Machine learning applications in macromolecular X-ray crystallography

Vollmar, M.; Evans, Gwyndaf

doi:10.1080/0889311x.2021.1982914

Cited by 11 publications

(6 citation statements)

References 119 publications

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“…The large interest raised by ML techniques for the development of FF is due to the unprecedented trade-off between accuracy and computational speed allowed by ML 19 . The high level of flexibility provided by ML models such as Neural Networks (NN) 14 , 20 , 21 and Gaussian Processes 16 , 22 – 24 have been successfully exploited to faithfully reproduce first-principle atomistic calculations. This is achieved by evaluating large databases of atomistic configurations starting from ab-initio approaches such as Density Functional Theory (DFT) 17 , 18 , 25 , 26 or Coupled Cluster 27 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning potential for interacting dislocations in the presence of free surfaces

Lanzoni

Rovaris

Montalenti

2022

Sci Rep

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Computing the total energy of a system of N interacting dislocations in the presence of arbitrary free surfaces is a difficult task, requiring Finite Element (FE) numerical calculations. Worst, high accuracy requires very fine meshes in the proximity of each dislocation core. Here we show that FE calculations can be conveniently replaced by a Machine Learning (ML) approach. After formulating the elastic problem in terms of one and two-body terms only, we use Sobolev training to obtain consistent information on both energy and forces, fitted using a feed-forward neural network (NN) architecture. As an example, we apply the proposed methodology to corrugated, heteroepitaxial semiconductor films, searching for the minimum-energy dislocation distributions by using Monte Carlo. Importantly, the presence of an interaction cutoff allows for the application of the method to systems of different sizes without the need to repeat training. Millions of energy evaluations are performed, a task which would have been impossible by brute-force FE calculations. Finally, we show how forces can be exploited in running 2D ML-based dislocation dynamics simulations.

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Section: Introductionmentioning

confidence: 99%

Machine learning potential for interacting dislocations in the presence of free surfaces

Lanzoni

Rovaris

Montalenti

2022

Sci Rep

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“…Feature extraction and recognition of potential outliers often require domain expertise, because automatic feature extraction may compromise the interpretability of the model. A significant body of exploratory data analysis and initial assessment techniques is available in a previous review on this topic (Vollmar & Evans, 2021).…”

Section: Data Assessmentmentioning

confidence: 99%

Deep learning applications in protein crystallography

Matinyan,

Filipcik,

Abrahams

2024

Acta Cryst Sect A

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Deep learning techniques can recognize complex patterns in noisy, multidimensional data. In recent years, researchers have started to explore the potential of deep learning in the field of structural biology, including protein crystallography. This field has some significant challenges, in particular producing high-quality and well ordered protein crystals. Additionally, collecting diffraction data with high completeness and quality, and determining and refining protein structures can be problematic. Protein crystallographic data are often high-dimensional, noisy and incomplete. Deep learning algorithms can extract relevant features from these data and learn to recognize patterns, which can improve the success rate of crystallization and the quality of crystal structures. This paper reviews progress in this field.

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“…The strategy, called DEFMap, relies on a multidisciplinary approach, which combines deep learning with molecular dynamics (MD) simulation and experimental data. Noteworthy, ML contributions can also be found in the fields of X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. , …”

Section: Biomolecular Systems Via Machine Learningmentioning

confidence: 99%

Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go?

Gianti

Percec

2022

Biomacromolecules

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This Perspective outlines recent progress and future directions for using machine learning (ML), a data-driven method, to address critical questions in the design, synthesis, processing, and characterization of biomacromolecules. The achievement of these tasks requires the navigation of vast and complex chemical and biological spaces, difficult to accomplish with reasonable speed. Using modern algorithms and supercomputers, quantum physics methods are able to examine systems containing a few hundred interacting species and determine the probability of finding them in a particular region of phase space, thereby anticipating their properties. Likewise, modern approaches in chemistry and biomolecular simulation, supported by high performance computing, have culminated in producing data sets of escalating size and intrinsically high complexity. Hence, using ML to extract relevant information from these fields is of paramount importance to advance our understanding of chemical and biomolecular systems. At the heart of ML approaches lie statistical algorithms, which by evaluating a portion of a given data set, identify, learn, and manipulate the underlying rules that govern the whole data set. The assembly of a quality model to represent the data followed by the predictions and elimination of error sources are the key steps in ML. In addition to a growing infrastructure of ML tools to address complex problems, an increasing number of aspects related to our understanding of the fundamental properties of biomacromolecules are exposed to ML. These fields, including those residing at the interface of polymer science and biology (i.e., structure determination, de novo design, folding, and dynamics), strive to adopt and take advantage of the transformative power offered by approaches in the ML domain, which clearly has the potential of accelerating research in the field of biomacromolecules.

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Machine learning applications in macromolecular X-ray crystallography

Cited by 11 publications

References 119 publications

Machine learning potential for interacting dislocations in the presence of free surfaces

Machine learning potential for interacting dislocations in the presence of free surfaces

Deep learning applications in protein crystallography

Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go?

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