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Machine Learning for Designing Perovskites and Perovskite‐Inspired Solar Materials: Emerging Opportunities and Challenges

open access: yesAdvanced Science, EarlyView.
This review offers a comprehensive comparison between perovskites and perovskite‐inspired materials (PIMs), focusing on their crystal structures, electronic properties, and chemical compositions. It evaluates the applicability of machine learning (ML) descriptors and models across both material classes.
Yangfan Zhang   +6 more
wiley   +1 more source

A social solution to the puzzle of doxastic responsibility: a two-dimensional account of responsibility for belief [PDF]

open access: yes, 2020
In virtue of what are we responsible for our beliefs? I argue that doxastic responsibility has a crucial social component: part of being responsible for our beliefs is being responsible to others.
Osborne, Robert Carry
core   +1 more source

AI in chemical engineering: From promise to practice

open access: yesAIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew   +4 more
wiley   +1 more source

The Institution of Gender-Based Asylum and Epistemic Injustice: A Structural Limit [PDF]

open access: yes, 2018
One of the recent attempts to explore epistemic dimensions of forced displacement focuses on the institution of gender-based asylum and hopes to detect forms of epistemic injustice within assessments of gender related asylum applications.
Sertler, Ezgi
core   +2 more sources

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Hate, crime and epistemic vulnerability: on sense-making and feelings of (un)safety among Danish Muslims

open access: yesFrontiers in Sociology
This article investigates feelings of (un)safety emerging from knowing and sharing knowledge about hate crime and hate incidents. Drawing on fieldwork and interviews with young Muslims living in the greater Copenhagen area, the article explores the way ...
Anne-Mai Flyvholm   +1 more
doaj   +1 more source

Aesthetic objects, aesthetic judgments and the crafting of organizational style in creative industries [PDF]

open access: yes, 2020
In this article, we conceptually engage with style as central to creative industries. We specifically argue that style is crafted into being via an interplay between aesthetic judgments and “aesthetic objects.” We define aesthetic objects as temporary ...
Bazin, Yoann, Korica, Maja
core   +1 more source

Taguchi–Bayesian Sampling: A Roadmap for Polymer Database Construction Toward Small Representative Machine Learning

open access: yesAdvanced Intelligent Discovery, EarlyView.
This article establishes a Taguchi–Bayesian sampling strategy to reconstruct polymer processing–property landscape at minimal sampling cost, generically building the roadmap for materials database construction from sampling their vast design space. This sampling strategy is featured by an alternating lesson between uniformity and representativeness ...
Han Liu, Liantang Li
wiley   +1 more source

Resisting Structural Epistemic Injustice

open access: yesFeminist Philosophy Quarterly, 2018
What form must a theory of epistemic injustice take in order to successfully illuminate the epistemic dimensions of struggles that are primarily political?
Michael Doan
doaj   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

open access: yesAdvanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park   +8 more
wiley   +1 more source

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