Science‐Driven Atomistic Machine Learning

to related objects

Abstract: Machine learning (ML) algorithms are currently emerging as powerful tools in all areas of science. Conventionally, ML is understood as a fundamentally data‐driven endeavour. Unfortunately, large well‐curated databases are sparse in chemistry. In this contribution, I therefore review science‐driven ML approaches which do not rely on “big data”, focusing on the atomistic modelling of materials and molecules. In this context, the term science‐driven refers to approaches that begin with a scientific question and then ask what training data and model design choices are appropriate. As key features of science‐driven ML, the automated and purpose‐driven collection of data and the use of chemical and physical priors to achieve high data‐efficiency are discussed. Furthermore, the importance of appropriate model evaluation and error estimation is emphasized.

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: Science‐Driven Atomistic Machine Learning ; day:13 ; month:04 ; year:2023 ; extent:15
Angewandte Chemie ; (13.04.2023) (gesamt 15)

Creator: Margraf, Johannes T.

DOI: 10.1002/ange.202219170

URN: urn:nbn:de:101:1-2023041315341635638348

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:50 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Margraf, Johannes T.

Other Objects (12)

Science‐Driven Atomistic Machine Learning

Science‐Driven Atomistic Machine Learning

Machine learning for atomistic modeling: representations and thermal transport

Machine learning for atomistic modeling: representations and thermal transport

MLatom 2: An Integrative Platform for Atomistic Machine Learning

MLatom 2: An Integrative Platform for Atomistic Machine Learning

Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning

Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning

Machine learning driven argument mining

Machine learning driven argument mining

Machine learning and atomistic origin of high dielectric permittivity in oxides

Machine learning and atomistic origin of high dielectric permittivity in oxides

Machine learning of correlated dihedral potentials for atomistic molecular force fields

Machine learning of correlated dihedral potentials for atomistic molecular force fields

De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning

De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications

Machine-Learning-Driven Haptic Sensor Design

Machine-Learning-Driven Haptic Sensor Design

Automated Analysis of Continuum Fields from Atomistic Simulations Using Statistical Machine Learning

Automated Analysis of Continuum Fields from Atomistic Simulations Using Statistical Machine Learning

Universal machine learning for the response of atomistic systems to external fields

Universal machine learning for the response of atomistic systems to external fields

Science‐Driven Atomistic Machine Learning

Science‐Driven Atomistic Machine Learning

Machine learning for atomistic modeling: representations and thermal transport

Machine learning for atomistic modeling: representations and thermal transport

MLatom 2: An Integrative Platform for Atomistic Machine Learning

MLatom 2: An Integrative Platform for Atomistic Machine Learning

Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning

Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning

Machine learning driven argument mining

Machine learning driven argument mining

Machine learning and atomistic origin of high dielectric permittivity in oxides

Machine learning and atomistic origin of high dielectric permittivity in oxides

Machine learning of correlated dihedral potentials for atomistic molecular force fields

Machine learning of correlated dihedral potentials for atomistic molecular force fields

De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning

De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications

Machine-Learning-Driven Haptic Sensor Design

Machine-Learning-Driven Haptic Sensor Design

Automated Analysis of Continuum Fields from Atomistic Simulations Using Statistical Machine Learning

Automated Analysis of Continuum Fields from Atomistic Simulations Using Statistical Machine Learning

Universal machine learning for the response of atomistic systems to external fields

Universal machine learning for the response of atomistic systems to external fields

Science‐Driven Atomistic Machine Learning

Science‐Driven Atomistic Machine Learning

Machine learning for atomistic modeling: representations and thermal transport

Machine learning for atomistic modeling: representations and thermal transport

MLatom 2: An Integrative Platform for Atomistic Machine Learning

MLatom 2: An Integrative Platform for Atomistic Machine Learning

Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning

Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning

Machine learning driven argument mining

Machine learning driven argument mining

Machine learning and atomistic origin of high dielectric permittivity in oxides

Machine learning and atomistic origin of high dielectric permittivity in oxides

Machine learning of correlated dihedral potentials for atomistic molecular force fields

Machine learning of correlated dihedral potentials for atomistic molecular force fields

De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning

De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications

Machine-Learning-Driven Haptic Sensor Design

Machine-Learning-Driven Haptic Sensor Design

Automated Analysis of Continuum Fields from Atomistic Simulations Using Statistical Machine Learning

Automated Analysis of Continuum Fields from Atomistic Simulations Using Statistical Machine Learning

Universal machine learning for the response of atomistic systems to external fields

Universal machine learning for the response of atomistic systems to external fields

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.