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Obtaining the free energies of condensed phase chemical reactions remains computationally prohibitive for high-level quantum mechanical methods. We introduce a hierarchical machine learning framework that bridges this gap by distilling…

Chemical Physics · Physics 2026-03-19 Chenghan Li , Garnet Kin-Lic Chan

Computer-assisted synthesis planning aims to help chemists find better reaction pathways faster. Finding viable and short pathways from sugar molecules to value-added chemicals can be modeled as a retrosynthesis planning problem with a…

Other Computer Science · Computer Science 2019-11-19 Peihong Jiang , Hieu Doan , Sandeep Madireddy , Rajeev Surendran Assary , Prasanna Balaprakash

Polymerases are protein enzymes that move along nucleic acid chains and catalyze template-based polymerization reactions during gene transcription and replication. The polymerases also substantially improve transcription or replication…

Biological Physics · Physics 2017-01-19 Jin Yu

An overview of computational methods to describe high-dimensional potential energy surfaces suitable for atomistic simulations is given. Particular emphasis is put on accuracy, computability, transferability and extensibility of the methods…

Chemical Physics · Physics 2020-07-08 Oliver T. Unke , Debasish Koner , Sarbani Patra , Silvan Käser , Markus Meuwly

Computing reaction rates in biomolecular systems is a common goal of molecular dynamics simulations. The reactions considered often involve conformational changes in the molecule, either changes in the structure of a protein or the relative…

Dynamical Systems · Mathematics 2013-07-03 Eric Darve , Ernest Ryu

Accurate modelling of electrostatic interactions and charge transfer is fundamental to computational chemistry, yet most machine learning interatomic potentials (MLIPs) rely on local atomic descriptors that cannot capture long-range…

Predicting structural and energetic properties of a molecular system is one of the fundamental tasks in molecular simulations, and it has use cases in chemistry, biology, and medicine. In the past decade, the advent of machine learning…

Chemical Physics · Physics 2022-08-23 Sajjad Heydari , Stefano Raniolo , Lorenzo Livi , Vittorio Limongelli

Over the last century, a large number of physical and mathematical developments paired with rapidly advancing technology have allowed the field of quantum chemistry to advance dramatically. However, the lack of computationally efficient…

Quantum Physics · Physics 2011-03-08 James D. Whitfield , Jacob Biamonte , Alán Aspuru-Guzik

We show how a quantum computer can be employed to elucidate reaction mechanisms in complex chemical systems, using the open problem of biological nitrogen fixation in nitrogenase as an example. We discuss how quantum computers can augment…

Quantum Physics · Physics 2017-08-14 Markus Reiher , Nathan Wiebe , Krysta M Svore , Dave Wecker , Matthias Troyer

The study of chemical reactions in environments under nonequilibrium conditions has been of interest recently in a variety of contexts, including current-induced reactions in molecular junctions and scanning tunneling microscopy…

Chemical Physics · Physics 2022-07-27 Yaling Ke , Christoph Kaspar , André Erpenbeck , Uri Peskin , Michael Thoss

Free energies play a central role in characterising the behaviour of chemical systems and are among the most important quantities that can be calculated by molecular dynamics simulations. Solvation free energies in various organic solvents,…

Chemical Physics · Physics 2026-02-11 J. Harry Moore , Daniel J. Cole , Gabor Csanyi

Quantum chemical studies of reactivity involve calculations on a large number of molecular structures and comparison of their energies. Already the set-up of these calculations limits the scope of the results that one will obtain, because…

Computational Physics · Physics 2016-08-23 Alain C. Vaucher , Markus Reiher

Due to the intrinsic complexity and nonlinearity of chemical reactions, direct applications of traditional machine learning algorithms may face with many difficulties. In this study, through two concrete examples with biological background,…

Molecular Networks · Quantitative Biology 2020-06-02 Wuyue Yang , Liangrong Peng , Yi Zhu , Liu Hong

Foundational Machine Learning Potentials can resolve the accuracy and transferability limitations of classical force fields. They enable microscopic insights into material behavior through Molecular Dynamics simulations, which can crucially…

Computational Physics · Physics 2025-12-04 Paul Fuchs , Julija Zavadlav

Developing accurate and efficient coarse-grained representations of proteins is crucial for understanding their folding, function, and interactions over extended timescales. Our methodology involves simulating proteins with molecular…

Biomolecules · Quantitative Biology 2023-10-11 Carles Navarro , Maciej Majewski , Gianni de Fabritiis

A novel method combining the ensemble refinement by maximum entropy principle and the force field fitting approach is presented. Its formulation allows to continuously interpolate in between these two methods, which can thus be interpreted…

Chemical Physics · Physics 2024-01-30 Ivan Gilardoni , Thorben Fröhlking , Giovanni Bussi

Computational techniques are required for narrowing down the vast space of possibilities to plausible prebiotic scenarios, since precise information on the molecular composition, the dominant reaction chemistry, and the conditions for that…

Molecular Networks · Quantitative Biology 2018-02-07 Jakob L. Andersen , Christoph Flamm , Daniel Merkle , Peter F. Stadler

Large parallel gains in the development of both computational resources as well as sampling methods have now made it possible to simulate dissociation events in ligand-protein complexes with all--atom resolution. Such encouraging progress,…

Activated processes from chemical reactions up to conformational transitions of large biomolecules are hampered by barriers which are overcome only by the input of some free energy of activation. Hence, the characteristic and…

Statistical Mechanics · Physics 2011-12-21 Jürgen Schlitter

Chemical reaction network is an important method for modeling and exploring complex biological processes, bio-chemical interactions and the behavior of different dynamics in system biology. But, formulating such reaction kinetics takes…

Artificial Intelligence · Computer Science 2025-03-28 Sadikshya Gyawali , Ashwini Mandal , Manish Dahal , Manish Awale , Sanjay Rijal , Shital Adhikari , Vaghawan Ojha