Gaussian process regression for materials and molecules
VL Deringer, AP Bartók, N Bernstein… - Chemical …, 2021 - ACS Publications
We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …
methods in computational materials science and chemistry. The focus of the present review …
Integrating scientific knowledge with machine learning for engineering and environmental systems
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
[PDF][PDF] Integrating physics-based modeling with machine learning: A survey
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Deep learning in protein structural modeling and design
Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and
powerful computational resources, impacting many fields, including protein structural …
powerful computational resources, impacting many fields, including protein structural …
86 PFLOPS deep potential molecular dynamics simulation of 100 million atoms with ab initio accuracy
We present the GPU version of DeePMD-kit, which, upon training a deep neural network
model using ab initio data, can drive extremely large-scale molecular dynamics (MD) …
model using ab initio data, can drive extremely large-scale molecular dynamics (MD) …
A recipe for cracking the quantum scaling limit with machine learned electron densities
A long-standing goal of science is to accurately simulate large molecular systems using
quantum mechanics. The poor scaling of current quantum chemistry algorithms on classical …
quantum mechanics. The poor scaling of current quantum chemistry algorithms on classical …
DeePKS: A comprehensive data-driven approach toward chemically accurate density functional theory
We propose a general machine learning-based framework for building an accurate and
widely applicable energy functional within the framework of generalized Kohn–Sham …
widely applicable energy functional within the framework of generalized Kohn–Sham …
Integrating expert ODEs into neural ODEs: pharmacology and disease progression
Modeling a system's temporal behaviour in reaction to external stimuli is a fundamental
problem in many areas. Pure Machine Learning (ML) approaches often fail in the small …
problem in many areas. Pure Machine Learning (ML) approaches often fail in the small …
Machine learning and physics: A survey of integrated models
A Seyyedi, M Bohlouli, SN Oskoee - ACM Computing Surveys, 2023 - dl.acm.org
Predictive modeling of various systems around the world is extremely essential from the
physics and engineering perspectives. The recognition of different systems and the capacity …
physics and engineering perspectives. The recognition of different systems and the capacity …
Deep-learning electronic-structure calculation of magnetic superstructures
Ab initio studies of magnetic superstructures are indispensable to research on emergent
quantum materials, but are currently bottlenecked by the formidable computational cost …
quantum materials, but are currently bottlenecked by the formidable computational cost …