## Matlantis™: Core Technology and Mechanics

### Our proprietary neural network potential (NNP)

significantly accelerates computation with high versatility and accuracy.

Matlantis supports large-scale materials discovery by simulating the behavior of various materials at an atomic level. The versatile atomistic simulator is powered by a neural network potential (NNP) in which a deep learning model is incorporated into a conventional atomic simulator.

Conventional physics-based atomistic simulation of material behavior requires high-cost calculations for solving the electronic state. Matlantis bypasses such calculations thanks to the deep learning model trained with energy, force, and other data necessary for simulating atomic behavior.

This allows researchers to quickly simulate a massive number of realistic, complex systems with relatively low calculation costs without compromising versatility.

There are some existing methods to perform atomic-scale simulation without the electronic state calculation. However, the governing equations are known to be too complex, and balancing versatility and accuracy is a long-standing challenge.

Matlantis can perform highly accurate simulations for atomic structures of any combination of 55 elements thanks to Preferred Networks (PFN)'s proprietary neural network architecture that has been trained with a vast amount of atomic structure data using PFN's computer clusters.

#### PFN's In-house Computer Cluster

#### Matlantis's simulation results

accurately predicts DFT results

## Matlantis: 3 Key Features

Matlantis supports companies exploring innovative materials for a sustainable future.

### Versatility

**Supports a wide range of elements and structures**

Matlantis can simulate properties of molecules and crystal systems, including unknown materials. Any combinations of 55 elements are currently supported, and more elements will be added.

### Speed

**Over 10,000x faster than conventional methods**

The atomistic simulation tasks that take hours to months using density functional theory (DFT) on a high-performance computer can be finished in only a few seconds using Matlantis.

### User-Friendliness

**Just open your browser to run simulations**

Thanks to the pre-trained deep learning model, physical property calculation library, and high-performance computing environment, no hardware or software installations are required for performing simulations. Unlike conventional machine learning potentials, Matlantis requires no data collection or training by users.

### Physical property calculation library

With additional codes, users can also simulate physical properties and phenomena from the NNP output (energy, force) without using the library.- Vibration analysis of organic molecules
- Vibration mode・Vibration frequencyInfrared spectroscopy

- Elastic constants
- Elastic constantsYoung's modulusBulk modulusPoisson's ratio

- Phonon dispersion and thermal properties of solid
- Phonon dispersionHelmholtz free energySpecific heat

- Dynamics calculation
- Viscosity coefficientDiffusion coefficientSpecific heatThermal conductivity

- Reaction pathway analysis
- Nudged elastic band method (NEB)

### Simulation

Case Studies

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## How to get started with Matlantis

STEP01

**Log in to Matlantis**

STEP02

**Write your programs in Jupyter Notebook**

Launch Jupyter Notebook and write simulation scripts in Python. Matlantis’s default environment allows you to run various packages such as first principles calculations and molecular dynamics methods using Python. Matlantis' original physical property calculation library and sample notebooks are also available for you to immediately start your research.

STEP03

**Execute the program**

Matlantis executes the contents described in the Jupyter Notebook. It will take you only a few seconds to complete the structural optimization calculation.

STEP04

**Export the result**

Matlantis can also visualize the simulation results or perform further analysis on Jupyter Notebook.

### Try Matlantis

Contact Us### FAQ

I want to use Matlantis to simulate a proprietary material that our company is developing. What kind of data do I need to prepare?

We provide Matlantis as a pre-trained and versatile potential. So you do not need to generate your own training data for pre-training before getting started.

Should we select appropriate interatomic potential depending on the target system and phenomena? How many types of potentials are provided?

Matlantis is designed to function as a versatile potential that can handle a variety of systems with a single potential.

What is the calculation accuracy of Matlantis?

MAE 0.03 eV/atom for disordered system, and 0.01 eV/atom for bulk, approximately.

What is the maximum number of atoms that Matlantis can calculate?

It depends on the number of neighboring atoms of the calculation target, but it is around several thousand atoms. For instance, up to 3000 atoms can be calculated for platinum with fcc structure.

Can Matlantis be applied to the research of resins and polymers?

For example, it can be used for simulating the reactivity of monomers or the polymer substructures. In comparison with classical potentials, the speed or the number of atoms are more constrained but the emphasis is more placed on an accurate calculation.

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