How to train a neural network potential

The Journal of Chemical Physics, Journal Year: 2023, Volume and Issue: 159(12)

Published: Sept. 27, 2023

The introduction of modern Machine Learning Potentials (MLPs) has led to a paradigm change in the development potential energy surfaces for atomistic simulations. By providing efficient access energies and forces, they allow us perform large-scale simulations extended systems, which are not directly accessible by demanding first-principles methods. In these simulations, MLPs can reach accuracy electronic structure calculations, provided that have been properly trained validated using suitable set reference data. Due their highly flexible functional form, construction be done with great care. this Tutorial, we describe necessary key steps training reliable MLPs, from data generation via final validation. procedure, is illustrated example high-dimensional neural network potential, general applicable many types MLPs.

Language: Английский

---

Stability and lifetime of diffusion-trapped oxygen in oxide-derived copper CO2 reduction electrocatalysts

Nature Catalysis, Journal Year: 2024, Volume and Issue: 7(4), P. 401 - 411

Published: April 1, 2024

Abstract Oxide-derived Cu has an excellent ability to promote C–C coupling in the electrochemical carbon dioxide reduction reaction. However, these materials largely rearrange under reaction conditions; therefore, nature of active site remains controversial. Here we study process oxide-derived via large-scale molecular dynamics with a precise neural network potential trained on first-principles data and introducing experimental conditions. The oxygen concentration most stable increases increase pH, or specific surface area. In long experiments, catalyst would be fully reduced Cu, but removing all trapped takes considerable amount time. Although highly reconstructed provides various sites adsorb more strongly, atoms are not common This work insight into evolution catalysts residual during also deep understanding sites.

Language: Английский

---

Machine learned force-fields for an Ab-initio quality description of metal-organic frameworks

npj Computational Materials, Journal Year: 2024, Volume and Issue: 10(1)

Published: Jan. 20, 2024

Language: Английский

---

Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023

Chemical Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

We present a comprehensive analysis of the capabilities modern machine learning force fields to simulate long-term molecular dynamics at near-ambient conditions for molecules, molecule-surface interfaces, and materials within TEA Challenge 2023.

Language: Английский

---

Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles

npj Computational Materials, Journal Year: 2023, Volume and Issue: 9(1)

Published: Dec. 16, 2023

Language: Английский

---

Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials—A Review

Advanced Materials, Journal Year: 2023, Volume and Issue: 36(22)

Published: Aug. 28, 2023

Abstract The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental computational studies. advent machine learning interatomic potential (MLIP) addresses some the limitations associated empirical force fields, presenting a valuable avenue accurate simulations these surfaces/interfaces nanomaterials. Central to this approach is idea capturing relationship between system configuration energy, leveraging proficiency (ML) precisely approximate high‐dimensional functions. This review offers an in‐depth examination MLIP principles their execution elaborates on applications in realm surface interface systems. prevailing challenges faced by potent methodology are discussed.

Language: Английский

---

Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials’ Surfaces

ACS Applied Materials & Interfaces, Journal Year: 2024, Volume and Issue: unknown

Published: July 11, 2024

Machine learning interatomic potentials (MLIPs) are one of the main techniques in materials science toolbox, able to bridge

Language: Английский

---

Data Generation for Machine Learning Interatomic Potentials and Beyond

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(24), P. 13681 - 13714

Published: Nov. 21, 2024

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides ML-based interatomic potentials have paved the way accurate modeling diverse chemical structural at atomic level. key determinant defining MLIP reliability remains quality training data. A paramount challenge lies constructing sets that capture specific domains vast space. This Review navigates intricate landscape essential components integrity data ensure extensibility transferability resulting models. We delve into details active learning, discussing its various facets implementations. outline different types uncertainty quantification applied to atomistic acquisition correlations between estimated true error. role samplers generating informative structures highlighted. Furthermore, we discuss via modified surrogate potential energy surfaces as innovative approach diversify also provides a list publicly available cover

Language: Английский

---

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Advanced Materials, Journal Year: 2024, Volume and Issue: 36(30)

Published: May 25, 2024

Abstract Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving Schrödinger equations increasing cost with size molecular system. In response, there has been a surge interest in leveraging artificial intelligence (AI) machine learning (ML) techniques silico experiments. Integrating AI ML into increases scalability speed exploration space. remain, particularly regarding reproducibility transferability models. This review highlights evolution from, complementing, or replacing energy property predictions. Starting from models trained entirely on numerical data, journey set forth toward ideal model incorporating physical laws quantum mechanics. paper also reviews existing their intertwining, outlines roadmap future research, identifies areas improvement innovation. Ultimately, goal develop architectures capable accurate transferable solutions equation, thereby revolutionizing experiments within materials science.

Language: Английский

---

A review of displacement cascade simulations using molecular dynamics emphasizing interatomic potentials for TPBAR components

npj Materials Degradation, Journal Year: 2025, Volume and Issue: 9(1)

Published: Jan. 2, 2025

Abstract This review explores molecular dynamics simulations for studying radiation damage in Tritium Producing Burnable Absorber Rod (TPBAR) materials, emphasizing the role of interatomic potentials displacement cascades. Recent machine learning (MLPs), trained on quantum data, enhance prediction accuracy over traditional models like EAM. We highlight temperature, PKA energy, and composition effects evolution TPBAR components, recommending suitable discussing advancements materials extreme environments.

Language: Английский

---