Machine learning and the physical sciences

Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. We review in a selective way the recent research on the interface between machine learning and physical sciences. This includes conceptual developments in machine learning (ML) motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross-fertilization between the two fields. After giving basic notion of machine learning methods and principles, we describe examples of how statistical physics is used to understand methods in ML. We then move to describe applications of ML methods in particle physics and cosmology, quantum many body physics, quantum computing, and chemical and material physics. We also highlight research and development into novel computing architectures aimed at accelerating ML. In each of the sections we describe recent successes as well as domain-specific methodology and challenges

Endpoint-restricted adiabatic free energy dynamics approach for the exploration of biomolecular conformational equilibria.

A method for calculating the free energy difference between two structurally defined conformational states of a chemical system is developed. A path is defined using a previously reported collective variable that interpolates between two or more conformations, and a restraint is introduced in order to keep the system close to the path. The evolution of the system along the path, which typically presents a high free energy barrier, is generated using enhanced sampling schemes. Although the formulation of the method in terms of a path is quite general, an important advance in this work is the demonstration that prior knowledge of the path is, in fact, not needed and that the free energy difference can be obtained using a simplified definition of the path collective variable that <i>only</i> involves the endpoints. We first validate this method on cyclohexane isomerization. The method is then tested for an extensive conformational change in a realistic molecular system by calculating the free energy difference between the <i>α</i> -helix and <i>β</i> -hairpin conformations of deca-alanine in solution. Finally, the method is applied to a biologically relevant system to calculate the free energy difference of an observed and a hypothetical conformation of an antigenic peptide bound to a major histocompatibility complex

Why Are Some Crystals Straight?

Copyright © 2020 American Chemical Society. More than one-quarter of molecular crystals that are able to be melted can be made to grow in the form of twisted lamellae or fibers. The mechanisms leading to such unusual crystal morphologies lacking long-range translational symmetry on the mesoscale are poorly understood. Benzil (C6H5C(O)-C(O)C6H5) is one such crystal. Here, we calculate the morphology of rod-shaped benzil nanocrystals and other related structures. The ground states of these ensembles were twisted by 0.05-0.75°/Å for rods with cross sections of 50-10 nm2, respectively; the degree of twisting decreased inversely proportional to the crystal cross-sectional area. In the aggregate, our computational studies, combined with earlier observations by light microscopy, suggest that in some cases very small crystals acquire 3D translational periodicity only after reaching a certain size. Twisting is accompanied by conformational changes of molecules on the {101¯ 0} surfaces of the six-sided rods, although it is not easily answered from our data whether such changes are causes of the twisting, consequences of surface stress where symmetry is broken, or consequences of intrinsic dissymmetry when two or more geometric tendencies are in conflict. Nevertheless, it has become clear that, in some cases, the development of a crystal with a lattice having long-range translational symmetry is not foretold in the thermodynamics of aggregates of molecules. Rather, a lattice is sometimes a device for allowing a growing crystal to take advantage of the thermodynamic driving force of growth, the best compromise for a large number of molecules, which on a smaller scale would be dissymmetric (have a point symmetry only). The relationship between these calculations and the ubiquity of crystal twisting on the mesoscale are discussed