Designing and fabricating materials from fire using sonification and deep learning.

Mario Milazzo, Markus J Buehler

Open access · goldAbstract read

In one paragraph

Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

1.4field-weighted citation impact, top 20% of its field

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 13 citations in OpenAlex.

Affective Neural Responses Sonified through Labeled Correlation Alignment.Sensors (Basel, Switzerland) · 2023
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Mario MilazzoLaboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue, Rm. 1-165, Cambridge, MA 02139, USA.

Markus J BuehlerLaboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue, Rm. 1-165, Cambridge, MA 02139, USA.

Massachusetts Institute of Technology · US

Funding

Models to Predict Protein Biomaterial PerformanceU01EB014976 · NIBIB · TUFTS UNIVERSITY MEDFORD · PI BUEHLER, MARKUS J., KAPLAN, DAVID L. · 2012 to 2020

$5.2M

NIBIB NIH HHS U01 EB014976

6 · The paper itself

Abstract

Fire has fascinated humankind since the prehistoric era. Rooted in the interactions between sound and flames, here we report a method to use fire for a variety of purposes, including sonification, art, and the design and manufacturing nature-inspired materials. We present a method to sonify fire, thereby offering a translation from the silent nature of flames, to represent audible information and to generate

Indexed as

Computational method in materials scienceMachine learningMaterials design

Identifiers

PMID34409268

DOI10.1016/j.isci.2021.102873

PMCPMC8361214

OpenAlexW3182879190

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.