Towards a machine learned thermodynamics: exploration of free energy landscapes in molecular fluids, biological systems and for gas storage and separation in metal–organic frameworks

2021 ◽  
Author(s):  
Caroline Desgranges ◽  
Jerome Delhommelle
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Metal Organic Frameworks ◽  
Molecular Simulations ◽  
Gas Storage ◽  
Energy Landscapes ◽  
Molecular Fluids ◽  
Metal Organic ◽  
Free Energy Landscapes ◽  
Energy Surfaces

Combined machine learning-molecular simulations protocols for the prediction and exploration of free energy surfaces.

scholarly journals Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

2017 ◽  
Vol 114 (28) ◽  
pp. E5494-E5503 ◽  
Author(s):  
Eliodoro Chiavazzo ◽  
Roberto Covino ◽  
Ronald R. Coifman ◽  
C. William Gear ◽  
Anastasia S. Georgiou ◽  
...  
Keyword(s):  
Free Energy ◽  
Added Value ◽  
Coarse Grained ◽  
Atomistic Simulations ◽  
Computer Assisted ◽  
Energy Landscapes ◽  
Learning Techniques ◽  
Free Energy Landscapes ◽  
Low Dimensional ◽  
Energy Surfaces

We describe and implement a computer-assisted approach for accelerating the exploration of uncharted effective free-energy surfaces (FESs). More generally, the aim is the extraction of coarse-grained, macroscopic information from stochastic or atomistic simulations, such as molecular dynamics (MD). The approach functionally links the MD simulator with nonlinear manifold learning techniques. The added value comes from biasing the simulator toward unexplored phase-space regions by exploiting the smoothness of the gradually revealed intrinsic low-dimensional geometry of the FES.

scholarly journals Enhanced, targeted sampling of high-dimensional free-energy landscapes using variationally enhanced sampling, with an application to chignolin

2016 ◽  
Vol 113 (5) ◽  
pp. 1150-1155 ◽  
Author(s):  
Patrick Shaffer ◽  
Omar Valsson ◽  
Michele Parrinello
Keyword(s):  
Free Energy ◽  
Sampling Methods ◽  
Molecular Simulations ◽  
High Dimensionality ◽  
High Dimensional ◽  
Energy Landscapes ◽  
Enhanced Sampling ◽  
Collective Variables ◽  
Targeted Sampling ◽  
Free Energy Landscapes

The capabilities of molecular simulations have been greatly extended by a number of widely used enhanced sampling methods that facilitate escaping from metastable states and crossing large barriers. Despite these developments there are still many problems which remain out of reach for these methods which has led to a vigorous effort in this area. One of the most important problems that remains unsolved is sampling high-dimensional free-energy landscapes and systems that are not easily described by a small number of collective variables. In this work we demonstrate a new way to compute free-energy landscapes of high dimensionality based on the previously introduced variationally enhanced sampling, and we apply it to the miniprotein chignolin.

scholarly journals Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Matter ◽  
2021 ◽  
Author(s):  
Andrew S. Rosen ◽  
Shaelyn M. Iyer ◽  
Debmalya Ray ◽  
Zhenpeng Yao ◽  
Alán Aspuru-Guzik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantum Chemical ◽  
Metal Organic Frameworks ◽  
Chemical Properties ◽  
Metal Organic

Synthesis, designing strategies and photocatalytic charge dynamics of Metal-Organic Frameworks (MOFs): A catalyzed Photo-degradation approach towards Organic Dyes

2021 ◽  
Author(s):  
Ayushi Singh ◽  
Ashish Kumar Singh ◽  
Jian-Qiang Liu ◽  
Abhinav Kumar
Keyword(s):  
Small Molecule ◽  
Coordination Polymers ◽  
Organic Dyes ◽  
Metal Organic Frameworks ◽  
Gas Storage ◽  
New Variety ◽  
Photo Degradation ◽  
Charge Dynamics ◽  
Potential Applications ◽  
Metal Organic

Metal-organic frameworks (MOFs) or coordination polymers (CPs) are regarded as new variety of materials that find potential applications in plethora of areas such as gas/small molecule absorption/separation, gas storage, membranes...

scholarly journals Recent Bio-Advances in Metal-Organic Frameworks

Molecules ◽  
2020 ◽  
Vol 25 (6) ◽  
pp. 1291 ◽  
Author(s):  
Isobel Tibbetts ◽  
George Kostakis
Keyword(s):  
Drug Delivery ◽  
Inorganic Material ◽  
Metal Organic Frameworks ◽  
Gas Storage ◽  
Industrial Applications ◽  
Biologically Relevant ◽  
Bioinspired Materials ◽  
Synthesis Properties ◽  
Metal Organic

Metal-organic frameworks (MOFs) have found uses in adsorption, catalysis, gas storage and other industrial applications. Metal Biomolecule Frameworks (bioMOFs) represent an overlap between inorganic, material and medicinal sciences, utilising the porous frameworks for biologically relevant purposes. This review details advances in bioMOFs, looking at the synthesis, properties and applications of both bioinspired materials and MOFs used for bioapplications, such as drug delivery, imaging and catalysis, with a focus on examples from the last five years.

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