AIMC Topic: Crystallization

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Nanostructured block copolymer muscles.

Nature nanotechnology
High-performance actuating materials are necessary for advances in robotics, prosthetics and smart clothing. Here we report a class of fibre actuators that combine solution-phase block copolymer self-assembly and strain-programmed crystallization. Th...

Protein Crystallization Identification via Fuzzy Model on Linear Neighborhood Representation.

IEEE/ACM transactions on computational biology and bioinformatics
X-ray crystallography is the most popular approach for analyzing protein 3D structure. However, the success rate of protein crystallization is very low (2-10 percent). To reduce the cost of time and resources, lots of computation-based methods are de...

Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins.

PloS one
Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simula...

CrystalM: A Multi-View Fusion Approach for Protein Crystallization Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Improving the accuracy of predicting protein crystallization is very important for protein crystallization projects, which is a critical step for the determination of protein structure by X-ray crystallography. At present, many machine learning metho...

A high-throughput system combining microfluidic hydrogel droplets with deep learning for screening the antisolvent-crystallization conditions of active pharmaceutical ingredients.

Lab on a chip
Crystallization of active pharmaceutical ingredients (APIs) is a crucial process in the pharmaceutical industry due to its great impact in drug efficacy. However, conventional approaches for screening the optimal crystallization conditions of APIs ar...

Machine learning-guided evolution of BMP-2 knuckle Epitope-Derived osteogenic peptides to target BMP receptor II.

Journal of drug targeting
Bone morphogenetic protein-2 (BMP-2) is a key regulator of bone formation, growth and regeneration, which contains a conformational wrist epitope and a linear knuckle epitope that are functionally responsible for the protein by mediating its interact...

Graph Convolutional Neural Networks as "General-Purpose" Property Predictors: The Universality and Limits of Applicability.

Journal of chemical information and modeling
Nowadays the development of new functional materials/chemical compounds using machine learning (ML) techniques is a hot topic and includes several crucial steps, one of which is the choice of chemical structure representation. The classical approach ...

3D nanostructural characterisation of grain boundaries in atom probe data utilising machine learning methods.

PloS one
Boosting is a family of supervised learning algorithm that convert a set of weak learners into a single strong one. It is popular in the field of object tracking, where its main purpose is to extract the position, motion, and trajectory from various ...

Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks.

Journal of chemical information and modeling
The surface energy of inorganic crystals is important in understanding experimentally relevant surface properties and designing materials for many applications. Predictive methods and data sets exist for surface energies of monometallic crystals. How...

Inexpensive robotic system for standard and fluorescent imaging of protein crystals.

Acta crystallographica. Section F, Structural biology communications
Protein-crystallization imaging and classification is a labor-intensive process typically performed either by humans or by instruments that currently cost well over $100 000. This cost puts the use of crystallization-trial imaging outside the reach o...