Conventional machine learning approaches for predicting material properties from elemental compositions have emphasized the importance of leveraging domain knowledge when designing model inputs. Here, we demonstrate that by using a deep approach, can bypass such manual feature engineering requiring and achieve much better results, even with only few thousand training samples. We present design implementation neural network referred to as ElemNet; it automatically captures physical chemical...

People have long sought answers to questions online, typically using either anonymous or pseudonymous forums social network platforms that primarily use real names. Systems allow communication afford freedom explore identity and discuss taboo topics, but can result in negative disinhibited behavior such as cyberbullying. Identifiable systems allows one reach a known audience avoid disinhibition, constrain with concerns about privacy reputation. One persistent design issue is understanding...

Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional objects from computer-aided design model by successively adding material layer layer. AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing well functional parts with complex geometries using processes laser metal deposition would be difficult create traditional machining. As process creating an intricate part expensive Titanium prohibitive respect cost,...

Additive manufacturing (AM) is an emerging technology that constructs complex parts through layer-by-layer deposition. The prediction and control of thermal fields during production AM are crucial importance as the temperature distribution gradient dictates microstructures, properties, performance. Finite element (FE) analyses commonly conducted to simulate history process, but known be costly time-consuming. This paper aims address challenge by presenting essential components a generic...

Organic solar cells are an inexpensive, flexible alternative to traditional silicon-based but disadvantaged by low power conversion efficiency due empirical design and complex manufacturing processes. This process can be accelerated generating a comprehensive set of potential candidates. However, this would require laborious trial error method modeling all possible polymer configurations. A machine learning model has the accelerate screening donor candidates associating structural features...

Abstract More frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times a high spatial resolution, remain challenging. In this study, we proposed an interpretable accurate hybrid machine learning (ML) model, that explicitly represented controls of fuel flammability, availability, human suppression effects on fires. The model demonstrated notable accuracy F 1 ‐score 0.846 ± 0.012,...

Organic Solar Cells are a promising technology for solving the clean energy crisis in world. However, generating candidate chemical compounds solar cells is time-consuming process requiring thousands of hours laboratory analysis. For cell, most important property power conversion efficiency which dependent on highest occupied molecular orbitals (HOMO) values donor molecules. Recently, machine learning techniques have proved to be very useful building predictive models HOMO structures...

Abstract Materials design aims to identify the material features that provide optimal properties for various engineering applications, such as aerospace, automotive, and naval. One of important but challenging problems materials is discover multiple polycrystalline microstructures with properties. This paper proposes an end-to-end artificial intelligence (AI)-driven microstructure optimization framework elastic materials. In this work, represented by Orientation Distribution Function (ODF)...

There are two broad modeling paradigms in scientific applications: forward and inverse. While estimates the observations based on known causes, inverse attempts to infer causes given observations. Inverse problems usually more critical as well difficult applications they seek explore that cannot be directly observed. used extensively various fields, such geophysics, health care materials science. Exploring relationships from properties microstructures is one of material It challenging solve...

The designing and development of near-infrared (NIR) red emitters have attracted significant attention owing to the challenges achieving necessary energy levels for harvesting both singlet triplet excitons their unique requirements this spectral range. Herein, we reported synthesis two novel TADF 4,4'-(3,6-bis(9,9-dimethylacridin-10(9H)-yl)dibenzo[a,c]phenazine-11,12-diyl)dibenzonitrile (Ac-PhCNDBPZ) 4,4'-(3,6-di(10H-phenoxazin-10-yl)dibenzo[a,c]phenazine-11,12-diyl)dibenzonitrile...

Microstructures significantly impact the performance of sensitively engineered components, such as wireless detectors used in military vehicles or sensors aircrafts. These components can operate safely only within a certain range frequencies, and frequencies outside that lead to instability because resonance. This paper addresses optimization microstructure design maximize yield stress galfenol beam under vibration tuning constraints defined for first torsional bending natural by using...

Objective: This research addresses the challenges of maintaining proper yoga postures, an issue that has been exacerbated by COVID-19 pandemic and subsequent shift to virtual platforms for instruction. aims develop a mechanism detecting correct poses providing real-time feedback through application computer vision machine learning (ML) techniques. Methods Procedures: study utilized vision-based pose estimation methods extract features calculate angles. A variety models, including extremely...

Microstructural materials design is one of the most important applications inverse modeling in science. Generally speaking, there are two broad paradigms scientific applications: forward and inverse. While estimates observations based on known parameters, attempts to infer parameters given observations. Inverse problems usually more critical as well difficult they seek explore that cannot be directly observed. used extensively various fields, such geophysics, healthcare However, it...

Fa Li1,*, Qing Zhu2, Kunxiaojia Yuan2, Fujiang Ji1, Arindam Paul3, Peng Lee3, Volker C. Radeloff1, Min Chen1,*1Department of Forest and Wildlife Ecology, University Wisconsin-Madison, Madison, WI, USA2Climate Ecosystem Sciences Division, Climate Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA3America Family Insurance*Corresponding authors: Li (fli235@wisc.edu) Chen (mchen392@wisc.edu)

The design and development of long-wavelength deep-red emitters have gained significant attention due to their potential prospective applications in optical communication, night-vision devices, sensors. However, the intrinsic limitations energy gap law, creating high-performing is still found be difficult. Herein, based on auxiliary cyanobenzene core attached phenazine acceptor unit, we reported two types orange-red emitting thermally activated delayed fluorescence (TADF) emitters,...