☆ 4.7 Article

Ab initio machine learning of phase space averages

JOURNAL OF CHEMICAL PHYSICS (2022)

Related references

Note: Only part of the references are listed.

Article Chemistry, Medicinal

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy

Yunsie Chung et al.

Summary: We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) for predicting solute parameters, as well as a machine learning model (DirectML) for predicting solvation free energy and enthalpy at 298 K. The DirectML model outperforms the other two models in terms of accuracy and can provide comparable results to advanced quantum chemistry methods. However, all three models are useful for different purposes and when combined, they can provide even more accurate predictions. Our compiled databases and prediction models are made publicly accessible through various tools and software packages.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2022)

Add to Collection

Article Chemistry, Physical

Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments

Viktor Zaverkin et al.

Summary: The study introduces a machine learning method based on the Gaussian moment neural network for modeling molecular tensorial quantities, specifically the magnetic anisotropy tensor, achieving high accuracy and generalization capability. By combining machine-learned interatomic potential energies, the approach can be used to investigate the dynamic behavior of magnetic anisotropy tensors and offer unique insights into spin-phonon relaxation.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2022)

Add to Collection

Article Multidisciplinary Sciences

Accurate determination of solvation free energies of neutral organic compounds from first principles

Leonid Pereyaslavets et al.

Summary: The main goal of molecular simulation is to accurately predict experimental observables of molecular systems, as well as to devise models for arbitrary neutral organic molecules. Researchers have successfully computed the solvation free energies of a diverse set of organic compounds using a polarizable force field fitted entirely to ab initio calculations, achieving high predictive accuracy.

NATURE COMMUNICATIONS (2022)

Add to Collection

Article Chemistry, Medicinal

Accurate Prediction of Aqueous Free Solvation Energies Using 3D Atomic Feature-Based Graph Neural Network with Transfer Learning

Dongdong Zhang et al.

Summary: In this study, a large and diverse calculated data set Frag20-Aqsol-100K of aqueous solvation free energy is built using electronic structure calculations with continuum solvent models. A novel 3D atomic feature-based GNN model is developed, and a transfer learning strategy is employed to achieve state-of-the-art prediction on the FreeSolv data set. The results indicate that integrating molecular modeling and DL provides a promising strategy for developing robust prediction models in molecular science.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2022)

Add to Collection

Article Chemistry, Physical

Unified theory of atom-centered representations and message-passing machine-learning schemes

Jigyasa Nigam et al.

Summary: Data-driven schemes provide a concise and effective description of the arrangement of atomic constituents in molecular and crystal structures. Atom-centered density correlations (ACDC) are used as a basis to expand targets in a body-ordered and symmetry-adapted manner. The ACDC framework can be extended to include multi-centered information and provide a linear basis for regressing symmetric functions of atomic coordinates, making it suitable for both atom-centered and message-passing machine-learning schemes.

JOURNAL OF CHEMICAL PHYSICS (2022)

Add to Collection

Article Chemistry, Physical

Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning

Marcel F. Langer et al.

Summary: Computational study of molecules and materials from first principles is essential in physics, chemistry, and materials science. Machine learning can significantly reduce the cost of simulations by interpolating between reference simulations. This article comprehensively reviews and discusses different representations and their relationships, and compares the energy predictions of selected state-of-the-art representations in various numerical experiments.

NPJ COMPUTATIONAL MATERIALS (2022)

Add to Collection

Article Multidisciplinary Sciences

Rapid discovery of stable materials by coordinate-free coarse graining

Rhys E. A. Goodall et al.

Summary: A fundamental challenge in materials science is to understand the relationship between stoichiometry, stability, structure, and property. Recent advances in machine learning have shown that it can be used to accurately predict the stability and functional properties of materials. This study solves the bottleneck of crystal structure identification for previously unidentified materials by coarse-graining the search space of atomic coordinates. The use of Wyckoff representations as inputs to a machine learning model achieves high precision in finding unknown theoretically stable materials.

SCIENCE ADVANCES (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

Generating stable molecules using imitation and reinforcement learning

Soren Ager Meldgaard et al.

Summary: This study proposes a reinforcement learning approach for generating molecules in chemical space and predicting their stability using quantum chemistry. By combining imitation learning and reinforcement learning, the sample efficiency is improved, and low energy molecules are generated under different stoichiometries conditions.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2022)

Add to Collection

Review Chemistry, Multidisciplinary

Machine Learning for Electronically Excited States of Molecules

Julia Westermayr et al.

Summary: This review focuses on how machine learning is used to speed up excited-state simulations and advance the research field. Applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, and others.

CHEMICAL REVIEWS (2021)

Add to Collection

Article Multidisciplinary Sciences

Quantum-mechanical exploration of the phase diagram of water

Aleks Reinhardt et al.

Summary: By combining machine learning methods and advanced free-energy techniques, this study successfully predicts and calculates the phase diagram of water at three levels of hybrid density-functional theory, showing good agreement with experimental results, especially at pressures below 8000 bar. The research demonstrates the completeness of the experimental water phase diagram in the region considered, provides a feasibility of predicting a polymorphic system's phase diagram from first principles, and tests the limits of quantum-mechanical calculations through a thermodynamic approach.

NATURE COMMUNICATIONS (2021)

Add to Collection

Review Chemistry, Multidisciplinary

General-Purpose Machine Learning Potentials Capturing Nonlocal Charge Transfer

Tsz Wai Ko et al.

Summary: The development of first-principles-quality machine learning potentials has gone through several generations, from being applicable only to low-dimensional systems to now needing to include nonlocal phenomena for greater accuracy in the fourth generation.

ACCOUNTS OF CHEMICAL RESEARCH (2021)

Add to Collection

Article Engineering, Environmental

Transfer learning for solvation free energies: From quantum chemistry to experiments

Florence H. Vermeire et al.

Summary: In this work, a transfer learning approach combining quantum calculations and experimental measurements is proposed for the prediction of solvation free energies, showing significant advantages for new data, small datasets and out-of-sample predictions. The pre-trained models based on quantum calculations demonstrate improved out-of-sample performance, with a mean absolute error of 0.21 kcal/mol achieved on random test splits.

CHEMICAL ENGINEERING JOURNAL (2021)

Add to Collection

Article Chemistry, Physical

High-dimensional neural network potentials for magnetic systems using spin-dependent atom-centered symmetry functions

Marco Eckhoff et al.

Summary: A new type of descriptor taking into account the atomic spin degrees of freedom has been proposed for constructing accurate potential energy surfaces of multicomponent systems, describing multiple collinear magnetic states. This method demonstrates excellent performance in predicting magnetically distorted structures in manganese oxide MnO.

NPJ COMPUTATIONAL MATERIALS (2021)

Add to Collection

Article Chemistry, Physical

Machine learning of free energies in chemical compound space using ensemble representations: Reaching experimental uncertainty for solvation

Jan Weinreich et al.

Summary: The Free energy Machine Learning (FML) model accurately predicts hydration free energies for chemical compounds, with prediction errors decreasing systematically with training set size and reaching experimental uncertainty levels after training on 80% of the FreeSolv database. FML's accuracy is comparable to state-of-the-art physics-based approaches, and it requires molecular dynamics runs to generate input representations for new query compounds. The model showcases usefulness in analyzing solvation free energies and identifying structure-property relationships for a large number of organic molecules.

JOURNAL OF CHEMICAL PHYSICS (2021)

Add to Collection

Editorial Material Chemistry, Physical

Machine learning meets chemical physics

Michele Ceriotti et al.

Summary: In recent years, the use of statistical learning techniques in solving chemical problems has gained significant momentum, especially in the field of physical chemistry. The balance between empiricism and physics-based theory, traditionally leaning towards the latter, has been highlighted. In this special topic on Machine Learning Meets Chemical Physics, a brief rationale and overview of the covered topics are provided, followed by some general remarks.

JOURNAL OF CHEMICAL PHYSICS (2021)

Add to Collection

Review Chemistry, Multidisciplinary

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

John A. Keith et al.

Summary: The article discusses the potential impact of machine learning models on chemical sciences and emphasizes the importance of collaboration between expertise in computer science and physical sciences. It provides concise tutorials of computational chemistry and machine learning methods, and demonstrates how they can be used together to provide insightful predictions.

CHEMICAL REVIEWS (2021)

Add to Collection

Review Chemistry, Multidisciplinary

Ab Initio Machine Learning in Chemical Compound Space

Bing Huang et al.

Summary: Chemical compound space (CCS) is vast and exploring it using modern machine learning techniques based on quantum mechanics principles can improve computational efficiency while maintaining predictive power. These methods have potential applications in discovering novel molecules or materials with desirable properties.

CHEMICAL REVIEWS (2021)

Add to Collection

Article Chemistry, Physical

Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space

Stefan Heinen et al.

Summary: The interplay of kinetics and thermodynamics governs reactive processes, with a newly introduced R2B machine learning model capable of rapidly and accurately inferring activation energies and transition state geometries, enabling experimental reaction design and providing new possibilities for synthesis efforts.

JOURNAL OF CHEMICAL PHYSICS (2021)

Add to Collection

Article Chemistry, Physical

Perspective on integrating machine learning into computational chemistry and materials science

Julia Westermayr et al.

Summary: Machine learning methods are widely used in electronic structure theory and molecular simulation, particularly in constructing high-dimensional interatomic potentials. These methods can effectively represent and predict quantum mechanical properties, impacting the prediction workflows for structure, dynamics, and spectroscopy.

JOURNAL OF CHEMICAL PHYSICS (2021)

Add to Collection

Article Multidisciplinary Sciences

Improved prediction of solvation free energies by machine-learning polarizable continuum solvation model

Amin Alibakhshi et al.

Summary: Accurate theoretical evaluation of solvation free energy is challenging. Here the authors introduce a machine-learning based polarizable continuum solvation approach to improve the accuracy of widely accepted continuum solvation models by almost one order of magnitude without additional computational costs.

NATURE COMMUNICATIONS (2021)

Add to Collection

Article Multidisciplinary Sciences

Machine learning based energy-free structure predictions of molecules, transition states, and solids

Dominik Lemm et al.

Summary: The authors introduce a kernel-based machine learning model for accurate computational prediction of atomistic structure across different system classes. By exploiting implicit correlations in training data sets, the Graph-To-Structure (G2S) model generalizes across compound space to infer interatomic distances and directly reconstruct coordinates, bypassing traditional energy optimization methods. The numerical evidence shows promising results in predicting 3D atomic coordinates for various molecules and solids, with mean absolute interatomic distance prediction errors below 0.2 A for a relatively small training set size.

NATURE COMMUNICATIONS (2021)

Add to Collection

Article Chemistry, Multidisciplinary

MLSolvA: solvation free energy prediction from pairwise atomistic interactions by machine learning

Hyuntae Lim et al.

Summary: Recent advances in machine learning have allowed for the development of diverse structure-property relationship models for crucial chemical properties, including the solvation free energy. The novel ML-based solvation model introduced in this study has a simple architecture and achieves outstanding performance and transferability for enlarging training data. The analysis of the interaction map shows that the model has significant potential for producing group contributions on the solvation energy, providing both predictions of target properties and detailed physicochemical insights.

JOURNAL OF CHEMINFORMATICS (2021)

Add to Collection

Review Chemistry, Multidisciplinary

Molecular excited states through a machine learning lens

Pavlo O. Dral et al.

Summary: This review assesses the application of machine learning in molecular excited-state simulations and highlights key issues for the future, demonstrating the potential of machine learning in predicting molecular properties, improving quantum mechanical methods, and searching for new materials.

NATURE REVIEWS CHEMISTRY (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Improved description of atomic environments using low-cost polynomial functions with compact support

Martin P. Bircher et al.

Summary: The paper introduces a new class of atom-centred symmetry functions (PSF) based on polynomials with compact support, which provides the flexibility to choose both the angular and radial coverage domains. The accuracy of PSFs is shown to be on par or better than conventional atom-centred SFs, particularly improving prediction accuracy for organic molecules in the gaseous and liquid phase. Calculation of PSFs does not require exponentials and their efficient implementation enables significant speedups compared to other state-of-the-art SFs, simplifying their use in new programs and platforms.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Revving up 13C NMR shielding predictions across chemical space: benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules

Amit Gupta et al.

Summary: This study presents a machine learning strategy and accurate reference dataset for accelerated and accurate screening of nuclear magnetic resonance spectra. By creating the QM9-NMR dataset and using kernel-ridge regression models, isotropic shielding was successfully predicted, and validation was performed on non-trivial benchmark sets.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

An assessment of the structural resolution of various fingerprints commonly used in machine learning

Behnam Parsaeifard et al.

Summary: The study compares the performance of different atomic environment fingerprints and their ability to resolve differences in local environments, while also examining the related atomic movements and fingerprint invariance.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2021)

Add to Collection

Article Chemistry, Physical

FCHL revisited: Faster and more accurate quantum machine learning

Anders S. Christensen et al.

JOURNAL OF CHEMICAL PHYSICS (2020)

Add to Collection

Article Engineering, Chemical

Temperature-dependent vapor-liquid equilibria and solvation free energy estimation from minimal data

Yunsie Chung et al.

AICHE JOURNAL (2020)

Add to Collection

Article Chemistry, Medicinal

Hybrid Alchemical Free Energy/Machine-Learning Methodology for the Computation of Hydration Free Energies

Jenke Scheen et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)

Add to Collection

Article Chemistry, Medicinal

Combining Molecular Dynamics and Machine Learning to Predict Self-Solvation Free Energies and Limiting Activity Coefficients

Julia Gebhardt et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)

Add to Collection

Review Chemistry, Multidisciplinary

Exploring chemical compound space with quantum-based machine learning

O. Anatole von Lilienfeld et al.

NATURE REVIEWS CHEMISTRY (2020)

Add to Collection

Article Chemistry, Physical

Predicting oxidation and spin states by high-dimensional neural networks: Applications to lithium manganese oxide spinels

Marco Eckhoff et al.

JOURNAL OF CHEMICAL PHYSICS (2020)

Add to Collection

Article Chemistry, Physical

Evaluation of an Efficient 3D-RISM-SCF Implementation as a Tool for Computational Spectroscopy in Solution

Marc Reimann et al.

JOURNAL OF PHYSICAL CHEMISTRY A (2020)

Add to Collection

Article Chemistry, Multidisciplinary

Quantum machine learning using atom-in-molecule-based fragments selected on the fly

Bing Huang et al.

NATURE CHEMISTRY (2020)

Add to Collection

Article Physics, Multidisciplinary

Incompleteness of Atomic Structure Representations

Sergey N. Pozdnyakov et al.

PHYSICAL REVIEW LETTERS (2020)

Add to Collection

Editorial Material Multidisciplinary Sciences

Machine learning for chemical discovery

Alexandre Tkatchenko

NATURE COMMUNICATIONS (2020)

Add to Collection

Article Multidisciplinary Sciences

Predicting materials properties without crystal structure: deep representation learning from stoichiometry

Rhys E. A. Goodall et al.

NATURE COMMUNICATIONS (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation

Mario Krenn et al.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Thousands of reactants and transition states for competing E2 and SN2 reactions

Guido Falk Von Rudorff et al.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2020)

Add to Collection

Article Chemistry, Physical

Exploration of Chemical Compound, Conformer, and Reaction Space with Meta-Dynamics Simulations Based on Tight-Binding Quantum Chemical Calculations

Stefan Grimme

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)

Add to Collection

Article Chemistry, Physical

Performance of 3D-RISM-KH in Predicting Hydration Free Energy: Effect of Solute Parameters

Dipankar Roy et al.

JOURNAL OF PHYSICAL CHEMISTRY A (2019)

Add to Collection

Article Chemistry, Multidisciplinary

Bayesian optimization for conformer generation

Lucian Chan et al.

JOURNAL OF CHEMINFORMATICS (2019)

Add to Collection

Article Chemistry, Multidisciplinary

Fast, efficient fragment-based coordinate generation for Open Babel

Naruki Yoshikawa et al.

JOURNAL OF CHEMINFORMATICS (2019)

Add to Collection

Article Multidisciplinary Sciences

Molecular Geometry Prediction using a Deep Generative Graph Neural Network

Elman Mansimov et al.

SCIENTIFIC REPORTS (2019)

Add to Collection

Article Materials Science, Multidisciplinary

On-the-fly machine learning force field generation: Application to melting points

Ryosuke Jinnouchi et al.

PHYSICAL REVIEW B (2019)

Add to Collection

Article Chemistry, Multidisciplinary

PYSCF: the Python-based simulations of chemistry framework

Qiming Sun et al.

WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE (2018)

Add to Collection

Article Chemistry, Physical

SchNet - A deep learning architecture for molecules and materials

K. T. Schuett et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

Add to Collection

Article Chemistry, Physical

Alchemical and structural distribution based representation for universal quantum machine learning

Felix A. Faber et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

Add to Collection

Article Chemistry, Physical

Predicting ionic liquid melting points using machine learning

Vishwesh Venkatraman et al.

JOURNAL OF MOLECULAR LIQUIDS (2018)

Add to Collection

Article Chemistry, Multidisciplinary

OPERA models for predicting physicochemical properties and environmental fate endpoints

Kamel Mansouri et al.

JOURNAL OF CHEMINFORMATICS (2018)

Add to Collection

Review Thermodynamics

Approaches for Calculating Solvation Free Energies and Enthalpies Demonstrated with an Update of the FreeSolv Database

Guilherme Duarte Ramos Matos et al.

JOURNAL OF CHEMICAL AND ENGINEERING DATA (2017)

Add to Collection

Article Chemistry, Medicinal

Molecular Dynamics Fingerprints (MDFP): Machine Learning from MD Data To Predict Free-Energy Differences

Sereina Riniker

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2017)

Add to Collection

Article Chemistry, Physical

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

Felix A. Faber et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2017)

Add to Collection

Article Biochemical Research Methods

OpenMM 7: Rapid development of high performance algorithms for molecular dynamics

Peter Eastman et al.

PLOS COMPUTATIONAL BIOLOGY (2017)

Add to Collection

Article Multidisciplinary Sciences

Machine learning of accurate energy-conserving molecular force fields

Stefan Chmiela et al.

SCIENCE ADVANCES (2017)

Add to Collection

Review Chemistry, Multidisciplinary

Coarse-Grained Protein Models and Their Applications

Sebastian Kmiecik et al.

CHEMICAL REVIEWS (2016)

Add to Collection

Article Chemistry, Physical

Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity

Bing Huang et al.

JOURNAL OF CHEMICAL PHYSICS (2016)

Add to Collection

Article Chemistry, Physical

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

O. Anatole von Lilienfeld et al.

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)

Add to Collection

Article Chemistry, Medicinal

Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation

Sereina Riniker et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2015)

Add to Collection

Article Chemistry, Physical

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

Raghunathan Ramakrishnan et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2015)

Add to Collection

Article Chemistry, Physical

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

Katja Hansen et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2015)

Add to Collection

Article Biochemistry & Molecular Biology

A unified coarse-grained model of biological macromolecules based on mean-field multipole-multipole interactions

Adam Liwo et al.

JOURNAL OF MOLECULAR MODELING (2014)

Add to Collection

Article Chemistry, Physical

Generalized Born Solvation Model SM12

Aleksandr V. Marenich et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)

Add to Collection

Article Chemistry, Physical

Fast Domain Decomposition Algorithm for Continuum Solvation Models: Energy and First Derivatives

Filippo Lipparini et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)

Add to Collection

Article Chemistry, Medicinal

Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17

Lars Ruddigkeit et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2012)

Add to Collection

Article Multidisciplinary Sciences

Decoding the energy landscape: extracting structure, dynamics and thermodynamics

David J. Wales

PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2012)

Add to Collection

Article Chemistry, Physical

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

Joerg Behler

JOURNAL OF CHEMICAL PHYSICS (2011)

Add to Collection

Article Chemistry, Multidisciplinary

Confab - Systematic generation of diverse low-energy conformers

Noel M. O'Boyle et al.

JOURNAL OF CHEMINFORMATICS (2011)

Add to Collection

Article Chemistry, Medicinal

Conformer Generation with OMEGA: Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database

Paul C. D. Hawkins et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2010)

Add to Collection

Article Chemistry, Medicinal

Extended-Connectivity Fingerprints

David Rogers et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2010)

Add to Collection

Article Chemistry, Physical

Property-optimized Gaussian basis sets for molecular response calculations

Dmitrij Rappoport et al.

JOURNAL OF CHEMICAL PHYSICS (2010)

Add to Collection

Article Chemistry, Multidisciplinary

Molecular Simulation of ab Initio Protein Folding for a Millisecond Folder NTL9(1-39)

Vincent A. Voelz et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2010)

Add to Collection

Article Biochemistry & Molecular Biology

Frog2: Efficient 3D conformation ensemble generator for small compounds

Maria A. Miteva et al.

NUCLEIC ACIDS RESEARCH (2010)

Add to Collection

Article Chemistry, Physical

DL-FIND: An Open-Source Geometry Optimizer for Atomistic Simulations

Johannes Kaestner et al.

JOURNAL OF PHYSICAL CHEMISTRY A (2009)

Add to Collection

Review Chemistry, Physical

Universal Solvation Model Based on Solute Electron Density and on a Continuum Model of the Solvent Defined by the Bulk Dielectric Constant and Atomic Surface Tensions

Aleksandr V. Marenich et al.

JOURNAL OF PHYSICAL CHEMISTRY B (2009)

Add to Collection

Article Computer Science, Hardware & Architecture

Anton, a special-purpose machine for molecular dynamics simulation

David E. Shaw et al.

COMMUNICATIONS OF THE ACM (2008)

Add to Collection

Article Chemistry, Physical

The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals

Yan Zhao et al.

THEORETICAL CHEMISTRY ACCOUNTS (2008)

Add to Collection

Article Chemistry, Medicinal

Generating conformer ensembles using a multiobjective genetic algorithm

Mikko J. Vainio et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2007)

Add to Collection

Article Biochemical Research Methods

Automatic atom type and bond type perception in molecular mechanical calculations

Junmei Wang et al.

JOURNAL OF MOLECULAR GRAPHICS & MODELLING (2006)

Add to Collection

Article Chemistry, Physical

Efficient and precise solvation free energies via alchemical adiabatic molecular dynamics

Jerry B. Abrams et al.

JOURNAL OF CHEMICAL PHYSICS (2006)

Add to Collection

Article Chemistry, Physical

Accurate Coulomb-fitting basis sets for H to Rn

F Weigend

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2006)

Add to Collection

Review Chemistry, Multidisciplinary

The GROMOS software for biomolecular simulation:: GROMOS05

M Christen et al.

JOURNAL OF COMPUTATIONAL CHEMISTRY (2005)

Add to Collection

Article Chemistry, Physical

Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy

F Weigend et al.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2005)

Add to Collection

Article Chemistry, Multidisciplinary

Development and testing of a general amber force field

JM Wang et al.

JOURNAL OF COMPUTATIONAL CHEMISTRY (2004)

Add to Collection

Article Physics, Multidisciplinary

Learn on the fly:: A hybrid classical and quantum-mechanical molecular dynamics simulation -: art. no. 175503

G Csányi et al.

PHYSICAL REVIEW LETTERS (2004)

Add to Collection

Article Chemistry, Multidisciplinary

A biomolecular force field based on the free enthalpy of hydration and solvation: The GROMOS force-field parameter sets 53A5 and 53A6

C Oostenbrink et al.

JOURNAL OF COMPUTATIONAL CHEMISTRY (2004)

Add to Collection

Article Biochemistry & Molecular Biology

Exploring protein native states and large-scale conformational changes with a modified generalized born model

A Onufriev et al.

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS (2004)

Add to Collection

Review Biochemistry & Molecular Biology

The Poisson-Boltzmann equation for biomolecular electrostatics: a tool for structural biology

F Fogolari et al.

JOURNAL OF MOLECULAR RECOGNITION (2002)

Add to Collection

Article Chemistry, Physical

Polarizable continuum model (PCM) calculations of solvent effects on optical rotations of chiral molecules

B Mennucci et al.

JOURNAL OF PHYSICAL CHEMISTRY A (2002)

Add to Collection

Article Engineering, Chemical

Fast solvent screening via quantum chemistry: COSMO-RS approach

F Eckert et al.

AICHE JOURNAL (2002)

Add to Collection

Article Chemistry, Physical

ReaxFF: A reactive force field for hydrocarbons

ACT van Duin et al.

JOURNAL OF PHYSICAL CHEMISTRY A (2001)

Add to Collection

© Peeref 2019-2024. All rights reserved.