUs	1.03
Allegro	2023	Ag	1M	128 A100 GPUs	49.4
DeePMD-kit (baseline)	2022	Cu	2.1M	218.8K Fugaku nodes	4.7
This work (optimized)	2024	Cu	0.5M	576K Fugaku cores	149

Table 2: Performance Across Different Systems After Optimization
System	Number of Atoms	Time Step	Performance (ns/day)
Copper (Cu)	0.54 million	1 fs	149
Water (H₂O)	0.56 million	0.5 fs	68.5

This performance breakthrough is particularly significant because it maintained ab initio accuracy throughout. The researchers validated their results by comparing physical properties like radial distribution functions of water, stacking fault energies of magnesium, and strain-stress curves of copper against established MLMD and DFT methods, finding excellent agreement.⁴

The ability to simulate 149 nanoseconds per day with quantum mechanical precision opens the door to millisecond-scale simulations within approximately one week—crossing a critical threshold where many biologically and chemically significant processes become accessible to computation.

Performance Improvement

The dramatic 31.7x performance improvement achieved through strong scaling optimizations on the Fugaku supercomputer.

Simulation Speed Comparison

Comparison of simulation speeds across different MD approaches and systems, highlighting the breakthrough achieved.

The Scientist's Toolkit: Essential Tools for Next-Generation MD

Advancements in strong scaling rely on a sophisticated ecosystem of software and hardware solutions:

Table 3: Key Research Reagent Solutions for Advanced MD Simulations
Tool	Function	Examples
Machine Learning Potentials	Approximate quantum mechanical forces at linear scaling cost	DeePMD-kit, Allegro, ANI, Schnet
Specialized Hardware	Accelerate computation while reducing power consumption	MDPU (MD Processing Unit), Fugaku, Anton series
Fragmentation Approaches	Enable ab initio accuracy for large biomolecules	AI2BMD's protein fragmentation scheme
Simulation Packages	Provide frameworks for running and analyzing simulations	AMBER, GROMACS, NAMD, LAMMPS
Validation Datasets	Benchmark and train machine learning potentials	ElectroFace dataset for electrochemical interfaces

Molecular Dynamics Processing Unit (MDPU)

The emergence of specialized hardware like the Molecular Dynamics Processing Unit (MDPU) promises further revolutionary gains. One proposed MDPU design could reduce time and power consumption by approximately 1,000 times compared to MLMD and 1 billion times compared to AIMD while maintaining ab initio accuracy.⁴

AI2BMD for Biomolecules

For biomolecular applications, systems like AI2BMD use innovative protein fragmentation approaches, splitting proteins into smaller units (dipeptides) that can be accurately simulated and then reassembled, enabling ab initio accuracy for proteins exceeding 10,000 atoms.⁹

Advanced visualization of molecular dynamics simulations showing protein folding, made possible by the latest computational breakthroughs.

Tool Adoption Timeline

Evolution of key tools and technologies in molecular dynamics simulations over time.

The New Era of Computational Microscopy

The recent breakthroughs in strong scaling molecular dynamics with ab initio accuracy represent more than just technical achievements—they fundamentally expand the boundaries of scientific inquiry. With the ability to simulate 149 nanoseconds per day, processes once beyond computational reach, including protein folding, catalytic reactions, and phase transitions, are now becoming accessible to detailed atomistic study.

Drug Design

Accelerated discovery of new pharmaceuticals through detailed molecular interaction simulations

Energy Materials

Development of advanced materials for batteries, fuel cells, and renewable energy applications

Fundamental Science

Unprecedented insights into chemical reactions, molecular interactions, and material properties

As these tools become more refined and accessible, they promise to transform our understanding of molecular processes central to biology, chemistry, and materials science. The "computational microscope" is not only becoming faster but also sharper, revealing details of the molecular world that were previously obscured by technical limitations. In the coming years, we can anticipate accelerated discoveries in drug design, energy materials, and fundamental molecular science as these powerful simulation capabilities empower researchers to explore previously inaccessible aspects of the nanoscale world.

The revolution in molecular simulation continues to accelerate, promising to reveal the deepest secrets of the atomic world—one femtosecond at a time.