scholarly journals Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation

2019 ◽  
Author(s):  
WooSeok Jeong ◽  
Samuel J. Stoneburner ◽  
Daniel King ◽  
Ruye Li ◽  
Andrew Walker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chemical Bond ◽  
Density Functional ◽  
Large Scale ◽  
Black Box ◽  
Target System ◽  
Complete Active Space ◽  
Active Space ◽  
Bond Dissociation ◽  
Selection Of

<div>Predicting and understanding the chemical bond is one of the major challenges of computational quantum chemistry. Kohn−Sham density functional theory (KS-DFT) is the most common method, but approximate density functionals may not be able to describe systems where multiple electronic configurations are equally important. Multiconfigurational wave functions, on the other hand, can provide a detailed understanding of the electronic structure and chemical bond of such systems. In the complete-active-space self-consistent field (CASSCF) method one performs a full configuration interaction calculation in an active space consisting of active electrons and active orbitals. However, CASSCF and its variants require the selection of these active spaces. This choice is not black-box; it requires significant experience and testing by the user, and thus active space methods are not considered particularly user-friendly and are employed only by a minority of quantum chemists. Our goal is to popularize these methods by making it easier to make good active space choices. We present a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules. The protocol shows high prediction performance for a given target system as long as a properly correlated system is chosen for training. Good active spaces are correctly predicted with a considerably better success rate than random guess (larger than 80% precision for most systems studied). Our automated machine learning protocol shows that a “black-box” mode is possible for facilitating and accelerating the large-scale calculations on multireference systems where single-reference methods such as KS-DFT cannot be applied.</div>

scholarly journals Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation

2019 ◽  
Author(s):  
WooSeok Jeong ◽  
Samuel J. Stoneburner ◽  
Daniel King ◽  
Ruye Li ◽  
Andrew Walker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chemical Bond ◽  
Density Functional ◽  
Large Scale ◽  
Black Box ◽  
Target System ◽  
Complete Active Space ◽  
Active Space ◽  
Bond Dissociation ◽  
Selection Of

<div>Predicting and understanding the chemical bond is one of the major challenges of computational quantum chemistry. Kohn−Sham density functional theory (KS-DFT) is the most common method, but approximate density functionals may not be able to describe systems where multiple electronic configurations are equally important. Multiconfigurational wave functions, on the other hand, can provide a detailed understanding of the electronic structure and chemical bond of such systems. In the complete-active-space self-consistent field (CASSCF) method one performs a full configuration interaction calculation in an active space consisting of active electrons and active orbitals. However, CASSCF and its variants require the selection of these active spaces. This choice is not black-box; it requires significant experience and testing by the user, and thus active space methods are not considered particularly user-friendly and are employed only by a minority of quantum chemists. Our goal is to popularize these methods by making it easier to make good active space choices. We present a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules. The protocol shows high prediction performance for a given target system as long as a properly correlated system is chosen for training. Good active spaces are correctly predicted with a considerably better success rate than random guess (larger than 80% precision for most systems studied). Our automated machine learning protocol shows that a “black-box” mode is possible for facilitating and accelerating the large-scale calculations on multireference systems where single-reference methods such as KS-DFT cannot be applied.</div>

scholarly journals Multiconfiguration Pair-Density Functional Theory for Transition Metal Silicide Bond Dissociation Energies, Bond Lengths, and State Orderings

Molecules ◽  
2021 ◽  
Vol 26 (10) ◽  
pp. 2881
Author(s):  
Meagan S. Oakley ◽  
Laura Gagliardi ◽  
Donald G. Truhlar
Keyword(s):  
Wave Function ◽  
Transition Metal ◽  
Density Functional ◽  
Bond Dissociation Energies ◽  
Complete Active Space ◽  
Active Space ◽  
Bond Dissociation ◽  
Dissociation Energies ◽  
Pair Density ◽  
Self Consistent Field

Transition metal silicides are promising materials for improved electronic devices, and this motivates achieving a better understanding of transition metal bonds to silicon. Here we model the ground and excited state bond dissociations of VSi, NbSi, and TaSi using a complete active space (CAS) wave function and a separated-pair (SP) wave function combined with two post-self-consistent field techniques: complete active space with perturbation theory at second order and multiconfiguration pair-density functional theory. The SP approximation is a multiconfiguration self-consistent field method with a selection of configurations based on generalized valence bond theory without the perfect pairing approximation. For both CAS and SP, the active-space composition corresponds to the nominal correlated-participating-orbital scheme. The ground state and low-lying excited states are explored to predict the state ordering for each molecule, and potential energy curves are calculated for the ground state to compare to experiment. The experimental bond dissociation energies of the three diatomic molecules are predicted with eight on-top pair-density functionals with a typical error of 0.2 eV for a CAS wave function and a typical error of 0.3 eV for the SP approximation. We also provide a survey of the accuracy achieved by the SP and extended separated-pair approximations for a broader set of 25 transition metal–ligand bond dissociation energies.

scholarly journals Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation

2020 ◽  
Vol 16 (4) ◽  
pp. 2389-2399 ◽  
Author(s):  
WooSeok Jeong ◽  
Samuel J. Stoneburner ◽  
Daniel King ◽  
Ruye Li ◽  
Andrew Walker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chemical Bond ◽  
Active Space ◽  
Bond Dissociation ◽  
Selection For ◽  
Multireference Methods

scholarly journals Assessing Conformer Energies using Electronic Structure and Machine Learning Methods

2020 ◽  
Author(s):  
Dakota Folmsbee ◽  
Geoffrey Hutchison
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Density Functional ◽  
Large Scale ◽  
Single Point ◽  
Semiempirical Method ◽  
Coupled Cluster ◽  
Scale Evaluation ◽  
Machine Learning Methods ◽  
Electronic Structure Methods

We have performed a large-scale evaluation of current computational methods, including conventional small-molecule force fields, semiempirical, density functional, ab initio electronic structure methods, and current machine learning (ML) techniques to evaluate relative single-point energies. Using up to 10 local minima geometries across ~700 molecules, each optimized by B3LYP-D3BJ with single-point DLPNO-CCSD(T) triple-zeta energies, we consider over 6,500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find promise from current ML methods and recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies. The ANI family of ML methods shows promise, particularly the ANI-1ccx variant trained in part on coupled-cluster energies. Multiple methods suggest continued improvements should be expected in both performance and accuracy.

scholarly journals Assessing Conformer Energies using Electronic Structure and Machine Learning Methods

2020 ◽  
Author(s):  
Dakota Folmsbee ◽  
Geoffrey Hutchison
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Density Functional ◽  
Large Scale ◽  
Single Point ◽  
Semiempirical Method ◽  
Coupled Cluster ◽  
Scale Evaluation ◽  
Machine Learning Methods ◽  
Electronic Structure Methods

We have performed a large-scale evaluation of current computational methods, including conventional small-molecule force fields, semiempirical, density functional, ab initio electronic structure methods, and current machine learning (ML) techniques to evaluate relative single-point energies. Using up to 10 local minima geometries across ~700 molecules, each optimized by B3LYP-D3BJ with single-point DLPNO-CCSD(T) triple-zeta energies, we consider over 6,500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find promise from current ML methods and recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies. The ANI family of ML methods shows promise, particularly the ANI-1ccx variant trained in part on coupled-cluster energies. Multiple methods suggest continued improvements should be expected in both performance and accuracy.

Complete active space self‐consistent field and density functional study of FNO

1994 ◽  
Vol 100 (1) ◽  
pp. 459-463 ◽  
Author(s):  
Theodore S. Dibble ◽  
Joseph S. Francisco ◽  
Robert J. Deeth ◽  
Michael R. Hand ◽  
Ian H. Williams
Keyword(s):  
Density Functional ◽  
Functional Study ◽  
Density Functional Study ◽  
Complete Active Space ◽  
Active Space ◽  
Consistent Field ◽  
Self Consistent ◽  
Self Consistent Field

scholarly journals Building Machine Learning Force Fields of Proteins with Fragment-Based Approach and Transfer Learning

2021 ◽  
Author(s):  
Zheng Cheng ◽  
Jiahui Du ◽  
Lei Zhang ◽  
Jing Ma ◽  
Wei Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Force Fields ◽  
Density Functional Theory Calculations ◽  
Md Simulations ◽  
Target System ◽  
Functional Theory ◽  
Protein Functions ◽  
Data Library ◽  
Good Agreement

<p>Molecular dynamic (MD) simulation plays an essential role in understanding protein functions at atomic level. At present, MD simulations on proteins are mainly based on classical force fields. However, the accuracy of classical force fields for proteins is still insufficient for accurate descriptions of their structures and dynamical properties. Here we present a novel protocol to construct machine learning force field (MLFF) for a given protein with full quantum mechanics (QM) accuracy. In this protocol, the energy of the target system is obtained by fitting energies of its various subsystems constructed with the generalized energy-based fragmentation (GEBF) approach. To facilitate the construction of MLFF for various proteins, a protein’s data library is created to store all data of subsystems generated from trained proteins. With this protein’s data library, for a new protein only its subsystems with new topological types are required for the construction of the corresponding MLFF. This protocol is illustrated with two polypeptides, 4ZNN and 1XQ8 segment, as examples. The energies and forces predicted from this MLFF are in good agreement with those from density functional theory calculations, and dihedral angle distributions from GEBF-MLFF MD simulations can also well reproduce those from <i>ab initio</i> MD simulations. Therefore, this GEBF-ML protocol is expected to be an efficient and systematic way to build force fields for proteins and other biological systems with QM accuracy.<b></b></p>

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