A5067642969- Machine Learning in Materials Science
- Computational Drug Discovery Methods
- Photonic Crystals and Applications
- X-ray Diffraction in Crystallography
- Crystallography and molecular interactions
- Pickering emulsions and particle stabilization
- Slime Mold and Myxomycetes Research
- Photonic and Optical Devices
- Material Dynamics and Properties
- Phase Equilibria and Thermodynamics
- Spectroscopy and Chemometric Analyses
- Pregnancy and preeclampsia studies
- Maternal and fetal healthcare
- Molecular Sensors and Ion Detection
- Electrophoretic Deposition in Materials Science
- Block Copolymer Self-Assembly
- Innovative Teaching Methods
- Doctoral Education Challenges and Solutions
- Luminescence and Fluorescent Materials
- Various Chemistry Research Topics
- Problem and Project Based Learning
- Inorganic Chemistry and Materials
- Biomedical and Engineering Education
- Fern and Epiphyte Biology
- Laser-Ablation Synthesis of Nanoparticles
University of Wisconsin–Madison
2023-2024
École Polytechnique Fédérale de Lausanne
2020-2023
University of Michigan
2017-2021
Abstract Many butterflies, birds, beetles, and chameleons owe their spectacular colors to the microscopic patterns within wings, feathers, or skin. When these patterns, photonic crystals, result in omnidirectional reflection of commensurate wavelengths light, it is due a complete band gap (PBG). The number natural crystal structures known have PBG relatively small, those even smaller subset notoriety, including diamond inverse opal, proven difficult synthesize. Here, we report more than...
Significance Understanding how structural order forms in matter is a key challenge designing materials. In the 1920s, Pauling proposed packing as mechanism for driving based on observed correlations between structure of crystals and mathematical hard spheres. We study ordering several systems colloids which correlates with find, surprisingly, that cannot arise from packing. Our approach provides statistical mechanics approaches investigating mathematics raises questions about role determining matter.
Abstract Why are materials with specific characteristics more abundant than others? This is a fundamental question in science and one that traditionally difficult to tackle, given the vastness of compositional configurational space. We highlight here anomalous abundance inorganic compounds whose primitive unit cell contains number atoms multiple four. occurrence—named rule four —has our knowledge not previously been reported or studied. Here, we first rule’s existence, especially notable...
Abstract Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to automatic processing of large amounts data for building supervised or unsupervised machine learning models. Principal covariates regression (PCovR) is an underappreciated method that interpolates between principal component analysis can be used conveniently reveal structure-property relations in terms simple-to-interpret, low-dimensional maps. Here we provide a pedagogic...
Abstract Selecting the most relevant features and samples out of a large set candidates is task that occurs very often in context automated data analysis, where it improves computational performance transferability model. Here we focus on two popular subselection schemes applied to this end: CUR decomposition, derived from low-rank approximation feature matrix, farthest point sampling (FPS), which relies iterative identification diverse discriminating features. We modify these unsupervised...
The number of materials or molecules that can be created by combining different chemical elements in various proportions and spatial arrangements is enormous.Computational chemistry used to generate databases containing billions potential structures (Ruddigkeit, Deursen, Blum, & Reymond, 2012), predict some the associated properties (Montavon et al., 2013;Ramakrishnan, Dral, Rupp, Lilienfeld, 2014).Unfortunately, very large makes exploring such database -to understand structureproperty...
Due to the subtle balance of molecular interactions, predicting stability crystals is a non-trivial scientific problem. Physically-motivated machine learning models can not only “rediscover” maxims crystal engineering, but also guide design.
<ns7:p>Easy-to-use libraries such as scikit-learn have accelerated the adoption and application of machine learning (ML) workflows data-driven methods. While many algorithms implemented in these originated specific scientific fields, they gained popularity part because their generalisability across multiple domains. Over past two decades, researchers chemical materials science community put forward general-purpose The deployment methods into other domains, however, is often burdensome due to...
<ns3:p>Easy-to-use libraries such as scikit-learn have accelerated the adoption and application of machine learning (ML) workflows data-driven methods. While many algorithms implemented in these originated specific scientific fields, they gained popularity part because their generalisability across multiple domains. Over past two decades, researchers chemical materials science community put forward general-purpose The deployment methods into other domains, however, is often burdensome due to...
Despite the prevalence of polymers in modern everyday life, there is little introduction to topic science education throughout primary or secondary schooling United States. Of few states that do include polymer education, this only found at high school level, primarily biology chemistry. Over past year, we have developed a graduate-student-run outreach initiative aimed providing young students with an understanding and appreciation class materials through interactive teaching...
ADVERTISEMENT RETURN TO ISSUEEditorialNEXTMachine Learning for Generating and Analyzing Thermophysical Data: Where We Are We're GoingRose K. Cersonsky*Rose CersonskyDepartment of Chemical Biological Engineering, University Wisconsin-Madison, Madison, Wisconsin 53706, United States*[email protected]More by Rose Cersonskyhttps://orcid.org/0000-0003-4515-3441, Bingqing ChengBingqing ChengDepartment Chemistry, California-Berkeley, Berkeley, California 94720, StatesMore Cheng, David KofkeDavid...
Materials adopting the diamond structure possess useful properties in atomic and colloidal systems are a popular target for synthesis colloids where photonic band gap is possible. The desirable of pose an interesting opportunity reconfigurable matter: Can we create crystal able to switch reversibly from with visible light range? Drawing inspiration high-pressure transitions diamond-forming systems, design system polyhedrally shaped particles spherical cores that tetragonal derivative upon...
Abstract Why are materials with specific characteristics more abundant than others? This is a fundamental question in science and one that traditionally difficult to tackle, given the vastness of compositional configurational space. We highlight here anomalous abundance inorganic compounds whose primitive unit cell contains number atoms multiple four. occurrence - named rule four has our knowledge not previously been reported or studied. Here, we first rule's existence, especially notable...
Photonic crystals, appealing for their ability to control light, are constructed from periodic regions of different dielectric constants. Yet, the structural holy grail in photonic materials, diamond, remains challenging synthesize at colloidal length scale. Here we explore new ways assemble diamond using modified gyrobifastigial (mGBF) nanoparticles, a shape that resembles two anti-aligned triangular prisms. We investigate parameter space leads self-assembly and compare likelihood defects...
Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within atomistic simulation community. Many these build off idea atoms as having spherical, or isotropic, interactions. In many communities, there is often a need represent groups atoms, either increase computational efficiency via coarse-graining understand molecular influences on system behavior. such cases, will limited utility, may not be well-approximated spheres....
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Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within atomistic simulation community. Many these build off idea atoms as having spherical, or isotropic, interactions. In many communities, there is often a need represent groups atoms, either increase computational efficiency via coarse-graining understand molecular influences on system behavior. such cases, will limited utility, may not be well-approximated spheres....
Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to automatic processing of large amounts data for building supervised or unsupervised machine learning models. Principal covariates regression (PCovR) is an underappreciated method that interpolates between principal component analysis regression, can be used conveniently reveal structure-property relations in terms simple-to-interpret, low-dimensional maps. Here we provide a pedagogic...