What is condensed matter physics?

 Every day we encounter a diversity of materials: liquids, glass, ceramics, metals, crystals, magnets, plastics, semiconductors, foams, … These materials look and feel different from one another. Their physical properties vary significantly: are they soft and squishy or hard and rigid? Shiny, black, or colourful? Do they absorb heat easily? Do they conduct electricity? The distinct physical properties of different materials are central to their use in technologies around us: smartphones, alloys, semiconductor chips, computer memories, cooking pots, magnets in MRI machines, LEDs in solid state lighting, and fibre optic cables. Consequently, the science of materials attracts researchers in a wide range of disciplines: physics, chemistry, biology, mathematics, and the varieties of engineering (electrical, chemical, mechanical, material…). But why do different materials have different physical properties? 

There are more than one hundred different types of atoms, or chemical elements, in the universe. Any material is composed of a specific collection of different atoms, and they are arranged in a particular spatial pattern within the material. A central question is: 

How are the physical properties of a material related to the properties of the atoms from which the material is made?

Extract from Chapter 1, Condensed Matter Physics: A Very Short Introduction

Materials physics versus condensed matter physics

How do you define a distinct scientific discipline? Should it be defined in terms of the subject of study, methods used, concepts, goals, history, and/or sociology? Who gets to decide the definition: the practitioners, a broader scientific community, or administrators? How clear do the boundaries between disciplines need to be? And, does it really matter?

Condensed matter physics has a close relationship between materials physics, both intellectually and organisationally. The flagship journal Physical Review B has the subtitle "covering condensed matter and materials physics". The largest physics meeting in the world is the American Physical Society March Meeting which is largely organised by two APS divisions, those of Condensed Matter and Materials Physics. According to the APS website

The Division of Condensed Matter Physics

Originally called the Division of Solid State Physics (DSSP), the unit was formed in 1947, the third society division. In 1978 the DSSP was renamed the Division of Condensed Matter Physics to recognize that disciplines covered in the division included liquids (quantum fluids) as well as solids. Today the DCMP is the largest of all APS divisions. Condensed Matter Physics concentrates on such topics as superconductivity, semiconductors, magnetism, complex fluids, and thin films. A broad range of physical problems, both applied and basic, are investigated.

The Division of Materials Physics was established in 1984. Materials Physics applies fundamental condensed matter concepts to complex and multiphase media, including materials of technological interest. 

I suggest that although there is much common ground, particularly in the materials and techniques that are involved, there is a significant difference in the goals, values, orientation, and questions that are the focus of the two fields, CMP and MP.

CMP is largely concerned with

-the big picture

-states of matter and the transitions between them

-ideal systems (e.g, where the amount of disorder is minimal or where it is homogeneous)

-universality, i.e. properties that are largely independent of the structural and chemical details of a material

-emergence

-qualitative changes in the properties of a material

-fundamental questions

-understanding for its own sake

In contrast, MP is largely concerned with

-the details

-materials

-real imperfect  ``everyday'' materials, especially including impurities, defects, and inhomogeneities.

-reductionism

-incremental improvements in a specific property of a material

-practical questions

-understanding to enable technological applications

I want to stress that there is significant overlap and one cannot clearly separate the two fields. Furthermore, it is not that one is better than the other. They are just different. We need both and we need healthy interaction between the two fields. That is why I think understanding the differences between MP and CMP really does matter.

This post was stimulated by my son asking me the simple but profound question, ``What is the difference between a material and a state of matter?"

What do you think about the difference between MP and CMP? Does it matter?

Don't be written off!

One of the most basic skills needed to succeed, or even survive, in professional life is to be able to write well. This is true whether you work in science, industry, business, or an NGO.
Of course, there are exceptions where an individual is incredibly gifted at the technical side of a job and can't even write a coherent paragraph. But, sorry, that individual is probably not you! Furthermore, even they need a collaborator or manager who is good at writing.

Most young scientists struggle to write a paper or a grant application, particularly when English is not their first language.

Here are a few suggestions on how to improve your writing skills over time.

First, accept that writing is hard work. Even John Grisham says that!

Accept that developing your writing skills is a project of a lifetime. This means starting early.

If you are an undergrad, take some humanities courses that require writing essays. Take writing lab reports seriously.
If you have to write a thesis, start writing it now.
Take a writing course. Take another.

Practise.
Write papers yourself. If you are the first author you really should write the first draft, including the introduction yourself. Don't let your boss (or someone more experienced) do it or expect them to. Your draft may be poor and get heavily edited or even discarded completely. But you will learn from the process and with time confidence and competence will follow.
Write a blog, even if no one reads it.

Learn by osmosis.
Read scientific authors known for the clarity and beauty of their writing. eg. David Mermin and Roald Hoffmann.
Read a lot and read broadly publications (newspapers and magazines) that are known for their excellent writing: The New York Times, The Economist, The New Yorker,...
Read famous novels and non-fiction books.

Read slowly and thoughtfully. Don't just skim everything.
I also suspect you may be better off reading hard copies.
Try to notice whether a piece of writing: makes sense, is hard to understand, is enjoyable to read?

Any other suggestions?

What is condensed matter physics?

What do condensed matter physicists study?

High school students are often taught there are three states of matter: solids, liquids, and gases. However, this is misleading as there are many more states of matter. Liquid crystals, superconductors, and ferromagnets are distinct states of matter that do not fit in the high school classification. Condensed matter physics (CMP) is concerned with practically any material system that involves a large number (say at least a million) of interacting atoms or molecules. We can consider this to be a complex system because there are many different ways of arranging the constituents (atoms or molecules) of the system.

What approaches and techniques do condensed matter physicists use to study and understand these systems?

CMP provides a coherent intellectual framework for a multi-faceted approach to investigate and understand complex material systems.
First, one can look at the material at many different scales ranging from the microscopic level (scale of individual atoms and molecules) to the mesoscopic (roughly thousands of atoms or molecules, micrometer scale) to the macroscopic (what can be seen with the naked eye). The different scales can be different system sizes, length scales, energy scales, and time scales.
At every scale one can use different tools and approaches, which fall into three broad categories: experimental, theoretical, and computational. All three are intellectually and technically challenging. All are important.

Experiment
There are several distinct parts to this.
Synthesis and fabrication: one has to make a sample of the material. This involves chemistry. Making large clean samples is an art in itself.
Characterisation: this concerns testing that one actually has a sample of chemical composition and purity desired.
Property measurement: this concerns determining what the physical properties (for example, crystal structure or electrical resistance) of the sample are. Often one varies external conditions such as temperature, magnetic field, and pressure, and determines how the properties of interest vary with these parameters. Some of the most interesting condensed matter physics happens under extreme conditions: low temperatures, high magnetic fields, or high pressures.

Theory and model building
The fundamental question that one is trying to answer is: How do the material properties emerge from the chemical composition and atomic structure of the material? In particular, what are the physical mechanisms responsible for the different states of matter found in the material? In CMP it is found that these questions are best understood in terms of deciding on the essential system components and  physical interactions between them that occur at different length and energy scales. Constructing (or dreaming up!) the simplest possible model for these interactions is a real art.

Computation
This has several aspects often requiring the use of state-of-the-art supercomputers and algorithms. One is broadly known as quantum chemistry and concerns starting with a knowledge of the basic chemical composition and calculating from quantum theory the properties of the system. In spite of massive advances in computational power and algorithms over the past 60 years, one is still confined to relatively small numbers of atoms and/or unreliable approximation schemes.
The second computational side is calculating properties of the theoretical models that can be compared to experiment. Even for "simple" models usually requires either massive computational power on small systems or unreliable approximation schemes.

Finally, an important challenge is that of intellectual synthesis and critical evaluation. Here, one tries to bring together the results of all these complementary investigations to gain a coherent picture of the material and its properties. Inevitably, there are inconsistencies, sometimes minor and sometimes major. Investigators then have to decide in which element the problem lies.

I think CMP is more complex, challenging, and full of surprises than other areas of physics, such as atomic physics, elementary particle physics, fluid mechanics, and optics. There is a lot more that is unknown in CMP and a lot more that can go wrong.

What makes a good theory or modelling paper?

There is an excellent editorial in the journal Langmuir
Writing Theory and Modeling Papers for Langmuir: The Good, the Bad, and the Ugly
Han Zuilhof, Shu-Hong Yu, David S. Sholl

The article is written in the context of a specific journal, that has a focus on surface and colloid chemistry, and predominantly experimental papers and readers.
The article is structured around the five questions below, that should actually be asked about any theory or computational paper.

Who is the intended audience?
Specifically, will the paper have an influence on the experimental community?

Are approximations and limitations clearly described? 

What physical insight is gained? 

Where does theory touch reality? 
Specifically, how does the work relate to experiment? Does it suggest new experiments to test the theory?

How can calculations be made reproducible? 

This is helpful advice and good for anyone to reflect on. On the other hand, this is so basic that the need for such an editorial reflects how bad science, and particularly computational modelling, has gotten. It is just too easy to download some software, run it for some complex chemical system that is fashionable, produce some pretty graphs, and write a paper....

The educational value of undergraduate research projects

This past semester I have been supervising two undergraduate research projects. One student is doing a one semester course (1/4 of the students load) for a third year student. The second student has a year long project for a fourth year student (1/2 of their load). I am very happy with how both have gone in terms of their educational value. The amount of research results is of secondary importance to me. Previously, I posted about possible ingredients for a good undergrad project. Both students are working on a simple model for hydrogen bonds. I recommend this because it has an "easy" learning curve and so they can start "doing science" quick. It also has a nice mix of theory and experiment, chemistry and physics.

Things that struck me as particularly valuable include the following very basic things. Some of which relate to basic but important skills.

Seeing calculations to completion. 
In an undergrad problem set or exam the student has limited time and gets partial marks for incomplete or wrong answers. In research you have to keep working on the problem until you have an answer and have checked it enough that you are confident it is the correct answer.

Personal attention.
Each week they get to meet one-to-one with a faculty member and get advice and feedback.

Units! 
Learning that they really do matter and you have to get them right. This converting between different unit systems.

Writing and debugging code.
Even a short Matlab or Mathematica code.

Reading papers not textbooks.
Gifted students can find textbooks quite manageable and understandable. Papers are in a different league.

Experiencing what research is often like.
Hard. Confusing. Boring. Tedious... But, progress and understanding can be quite satisfying.

Communication skills.
Giving a talk and writing reports, and getting feedback on them.

Job skills.
Time management. Showing up for meetings on time. Writing meeting summaries. Coming up with action plans. Listening to constructive criticism. Working with others.

Many reasons why you should NOT work 13 hours per day

I am very disturbed at how I encounter people, particularly young people, who work ridiculously long hours. Furthermore, it worries me that some are deluded about what they might achieve by doing this. Due to a variety of cultural pressures I think Ph.D. students from the Majority World are particularly prone to this.

First let's not debate exactly how many hours is too many or exceptions to the generalisations below. At the end I will give some caveats.

Here are some reasons why very long hours are not a good idea.

Something may snap.
And, when it does it will be very costly.
It may be your mental or physical health, or your spouse, or your children, ...
Don't think it won't happen. It does.

Long hours may be making you quite inefficient and unproductive.
You become tired and can't think as clearly and so make more mistakes, have less ideas, and find it harder to prioritise.

It is a myth that long hours is mostly what you need to do to survive or prosper in science.
I claim dumb luck is the biggest determining factor in getting a faculty position. Furthermore, when I look at people [students, postdocs, facutly] I don't observe a lot of correlation between real productivity and the hours they work.
There are other things that are much more important than long hours. Some of these I have covered in posts about basic but important skills. Others include knowing the big picture, giving good talks, ...
These are necessary but not sufficient conditions for survival.
Yet many who are "lab slaves" seem oblivious to do this. They may have unrealistic expectations about what the long hours will lead to. Some even think long hours are a sufficient condition for survival.

You may be wasting a lot of time.
Because you can't think clearly and/or just do whatever your boss or manager tells you to, you may spend a lot of time on tasks that have almost no chance of succeeding: poorly formulated experiments or calculations, applying for grants or jobs out of your league, submitting papers to luxury journals, ...
There are also all those papers that you or your boss did not finish. You worked long hours in the lab to get the data and then the paper was never brought to completion because you and/or your boss had moved on to the next crisis/opportunity/hot topic.

It may rob you of your joy of doing science.

It may be an addiction. 
Workaholism is as dangerous and as costly as alcoholism, drug and sexual addictions. The only difference is that workaholism is often seen as a virtue.

You DO have a choice.
One of the great lies of life in the affluent modern West is that people do not have many choices. This is exactly what employers and governments want us to believe. A problem is that people make choices [e.g. I have to get a permanent job in a research university, I have to have a big house, I have to send my kids to a private school, ...] that then severely constrain other choices.

You may be being exploited.
Universities and many PI's love cheap and compliant labour, whether it is grad students, "adjunct faculty" [teaching staff on short term contracts],  or "visiting scholars" from the Majority World.

A few years from now you may regret it.
You may have left academia and realise you could have got your current job without working 3 extra hours a day. Why did you do it? Your spouse [if they are still around] sure wishes you hadn't.

How many hours is too many?
I don't know.
There is significant variability in people's stamina and makeup.
There are also differences in personal circumstances [e.g. a single person versus someone with two young children at home].
Different tasks in science [analytical calculations, writing, discussing, device fabrication, computer coding, babysitting experiments, ...] differ significantly in how taxing they are intellectually, physically, or emotionally. Also, there may be certain deadlines or tasks that require long hours for a short period of time [a visit to a synchrotron, monitoring a chemical reaction that takes 18 hours, the last week of finishing a thesis, ...] .
This is not what I am talking about.
I am talking about an unhealthy lifestyle that does not deliver what it claims to.

How do you get out of this?
First take a break so you can see more clearly the problem.
Set some boundaries. Just say NO!
Talk to others about the issue.
Aim to work smarter not longer.

I welcome comments.

Taylor expansions are a really basic skill and concept that undergraduate physics majors need to master

This past semester I taught part of two undergraduate courses: thermodynamics for second year, and solid state physics for fourth years. I was particularly struck by two related things.

1. Taylor expansions kept coming up in many contexts: approximate forms for the Gibbs free energy (e.g. G vs. pressure is approximately a straight line with slope equal to the volume), Ginzburg-Landau theory, Sommerfeld expansion, linear response theory, perturbation theory, and solving many specific problems (e.g. where one dimensionless parameter is very small).

2. Many students really struggled with the idea and/or its application. They have all done mathematics courses where they have covered the topic but understanding and using it in a physics course eludes them.

Physics is all about approximations, both in model building and in applying specific theories to specific problems. Taylor expansions is one of the most useful and powerful methods for doing this. But, it is not just about a mathematical technique but also concepts: continuity, smoothness, perturbations, and error estimation.

Does anyone have similar experience?
Can anyone recommend helpful resources for students?

A basic but important skill: critical reading of theoretical papers

Previously I posted about learning how to critically read experimental papers. 

A theory paper may claim
"We can understand property X of material Y by studying effective Hamiltonian A with approximation B and calculating property C."

Again it is as simple as ABC.

1. Effective Hamiltonian A may not be appropriate for material Y.
The effective Hamiltonian could be a Hubbard model or something more "ab initio" or a classical force field in molecular dynamics. It could be the model itself of the parameters in the model that are not appropriate. An important question is if you change the parameters or the model slightly how much do the results change. Another question, is what justification is there for using A? Sometimes there are very solid and careful justifications. Other times there is just folklore.

2. Approximation B may be unreliable, at least in the relevant parameter regime.

Once one has defined an interesting Hamiltonian calculating a measurable observable is usually highly non-trivial. Numerous consistency checks and benchmarking against more reliable (but more complicated and expensive) methods is necessary to have some degree of confidence in results. This is time consuming and not glamorous. The careful and the experienced do this. Others don't.

3. The calculated property C may not be the same as the measured property X.

What is "easy" (o.k. possible or somewhat straightforward) to measure is not necessarily "easy" to calculate and visa versa. For example, measuring the temperature dependence of the electrical resistance is "easier" than calculating it. Calculating the temperature dependence of the chemical potential in a Hubbard model is "easier" than measuring it.
Hence, connecting C and X can be non-trivial.

4. There may be alternative (more mundane) explanations.
The experiment was wrong. Or, a more careful calculation of a simpler model Hamiltonian can describe the experiment.

Theory papers are simpler to understand and critique when they are not as ambitious and more focused than the claim above. For example, if they just claim
"effective Hamiltonian A for material Y can be justified"
or
"approximation B is reliable for Hamiltonian A in a specific parameter regime"
or
"property C and X are intricately connected".

Finally, one should consider whether the results are consistent with earlier work. If not, why not?

Can you think of other considerations for critical reading of theoretical papers?
I have tried to keep it simple here.

A basic but important skill: critical reading of experimental papers

Previously, I highlighted the important but basic skill of being skeptical. Here I expand on the idea.

An experimental paper may make a claim, "We have observed interesting/exciting/exotic effect C in material A by measuring B."
How do you critically assess such claims?
Here are three issues to consider.
It is as simple as ABC!

1. The material used in the experiment may not be pure A.
Preparing pure samples, particularly "single" crystals of a specific material of know chemical composition is an art. Any sample will be slightly inhomogeneous and will contain some chemical impurities, defects, ... Furthermore, samples are prone to oxidation, surface reconstruction, interaction with water, ... A protein may not be in the native state...
Even in a ultracold atom experiment one may have chemically pure A, but the actual density profile and temperature may not be what is thought.
There are all sorts of checks one can do to characterise the structure and chemical composition of  the sample. Some people are very careful. Others are not. But, even for the careful and reputable things can go wrong.

2. The output of the measurement device may not actually be a measurement of B.
For example, just because the ohm meter gives an electrical resistance does not mean that is the electrical resistance of the material in the desired current direction. There are all sorts of things that can go wrong with resistances in the electrical contacts and in the current path within the sample.
Again there are all sorts of consistency checks one can make. Some people are very careful. Others are not. But, even for the careful and reputable things can go wrong.

3. Deducing effect C from the data for B is rarely straightforward.
Often there is significant theory involved. Sometimes, there is a lot of curve fitting. Furthermore, one needs to consider alternative (often more mundane) explanations for the data.
 Again there are all sorts of consistency checks one can make. Some people are very careful. Others are not. But, even for the careful and reputable things can go wrong.

Finally, one should consider whether the results are consistent with earlier work. If not, why not?

Later, I will post about critical reading of theoretical papers.

Can you think of other considerations for critical reading of experimental papers?
I have tried to keep it simple here.

Teaching students to think like a condensed matter physicist

Yesterday I heard Carl Wieman give a talk at UQ, Taking a scientific approach to science education. I hope I will say more about it later. Here I just want to highlight one helpful point he made concerning relating teaching to the psychology and practise of "becoming an expert".
We need to teach students to "think like a physicist". This is quite different to imparting (memorising) information in textbooks.

Later in the day I taught my class PHYS4030 Condensed Matter Physics which is really the basics of solid state physics, a la Ashcroft and Mermin. I led a discussion with the students about
"What is the conceptual strategy that we are following in this course?"
We came up with something like the following.

1. Define the simplest possible model.
2. Calculate some properties of materials that are predicted by the model.
3. Compare the predicted properties with experimental results. What are the successes and failures of the model?
4. Refine the model in the hope of better agreement with experiment.
5. Repeat the process.

This is what we are doing as we go from Drude to Sommerfeld to Bloch models, and then consider the role of electron-electron interactions.

I stressed that this is not just what we do in this course but this is the general research strategy in condensed matter physics.

Note this strategy is quite different to how one teaches most physics courses; e.g., quantum mechanics and electromagnetism.
The latter is largely an exercise in the applied mathematics of Maxwell's equations. One never really considers whether they are right or not, or need to be modified.

An important but basic skill: how to quickly "read" a scientific paper

Basically, look at the figures.

The amount of literature we might read is increasing exponentially. It is overwhelming. We all need some strong filters to focus on a few papers. This can save a lot of time.

The question is really, "Should I read this particular paper?"
This means answering two questions.
1. Does the paper contain some results that are of interest to me personally?
2. Are the results valid and important?

These days I tend to only look at papers that someone else recommends to me or are cited or linked to in papers I have decided to "read" using the procedure below.

The quickest and most efficient way to answer these questions is.

a. Read the title and abstract.
Is there potentially something of concrete interest to me?
If not, ditch the paper.

b. Look at the figures.
Are they comprehensible? If not, ditch the paper.
Do they contain new results, I did not know about? Are they interesting and important?
Are they valid? Do they make sense in certain limits I already know about? Are they consistent with other work?

If I get positive answers, only then will I actually look at details in the paper, such as the methods used. Then I may actually read the paper properly, perhaps even trying to work through some details.

Many senior scientists follow similar procedures.

Corollary. There are important implications of this for you when you write a paper.
Pick your title carefully.
Polish your abstract.
Work particularly hard on your figures and their captions.
Indeed, my mentor John Wilkins taught me to "write" a PRL by first constructing polished figures and captions, and then writing the text. The abstract is written last.
If you don't the paper may never get read, even though it does contain important and interesting results.

An important but basic skill: bringing a paper to publication

In trying to turn research into an actual journal publication there are several stages at which the process can stall or be significantly delayed (sometimes by months or years).

* Combining, selecting, and condensing some specific research results into a publon with a well defined message.

* Writing a rough first draft.

* Polishing the draft into an acceptable form for submission to a journal.

* Revising and resubmitting the paper, possibly to a different journal, if rejected from one.

Moving beyond these obstacles can be a significant struggle even for senior scientists. Furthermore,  junior collaborators can be frustrated and anxious as they wait for action. Their survival and careers depend on getting papers published in a timely manner. I even know of cases of students who did not get a Ph.D because a manuscript or draft thesis just sat on the desk of their advisor.
I am also struck by the fact that I know senior people who have impressive publication records but if you talk to their collaborators, both senior and junior, you will hear how this basic skill has not been learnt or mastered.

Aside: I myself am far from perfect. I have not been as quick as I could/should be with some of my collaborations. I still have two papers on the arXiv that have never been published and currently have at least three manuscripts stalled on my laptop. I do take a little consolation/excuse from the fact that these are single author papers and so I am the only one suffering.

I don't have simple solutions but do offer several suggestions. The first is by far the most important.

You must learn to do this yourself. Don't wait for others to do it for you. Take charge. Be responsible.

Don't be shy about bugging your collaborators to move things along.
I know this is can be difficult for junior people [graduate students and postdocs] from countries and cultures that are overly deferential to seniority and authority figures. Don't just email.
Knocking on doors and talking in person helps. .... even, if that means getting on a plane to the other side of the world. I am struck how some of my collaborations are moved forward just because my co-authors know that I am about to visit.

Consider your own possible underlying psychological issues such as perfectionism, procrastination, lack of confidence, laziness, or fear of offending authority.

I welcome other suggestions on how to develop this important but basic skill.

A basic but important research skill, 6: skepticism

Feynman said "The first principle is that you must not fool yourself and you are the easiest person to fool."

Walter Kauzmann emphasised that people will often believe what they want to believe rather than what the evidence before them suggests they should believe.

Students need to learn skepticism. Furthermore, it needs to be modelled to them by their advisors.
In particular, students should not just believe something because

- their advisor/supervisor believes it or tells them it is true
- it has been published, especially if it is in a luxury journal
- someone famous [or a group of famous people] claims it is true
- it is an exciting idea.

Basic but important questions to ask are:

What is the evidence? How reliable is the evidence?
Is there an alternative explanation, particularly a simpler one?

Maybe I am just becoming a grumpy old man, but I think I do increasingly encounter students and young researchers who lack this basic skill.
I fear that this is because of the seductive power of "sexy" explanations and topics. Furthermore, some of the students mentors and role models don't model or practise skepticism, particularly if their career success and funding [or hope thereof] depends on the exotica favoured by the luxury journals.

Good science is just plain hard work and not as exciting or clear cut as we might wish.

A basic but important research skill, 5: solving homework problems

Carl Caves has a helpful two pager, tips for solving physics homework problems. It nicely emphasises the importance of drawing a clear diagram, dimensional analysis, thinking before you calculate, and checking the answer.

He also discusses moving from homework problems to "real world" problems, e.g. research. Then, just formulating the problem is crucial.

I wonder if the goals of some Ph.D projects might be revised if the supervisor and/or student simply combined dimensional analysis with a realistic order of magnitude estimate. Just doing the exercise might also significantly increase the students understanding of the underlying physics.

A basic but important research skill, 4: breaking the project down

Any worthwhile scientific project will be large, challenging, and ambitious. Even a small project, particularly for a beginner, can be intimidating and overwhelming. A key skill is to learn how to break a project down into small and manageable parts.

This applies whether one is trying to solve a particular scientific problem [how does a particular enzyme work? what is the origin of superconductivity in the iron pnictides?], write a large computer code, perform a multi-step chemical synthesis, solve a quantum many-body Hamiltonian with a specific approximation, fabricating a solar cell....

How does one do this? Are there some general principles?

I am not sure and I welcome comments and suggestions.
I also fear that current pressures to publish quickly lead to hastily put together projects without due attention to the robustness of the sub-projects.

My main suggestions are:

*be realistic. make sure each part/step is arguably manageable/doable.

* start with easy steps and processes that will build confidence and understanding. first trying to reproduce someone else's results is always a good thing!

*make sure that you have "control" of each step or "sub-module", i.e. you are sure it really does work and know what is going on. For example, if you have a large computer code using lots of sub-routines you need to be sure that each of them is numerically stable. Just, quickly throwing them together may produce garbage or be very hard to debug.

*plan steps that will produce publons.

*talk to others about how they do it, both in general and for specific projects.

Any suggestions?

A basic but important research skill, 3: talking, asking, and listening

One of the quickest ways to learn about a research field is to talk to others working in the area. Trying to learn the fundamentals [key questions, techniques, background, …] by only reading can be a slow and inefficient process. Furthermore, key pieces of information, can be buried or not even there. So reading needs to be complemented by talking to others. They don't have to be the worlds leading expert.

Yet this is a very hard process and many students give up too easily. First, there is the problem of finding someone who both knows enough and will take the time to talk to you. Second, you will probably feel dumb. It requires courage and confidence to do this. You may not even know what questions to ask. Much of the jargon/language they use may be unfamiliar or meaningless. Third, it is just plain hard work and requires a sustained effort.
Theorists and experimentalists talking to each other presents a special set of challenges.
So does talking across disciplines (chemists and physicists, biologists and physicists, …)

Here are some basic questions to ask:
What are you working on?
Why is this important?
What are the key papers in the field? Why are they significant?
What is the "holy grail" of the field?
What "results" in the field do you think are dubious?
What is X? I don't understand. Can you explain it to me with a simple physical or chemical picture?
What are the limitations of this technique?
Could you explain that to me again?

Listen carefully.
Listen for concepts, names, pictures, graphs, equations, results, problems, …. that keep coming up.
Complement your discussions with reading. You will begin to know what to look for and certain things will start to stand out.

If I could have my time over I would have done a lot more talking to people.

A basic but important research skill, 2: checking results

Earlier I posted about a basic skill: take initiative! Don't wait for someone else to tell you what to do. Try stuff.

It is exciting when you think that you have finally obtained some research results. It is even more exciting if they seem interesting and potentially important. But, don't fool yourself. They may be wrong! Mistakes happen in research. More often than many want to admit. Furthermore, the more complicated the technique and the system under investigation, the more likely something will go wrong. Murphy's law!

So how do you check your results? I am not sure. There is no simple universal procedure to check results. Just repeating the experiment or calculation is not good enough. You [or the instrument or software...] may be making the same mistake.

Learning to check results is an art and requires patience, discipline, and creativity.
Furthermore, different individuals and different research fields often have quite different standards as to how many different checks one should perform. Some seem to rush to publish once they get an "interesting" result. Others, are very cautious and careful and perform multiple checks.
I am very thankful that many of my collaborators over the years have been more conscientious than me.

For students: here are a few ideas as to some basic checks that one should do.

Compare your results to relevant published work. Make sure you can reproduce earlier work. If not, do you have a good reason to believe you are right and they are wrong.

Computational work.
Compare your results to limits [e.g. weak or strong coupling, for which one can obtain analytical results].
Use different versions of software or numerical methods.
For short programs, write two codes from scratch.

Analytical calculations.
Compare your results to Mathematica or a numerical calculation.

Experiments.
Change the sample, device, material, instrument, or procedure.

Computational chemistry.
Try different basis sets and levels of theory. Don't just do DFT! When possible, benchmark it against smaller systems.

Curve fitting.
Have different individuals do it independently and see if they get the same result.

What do you think are good procedures for checking results?
When should you quit checking?

A basic but important research skill: initiative

Some basic research skills that I feel are important may be increasingly neglected in the training of students. I fear this neglect is partly due to the pressure to "produce" leading to people cutting corners and supervisors treating students like technicians.

The first skill is Take initiative.
Students, regardless of their limited knowledge and experience, should not sit around waiting for directions and feedback. They should not just do what they are told.
They should just "try stuff". What does this mean?

• Look at the literature on your own, beyond what has been recommended to you.
• Try to reproduce other peoples results.
• Try doing the experiment under slightly different conditions. Try a different sample or device.
• Try doing the computer simulation with slightly different parameters.
• Try doing the analytical calculation using a different method.
• Ask questions.

Don't be scared of looking and feeling "dumb".

To begin with most of what you try may not work or will produce little of new interest.
But, you will slowly learn what works and what does not. Your intuition will increase. But you may get lucky and find something completely new.

Other basic skills include:

• knowing the literature
• double checking results
• making comparisons
• getting feedback from others
• listening to referees