What is the difference between scientific discovery and circumstantial discovery

The other leading theory, called leptogenesis, stems instead from neutrinos. These particles are much, much lighter than quarks and pass through the cosmos ethereally, rarely stopping to interact with anything at all. According to this scenario, in addition to the regular neutrinos we know of, there are extremely heavy neutrinos that are so gargantuan that they could have been forged only from the tremendous energies and temperatures present just after the big bang, when the universe was very hot and dense.

When these particles inevitably broke down into smaller, more stable species, the thinking goes, they might have produced slightly more matter than antimatter by-products, leading to the arrangement we see today. The recent announcement, which was made by scientists at the T2K Tokai to Kamioka experiment in Japan, offers hopeful signs for the leptogenesis concept. The experiment observes neutrinos as they travel through kilometers underground and change between three types, or flavors—a peculiar ability of neutrinos called oscillation.

The T2K researchers detected more oscillations in neutrinos than in antineutrinos, suggesting the two do not just act as mirror images of each other but, in fact, behave differently.

Such a difference between a particle and its antimatter counterpart is termed CP violation, and it is a strong clue in the quest to understand how matter outran antimatter after the universe was born. The experiment has now ruled out the possibility that neutrinos have zero CP violation with 95 percent confidence, and it shows hints that the particles might display the maximum possible amount of CP violation allowed.

Yet more data, and probably future experiments, will be needed to precisely measure just how much neutrinos and antineutrinos differ. Even if physicists make a definitive discovery of CP violation in neutrinos, they will not have completely solved the cosmic antimatter question. Another requirement of the theory is that neutrinos and antineutrinos turn out to be the same thing. How is that seeming contradiction possible? Matter and antimatter are thought to be identical except for a reversed electrical charge.

Neutrinos, having no charge, could be both at the same time. The solution he proposes is to dissolve that capture by making science accountable to end users. So far, I agree with his point. In brief, we would simply be exchanging one form of capture for another. The first concern about this capture-swap is that scientific rigor is a darling child that should not be tossed out along with the bath water.

Tim Caulfield never tires of his crusade against health advice spewed by Gwyneth Paltrow and other celebs because much of what is appealing to and adopted by non-experts is just bunk.

Sarewitz articulates well the problems of letting scientists call all the shots, prompting us to recognize that we need others involved in this process, too. As for how we should be collaborating, true to my Socratic roots I maintain that we all need to be a bit more open to being wrong. Surrounding ourselves with a diverse group of people, and respecting them enough to actually listen, is an important step in recognizing our own preconceptions, identifying alternative views, examining them to develop novel insights, and using them to better our world.

Better steerage, he argues, will produce better science. These may be worthy propositions, but Sarewitz should not dismiss Vannevar Bush so readily.

Bush steered technology development by appropriating new, competitive science. The question Bush addressed in Science, The Endless Frontier was whether such an innovation engine was possible for civilian purposes. He argued that it was. Bush proposed a National Research Foundation, led by a director accountable to the president, to support this frontier scientific research.

University administrators insisted that government funding of science should not be concentrated in a single agency; government agencies clambered to extend their own research programs. Sarewitz insists that scientists should discover the new science they need by working on problems important to society. They just need to have a better sense of those problems and better steerage. According to Sarewitz, scientists led by charismatic, organized non-scientists, or by scientists working outside of their capacity as scientists, will solve socially urgent problems sooner, producing the sound, testable science they need to get the job done.

New science may arise when scientists work on institutional problems, but that science may not be on a frontier, and even when it is, scientists are not at liberty to chase after mystery — especially if charismatic and organized leaders outside of science prevent such wandering off.

Or is it an argument against the dominance of science by institutions? Sarewitz advocates for scientists to get out more and deal with practical problems, under the direction of most anyone other than the current institutional managers of science.

One can see why. Institutional managers — even those with scientific training — are not accountable for outputs, and in turn they do not hold scientists accountable for outputs. Every worry is here that concerns Sarewitz — mediocrity, grandiose projects, and hardening of administrative consciences.

That may be so. Rather, it argues for limits on institutional control of research. Free play of free intellects is not the problem with institutional science — it is an alternative, if not a remedy. The problem for institutional science is that the managers of science are not accountable, not even visible.

For institutional science, the problem is the free play of management intellects. We might argue that science administrators need to be better trammeled rather than that Bush was a seductive manipulator. Untrammeled scientists may create new science that can spark technological innovation. So might better-trammeled institutional managers of science.

In short, Sarewitz wants different management to direct scientific discovery when what we need is less management. And these are testable proposals. In my own experience the actual, mechanical aspects of even getting to a place where one can do science are confining.

The Kafkaesque description E. To climb out of poverty and come so close to achieving a lifelong goal I am wrapping my up my Ph. As an example of this, in earlier days of my research experience, during my undergraduate, I saw many physics labs including my own pivoting to graphene for promise of grants and glory.

Graphene was hyped and marketed heavily and even at the time, in that moment, I felt the strong tug of skepticism and looming disappointment. That moment was when the would-be Nobel Prize winners were giving a talk about graphene at the American Physical Society conference in Portland, Oregon. This is where I diverge from the tone of the article. It will be contained by economic constraints and the very niches that we occupy to form the basis of our careers.

Yes, agendas and goals are an excellent basis with which to embark on any complex task, but without scientists having resources and a secure position, the resolution of these goals will always be at risk of compromise. We can contrast empirical evidence with other types of evidence to understand its value.

Hearsay evidence is what someone says they heard another say; it is not reliable because you cannot check its source. Better is testimonial evidence, which, unlike hearsay evidence, is allowed in courts of law. But even testimonial evidence is notoriously unreliable, as numerous studies have shown.

Courts also allow circumstantial evidence e. Revelatory evidence or revelation is what someone says was revealed to them by some deity or supernatural power; it is not reliable because it cannot be checked by others and is not repeatable.

Spectral evidence is evidence supposedly manifested by ghosts, spirits, and other paranormal or supernatural entities; spectral evidence was once used, for example, to convict and hang a number of innocent women on charges of witchcraft in Salem, Massachusetts, in the seventeenth century, before the colonial governor banned the use of such evidence, and the witchcraft trials ended.

Emotional evidence is evidence derived from one's subjective feelings; such evidence is often repeatable, but only for one person, so it is unreliable.

The most common alternative to empirical evidence, authoritarian evidence, is what authorities people, books, billboards, television commercials, etc. Sometimes, if the authority is reliable, authoritarian evidence is reliable evidence, but many authorities are not reliable, so you must check the reliability of each authority before you accept its evidence.

In the end, you must be your own authority and rely on your own powers of critical thinking to know if what you believe is reliably true. Transmitting knowledge by authority is, however, the most common method among humans for three reasons: first, we are all conditioned from birth by our parents through the use of positive and negative reinforcement to listen to, believe, and obey authorities; second, it is believed that human societies that relied on a few experienced or trained authorities for decisions that affected all had a higher survival value than those that didn't, and thus the behaviorial trait of susceptibility to authority was strengthened and passed along to future generations by natural selection; third, authoritarian instruction is the quickest and most efficient method for transmitting information we know about.

But remember: some authoritarian evidence and knowledge should be validated by empirical evidence, logical reasoning, and critical thinking before you should consider it reliable, and, in most cases, only you can do this for yourself. It is, of course, impossible to receive an adequate education today without relying almost entirely upon authoritarian evidence. Teachers, instructors, and professors are generally considered to be reliable and trustworthy authorities, but even they should be questioned on occasion.

The use of authoritarian evidence in education is so pervasive, that its use has been questioned as antithetical to the true spirit of scholarly and scientific inquiry, and attempts have been made in education at all levels in recent years to correct this bias by implementing discovery and inquiry methodologies and curricula in classrooms and laboratories.

It is easier to utilize such programs in humanities and social sciences, in which different yet equally valid conclusions can be reached by critical thinking, rather than in the natural sciences, in which the objective reality of nature serves as a constant judge and corrective mechanism.

Another name for empirical evidence is natural evidence: the evidence found in nature. Naturalism is the philosophy that says that "Reality and existence i. Another popular definition of naturalism is that "The universe exists as science says it does. This is not bad, however, for, whether naturalism is ultimately true or not, science and naturalism reject the concept of ultimate or absolute truth in favor of a concept of proximate reliable truth that is far more successful and intellectually satisfying than the alternative, the philosophy of supernaturalism.

The supernatural, if it exists, cannot be examined or tested by science, so it is irrelevant to science. It is impossible to possess reliable knowledge about the supernatural by the use of scientific and critical thinking. Individuals who claim to have knowledge about the supernatural do not possess this knowledge by the use of critical thinking, but by other methods of knowing.

Science has unquestionably been the most successful human endeavor in the history of civilization, because it is the only method that successfully discovers and formulates reliable knowledge. The evidence for this statement is so overwhelming that many individuals overlook exactly how modern civilization came to be our modern civilization is based, from top to bottom, on the discoveries of science and their application, known as technology, to human purposes.

Philosophies that claim to possess absolute or ultimate truth invariably find that they have to justify their beliefs by faith in dogma, authority, revelation, or philosophical speculation, since it is impossible to use finite human logic or natural evidence to demonstrate the existence of the absolute or ultimate in either the natural or supernatural worlds. Scientific and critical thinking require that one reject blind faith, authority, revelation, and subjective human feelings as a basis for reliable belief and knowledge.

These human cognitive methods have their place in human life, but not as the foundation for reliable knowledge. Scientists and critical thinkers always use logical reasoning. Logic allows us to reason correctly, but it is a complex topic and not easily learned; many books are devoted to explaining how to reason correctly, and we can not go into the details here. However, I must point out that most individuals do not reason logically, because they have never learned how to do so.

Logic is not an ability that humans are born with or one that will gradually develop and improve on its own, but is a skill or discipline that must be learned within a formal educational environment. Emotional thinking, hopeful thinking, and wishful thinking are much more common than logical thinking, because they are far easier and more congenial to human nature.

Sathiya, M. Balachandran, P. Predictions of new ABO 3 perovskite compounds by combining machine learningand density functional theory. Shukla, R. Interconversion of perovskite and fluorite structures in Ce-Sc-O system. Akamatsu, H. Hautier, G. Accuracy of density functional theory in predicting formation energies of ternary oxides frombinary oxides and its implication on phase stability.

B 85 , Lejaeghere, K. Reproducibility in density functional theory calculations of solids. Science , aad Hagberg, A. In Proc. Clauset, A. Power-Law distributions in empirical data. SIAM Rev. Alstott, J. Pedregosa, F. Scikit-learn: machine learning in python. Download references. You can also search for this author in PubMed Google Scholar. All authors contributed to the discussions of the results. Correspondence to Muratahan Aykol.

The remaining authors declare no competing interests. Journal peer review information: Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. Reprints and Permissions. Network analysis of synthesizable materials discovery. Nat Commun 10, Download citation. Received : 26 September Accepted : 08 April Published : 01 May Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article.

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Download PDF. Subjects Design, synthesis and processing Inorganic chemistry Theory and computation. Abstract Assessing the synthesizability of inorganic materials is a grand challenge for accelerating their discovery using computations. Introduction Synthesis prediction for inorganic materials remains one of the major challenges in accelerating materials discovery 1 , 2 , 3 , 4 , mostly because the complexity of the synthesis process itself hinders the development of a general, first-principles approach to it 3 , 5.

Full size image. Results The materials stability network and its time evolution The complete network formed by the current convex-hull in the chemical space of all elements is extremely dense with 41 million tie-lines Discussion The trained models can be used in multiple ways, for example, to predict class labels or probabilities for synthesizability in network environments pertaining to the present time or a past time. Methods Network data and analysis The network presented in this work is constructed from the energy-composition convex-hull of OQMD, which is a collection of systematic DFT calculations of inorganic crystalline materials, and subsequent properties derived from them, such as formation energies 6 , 7.

Model construction To prepare the input vectors for training the machine-learning models, we create multiple sequential training examples x i , t , y i , t for each material i from its temporal data, where feature vector for time t , x i , t extends to features for the past times within a window w , and where the target y i , t encodes binary labels 1 and 0, respectively, indicating whether a material was discovered at that point in time or not Fig.

Model training and evaluation Model training and parts of the evaluation were performed using scikit-learn References 1. Article Google Scholar Acknowledgements V.

View author publications. Ethics declarations Competing interests M. Additional information Journal peer review information: Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work.

Supplementary information. Supplementary Information. Description of Additional Supplementary Files. Supplementary Data 1.