scholarly journals Automatic discovery of photoisomerization mechanisms with nanosecond machine learning photodynamics simulations

2021 ◽  
Author(s):  
Jingbai Li ◽  
Patrick Reiser ◽  
Benjamin R. Boswell ◽  
André Eberhard ◽  
Noah Z. Burns ◽  
...  
Keyword(s):  
Machine Learning ◽  
Photochemical Reactions ◽  
Automatic Discovery ◽  
Molecular Architectures

Photochemical reactions are widely used by academia and industry to construct complex molecular architectures via mechanisms that are often inaccessible by other means.

Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning

2020 ◽  
Author(s):  
Jingbai Li ◽  
Patrick Reiser ◽  
André Eberhard ◽  
Pascal Friederich ◽  
Steven Lopez
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Excited State ◽  
Adaptive Sampling ◽  
Computational Cost ◽  
Ground Truth ◽  
Absolute Error ◽  
Photochemical Reactions ◽  
Computational Techniques ◽  
Full Potential

<p>Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr <i>et al.</i> demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH<sub>2</sub>NH<sub>2</sub><sup>+</sup>).</p><p>We have developed a Python-based code, Python Rapid Artificial Intelligence <i>Ab Initio</i> Molecular Dynamics (PyRAI<sup>2</sup>MD), to accomplish the unprecedented 10 ns <i>cis-trans</i> photodynamics of <i>trans</i>-hexafluoro-2-butene (CF<sub>3</sub>–CH=CH–CF<sub>3</sub>) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S<sub>1</sub> half-life (60.5 fs), the photochemical product ratio (<i>trans</i>: <i>cis</i> = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (<i>trans</i> → <i>cis</i> but not <i>cis</i> → <i>trans</i> pathways) that may offer future automated photochemical reaction discoveries.</p>

scholarly journals Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Namid R. Stillman ◽  
Igor Balaz ◽  
Michail-Antisthenis Tsompanas ◽  
Marina Kovacevic ◽  
Sepinoud Azimi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Cells ◽  
In Silico ◽  
Nanoparticle Transport ◽  
Cancer Treatments ◽  
Nanoparticle Distribution ◽  
Anti Cancer ◽  
Automatic Discovery ◽  
In Silico Models ◽  
Computational Platform

AbstractWe present the EVONANO platform for the evolution of nanomedicines with application to anti-cancer treatments. Our work aims to decrease both the time and cost required to develop nanoparticle designs. EVONANO includes a simulator to grow tumours, extract representative scenarios, and simulate nanoparticle transport through these scenarios in order to predict nanoparticle distribution. The nanoparticle designs are optimised using machine learning to efficiently find the most effective anti-cancer treatments. We demonstrate EVONANO with two examples optimising the properties of nanoparticles and treatment to selectively kill cancer cells over a range of tumour environments. Our platform shows how in silico models that capture both tumour and tissue-scale dynamics can be combined with machine learning to optimise nanomedicine.

Grammar Factorization by Tree Decomposition

2011 ◽  
Vol 37 (1) ◽  
pp. 231-248 ◽  
Author(s):  
Daniel Gildea
Keyword(s):  
Machine Learning ◽  
Graphical Models ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Theoretic Property ◽  
Graph Theoretic ◽  
Rewriting Systems ◽  
Automatic Discovery ◽  
Context Free ◽  
General Graphs

We describe the application of the graph-theoretic property known as treewidth to the problem of finding efficient parsing algorithms. This method, similar to the junction tree algorithm used in graphical models for machine learning, allows automatic discovery of efficient algorithms such as the O(n4) algorithm for bilexical grammars of Eisner and Satta. We examine the complexity of applying this method to parsing algorithms for general Linear Context-Free Rewriting Systems. We show that any polynomial-time algorithm for this problem would imply an improved approximation algorithm for the well-studied treewidth problem on general graphs.

Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning

2020 ◽  
Author(s):  
Jingbai Li ◽  
Patrick Reiser ◽  
André Eberhard ◽  
Pascal Friederich ◽  
Steven Lopez
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Excited State ◽  
Adaptive Sampling ◽  
Computational Cost ◽  
Ground Truth ◽  
Absolute Error ◽  
Photochemical Reactions ◽  
Computational Techniques ◽  
Full Potential

<p>Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr <i>et al.</i> demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH<sub>2</sub>NH<sub>2</sub><sup>+</sup>).</p><p>We have developed a Python-based code, Python Rapid Artificial Intelligence <i>Ab Initio</i> Molecular Dynamics (PyRAI<sup>2</sup>MD), to accomplish the unprecedented 10 ns <i>cis-trans</i> photodynamics of <i>trans</i>-hexafluoro-2-butene (CF<sub>3</sub>–CH=CH–CF<sub>3</sub>) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S<sub>1</sub> half-life (60.5 fs), the photochemical product ratio (<i>trans</i>: <i>cis</i> = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (<i>trans</i> → <i>cis</i> but not <i>cis</i> → <i>trans</i> pathways) that may offer future automated photochemical reaction discoveries.</p>

Automatic discovery of Web Query Interfaces using machine learning techniques

2012 ◽  
Vol 40 (1) ◽  
pp. 85-108 ◽  
Author(s):  
Heidy M. Marin-Castro ◽  
Victor J. Sosa-Sosa ◽  
Jose F. Martinez-Trinidad ◽  
Ivan Lopez-Arevalo
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Query Interfaces ◽  
Learning Techniques ◽  
Automatic Discovery

scholarly journals A Novel Tool For Fast Feature Selection

2020 ◽  
Author(s):  
Ivan Alejandro Garcia Ramirez ◽  
Arturo Calderon ◽  
Andrés Méndez ◽  
Susana Ortega
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Computation Time ◽  
Learning Model ◽  
Large Datasets ◽  
High Dimensionality ◽  
Learning Methods ◽  
Machine Learning Model ◽  
Automatic Discovery

Abstract Motivation: Datasets with high dimensionality represent a challenge to existing learning methods. The presence of irrelevant and redundant features in a dataset can degrade the performance of the models inferred from it. In large datasets, manual management of features tends to be impractical. Therefore, the development of automatic discovery techniques to remove useless features has attracted increasing interest. In this paper, we propose a novell framework to select relevant features in supervised datasets. Availability: This tool can be downloaded from https://github.com/ivangarcia88/selectionResults: This tool allow to identify relevant and remove redundant features, reducing computation time on training a machine learning model while improving the performance.

Self-learning and self-organization as tools for speech research

1998 ◽  
Vol 21 (2) ◽  
pp. 262-263
Author(s):  
R. I. Damper
Keyword(s):  
Machine Learning ◽  
Scientific Inquiry ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Self Organization ◽  
Knowledge Based ◽  
Automatic Discovery ◽  
Perceptual Invariance ◽  
Speech Research ◽  
Self Learning

Locus equations offer promise for an understanding of at least some aspects of perceptual invariance in speech, but they were discovered almost fortuitously. With the present availability of powerful machine learning algorithms, ignorance-based automatic discovery procedures are starting to supplant knowledge-based scientific inquiry. Principles of self-learning and self-organization are powerful tools for speech research but remain somewhat under-utilized.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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